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 12 times a year, in January, February, March, April, May, June, July, August, September, October, November and December. 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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Identification of Usability Issues in the ELLS Website at Amikom University Purwokerto
The transformation of information technology has significantly influenced the education sector, particularly in the implementation of online learning. One of its key contributions is enabling lecturers and students to interact through e-learning platforms without the constraints of time and space. Amikom University Purwokerto, as one of the higher education institutions, has developed an e-learning platform called ELLS. Based on observational analysis, several issues are frequently encountered by users, including confusion in navigation, difficulty in locating information, repeated uploading of course materials for the same subject across multiple classes, the absence of notification messages, unexpected system logouts during grading activities, and a visually unappealing interface. To further investigate usability issues in the ELLS website, Nielsen’s heuristic evaluation method—consisting of 10 principles—was applied. The findings reveal that, out of 8 main features tested, a total of 19 usability issues were identified. The most frequently occurring problems are related to the principles of visibility of system status, user control and freedom, and consistency and standards across interface elements. One critical issue with a severity level of 4 (usability catastrophe) was identified in the assignment grading feature, which poses a risk of sudden loss of student grade data. In addition, 15 issues were categorized with severity level 3 and 3 issues with severity level 2. These findings indicate that although the ELLS system is functionally operational, there are still numerous interface design aspects that need improvement to enhance efficiency, clarity, and user comfort, particularly for lecturers and students as the primary users.
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Integrated Chicken–Fish–Vegetable Farming based on a Multi-Modal Sensor System using ESP32 and Raspberry Pi
This study aims to develop an integrated Chicken–Fish–Vegetable (AISa) farming system based on a multi-modal sensor system controlled by ESP32 and Raspberry Pi. In this system, the ESP32 functions as a microcontroller responsible for reading multi-modal sensor inputs in the form of analog data, converting them into digital signals, and transmitting the data to the Raspberry Pi via a serial communication protocol. The Raspberry Pi, in turn, processes the digital sensor data and manages overall control operations within the AISa farming system. The multi-modal sensors used in this study include a camera for farm monitoring, a water level sensor for detecting water height, a DHT11 sensor for air temperature and humidity, an LDR for light intensity detection, an infrared sensor for sunlight detection, a TDS sensor for measuring water nutrient levels, a pH sensor for detecting water acidity, and a DS18B20 sensor for monitoring fish pond temperature. The method applied in this study for converting analog sensor data involves utilizing the GPIO pins of the ESP32 to perform analog-to-digital conversion, while the Raspberry Pi is employed to process the AISa farming data due to its computational capabilities similar to a computer. The results indicate that the multi-modal sensor system is capable of providing comprehensive environmental information about the AISa farming conditions. The data are initially captured by the ESP32 in analog form, converted into digital data, and then transmitted to the Raspberry Pi for further processing. In addition, this study establishes a data design framework for future research involving the integration of AISa greenhouse farming systems with Internet of Things (IoT) technologies.
Exploration of Data Augmentation in Xception for Waste Classification
The increasing volume of waste worldwide has led to significant challenges related to pollution, waste management, and recycling. These issues require innovative solutions to enhance the waste management ecosystem, such as the implementation of Smart Waste Management, which leverages information technology and artificial intelligence. This study aims to implement the Xception Convolutional Neural Network (CNN) model for waste classification, explore various data augmentation techniques, and identify optimal model configurations for this task. The research methodology consists of several stages, including data preparation, model building and training, model adaptation for classification tasks, model evaluation, iterative experimentation, and saving and reloading the trained model. The dataset used in this study is the TrashNet dataset obtained from Kaggle, consisting of 2,527 images across several classes: cardboard, glass, metal, paper, plastic, and trash. Based on the optimization process, the selected hyperparameters include a batch size of 32, 64 convolutional filters, the Adam optimizer (learning rate = 0.0001), and a dropout rate of 0.25. After training for 100 epochs, the model achieved a training accuracy of 99% with a loss of 0.7%, and a validation accuracy of 87% with a validation loss of 52%. Evaluation on the test dataset yielded an accuracy of 76%, precision of 79%, recall of 75%, and an F1-score of 75%. The application of data augmentation techniques—such as scaling, translation, and color space transformation—resulted in performance improvements, increasing accuracy by 13%, precision by 11%, recall by 13%, and F1-score by 12%. This study contributes by implementing the Xception model on the TrashNet dataset for waste classification and proposing several data augmentation methods that provide empirical evidence to support or challenge existing approaches. The findings offer practical insights for the development of Smart Waste Management systems, enrich the literature through experimental results, and provide a comparative analysis of data augmentation techniques suitable for the TrashNet dataset.
