Sistemasi: Jurnal Sistem Informasi is a scientific journal in the field of computer science and engineering. General Focus and Scope of Systemation, are Information Systems, Information Technology, Computer Science, Software Engineering, and Computer Engineering. Journal of Information Systems is abstracted and indexed in Directory of Open Access Journal (DOAJ) since 2018 and Science and Technology Index (SINTA) since 2018. Systemation journals are published 6 times a year, in January, March, May, July, September and November. Sistemasi: Jurnal Sistem Informasi (e-ISSN: 2540 – 9719) published by the Information Systems Study Program, Faculty of Engineering and Computer Science, Universitas Islam Indagiri, Tembilahan Riau Indonesia.
Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer
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Decision Support System for Porang Land Selection based on Multi Attribute Utility Theory (MAUT)
This study aims to develop a web-based decision support system using the Multi-Attribute Utility Theory (MAUT) algorithm to assess land suitability for porang cultivation. The system is designed to assist farmers and land developers in selecting optimal planting sites. The research methodology includes problem identification, primary data collection through field surveys, determination of criteria and weights based on land characteristics, and the implementation of the MAUT algorithm to generate land recommendations. The five main criteria considered are soil texture, altitude, temperature, soil pH, and shading level. The results indicate that the three tested land alternatives achieved suitability levels of 86.67%, 75.57%, and 73.33%, respectively. Based on the suitability threshold of ≥70%, all land alternatives are deemed suitable for porang cultivation. These findings demonstrate the effectiveness of MAUT in supporting data-driven decision-making in the agricultural sector. Furthermore, this approach can be replicated for other commodities and further enhanced through the integration of spatial mapping and financial benefit analysis.
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Sentiment Analysis of Academic Application User Comments using Naïve Bayes and Particle Swarm Optimization for Feature Selection
The Academic Information System supports educational institutions by providing quality management programs to all students and stakeholders, relying on information and communication technologies such as the internet and local networks. Over time, this application has been consistently used by students and lecturers. However, the university has not yet evaluated the feasibility and effectiveness of the system, making it difficult to plan future improvements. Therefore, feedback from both students and lecturers is essential for guiding the system’s development. Given the current state of the application, such evaluation can be carried out through user comments. This study investigates the performance of the Naïve Bayes algorithm, one of the most commonly used algorithms in various research libraries, in analyzing sentiment from these comments. To further improve the accuracy of the Naïve Bayes method, we applied an additional Particle Swarm Optimization (PSO) feature selection process. The results demonstrate that the Naïve Bayes method with PSO achieved an accuracy of 86.27%, precision of 84.78%, and recall of 84.78%, which are higher than the results obtained using the standard Naïve Bayes method alone.
Comparison of Machine Learning Models for Predicting Lung Cancer Severity
This study aims to compare the performance of four machine learning algorithms Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), and K-Nearest Neighbors (KNN) in predicting lung cancer severity based on patient medical data. The dataset includes clinical information with the target variable categorized into three severity levels: low, medium, and high. Experiments were conducted using an 80:20 train-test split without feature scaling. The results show that RF achieved 100% accuracy, LR 99%, KNN 82%, and SVM 43%. The superior performance of Random Forest can be attributed to its ensemble of decision trees, which mitigates overfitting in medium-dimensional numerical features, whereas SVM (kernel = RBF, C = 1.0, gamma = "scale") failed to adapt due to the absence of scaling and hyperparameter tuning. Recall, precision, and F1-score further confirm the dominance of RF and LR. This study provides insights into the effectiveness of machine learning algorithms in lung cancer diagnosis and highlights the contribution of a multi-algorithm approach. The findings recommend using RF as the primary model and LR as a complementary control within clinical decision support systems, enabling physicians to make earlier, more personalized treatment decisions and ultimately improve lung cancer patient prognosis.
Development of a Website-based Inventory Information System at Konveksi Mitra Jaya Abadi
The utilization of information technology has increased the demand for more efficient and accurate inventory management systems, particularly for businesses such as Mitra Jaya Abadi Garment Company, which previously relied on manual record-keeping. This study aims to design and implement a web-based inventory information system using the Extreme Programming (XP) methodology. XP was chosen for its flexibility in software development and its support for direct user involvement in the iterative process. The system was developed using the CodeIgniter framework and applied the Model-View-Controller (MVC) architecture to ensure a clear separation between application logic, user interface, and data management. Compared to the previous system, the new system provides more flexible reporting features, including filters by date and item category, as well as report export capabilities. Unlike the waterfall approach, the XP methodology allows for faster iterations, better adaptability to changing requirements, and stronger user engagement. System testing based on the ISO 25010 standard demonstrated excellent feasibility, achieving an overall score of 95.36%. The system is expected to accelerate and simplify inventory management processes, reduce errors in data recording, and improve the efficiency of decision-making.
Design of an Archiving Information System using the Agile Scrum Method
This study aims to design and implement a web-based archiving information system at PT Sarana Tirta Ungaran using the Agile Scrum methodology. The background of this research lies in the challenges of manual archive management, which often lead to difficulties in document retrieval, risks of data loss, and inefficient use of storage space. By adopting the Agile approach, the system was developed iteratively through stages such as product backlog, sprint planning, and daily Scrum, allowing for dynamic adjustments to user requirements. The system incorporates four types of user roles—super admin, admin, director, and general administrative staff—with key features including login, archive management (CRUD), document search, and activity logs. The design process employed UML, covering use case diagrams, activity diagrams, and class diagrams. Testing results demonstrate that the system improves archiving efficiency, accelerates information access, and supports the digitalization process within the company. Therefore, this information system is expected to enhance overall organizational performance.
