Classification of Thyroid Class using ID3 Algorithm and Artificial Neural Network (ANN)

Nabila Henisaniyya, Citra Pertiwi, Anita Desiani, Ali Amran, Muhammad Arhami

Abstract


Thyroid disease refers to a range of conditions or issues affecting the thyroid gland. This gland, located below the Adam’s apple, is responsible for coordinating various metabolic processes in the body, making its function essential. Early detection of thyroid symptoms is crucial as an initial step in planning the necessary treatments to prevent more severe thyroid-related health risks. One commonly applied method for early detection involves classification using a data mining approach. Among the algorithms frequently used for classification are the ID3 algorithm and Artificial Neural Networks (ANN).
This study aims to obtain the best classification results for detecting thyroid disease by comparing these two algorithms. The accuracy results for percentage split testing were 88% for ID3 and 90% for ANN. Meanwhile, the accuracy values for K-Fold cross-validation were 93% for the ID3 algorithm and 95% for the ANN algorithm. Additionally, the overall average precision and recall values for both algorithms were above 75% for percentage split testing and above 90% for K-Fold cross-validation. The results indicate that ANN achieved higher percentages compared to ID3. Based on the accuracy, precision, and recall values obtained from both algorithms, it can be concluded that the ANN algorithm performs better than ID3 in classifying thyroid disease.

Keywords


thyroid disease, classification, data mining, ID3 algorithm, ANN Algorithm

Full Text:

PDF

References


A. Putra ZM, Ernawati, and A. Erlansari, “Sistem Pakar Diagnosa Penyakit Tiroid menggunakan Metode Naive Bayes Berbasis Android,” J. Rekursif, vol. 5, no. 3, pp. 270–284, 2017.

S. Agustiani, A. Mustopa, A. Saryoko, W. Gata, and S. K. Wildah, “Penerapan Algoritma J48 untuk Deteksi Penyakit Tiroid,” Paradig. - J. Komput. dan Inform., vol. 22, no. 2, pp. 153–160, 2020.

B. Wijonarko, “Perbandingan Algoritma Data Mining Naive Bayes dan Bayes Network untuk Mengidentifikasi Penyakit Tiroid,” J. Pilar Nusa Mandiri, vol. 14, no. 1, pp. 21–26, 2018.

A. Nurmasani and Y. Pristyanto, “Algoritme Stacking untuk Klasifikasi Penyakit Jantung pada Dataset Imbalanced Class,” Pseudocode, vol. 8, no. 1, pp. 21–26, Mar. 2021.

D. Himawan, “Aplikasi Data Mining menggunakan Algoritma ID3 untuk Mengklasifikasi Kelulusan Mahasiswa pada Universitas Dian Nuswantoro Semarang,” pp. 1–10, 2014.

A. E. Pramadhani and T. Setiadi, “Penerapan Data Mining untuk Klasifikasi Prediksi Penyakit ISPA (Infeksi Saluran Pernapasan Akut) dengan Algoritma Decision Tree (ID3),” J. Sarj. Tek. Inform., vol. 2, no. 1, pp. 831–839, 2014.

V. Sihombing, “Klasifikasi Algoritma Iterative Dichotomizer (ID3) untuk Tingkat kepuasan pada Sarana Laboratorium Komputer,” J. Teknol. dan Ilmu Komput. Prima, vol. 1, no. 2, pp. 180–187, 2018.

S. Defiyanti and D. L. Pardede, “Perbandingan Kinerja Algoritma Id3 dan C4.5 dalam Klasifikasi Spam-Mail,” 2010.

G. Gunawan, A. C. Fauzan, and H. Harliana, “Implementasi Algoritma Decision Tree Iterative Dichotomiser 3 (ID3) untuk Prediksi Keberhasilan Pengobatan Penyakit Kutil menggunakan Cryotherapy,” J. Bumigora Inf. Technol., vol. 4, no. 1, pp. 73–82, 2022.

J. A. Sidette, E. Eko, and O. D. Nurhayati, “Pendekatan Metode Pohon Keputusan Menggunakan Algoritma ID3 untuk Sistem Informasi Pengukuran Kinerja PNS,” J. Sist. Inf. Bisnis, vol. 4, no. 2, pp. 75–86, 2014.

I. P. Sutawinaya, I. N. G. A. Astawa, and N. K. D. Hariyanti, “Perbandingan Metode Jaringan Saraf Tiruan pada Peramalan Curah Hujan,” Log. J. Ranc. Bangun dan Teknol., vol. 17, no. 2, pp. 92–97, 2017.

A. Gafururrahim, “Prediksi Daya Output Pembangkitan Pltb Jeneponto untuk Satu Tahun ke depan Menggunakan ANN (Artificial Neural Network).” Universitas Hasanuddin, 2021.

Euis Saraswati, Yuyun Umaidah, and Apriade Voutama, “Penerapan Algoritma Artificial Neural Network untuk Klasifikasi Opini Publik terhadap Covid-19,” Gener. J., vol. 5, no. 2, pp. 109–118, 2021.

D. A. Ihsani, A. Arifin, and M. H. Fatoni, “Klasifikasi DNA Microarray menggunakan Principal Component Analysis (PCA) dan Artificial Neural Network (ANN),” J. Tek. ITS, vol. 9, no. 1, pp. A124–A129, 2020.

S. Melangi, “Klasifikasi Usia berdasarkan Citra Wajah menggunakan Algoritma Artificial Neural Network dan Gabor Filter,” Jambura J. Electr. Electron. Eng., vol. 2, no. 2, pp. 60–67, 2020.

E. D. Wahyuni, A. A. Arifiyanti, and M. Kustyani, “Exploratory Data Analysis dalam Konteks Klasifikasi Data Mining,” Pros. Nas. Rekayasa Teknol. Ind. dan Inf. XIV Tahun 2019, vol. 2019, no. November, pp. 263–269, 2019, [Online].

I. Sugiyarto and U. Faddillah, “Optimasi Artificial Neural Network dengan Algorithm Genetic pada Prediksi Approval Credit Card,” J. Tek. Inform. Stmik Antar Bangsa, vol. III, no. 2, pp. 151–156, 2017.

B. P. Pratiwi, A. S. Handayani, and S. Sarjana, “Pengukuran Kinerja Sistem Kualitas Udara dengan Teknologi WSN menggunakan Confusion Matrix,” J. Inform. Upgris, vol. 6, no. 2, pp. 66–75, 2021.

A. Desiani, “Perbandingan Implementasi Algoritma Naïve Bayes dan K-Nearest Neighbor pada Klasifikasi Penyakit Hati,” Simkom, vol. 7, no. 2, pp. 104–110, 2022.




DOI: https://doi.org/10.32520/stmsi.v14i1.3440

Article Metrics

Abstract view : 172 times
PDF - 119 times

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.