Accessibility Evaluation of University Websites in Indonesia based on WCAG 2.2 Guidelines
Universities use websites as their main portal and main source of information. However, some university websites in Indonesia still show accessibility issues and do not meet the Web Content Accessibility Guidelines (WCAG) 2.2 standards. This research aims to evaluate the accessibility of five university websites in Indonesia based on the Similarweb platform. The evaluation focused on color blindness, attention deficit, and limited movement. These three disabilities are considered to have a significant impact on web accessibility. The method used in this study was WCAG-EM by using a combination of automated and manual evaluation. The automated evaluation used WAVE and Siteimprove Accessibility Checker tools. Meanwhile, manual evaluation adopted the evaluation steps from the Accessibility Insights tool by Microsoft. The results of the automated evaluation showed that the accumulated issues obtained from all university homepages were 188 issues. There are 7 types of issues identified through manual evaluation. The most common accessibility issue found is components with low contrast between the foreground and background. These results indicate that university websites in Indonesia, based on Similarweb rankings, still do not meet the international standards WCAG.
Instruction-Efficient and Parallelized AES using CUDA and PTX for Data Encryption
This research presents an instruction-efficient and parallelized implementation of the AES-256 encryption algorithm using NVIDIA CUDA with inline PTX to optimize instruction usage and execution performance on GPUs. Conventional AES implementation on CUDA often suffers from redundant instructions and high computational overhead, limiting encryption throughput. To address this, three implementation variants were developed: a baseline version, a parallelized version, and an inline PTX-optimized version. The proposed PTX-enhanced AES reduces instruction redundancy through vectorized memory access, byte permutation, and logical operation consolidation using PTX instructions. Instruction analysis using NVIDIA Nsight Compute and cuobjdump revealed a 66% reduction in executed instructions compared to the baseline, primarily due to optimized AES operations. Performance evaluation of 32 MB plaintext demonstrates a 16-fold improvement in throughput, increasing from 2.73 Gbps in the baseline to 43.9 Gbps in the PTX-optimized version, with cycles per byte decreasing from 2.63 to 0.164. These results confirm that integrating fine-grained PTX control within CUDA enables substantial gains in encryption efficiency while maintaining correctness and security. The proposed approach provides a scalable foundation for high-performance cryptographic operations in GPU-based computing environments.
Analysis of Service Quality of the Sorong Regency Government Website using the WebQual 4.0 Method
The rapid development of information technology has encouraged local government to adopt websites as platforms for delivering public information and services. This study aims to analyze the service quality of the Sorong Regency Government website based on user perceptions using the WebQual 4.0 method, which consists of three dimensions: usability, information quality, and service interaction quality. This study adopts a quantitative approach, with data collected through an online questionnaire administered to 49 respondents. The data were analyzed using SPSS, including validity and reliability tests and descriptive statistics. The results show that all 18 questionnaire items are valid and reliable, with a Cronbach’s Alpha value of 0.763. The mean scores for each dimension are 3.43 for usability (agree), 3.39 for information quality (somewhat agree), and 3.37 for service interaction quality (somewhat agree). These findings indicate that the overall service quality of the Sorong Regency Government website ranges from moderate to good, with usability identified as the strongest dimension. The novelty of this study lies in its focus on evaluating a local government website in a developing regional context, providing user-based evidence on WebQual 4.0 dimensions that remain underexplored in similar settings. This study provides empirical evidence that can support efforts to improve the quality of digital public services in Sorong Regency.
Enhancing the Diagnosis of Cerebral Vascular Occlusion in Brain MRI Images using Advanced Image Processing Techniques
The paper presents an automated and computationally lightweight system that aims at diagnosing the presence of the ischemic stroke in the brain based on the MRI images and prioritizes mathematical clarity over the overpowering complexity. Workflow starts with AWCES algorithm to improve the quality of images, which is applied based on adaptive windowing determined by the entropy, and then proceeds to Watershed segmentation based on markers to word out the areas that are suspected of being vascularly blocked. The texture descriptors are then obtained using the Local Binary Patterns (LBP) and sent to a Random Forest classifier which differentiates between the damaged and the healthy brain tissue. The training stage has been integrated with the SMOTE technique to address the problem of class imbalance that is severe in the dataset. With a stratified five-fold cross-validation, the system demonstrated an AUC of 0.99, a specificity of 94% and a recall of 70%. These results indicate that the classical methods based on open mathematics can be competitive with deep learning networks, providing a high-quality and quick diagnostic methodology that could be used as a primary solution in the diagnosis of a stroke and as a complement to the exploratory three-dimensional visualization.
Mountain Hiking Route Recommendation System using Content-based Filtering with SBERT and Cosine Similarity
Mountain hiking activities in Indonesia have increased significantly; however, information on hiking routes is often dispersed informally and remains subjective, increasing the risk of a mismatch between hikers’ abilities and route characteristics. This study develops a hiking route recommendation system using a content-based filtering approach powered by Sentence-BERT (SBERT) to extract semantic features from narrative route descriptions, which are integrated with numerical GPX attributes. Similarity is computed using cosine similarity. Experimental evaluation on 39 hiking routes on Java Island shows an average Precision@5 of 0.60, outperforming TF-IDF (0.25) with a 140% improvement. The system proves effective in capturing implicit semantic relationships, thereby providing more relevant and context-aware recommendations.