Implementation of a Network Security System using an Intrusion Prevention System with Machine Learning
This research develops a machine learning-based Intrusion Prevention System (IPS) to automatically detect and prevent network attacks. The system was designed using the Random Forest algorithm, trained on the CICIDS2017 and CICIDS2019 datasets—standard benchmarks developed by the Canadian Institute for Cybersecurity, widely used in cybersecurity research for their realistic network traffic and diverse attack types. The system focuses on three common attacks: SYN Flood, Port Scanning, and SSH Patator. After preprocessing, training, and evaluation, the model was integrated into the IPS, enabling real-time network monitoring, attacker IP blocking, and automated notifications via Telegram. Testing results indicate that the system achieves high detection accuracy while delivering fast and efficient responses. This system simplifies the work of network administrators by detecting and responding to attacks without the need for manual log monitoring. Through its automated and adaptive approach, the IPS makes a significant contribution to enhancing network security and can be directly implemented in organizational or institutional network environments to substantially reduce the risk of cyberattacks.
Web-based Real-Time Inventory Information System Design: An Operational Efficiency Solution at PT. Visual Media Creative
This study developed a web-based real-time inventory information system to address delays in data processing and the high potential for recording errors at PT Visual Media Creative, which previously relied on conventional methods using books and MS Excel. The system was designed using a prototyping method with an iterative and participatory approach, involving stages of user requirements analysis, interface design, implementation with the CodeIgniter framework and MySQL, and testing. Black-box testing was applied to verify functional compliance, while usability testing involved six internal respondents to evaluate ease of use. The results showed that all functions operated according to specifications with a 100% success rate, and usability testing received positive feedback from respondents. The system implementation reduced inventory report management time from 4–8 hours to 1–2 hours per day, decreased input errors by up to 75%, and accelerated data retrieval from approximately 3 minutes to less than 10 seconds. The contribution of this research lies in providing an integrated, real-time data-driven solution that enhances operational efficiency, improves data accuracy, and supports decision-making within the company.
User Satisfaction Analysis of PDAM’s CATER System using Heuristic-SUS Evaluation
The Regional Drinking Water Company (PDAM) of Salatiga City, as a provider of clean water services, has implemented a web-based Meter Recording System (CATER) to enhance operational efficiency. However, the system's effectiveness has not been comprehensively evaluated in terms of usability, despite this factor being crucial for employee productivity and service quality. This study aims to assess the usability level of the CATER system by combining the Heuristic Evaluation (HE) and System Usability Scale (SUS) methods to identify the system's strengths and weaknesses and provide evidence-based recommendations for improvement. The evaluation results show that the system generally demonstrates good usability, with the highest HE scores in Visibility of System Status (89%) and Aesthetic Design (88%), indicating an informative and consistent interface. However, weaknesses were identified in the areas of Recognition Rather Than Recall (68%) and Help Documentation (75%), suggesting a need for workflow simplification and more intuitive user guidance. The average SUS score of 75 (classified as Acceptable) supports the finding that the system is generally well-received by users, though there is still room for improvement. Based on these findings, the study recommends improvements focusing on workflow simplification, the development of more contextual help features, and optimization of error prevention mechanisms. This study’s results are not only valuable for the further development of the CATER system but also serve as a reference for evaluating similar systems in other public utility sectors.
Designing a Prototype Information System for a Building Material Store using the Zachman Framework
The growing need for digitalization of business processes in the retail sector requires information systems capable of supporting operational efficiency and accurate decision-making. This study aims to design an inventory management information system for CV. Berjuang Sakti using an Enterprise Architecture approach based on the Zachman Framework. The previously applied manual system had led to several issues, including inaccurate record-keeping, data inconsistencies, and delayed reporting. The Zachman Framework was applied to structure the system design across six main perspectives: Planner, Owner, Designer, Builder, Implementer, and User. The design results include stakeholder-based requirements analysis, system modeling (ERD, Use Case Diagram, and Activity Diagram), and a user interface prototype featuring real-time stock tracking, transaction management, and reporting. Usability testing using the System Usability Scale (SUS) with 15 respondents produced a score of 72.3, categorized as “Good,” indicating that the system is feasible and effective in supporting the daily operations of CV. Berjuang Sakti. These findings provide practical implications for improving efficiency and accuracy in inventory management through the adoption of a structured and measurable system architecture.
Multi-Platform System Development for Violence Complaint Services using the CodeIgniter Framework
Violence against women and children remains a prevalent social issue in Indonesia, including in Kudus Regency. The lack of fast, secure, and easily accessible reporting facilities is one of the factors contributing to the low reporting rate, leaving many cases unaddressed. This study aims to design and develop a web-based violence complaint information system using the CodeIgniter framework with a Model-View-Controller (MVC) architecture to ensure a more structured, secure, and efficient system. The development method follows the waterfall model, consisting of requirements analysis, design, implementation, integration, and system testing. The system provides key features such as an online reporting form, automated notifications to officers, real-time report status tracking, and case progress recording by authorized personnel. Black-box testing conducted by one reporter and three staff members of the Kudus Social Service (Dinas Sosial P3AP2KB) on six main features across four different scenarios resulted in a total of 96 test cases, achieving a functional success rate of 98.9%. One failure was identified in file upload validation, where the system still allowed unsupported file formats. Nevertheless, all other features functioned properly, and the system was proven responsive across devices. This reliability supports faster reporting and case handling, enabling victims to report more easily while allowing relevant institutions to respond quickly, accurately, and transparently.