Implementation of a Deep Learning Model for Real-Time Detection and Classification of Toraja Traditional Motifs (Pa’ssura’) for Digital Cultural Preservation
Toraja traditional motifs, known as Pa’ssura’, represent an intangible cultural heritage rich in philosophical values and deep cultural identity. However, the authenticity and understanding of the meanings behind these motifs are at risk of erosion among younger generations due to the lack of interactive and technologically relevant learning media. This study aims to bridge this gap through an innovative digital cultural preservation strategy by implementing deep learning technology. Specifically, the research focuses on developing a real-time object detection and classification system using a Convolutional Neural Network (CNN) architecture, particularly the YOLO11s model. The main research stages include constructing an annotated image dataset for seven primary Pa’ssura’ motifs: Pa’ Barre Allo, Pa’ Kapu Baka, Pa’ Tangke Lumu, Pa’ Tedong, Pa’ Ulu Karua, Pa’ Kadang Pao, and Pa’ Papan Kandaure. These data were collected from both planar media (such as textiles) and non-planar media, including wood carvings and stone engravings. The results show that the developed model achieved a precision of 0.7109, a recall of 0.6708, and an mAP@50 of 0.6910 after 100 training epochs. The implementation of data augmentation techniques proved effective in increasing the dataset size—from 1,050 images before augmentation to 2,520 images after augmentation—thereby significantly enhancing the model’s robustness in detecting and classifying motifs across both planar and non-planar media. This study produces an accurate and practical model that can be applied as an educational tool in mobile applications. Furthermore, the model plays an important role in preserving Toraja cultural heritage through digitalization.
Analysis of NGINX Static Content Delivery on the Performance of a Docker-based Photo Gallery Backend
Photo studio websites typically present large volumes of multimedia content through photo gallery modules. In many implementations, this content is served dynamically by the application backend, which can increase processing load and degrade system performance. This study analyzes the impact of static content delivery using NGINX on the performance of a Docker-based photo gallery backend. A quantitative experimental method was applied by comparing two scenarios: dynamic content delivery without NGINX and static content delivery using NGINX. The experiments were conducted using Apache Benchmark with identical request and concurrency parameters, accompanied by monitoring of backend resource utilization. The results show that dynamic delivery produces lower throughput (463.78 requests per second) and higher response time (108.27 ms), along with significant backend CPU usage. In contrast, the use of NGINX increases throughput to 1105.25 requests per second, reduces response time to 45.33 ms, and significantly lowers backend CPU and memory consumption. These findings demonstrate that separating static and dynamic content delivery using NGINX is effective in improving backend performance in Docker-based photo gallery applications.
Performance Evaluation of a Laravel-based Internship Website using MySQL Master–Slave Replication
Single database architectures often experience performance degradation as user load increases because all read and write operations are processed centrally, causing bottlenecks and limiting the scalability of web systems. To address this issue, this study implements database replication using a MySQL Master–Slave architecture combined with the separation of read and write operations in a containerized Laravel-based web system. A quantitative experimental method was applied by comparing the Single Database and Master–Slave Replication configurations implemented in a Docker environment on Windows Subsystem for Linux 2 (WSL2). Performance evaluation was conducted using Apache JMeter at three workload levels low (10 users), medium (50 users), and high (100 users) based on response time, throughput analysis, latency analysis, and error rate. The results show that the Master–Slave configuration provides more stable performance at medium to high workloads, with latency between 0.20 and 0.26 seconds and an error rate of 0% in all test scenarios. Additionally, the highest throughput of 2.01 requests per second was achieved at medium workload, indicating effective read-write separation and better workload distribution through database replication.
Open Government Data Analytics of Tourist Visits In West Java 2014–2024: A Data Science and Philosophy of Science Perspective
Open Government Data (OGD) in tourism provides opportunities for data-driven analytics to support destination management policies. In policy practice, tourism OGD is often accepted at face value as a direct representation of real-world conditions, even though such data are constructed through definitions, recording procedures, and measurement choices. Therefore, a philosophy of science perspective is essential in data governance. This article analyzes an Open Data Jabar dataset on the number of tourist visits by visitor type and district/city in West Java Province for the period 2014–2024 (n = 565; 27 districts/cities; two visitor categories: domestic and international). The data science approach includes data quality auditing (completeness and consistency), time-series aggregation, spatial concentration measurement using the Gini coefficient, and a comparison of shock–recovery patterns in tourist visits before and after the pandemic. The results indicate a decline in total visits of -50.6% in 2020 compared to 2019, with international visits experiencing the sharpest drop (-82.8%). By 2024, total visits reached 64,517,298, dominated by domestic tourists (63,963,443; international share 0.9%). Spatial concentration in 2024 is reflected by a Gini coefficient of 0.429, with the top five regions accounting for 44.2% of total visits. The discussion emphasizes that visitor counts are epistemic representations shaped by definitions, reporting practices, and data cleaning processes. Therefore, policy recommendations should be accompanied by data provenance, metadata, and explicit uncertainty annotations to avoid the reification of indicators.