The Best Nurse Performance Recommendation Model with Integration of AHP and Weighted Product Methods
This study aims to develop a recommendation model for identifying the best nurse performance by integrating the Analytical Hierarchy Process (AHP) and Weighted Product (WP) methods. Nurse performance plays a vital role in determining the quality of healthcare services; however, existing performance evaluations are often subjective and lack transparency. This situation leads to dissatisfaction among nurses and reduces work motivation. Therefore, a system that provides objective and fair evaluation is needed. The AHP method is employed to determine the priority weights of nurse performance criteria through pairwise comparisons, while the WP method is applied to rank nurses based on the assigned weights. The criteria used include Technical Competence, Professional Attitude, Teamwork, and Patient Satisfaction. This research adopts a Research and Development (R&D) approach, which involves data collection, criteria identification, AHP weighting, web-based system development, and model validation and evaluation. The results indicate that integrating the AHP and WP algorithms can produce a comprehensive and practical nurse performance recommendation model that enhances decision-making efficiency and accuracy in hospitals. The best nurse performance recommendation resulted in Wulandari achieving the highest score of 0.3251.
Enhancing Deep Learning-based Classification of Cassava Leaf Diseases using CLAHE and SMOTE
Efficient detection of foliar diseases in cassava (Manihot esculenta) is essential for sustaining crop productivity and ensuring food security, particularly in regions vulnerable to environmental stress. However, accurate identification remains a challenge due to the widespread occurrence of diseases such as Cassava Mosaic Disease (CMD), Cassava Bacterial Blight (CBB), and Cassava Brown Streak Disease (CBSD), which continue to threaten cassava yields. This study addresses two major obstacles in cassava disease classification—uneven image quality and imbalanced class distribution—by implementing Contrast Limited Adaptive Histogram Equalization (CLAHE) and the Synthetic Minority Over-sampling Technique (SMOTE). A publicly available dataset from the Cassava Leaf Disease Classification competition on Kaggle was used, and two pretrained convolutional neural networks, EfficientNetV2B2 and DenseNet169, were fine-tuned through transfer learning. The images were resized, enhanced using CLAHE, and augmented before being split into training, validation, and test sets. Both models were trained for 10 epochs using identical configurations. Results indicate that EfficientNetV2B2 achieved higher classification accuracy (88.1%) than DenseNet169 (86.4%), with CLAHE contributing a 2–3% improvement in accuracy. While these results are slightly lower than those reported in previous studies employing extended training durations and advanced techniques such as focal loss, the lightweight approach presented here proves effective under computational constraints. The findings demonstrate the feasibility of developing scalable and resource-efficient disease detection systems, especially for mobile or edge devices. Future research should focus on longer training schedules, advanced loss functions, and validation using field-acquired images to further improve model performance in real-world agricultural settings.
Graduation Prediction for Prospective University Students Using Stacking Ensemble Learning
Graduation is a crucial indicator for accreditation and forms an integral part of quality management strategies in higher education institutions. Therefore, early prediction of student graduation is essential to enhance the effectiveness of data-driven decision-making in student admissions. Differences in graduation rates are influenced by a combination of academic, demographic, economic, and family factors. This study applies a Stacking Ensemble Learning method by combining Random Forest, K-Nearest Neighbors, and Support Vector Machine, with XGBoost used as a meta-learner. The dataset integrates new student enrollment data and graduation status reports from the NeoFeeder PDDikti system, covering 16 academic and non-academic feature variables. The model was evaluated using accuracy, precision, recall, F1-score, and Area Under the Curve (AUC) metrics. The results show that the stacking ensemble model outperforms individual models, achieving an accuracy of 82%, a weighted F1-score of 80%, and an AUC of 87.15% on the test data. These findings contribute to the identification of relevant features and demonstrate the effectiveness of applying an ensemble model for building a machine learning-based prediction system, particularly in addressing data imbalance and improving classification accuracy.
Implementation of Safety Stock and ROP Methods in the Inventory Information System of Kava Kudus Coffee Shop
Manual stock management, particularly through paper based record keeping, remains a challenge for culinary businesses such as Kava Coffee Shop in Kudus. Issues such as inaccurate records, delayed reordering, and the absence of notifications for expired or damaged ingredients can lead to stockouts or overstocking. This study aims to design a web-based inventory information system incorporating the Safety Stock and Reorder Point (ROP) methods to optimize the management of perishable raw materials. The system was developed using the Waterfall model, covering requirements analysis, design with Use Case and Activity Diagrams, feature implementation, and testing through the Black-box method. Key system features include minimum stock notifications, expiration tracking, transaction management, and damaged goods returns. Testing results show that the system performs according to specifications, achieving a 100% success rate across functional scenarios. Compared to previous studies, this system demonstrates superiority by being web-based, focusing on perishable goods, and offering integrated features. Therefore, the system can enhance record accuracy, accelerate decision-making, and ensure the availability and quality of raw materials at Kava Coffee Shop.
Bitcoin Price Forecasting using Seasonal Log-Differenced XGBoost with 2014–2025 Data
Bitcoin, as a decentralized digital currency, experiences significant price fluctuations, making accurate price forecasting a complex yet valuable challenge. Price forecasting is essential in economic decision-making, serving as the foundation for portfolio construction, risk analysis, and investment strategy development. Bitcoin's high volatility makes it an attractive asset for investors but also poses significant risks, necessitating sophisticated forecasting tools and models to mitigate uncertainty. The XGBoost model in regression is widely known and effectively applied to handle time series data. This model can capture complex nonlinear relationships in Bitcoin price data, providing more accurate forecasts than traditional statistical models. The research methodology includes data collection, data preprocessing, stationarity checking, differencing, feature engineering, data division into training and testing sets, XGBoost model training, prediction and evaluation, and result visualization. The research results show that the XGBoost model achieves a Mean Absolute Error of 8.26% and an RMSE of 9.87%, indicating excellent forecasting accuracy. The implications of this research could potentially assist investors and traders in improving their strategies and risk management.
Implementation of the K-Means Clustering Algorithm for Customer Segmentation
With the rapid advancement of digital technology, companies are faced with an increasing volume and complexity of customer data. This necessitates a more effective approach to customer segmentation in order to better understand consumer behavior patterns and preferences. The large number of customers conducting transactions daily makes it challenging for managers to distinguish between highly frequent shoppers and less frequent ones based on transaction data and purchasing preferences. Therefore, the role of the K-Means clustering algorithm is crucial in addressing this issue. The objective of this study is to implement the K-Means Clustering algorithm for customer segmentation by grouping customers into three clusters: highly frequent shoppers, moderately frequent shoppers, and infrequent shoppers. The research methodology includes the following steps: data collection of transactions and customer preferences, data preprocessing, algorithm implementation, and validation and testing. The clustering parameters are based on the number of purchases, number of transactions, and quantity of items purchased. The functional testing results indicate that the system performs well, as all test scenarios were successfully executed. Furthermore, the evaluation using the Silhouette Coefficient (SC) method produced a strong structure status, with an average SC value of 0.97. This result demonstrates that the dataset is highly robust and suitable to serve as a reference model for customer segmentation.
Analysis of the Performance Comparison between Random Forest and SVM RBF in Detecting Cyberbullying on Imbalanced Data with the SMOTE Approach
Cyberbullying has emerged as a growing threat with the widespread adoption of social media, creating significant risks to online safety. Automatic detection of such behavior remains challenging, particularly when the training dataset is highly imbalanced. This study presents a comparative analysis of Random Forest and Support Vector Machine with Radial Basis Function kernel (SVM RBF) for cyberbullying detection, incorporating the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. The experiments utilized a publicly available, manually annotated dataset containing 47,693 English-language tweets from global users, labeled as cyberbullying or non-cyberbullying. Performance was evaluated using accuracy, precision, recall, and F1-score. Results indicate that Random Forest achieved the highest performance before SMOTE (accuracy = 88.52%, precision = 89.07%, recall = 94.00%, F1-score = 91.49%), while SMOTE improved recall for both algorithms but reduced accuracy and precision. These findings highlight that the choice of algorithm and effective handling of class imbalance are critical for enhancing the reliability of automated cyberbullying detection systems, thereby enabling more effective content moderation and safer online environments.
CNN-Based Model for Classifying Regional Types on Shipping Label Images
The rapid growth of the e-commerce sector has led to a significant surge in shipping volumes in Indonesia. In logistics systems, a shipping receipt serves as a crucial document containing destination information such as address, city/regency, and postal code. Errors or delays in classifying destination regions not only generate additional operational costs (e.g., reshipment fees and service penalties) but may also reduce customer satisfaction and harm the reputation of service providers. This study proposes the implementation of a Convolutional Neural Network (CNN) model to automatically classify region types in shipping receipt images, aiming to minimize manual errors and accelerate processing time. CNN was chosen for its ability to recognize complex visual patterns in digital documents without requiring manual feature extraction. The dataset used in this study consists of 1,540 shipping receipt images from various courier services, labeled as REG_JAWA and REG_LUARJAWA. The research process includes image preprocessing (resizing, normalization, augmentation), CNN architecture design, model training with early stopping, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The results demonstrate that the model achieved a testing accuracy of 99.87%, precision of 99.71%, and recall of 100%, highlighting its strong potential for real-world implementation in logistics systems to improve efficiency and reliability of deliveries.
Designing Chatbot Interface to Accelerate Services at Disdukcapil Salatiga
Managing population documents such as lost or damaged ID cards (KTP) and Family Cards (KK) remains a challenge for the public due to complex procedures, lengthy processing times, and limited access to information. This study aims to design a chatbot interface to simplify the document management process using a Design Thinking approach focused on user needs. Design Thinking consists of five stages: Empathize (identifying user problems), Define (formulating the core issues), Ideate (generating solution ideas), Prototype (creating initial designs), and Test (evaluating the solution). The prototype was tested on 37 respondents to assess its quality. The analysis results show that the average score for all statements ranged between 3 and 4, indicating positive feedback on the design. All items were validated with an r-count value > 0.3, with the highest value being 0.864, while the reliability test produced a Cronbach’s Alpha of 0.863, demonstrating excellent internal consistency. Overall, the chatbot was considered effective, user-friendly, and capable of supporting digital transformation at the Civil Registry Office (Disdukcapil). The trial also indicated a potential increase in user satisfaction of up to 85% and a 60% improvement in information retrieval efficiency compared to conventional methods. These findings suggest the proposed solution as a viable digital alternative to accelerate services, reduce queues, and enhance transparency and public satisfaction.
The use of iTCLab Kit as a Learning Media for Dynamic Systems and Control
The Internet-Based Temperature Control Lab (iTCLab) kit is an instructional tool designed to facilitate the understanding of fundamental concepts in dynamic systems and control, particularly Proportional-Integral-Derivative (PID) controllers. The background of this study lies in the gap between students’ theoretical knowledge and its application in real-world systems, which often poses a challenge in learning Dynamic Systems and Control courses. This research aims to evaluate the effectiveness of using iTCLab in improving students’ understanding of both PID control theory and its practical applications. The study was conducted in three stages: development of learning modules, implementation, and evaluation involving 30 Informatics students. Assessment was carried out through knowledge tests (pre-test and post-test) and perception surveys. The results indicated an increase in the average score from 52.30 to 76.48 (+46.26%, p < 0.001, Cohen’s d = 1.86), along with positive evaluations in terms of theory–practice integration (4.30), ease of use (4.10), content relevance (4.40), and learning satisfaction (4.20) on a 1–5 scale. These findings suggest that iTCLab is effective in strengthening students’ conceptual understanding and technical skills. From a practical standpoint, iTCLab is suitable for integration into Semester Learning Plans (RPS) and further development to support Internet of Things (IoT)-based distance learning.