The Role of Cognitive Offloading in Mediating the Effects of Creativity, Artificial Intelligence, and Critical Thinking on Higher Order Thinking Skills
This study investigates the role of cognitive offloading in mediating the effects of creativity, artificial intelligence (AI), and critical thinking on Higher Order Thinking Skills (HOTS). A quantitative design using Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied. The respondents were undergraduate students from three universities in Indonesia (Universitas Lampung, Universitas Aisyah Pringsewu, and IIB Darmajaya). Using the Slovin formula with a 10% margin of error, 295 questionnaires were distributed, and 202 valid responses were obtained from students who reported frequent use of AI in academic activities. The findings indicate that creativity, AI, and critical thinking positively and significantly influence cognitive offloading. Critical thinking and cognitive offloading significantly affect HOTS, while creativity and AI do not show significant direct effects on HOTS. Cognitive offloading fully mediates the effects of creativity and AI on HOTS and partially mediates the effect of critical thinking on HOTS. The structural model explains 51.9% of the variance in HOTS (R² = 0.519). These results highlight that AI integration alone does not automatically enhance higher-order thinking; instead, structured cognitive regulation strategies—particularly cognitive offloading and critical thinking—are essential for optimizing AI-based learning in higher education.
Internet Bandwidth Management using a MikroTik Router at Shanti Bhuana Institute
This study aims to evaluate the effectiveness of bandwidth management using a MikroTik router in optimizing network capacity distribution at Shanti Bhuana Institute. The methods employed include designing a bandwidth allocation scheme based on the priority needs of each unit, implementing the configuration on a MikroTik router, and measuring network performance parameters such as throughput, delay, packet loss, and jitter before and after the implementation of bandwidth management. The testing was conducted in a campus environment involving active users from various academic and administrative units. The results show that the implementation of bandwidth management increased the average throughput from 412 kbps to 1,105 kbps, reduced the average delay from 22.4 ms to 5.2 ms, and decreased jitter from 8.7 ms to 2.1 ms. Packet loss remained below the 1% threshold both before and after implementation. All units received proportional bandwidth allocation according to their operational needs, enabling activities such as web browsing, email communication, and lecture material downloads to run without mutual interference. This study concludes that MikroTik-based bandwidth management is more effective than relying solely on high bandwidth availability, as it is capable of preventing network congestion in multi-user campus environments.
Analysis of the Provider State Management Pattern for Performance and Maintainability in a Flutter-based Mobile Pharmacy Application
State management in modern mobile applications is a critical aspect that affects both user interface (UI) rendering performance and code maintainability. Previous studies on Flutter state management have generally used simple catalog applications as test objects and focused on performance comparisons between approaches without quantitatively measuring maintainability aspects. This study aims to analyze the effectiveness of implementing the Provider state management pattern in terms of UI rendering performance and code maintainability, using a real-world business application case study—namely, the Apotek Berkah mobile application. The research adopts an applied research methodology with a case study approach. The application consists of 20 Dart files with a total of 5,899 lines of code (LOC), developed using Flutter with a MultiProvider architecture and dependency injection. Performance measurements were conducted using Flutter DevTools, including widget rebuild count, frame rendering time, and memory usage, with comparisons against setState as the baseline. Maintainability was assessed through nine metric aspects, including code distribution across layers, provider class metrics, provider access patterns, screen complexity, architectural patterns, and dependency graph analysis. The results indicate that the Provider pattern reduces widget rebuild count by an average of 52.3% compared to setState, with frame rendering time consistently below 16 ms. In terms of maintainability, the Provider architecture demonstrates low coupling (all providers depend solely on ApiService), zero inter-provider dependency, and well-structured separation of concerns. The novelty of this study lies in the combined evaluation of performance and maintainability of the Provider pattern within the domain of a pharmacy business application, which involves CRUD operations, inventory management, and point-of-sale (POS) transactions—a domain that has not been extensively explored in prior Flutter state management literature. The main contribution is empirical evidence that the Provider pattern offers an optimal balance between performance, maintainability, and implementation complexity for medium-scale mobile applications.
Development of a Web-based Library Information System Integrated with a Webcam-based Barcode Scanner
The process of borrowing and returning books in some school libraries is still carried out manually, resulting in slower service times and a higher risk of data recording errors. Therefore, a library information system is needed to improve transaction efficiency and data management accuracy. This study aims to develop a web-based library information system integrated with a webcam-based barcode scanning feature, eliminating the need for additional scanning devices. The research employs a Research and Development (R&D) approach using the Waterfall model, which includes the stages of requirements analysis, system design, implementation, and testing. System evaluation was conducted using Black Box Testing, transaction time efficiency analysis, data accuracy measurement, assessment of lighting intensity on webcam-based barcode scanning performance, and usability testing using the System Usability Scale (SUS). The results indicate that all system functions operate properly. The system reduces the average transaction time from 72.06 seconds to 24.48 seconds (66% efficiency improvement) and increases data recording accuracy from 60% to 100%. Webcam-based barcode scanning performs optimally under lighting conditions of ≥150 lux. The usability evaluation achieved a SUS score of 85.8 (excellent). However, system performance is still influenced by lighting conditions and camera quality. Future research may focus on enhancing the system by incorporating image processing techniques or alternative identification technologies to further improve scanning accuracy.
Improving Cloud Task Scheduling in Cloud Sim Plus using Demand-Aware VM Selection
Deep reinforcement learning and metaheuristics are becoming popular ways to study cloud task scheduling to make things like makespan and completion time better; however, these methods often require a lot of computing power and can make it harder to understand and repeat the results. This paper suggests a simple Demand-Aware VM Selection policy for CloudSim Plus (simulated tasks) that improves the basic scheduling process by better matching tasks with virtual machines based on task needs, while leaving the rest of the simulation the same. The method was tested against the default time-shared baseline with three workload sizes (200, 500, and 1000 cloudlets) and six random seeds (42-47). The experimental results demonstrate that the proposed method drastically shortened the overall time of task completion: it raised the average time by 35.65% for 200 cloudlets (A computational task/work unit submitted for execution is CloudSim abstraction) and 28.86% for 500 cloudlets. In terms of average completion time, the newly planned lowered the average by 26.68% at 200 cloudlets and 28.53% at 1000 cloudlets; on the other hand, the 500-cloudlet scenario showed very high variability and even a slightly negative average improvement (-1.42%), indicating that completion-time averaging was sensitive under that workload regime even though there was a strong makespan gain. The results show that demand-aware binding is a clear, repeatable, and easy-to-add improvement for scheduling in CloudSim Plus, which can serve as a better starting point or as part of more advanced optimization and learning systems.
Advanced Sensor Data Analysis using Big Data-Enhanced Algorithms
Traditional IoT anomaly detection systems lack the ability to cope with the increase in dimensionality, the constraints related to processing big data and the problem of non-interpretable features extraction. This article describes a complete flow integrating Apache Spark data preparation, PCA for dimensionality reduction (from 744 to 12 components that retain 92.7% variance), and CatBoost gradient boosting for classification. Performing a thorough benchmarking of six algorithms on the Intel Berkeley Research lab dataset (n=30, 221 instances) demonstrates CatBoost as the best method obtaining F1-score=0.97, precision=0.97, accuracy=98.7% with 3-8% margin of improvements over XGBoost, LightGBM, Random Forest, and SVM methods. Temperature changes (PC1:0.37 factor) and humidity variations (PC2:0.29) became the major indicators of anomalies. The proof of computational feasibility by training finished in 45.2 seconds and making predictions under 35 seconds per batch on consumer Intel i7/16GB hardware, production level for environmental monitoring and industrial IoT applications is confirmed.
A Large-Scale Open Dataset of Computer Science Research Papers (2020–2025)
The rapid growth of publications in different fields, such as computer science, required well-structured datasets to support data-driven research. This paper presents an open large-scale dataset of computer science research papers published between 2020 and 2025, collected from Crossref metadata using the Crossref REST API. A structured keyword-based retrieval framework was developed to collect papers and their associated metadata. Preprocessing techniques, including cleaning, normalization, and validation were also made on the collected data. The introduced dataset has 4,313,328 research paper records which represents one of the largest structured collections of computer science publications for the specified period. The dataset provides comprehensive metadata fields that enable large-scale analysis, research trend identification, collaboration network exploration, and the recommendation systems development.
Development of a Flask-based Application for Bank Customer Churn Prediction as a Decision Support Tool
Customer churn prediction is a crucial aspect of the banking industry for maintaining customer loyalty and reducing the cost of acquiring new customers. This study aims to develop a web-based decision support system capable of predicting potential customer churn using the Gradient Boosting Machine (GBM) algorithm. The dataset used is the Bank Customer Churn Dataset, consisting of 10,000 customer records with 14 attributes. The research stages include exploratory data analysis and preprocessing, which involves data cleaning, categorical feature encoding, feature engineering (BalanceSalaryRatio, TenureByAge, CreditScoreGivenAge), and data balancing using SMOTE to address class imbalance. The GBM model was trained on the balanced dataset and evaluated using accuracy, precision, recall, and F1-score metrics. The evaluation results show that the model achieved an accuracy of 83.95%, with a recall of 67.32% for the churn class, indicating a strong capability in identifying customers at risk of churn. Feature importance analysis reveals that Age and NumOfProducts are the most dominant features, contributing approximately 77% to the prediction. The model was then implemented in a Flask-based web application with an HTML and CSS interface, enabling non-technical users to perform real-time churn predictions. This system is expected to assist banking institutions in designing more targeted and data-driven customer retention strategies.