A Comparative Study of LSTM and GRU Models for Wind Forecasting
The use of deep learning in the current technological era is increasingly widespread, including in the field of meteorology to support aviation safety. As an archipelagic country, Indonesia faces significant challenges in ensuring flight safety due to unpredictable weather conditions, particularly wind direction and speed, which greatly influence takeoff and landing operations. To address these challenges, the Automatic Weather Observing System (AWOS) plays a crucial role in providing real-time weather data. This study aims to compare the performance of two popular deep learning models for time series data, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), in forecasting wind direction and speed based on AWOS data from Sultan Hasanuddin International Airport for the period of January 2020–December 2022, obtained from the National Oceanic and Atmospheric Administration (NOAA) website. After preprocessing, five out of eight attributes were used for modeling. The evaluation results show that the LSTM model consistently outperformed GRU in all forecasting scenarios (30 minutes, 1 hour, and 1.5 hours). For wind direction, LSTM achieved MAE values of 10.92°–11.01°, MSE 242.45–247.89, and RMSE 15.57°–15.74°, all lower than those of GRU. For wind speed, LSTM recorded MAE values of 30.32–31.72 knots, MSE 1868.53–2013.92, and RMSE 43.23–44.88 knots, also outperforming GRU. This research is expected to contribute to the development of risk mitigation systems and the advancement of weather forecasting technology in the future.
Heuristic Analysis of Functionality and UI/UX in Five Digital Finance Applications
Digital financial applications, such as e-wallets and mobile banking services, have grown significantly in Indonesia as a result of advancements in information technology. However, the user interface (UI) and user experience (UX) of these applications still face several challenges, particularly in navigation, information clarity, and transaction efficiency. Using a heuristic evaluation method, this study aims to compare the functionality and UI/UX quality of five popular digital finance applications in Indonesia: DANA, OVO, LinkAja, myBCA, and Jenius. Three data collection techniques were employed: heuristic evaluation by trained evaluators, analysis of user comments from the Google Play Store, and semi-structured interviews with active users. The most frequently violated heuristic principle was “recognition rather than recall,” followed by issues related to Error Prevention and Flexibility and Efficiency of Use. Among the evaluated applications, LinkAja exhibited the highest number of usability violations, while DANA and OVO recorded the most critical usability issues. Overall, the findings from the three methods were consistent and mutually reinforcing, indicating that usability problems have a direct impact on user experience. This study demonstrates that a triangulated methodological approach is highly effective in identifying and understanding usability issues comprehensively and in providing actionable design recommendations to improve the UI/UX quality of digital financial applications.
Application of OLAP based Business Intelligence and Predictive Analysis (Random Forest Classifier and ARIMA) in a Fintech P2P Lending Company
The rising rate of non-performing loans (NPLs) in the financial technology (fintech) P2P lending industry has become a serious challenge, including for PT XYZ, which focuses on funding the MSME sector. This study applies Business Intelligence using OLAP and predictive analytics at PT XYZ over a two-year period. The dataset consists of records from January 2023 to December 2024, comprising 276,320 loan transactions and 267,542 repayment records. The OLAP approach was employed to explore loan transactions, repayment performance, and delinquency patterns across multiple dimensions, such as account, time, payment method, and bank used. The results of this exploration were visualized through three interactive dashboards tailored to the analysis focus. Predictive analysis was conducted using a Random Forest Classifier, which identified loan_amount as the most influential variable in predicting potential delinquency. However, the model’s performance was relatively limited, achieving an accuracy of 61.47%, precision of 38.33%, and recall of 36.89%. Delinquency trend forecasting using the ARIMA (2,1,1) model indicated an increase in overdue cases up to March 2025, followed by a decline in subsequent months. The model evaluation yielded MAPE of 39.74%, sMAPE of 33.32%, and RMSE of 1086.77, suggesting moderately adequate predictive accuracy but with a relatively high error rate. This study recommends implementing incentives for OBA (Online Borrower Accounts) with good repayment performance and restricting loan ceilings based on an automated risk score. Further development is advised by enriching the dataset with additional variables and enhancing predictive models through techniques such as oversampling (e.g., SMOTE) and hyperparameter tuning to maximize accuracy.
Proposing a Model of Technology Acceptance and use in Digital Banking: A Systematic Review and Meta-Analysis Approach
The advancement of information technology has driven a significant transformation in the banking sector through digital banking, which has now become the backbone of modern financial services. Digital banking offers efficiency, ease of transactions, and reduced operational costs. Despite these benefits, challenges remain, particularly the high initial investment costs and the complexity of customer adoption. Without a well-designed user acceptance strategy, substantial investments risk being underutilized. Therefore, a deep understanding of the factors influencing digital banking adoption is crucial to ensure the effectiveness of digital transformation initiatives. Previous studies have examined the acceptance and use of digital banking using popular models such as TAM, UTAUT, and UTAUT2. However, fragmented findings—caused by variations in results and the inclusion of additional variables—pose challenges for generalization. This study aims to develop a more comprehensive model of digital banking acceptance through a systematic review and meta-analysis. The results indicate that most core constructs of UTAUT2—such as Performance Expectancy, Effort Expectancy, Facilitating Conditions, Social Influence, Habit, Price Value, and Hedonic Motivation—are significant. Furthermore, external variables such as Trust, Perceived Security, Enterprise Image, Promotions, and Perceived Risk also play a role, thereby extending the model beyond the generic framework. The proposed model is expected to enrich the development of technology acceptance theory by introducing a context-specific framework for digital banking. It also provides strategic guidance for the banking industry to enhance adoption through targeted interventions on the most influential variables. Consequently, this model can serve as a stronger foundation for both institutional practices and future research in the field.