Analysis of Government Employees’ Information Security Awareness: A Case Study of Pusinfowas BPKP
The utilization of information technology as a tool that is widely believed to facilitate business processes within organizations is inseparable from challenges related to information security threats. According to the 2024 cyber incident report issued by the National Cyber and Crypto Agency (BSSN), data exposure threats accounted for 58.34%, with most incidents originating from the government administration sector. Humans are considered the weakest link in information security; therefore, the primary effort to improve security can begin with measuring the level of security awareness. Among the various work units within BPKP, Pusinfowas, as the central information technology management unit, is considered an appropriate sample for evaluation and is expected to contribute to improving information security awareness across other units. This study employs the Human Aspects of Information Security Questionnaire (HAIS-Q) model to measure the level of information security awareness among employees at Pusinfowas. The HAIS-Q model consists of three dimensions—knowledge, attitude, and behavior—and seven focus areas: password management, email use, internet use, social media use, mobile device use, information handling, and incident reporting. The results indicate that employees’ information security awareness is at a “Good” level, with scores ranging between 80% and 100% across all HAIS-Q dimensions and focus areas.
Feature-Level Usability and User Retention Prediction in TikTok Shop using Naïve Bayes
User retention is a critical factor in the sustainability of social commerce platforms; however, the relationship between usability and retention remains inconsistent across various studies. This study aims to analyze the effect of feature-level usability on user retention in TikTok Shop among Generation Z users, as well as to identify retention patterns using a predictive approach. The study adopts a quantitative approach with an explanatory–predictive research design. Data were collected from 115 respondents through a questionnaire based on the System Usability Scale (SUS), focusing on the usability of live shopping and product review features. The analysis was conducted using binary logistic regression and the Naïve Bayes algorithm. The results show that both features fall into the marginal-low usability category, with scores below the acceptable threshold. However, the logistic regression results indicate that usability does not have a significant effect on user retention. On the other hand, the Naïve Bayes model achieved an accuracy of 88.64%, with a macro-average F1-score of 58.71%, but showed limitations in detecting non-retained users, as indicated by a minority-class recall of 14.29% due to class imbalance. These findings suggest that feature-level usability has limited explanatory power in predicting user retention within the social commerce context. This study contributes by reinforcing the argument that user retention is not solely determined by usability and demonstrates that predictive approaches can provide additional insights that are not captured by explanatory models.
Enhancing Air Traffic Forecasting Accuracy at Hang Nadim Airport Using ARIMA-Neural Network
Passenger traffic fluctuations at Hang Nadim International Airport exhibit extreme volatility influenced by the unique characteristics of the Free Trade Zone (FTZ). Single statistical methods often fail to capture non-linear patterns in this high-variability data. Therefore, this study proposes a Hybrid ARIMA-Neural Network model to enhance forecasting accuracy. The primary variable used is the total monthly passenger volume (arrivals and departures). The research stages began with data preprocessing (80:20 train-test ratio), linear component modeling using ARIMA, residual extraction, and non-linear component modeling using Multi-Layer Perceptron (MLP) to correct residual errors on a one-step-ahead basis. Evaluation results show that the standalone ARIMA model is slow to anticipate extreme surges, resulting in a Mean Absolute Percentage Error (MAPE) of 23.75%. The hybrid model integration proved successful in compensating for these weaknesses, reducing the MAPE value to 12.51%. This achievement represents a 47.33% error reduction from the baseline. In terms of novelty, this hybrid approach provides a highly reliable computational solution for airport management with dual characteristics (tourism and industry) in mitigating uncertainty in capacity planning.
Analysis of User Acceptance and use of the GKI Salatiga+ Application using the UTAUT Model
The advancement of digital technology has encouraged religious institutions to adopt information systems to enhance services for their congregations; however, user acceptance is not always optimal. This study aims to analyze the factors influencing behavioral intention to use the GKI Salatiga+ application by applying the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The study employs a quantitative explanatory approach using Partial Least Squares–Structural Equation Modeling (PLS-SEM). Data were collected through a questionnaire administered to 95 active users of the application. The results indicate that performance expectancy, effort expectancy, and facilitating conditions have a positive and significant effect on behavioral intention, with effort expectancy emerging as the most dominant factor. In contrast, social influence does not play a significant role in explaining the intention to use technology in this context. These findings suggest that, within a voluntary religious community context, the intention to use technology is more strongly influenced by perceived ease of use and system support than by social pressure. This study contributes by demonstrating that the relationships among constructs in the UTAUT model are context-dependent, particularly in community-based and voluntary technology adoption settings.
Comparative Analysis of Accuracy and Performance between Zed Attack Proxy and Burp Suite Community on Website XYZ
Web application security is a critical aspect of protecting the confidentiality, integrity, and availability of data, where Vulnerability Assessment and Penetration Testing (VAPT) serves as a vital method within the system development lifecycle. This study is motivated by the dilemma faced by security practitioners when choosing testing tools between Zed Attack Proxy (ZAP), an open-source solution with full automation capabilities, and Burp Suite Community, an industry-standard tool that imposes throttling limitations in its free version. This study aims to conduct a comparative analysis of the performance of these two tools on Website XYZ, with a particular focus on the accuracy of detecting OWASP Top 10 vulnerabilities, computational resource efficiency, and the effectiveness of fuzzing and spidering in modern web architectures based on JavaScript/AJAX. The research adopts a systematic VAPT approach, including information gathering, vulnerability scanning, and risk analysis, and employs Youden’s Index as a statistical metric to evaluate diagnostic effectiveness. The results indicate that OWASP ZAP achieved a True Positive Rate (TPR) of 75% (6 out of 8 based on Youden’s Index), with a Youden’s Index value of 0.625. In terms of computational efficiency, OWASP ZAP completed the fuzzing process in an average of 4.72 seconds, significantly faster than Burp Suite Community, which required an average of 22.56 seconds due to speed limitations in its free Intruder module. Therefore, this study recommends OWASP ZAP as a more effective tool for penetration testing in environments with limited computational resources, given its superior performance in both endpoint detection accuracy and execution time efficiency.