UI/UX Design Planning of Ququ Bakery Application Using the User Centered Design (UCD) Method
This study aims to design the user interface (UI) and user experience (UX) of a birthday cake ordering application for Ququ Bakery in Semarang, Central Java, using the User-Centered Design (UCD) approach. The main issues faced by Ququ Bakery include limited promotional media, which currently relies solely on Instagram, as well as user difficulties in placing orders, checking prices, tracking order status, accessing promotions, and making payments. The objective of this research is to develop a web-based application UI/UX that addresses these challenges and enhances user satisfaction. The UCD method applied in this study consists of four stages: Understanding and Specifying the Context of Use, Specifying User Requirements, Designing Solutions to Meet User Requirements, and Evaluating Against Requirements. Evaluation was conducted using the System Usability Scale (SUS) through a questionnaire distributed to 96 respondents. The results indicate an SUS score of 90.35, categorized as “Excellent,” which demonstrates that the proposed application design achieves a very high level of usability and user satisfaction. These findings suggest that implementing the UCD approach effectively produces an intuitive, efficient, and user-oriented application design, which can significantly improve customer loyalty and engagement with Ququ Bakery.
Integrating Agile and Business Metrics into Backlog Prioritization: A Case Study at PT. XYZ
Backlog prioritization is an essential component of Agile development because it makes sure that resources are used in the best way possible and that business value is maximized. The Effort-influence Matrix gives you a way to prioritize items in your backlog based on how much effort they will take and how much influence they might have. However, just prioritizing them doesn't mean you'll be successful in the long term. Integrating Agile Metrics—such as velocity, cycle time, and lead time—with Business Metrics—such as customer satisfaction, retention, and market adoption—offers a more comprehensive approach to guiding decision-making. This study examines how Product Owners at PT. XYZ applies the Effort-Impact Matrix while incorporating Agile and Business Metrics to align development priorities with organizational objectives. This study employed qualitative research design, drawing on structured interviews, project documentation, and literature review. The findings show that combining prioritizing frameworks with performance indicators improves decision-making, increases alignment with company goals, and leads to more predictable delivery outcomes. This study contributes to the literature by being among the few to empirically demonstrate how Agile and Business Metrics can be systematically integrated into backlog prioritization using the Effort-Impact Matrix.
Does JASTIP as Facilitating Condition Affect the E-Marketplace Adoption in Developing Region?
E-marketplaces are expanding in developing regions but face barriers related to logistics costs and infrastructure. JASTIP, an informal proxy shopping service, has emerged as a potential solution to improve accessibility. This study examines the influence of perceived cost, social influence, hedonic motivation, and facilitating conditions on e-marketplace adoption using a modified UTAUT model analyzed through PLS-SEM with data from 185 respondents in West Papua. The findings show that hedonic motivation and social influence significantly increase user intention, perceived cost directly affects usage behavior, and facilitating conditions strengthen intention but do not directly influence usage. These results emphasize the role of JASTIP in reducing costs and improving accessibility. This study recommends improving JASTIP access and infrastructure, implementing flexible pricing policies, and promoting community-based marketing to enhance e-marketplace adoption in developing regions.
The Influence of Social Media and Online Shopping Habits on Consumer Behavior and Social Identity in Indonesia’s Digital Economy
This study aims to examine the impact of social media and online shopping habits on consumer behavior and social identity within the context of Indonesia’s digital economy. The research analyzes how social media and online shopping habits influence consumer behavior and social identity within the framework of Indonesia’s digital economy. A quantitative methodology was employed, collecting data from 280 participants using a Likert-scale questionnaire, and Structural Equation Modeling with Partial Least Squares (SEM-PLS) was applied to test the relationships. The findings reveal that social media influence and online shopping habits significantly affect consumer behavior and social identity. Furthermore, consumer behavior and social identity were found to strongly contribute to the growth of the digital economy. These results highlight the crucial role of digital platforms in shaping consumer actions and identities, thereby driving Indonesia’s digital economy. This study provides practical insights for business practitioners to leverage social media and e-commerce to influence consumer behavior and for policymakers to promote supportive digital infrastructure.
Website Database Development for Radyakartiyasa using the Directus Headless CMS
Kawadenan Radyakartiyasa, under the auspices of the Karaton Ngayogyakarta Hadiningrat, plays a vital role in cultural preservation and the management of historical tourism destinations. To broaden the reach of information and enhance the promotion of cultural events and Kagungan Dalem heritage sites, a website database was developed as the core foundation of the digital information system. The development process adopted the Agile methodology with the Scrum framework, involving sprint planning to prioritize collections, daily scrums to synchronize progress, sprint reviews to evaluate outcomes, and sprint retrospectives to improve processes. The system was built using the Directus Headless CMS, which decouples the backend and frontend, enabling non-technical teams to manage content efficiently while supporting cross-platform integration. The resulting system includes core collections such as Navigation, Destination Index, Event Index, Hero Banner, FAQ, and other supporting collections, all designed to systematically accommodate information and support multilingual display. These features significantly improve content management efficiency by accelerating information updates, reducing data redundancy, and simplifying content organization in multiple languages. Inter-collection integration ensures consistent information across all website pages, enabling users to quickly and systematically access the data they need. Collection endpoint testing was conducted using Postman to verify that all functions operate according to design specifications and support more organized content management.