Development of a Prototype Web-based System for Managing Customer Incoming Letters at PLN ULP Subang using a Design Thinking Approach
This study was conducted to design and evaluate a prototype web-based system for managing customer incoming letters in order to improve efficiency and user experience. The method employed is Design Thinking, which includes the stages of empathize, define, ideate, prototype, and testing, with a focus on identifying issues in incoming mail management that still rely on manual processes. The resulting prototype was evaluated using the System Usability Scale (SUS) and the User Experience Questionnaire (UEQ). The results indicate that the system demonstrates a high level of usability, with an average SUS score of 80.6 (categorized as excellent), and provides a positive user experience, as reflected in UEQ scores for attractiveness (1.6), perspicuity (1.7), efficiency (1.5), dependability (1.4), stimulation (1.6), and novelty (1.3). Therefore, the developed system is capable of improving the efficiency of customer incoming letter management while supporting the digitalization of administrative processes and enhancing service quality.
Design of a Prototype Web-based System for Monitoring New Customer Installation Administration using a User-Centered Design Approach
Monitoring the administrative status of new customers in electricity service environments is still largely dependent on conventional methods, resulting in delays in information delivery, data recording errors, and limitations in comprehensive data oversight. This study aims to develop a prototype web-based system for monitoring the administrative status of new customer installations at PT PLN (Persero) ULP Subang by applying a user-centered design approach. The research adopts the User-Centered Design (UCD) methodology, which consists of four main stages: context-of-use analysis, user requirements identification, design solution development, and evaluation of the proposed design. The prototype was developed using Figma and includes features such as a monitoring dashboard, customer data input, data search, and administrative status updates. The system was evaluated using the System Usability Scale (SUS). The findings indicate that the system achieved an average SUS score of 75.5, which falls into the “good” category, suggesting that the system has an acceptable level of usability and is capable of supporting administrative monitoring activities more effectively.
Analysis and Improvement of the User Interface of the Project Plan Website in the Construction Division of PT Telkom Akses Tasikmalaya using Heuristic Evaluation
A well-designed user interface (UI) is crucial for the effectiveness of information delivery and user experience in organizational systems. Currently, the project planning website of the Construction Division at PT Telkom Akses Tasikmalaya faces issues related to a monotonous interface design and overly complex feature presentation. This study aims to analyze and improve the UI of the website. The method used is heuristic evaluation, focusing on interface simplification and design consistency. Data were collected through a questionnaire consisting of 10 questions based on heuristic principles, distributed to five evaluators representing employees of the construction division. The proposed design was evaluated using a severity rating scale. The evaluation results show an overall average score of 0.74. This value falls within the 0 to 1 scale range, indicating that the new interface does not present significant usability problems, or only superficial (minor) issues. These improvement recommendations significantly simplify feature complexity and enhance visual consistency, thereby improving interaction quality and the effectiveness of project information delivery to users.
Comparative Analysis of Random Forest and Support Vector Machine Algorithms for Predicting Student Retention at Ibnu Sina University
Student retention is a critical challenge facing higher education institutions, including Ibnu Sina University (UIS), where a significant proportion of students risk not completing their studies. Purpose: This study develops and compares predictive models using Random Forest (RF) and Support Vector Machine (SVM) algorithms to classify student retention into three categories: Active, At-Risk, and Inactive. Methods: Administrative data from 2,389 students across 6 study programs (2021/2022–2023/2024 cohorts) were used, encompassing 18 predictor variables including academic performance (GPA, failed credits), demographic, and socio-economic factors. Class imbalance was handled using SMOTE, and hyperparameter optimization was performed via Grid Search with 5-Fold Cross Validation. Results: RF outperformed SVM across all metrics, achieving accuracy of 92.24%, weighted F1-Score of 92.38%, and macro F1-Score of 82.67%, compared to SVM's 87.63% and 87.79%. Feature importance identified Total Failed Credits (0.2847) and Cumulative GPA (0.2134) as the strongest predictors. Novelty: Unlike prior studies focusing solely on academic data, this research integrates non-academic variables (leave history, parental income) and explicitly addresses class imbalance via SMOTE in a multi-class Indonesian higher education context, providing a practical Early Warning System (EWS) framework.
Analysis of User Satisfaction and Loyalty in the BRI Mobile E-Banking Application using a Data Mining Approach (K-Means Clustering and Decision Tree)
The development of information technology has driven digital transformation in the banking sector, particularly through e-banking services that provide convenience, speed, and flexibility for users in conducting financial transactions. The BRI Mobile (BRImo) application, as a digital service of Bank Rakyat Indonesia, plays an important role in improving service quality for customers. However, the emergence of negative perceptions regarding transaction notifications, along with varying user experiences, may affect user satisfaction and loyalty levels. The main problem in this study is that the patterns of user satisfaction and loyalty, as well as the most influential factors affecting both aspects, have not been clearly identified. This study aims to analyze the level of user satisfaction and loyalty toward the BRI Mobile application and to identify the dominant factors influencing them, namely ease of use, security, trust, and system performance. The research employs a quantitative approach using data mining techniques, specifically the K-Means Clustering algorithm to group users based on satisfaction levels, and a Decision Tree to determine the most influential factors affecting user loyalty. Data were collected through questionnaires distributed to 100 active BRImo users. The results show that users can be grouped into several clusters with different satisfaction characteristics. In addition, the Decision Tree analysis reveals that system performance is the most dominant factor influencing user satisfaction and loyalty, followed by ease of use, security, and trust. Overall, the combination of K-Means Clustering and Decision Tree methods provides a more comprehensive understanding of user satisfaction and loyalty patterns, and can serve as a basis for strategic decision-making to improve the quality of the BRI Mobile application services.
Framework for Culinary Entrepreneurship Learning System Integrating Pedagogy and Analytics
Entrepreneurship education in Indonesian vocational higher education remains constrained by theoretical approaches and generic learning management systems (LMS) that do not fully capture the multidimensional competencies required for entrepreneurial readiness. The culinary sector, which contributes nearly 42% of the creative economy’s GDP and is dominated by micro, small, and medium enterprises (MSMEs), highlights the urgent need for tailored pedagogical and technological solutions. This study proposes a conceptual framework for a culinary entrepreneurship learning information system, designed by integrating Deep Learning pedagogy, Innovation Canvas, and learning analytics. Drawing on preliminary findings from focus group discussions with practitioners and surveys of student competence, the research employed a conceptual design methodology guided by human-centered design principles. The resulting framework maps stakeholder needs into system components, including competency dashboards, e-portfolios, gamified features, and analytics-driven monitoring of five core entrepreneurial competencies: mentality, management, legality, branding, and networking. The framework demonstrates how reflective pedagogy can be operationalized into an information system architecture, bridging the gap between entrepreneurship education and digital innovation. Academically, it extends the literature on integrating pedagogy and information systems, while practically, it offers a replicable blueprint for higher education institutions seeking to enhance graduate employability and entrepreneurial readiness. The novelty of this study lies in integrating deep learning pedagogy, innovation canvas, and learning analytics into a unified conceptual framework for vocational entrepreneurship education—an approach not previously applied in the culinary education context. Future research should focus on prototyping, usability testing, and empirical validation to ensure the framework’s effectiveness and scalability.
Development of a Web-Based Extracurricular Activity Monitoring Information System
Extracurricular activities play an important role in supporting students’ development, both in academic and non-academic aspects. However, the management of extracurricular activities in many schools is still conducted manually, making it difficult to monitor student participation, record attendance, and document achievements. This study aims to develop a web-based extracurricular activity monitoring information system to improve the effectiveness of activity management at SMP 2 Jati. The system development method used is the Waterfall model, which includes the stages of requirements analysis, system design, implementation, and testing. The system was developed using native PHP and MySQL and can be accessed by administrators, extracurricular supervisors, and students. The results of testing using the Black Box Testing method indicate that all system functions operate as intended. In addition, user evaluation using a Likert-scale questionnaire shows that the system has good usability, improves the efficiency of activity management, and provides a positive level of user satisfaction. The findings of this study demonstrate that the developed system can support a more effective and structured monitoring process for extracurricular activities.
Analysis of the Simple Additive Weighting and Similarity Methods in Decision Support Systems for SDLC Method Selection
Selecting an appropriate Software Development Life Cycle (SDLC) method is a critical factor in the success of software development projects. However, the selection process is often conducted subjectively without in-depth analysis of project characteristics. This study aims to analyze the comparison between the pure Simple Additive Weighting (SAW) method and SAW combined with the similarity method in a decision support system for SDLC method selection. The study utilized 14 SDLC method alternatives and 9 evaluation criteria, including estimated project duration, risk level, system complexity, budget, team size, team experience level, user involvement, changing requirements, and documentation needs. The calculation results show that the pure SAW method recommended DevOps with a score of 84.07%, while the SAW method combined with similarity recommended Test-Driven Development (TDD) with a score of 99.63%, representing an increase of 15.56 percentage points. Ranking changes occurred in 13 out of the 14 methods evaluated, with 7 methods experiencing rank increases and 6 methods experiencing rank decreases. The similarity-based approach proved to generate recommendations that are more aligned with project characteristics because it considers the level of similarity between the project profile and the characteristics of each SDLC method.