Lightning Risk Mapping in West Sumatra using Kernel Density Estimation and Simple Additive Weighting
This study analyzes the spatial distribution, occurrence frequency, and lightning hazard vulnerability levels in West Sumatra during March 2024 using a geospatial and multi-criteria approach. By integrating meteorological data with land vulnerability assessments, the research applies Kernel Density Estimation (KDE) to map lightning strike density and employs Simple Additive Weighting (SAW) to incorporate land cover and socio-economic factors into the vulnerability evaluation. This combined approach produces a comprehensive lightning hazard risk map, identifying high-risk zones covering 3.01%, medium-risk zones 21.72%, low-risk zones 25.75%, and safe zones 49.52% of the total area. This innovative methodology represents a significant step forward in improving lightning risk management strategies and disaster mitigation policies, particularly in regions vulnerable to climate change and rapid urbanization. The findings not only highlight the importance of integrating spatial lightning data with environmental vulnerability assessments but also support practical applications in early warning systems and spatial planning to effectively minimize the impact of lightning hazards.
Lung Cancer Classification using the Naïve Bayes Method with SMOTE
The primary challenges addressed in this study include delays in the early detection of lung cancer due to non-specific initial symptoms, the limitations of the Naïve Bayes algorithm in processing categorical data such as symptoms, gender, and smoking habits, as well as class imbalance issues in the dataset that can affect model accuracy. To overcome these challenges, the SMOTE (Synthetic Minority Over-sampling Technique) method was applied to improve classification performance. This study aims to implement the Naïve Bayes algorithm for lung cancer classification and compare its performance on imbalanced data versus data balanced using SMOTE. The methodology consists of data preprocessing, encoding, applying SMOTE for balancing, and classification using Naïve Bayes. Evaluation was performed using three data split ratios: 80:20, 70:30, and 60:40. The results show that applying SMOTE led to performance improvements, with the most significant gains observed at the 60:40 split ratio. In this case, model accuracy improved from 88.29% to 93.19%. For the “Yes” (positive) class, precision remained at 0.96, recall at 0.91, and F1-score at 0.93. However, for the “No” (negative) class, precision improved from 0.40 to 0.90, recall from 0.60 to 0.96, and F1-score from 0.48 to 0.93. Conversely, slight decreases in accuracy were observed for the 80:20 and 70:30 ratios after SMOTE application. These findings demonstrate that SMOTE significantly enhances model performance at the 60:40 ratio, not only in terms of accuracy but also in recall and F1-score, which are crucial for reducing false negatives in the minority (“Yes”) class. This is especially critical in early detection, as correctly identifying actual cancer cases is more important than merely maintaining overall accuracy. Although SMOTE did not always improve accuracy at other ratios, it still contributed to better cancer case detection. Therefore, its application should be considered carefully, balancing overall accuracy with clinically meaningful metrics.
WhatsApp-Based Drug Reminder Information System
Timely and accurate medication use is a critical factor in the success of treatment, especially for patients with chronic illnesses such as diabetes, hypertension, and heart disease. However, patient non-adherence remains a major challenge. Based on observations at Panglayungan Community Health Center (Puskesmas), Tasikmalaya City, data recording and visit reminders are still handled manually. This often leads to service delays, data errors, and high rates of patient absenteeism. To address these issues, this study designed and developed a WhatsApp-based medication reminder information system capable of sending automated notifications to patients according to schedules set by healthcare staff. The application includes features such as patient data management, visit scheduling, and automated message delivery via the WhatsApp API. The system was developed using the waterfall software engineering model, starting from requirements analysis, system design, implementation, and testing. Test results demonstrated that the system successfully delivered medication reminders on time and effectively reached patients. Moreover, integrating WhatsApp as a reminder medium proved to be user-friendly and widely accepted, as it does not require users to install additional applications. This system not only improves patient adherence to prescribed therapies but also assists healthcare providers in data management and patient monitoring more efficiently. Furthermore, the system was designed with a lightweight, responsive web-based interface to ensure ease of use for health center staff. With a structured database, it helps reduce administrative workload and minimizes the risk of data loss.
Design and Implementation of a Tourism Website User Interface for Yogyakarta using the Scrum Method
The Radya Kartiyasa Website, a cultural tourism promotion platform under the management of the Kawadenan Radya Kartiyasa unit, was designed and implemented as a medium for promoting cultural tourism with a user interface that emphasizes both aesthetics and local cultural values. The primary objective of developing this website is to provide comprehensive information about tourism destinations within the Keraton area and cultural events organized by the Keraton, while simultaneously enhancing the digital image of the Yogyakarta Palace as a premier cultural tourism destination. The development process adopted the Scrum methodology. This approach facilitated short development cycles, high flexibility, and strong team collaboration, as reflected in the stages of product backlog, sprint planning, sprint execution, daily scrum, sprint review, and retrospective. The outcome of this implementation includes the creation of the homepage, destination index, museum visit promotions, event index, museum gallery, about us page, event detail pages, destination detail pages, and a contact us form page. The contribution of this study lies in integrating a modern development methodology with a culturally inspired interface design, aiming to make the website an effective digital tool for promoting, educating, and preserving the cultural heritage of the Yogyakarta Palace.
Adaptive Integration Optimization of Naïve Bayes and Information Gain for Diabetes Complication Prediction
Diabetes mellitus is a major global health issue with a steadily increasing prevalence in Indonesia, including the Surakarta region. Diabetes complications are among the leading causes of mortality and reduced quality of life in patients, while also imposing a significant economic burden. This situation highlights the need for an accurate predictive model to identify both high- and low-risk patients, particularly in primary healthcare facilities that still face limitations in conducting comprehensive risk prediction. This study aims to develop a predictive model for diabetes complications by optimizing the adaptive integration of the Naïve Bayes algorithm with Information Gain for feature selection. The dataset used includes demographic variables (age, gender), clinical data (blood glucose level, HbA1c, blood pressure), medical history (family history of diabetes), and lifestyle factors (physical activity, dietary patterns). The results indicate that the pure Naïve Bayes algorithm achieved an accuracy of 75%. After applying Information Gain for feature selection, the accuracy improved to 87.5%, representing a 12.5% increase. These findings demonstrate that integrating Naïve Bayes with Information Gain can produce a more accurate prediction model for diabetes complications, making it a potentially effective decision-support tool for healthcare professionals in preventing complications and reducing the burden on the healthcare system.
A Comparative Study of K-Means, Gaussian Mixture Model, and Spectral Clustering for Facial Emotion Recognition
Facial emotion recognition is one of the key areas of artificial intelligence that has been widely applied in human–computer interaction. This study compares three clustering algorithms—K-Means, Gaussian Mixture Model (GMM), and Spectral Clustering—to group facial expressions from the FER-2013 dataset, which was sampled down to 1,820 images. Facial features were extracted using Gabor filters and reduced in dimensionality using Principal Component Analysis (PCA) to efficiently preserve essential information. The evaluation was conducted using Silhouette Score, Davies–Bouldin Index, and clustering accuracy estimation. The results show that Spectral Clustering achieved the best performance, with a Davies–Bouldin Index of 0.464 and an accuracy of 95.36%, followed by GMM (Silhouette Score 0.526) and K-Means (Silhouette Score 0.523). Furthermore, PCA with 80% variance retention produced an effective 1D feature representation, allowing clustering results to be visualized in a simple yet informative manner. These findings suggest that the choice of clustering algorithm should be aligned with the desired trade-off between system accuracy and efficiency.
Development of Waste Management Application using GIS
Waste management is a serious challenge in modern society due to rapid population growth and urbanization. The inefficiency of conventional systems such as limited coordination, infrastructure, and reporting calls for a technology-based approach. This study develops a web-based waste management application integrated with Geographic Information System (GIS) to improve waste collection, monitoring, and reporting. The development method used is prototyping, allowing iterative improvement based on user feedback. The application includes key features such as waste bin location data entry, interactive map visualization with Leaflet.js, and transaction reporting. The result showed that the app is able to provide real-time visualization of waste bin locations, display waste statistics, and improve the transparency and efficiency of waste management operations. This research contributes to the development of environmental information systems that support data-driven decision-making and environmental conservation.
High Precision Deep Learning Model for Road Damage Classification using Transfer Learning
Roads are critical infrastructure that frequently experience damage, directly impacting transportation safety and efficiency. Manual road damage inspection is time-consuming and resource-intensive, highlighting the need for automated, image-based approaches. This study compares two Convolutional Neural Network (CNN) architectures—MobileNetV2 with transfer learning and a custom-built CNN—for classifying road surface damage severity. The dataset consists of 1,800 road surface images evenly distributed across three categories: good, minor damage, and severe damage. All images were normalized, augmented, and resized, followed by evaluation using 5-Fold Cross-Validation to ensure robust performance. Experimental results show that MobileNetV2 achieved an accuracy of 98%, outperforming the custom CNN, which achieved 89%. These findings demonstrate the effectiveness of transfer learning in improving classification accuracy with limited data and highlight the potential of MobileNetV2 for efficient, real-time road damage detection systems that can be integrated into intelligent infrastructure monitoring solutions.
User Review Automation: Detecting Actionable Complaints on Gojek in the Play Store using the LSTM Method
This study aims to develop an automatic complaint detector for Gojek app reviews using Long Short Term Memory (LSTM). The dataset consists of 225,002 user reviews on the Google Play Store. The purpose of this study itself is to facilitate the service team in understanding the shortcomings of the application complained by users. Automatic complaint detection will facilitate the service team to take action to resolve the problems experienced by users. Therefore, the review data provided by users is properly processed using LSTM to create an effective and efficient detection system. Processing is carried out using three different data sharing ratios, namely 90:10, 80:20, and 70:30 to ensure that the system is stable and effective. The accuracy results of the three data sharing ratios reached above 90%, thus proving that the system is able to detect complaints well. A pre-built dashboard is used to visualize the results of the system built using LSTM to facilitate monitoring the classification results. This system is expected to facilitate companies in detecting all user complaints and finding solutions to improve services to provide comfort for users.
Analysis of Student Job Readiness in Facing AI Transformation Towards Society 5.0 in XYZ University Students
This study analyzes the factors influencing job readiness among XYZ University students in the context of Society 5.0. The novelty of this research lies in its application of Soft Systems Methodology (SSM) combined with quantitative questionnaire analysis, enabling both systemic and measurable problem identification—an approach still rarely applied in studies of student job readiness in Indonesia. A survey was conducted with 355 active students across five study programs and analyzed using IBM SPSS 2.5 to ensure data reliability. The findings reveal that AI literacy, psychological readiness, and the use of digital technologies have a significant impact, while human-centered skills such as communication and empathy serve as key reinforcing factors. Practically, this study encourages universities to integrate technology-based technical skills with soft skills in their curricula and training programs to produce graduates who are more adaptive, competitive, and prepared for the demands of Society 5.0. The study’s contribution lies in its use of SSM to map complex issues and formulate strategies for improving job readiness. The implication is that universities should embed technical competencies, soft skills, and AI-based training into adaptive learning ecosystems to better prepare graduates for the challenges of Society 5.0.
Computika: A Mobile-based Interactive Learning Media for Computer Systems Subject in Vocational High Schools
Vocational high school students often face difficulties in understanding computer systems, creating the need for an innovative and interactive learning medium. This study aims to develop Computika, a mobile-based learning platform designed to enhance students’ comprehension of computer systems. The research employed a Research and Development (R&D) approach using the 4D development model (Define, Design, Develop, Disseminate). Validation was conducted by subject-matter experts, media experts, and tested on 21 students. The validation results indicated a very high level of feasibility: 100% from subject-matter experts, 94% from media experts, and positive responses from both students (94%) and teachers (91%). These findings confirm that Computika is highly feasible to be implemented as an innovative interactive learning solution that improves students’ understanding and learning motivation in computer systems courses.