Comparison of K-Nearest Neighbor and Naive Bayes Algorithms for Tuberculosis Diagnosis Classification

Dedi Setiadi, Alfis Arif, Anik Oktaria

Sari


Tuberculosis is an infectious disease caused by the bacteria mycobacterium tuberculosis. Tuberculosis is a serious global health problem and can cause death if not treated properly. At the Sidorejo Health Center, the current process of diagnosing patients uses several benchmarks of medical history obtained from patients regarding complaints, symptoms, and risk factors, while the results of the diagnosis calculation are not yet known. Comparison of the K-nearest neighbor and naïve bayes algorithms in classifying tuberculosis can provide input for the Sidorejo Health Center in seeing the accuracy of the diagnosis of tuberculosis, with medical information such as symptoms and medical history, where later patient data will be processed using the rapid miner application. The system development method used in this study is CRISP-DM, which consists of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The testing method uses a confusion matrix to measure the accuracy of the algorithm model with the results being that the K-nearest neighbor algorithm produces a high accuracy of 98% while the naïve bayes algorithm is the lowest with an accuracy of 0.70%.

Teks Lengkap:

PDF

Referensi


R. Simamora, A. Alhafiz, and S. Julianita, “Sistem Pakar Mendiagnosis Tuberkulosis Pada Remaja Menggunakan Metode Dempster Shafer,†J. Sist. Inf. Triguna Dharma (JURSI TGD), vol. 3, no. 5, pp. 713–723, 2024.

A. A. Prameswaty, M. H. P. Swari, and W. S. J. Saputra, “Perancangan Sistem Pakar Diagnosis Penyakit Tbc Paru Dengan Metode Certainty Factor Dan Dempster Shafer,†JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 5, pp. 8658–8663, 2024.

A. A. Nabila, “Sistem Pakar Diagnosa Penyakit Tuberkulosis Dengan Metode Certainty,†J. Artif. Intell. Softw. Eng., vol. 3, no. 1, pp. 1–6, 2023.

A. A. Alimi, A. R. Adriansyah, and P. Prima, “Pengembangan Sistem Deteksi Tuberkulosis pada Citra X-Ray Menggunakan Metode Convolutional Neural Network (CNN) dengan Framework Laravel,†J. Inform. Terpadu, vol. 10, no. 2, pp. 165–171, 2024.

M. R. Syahwana and R. M. Simanjorang, “Analisa Sistem Pakar Metode Bayes Dalam Mendiagnosa Penyakit Tubercolosis,†J. Sist. Informasi, Tek. Inform. dan Teknol. Pendidik., vol. 1, no. 2, pp. 57–66, 2022.

D. S. Wulandari and M. G. Rohman, “Implementasi Metode Naïve Bayes Pada Sistem Pakar Diagnosa Penyakit Tuberculosis,†Gener. J., vol. 7, no. 3, pp. 64–76, 2023.

M. Ula, A. Zulfikri, A. F. Ulva, and R. A. Rizal, “Penerapan Machine Learning Clustering K-Means dan Linear Regression Dalam Penentuan Tingkat Resiko Tuberkulosis Paru,†Indones. J. Comput. Sci., vol. 12, no. 1, 2023.

W. Ramdhani, D. Bona, R. B. Musyaffa, and C. Rozikin, “Klasifikasi Penyakit Kangker Payudara Menggunakan Algoritma K-Nearest Neighbor,†J. Ilm. Wahana Pendidik., vol. 8, no. 12, pp. 445–452, 2022.

A. Khaidar, M. Arhami, and M. Abdi, “Application of the Random Forest Method for UKT Classification at Politeknik Negeri Lhokseumawe,†J. Artif. Intell. Softw. Eng., vol. 4, no. 2, pp. 94–103, 2024.

A. Ridwan, “Penerapan Algoritma Naïve Bayes Untuk Klasifikasi Penyakit Diabetes Mellitus,†J. SISKOM-KB (Sistem Komput. dan Kecerdasan Buatan), vol. 4, no. 1, pp. 15–21, 2020.

S. Widaningsih, “Penerapan Data Mining untuk Memprediksi Siswa Berprestasi dengan Menggunakan Algoritma K Nearest Neighbor,†JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 3, pp. 2598–2611, 2022.

E. Novianto, A. Hermawan, and D. Avianto, “Klasifikasi Algoritma K-Nearest Neighbor, Naive Bayes, Decision Tree Untuk Prediksi Status Kelulusan Mahasiswa S1,†Rabit J. Teknol. dan Sist. Inf. Univrab, vol. 8, no. 2, pp. 146–154, 2023.

M. Z. Haq, C. S. Octiva, A. Ayuliana, U. W. Nuryanto, and D. Suryadi, “Algoritma Naïve Bayes untuk Mengidentifikasi Hoaks di Media Sosial,†J. Minfo Polgan, vol. 13, no. 1, pp. 1079–1084, 2024.

T. P. R. Sanjaya, A. Fauzi, and A. F. N. Masruriyah, “Analisis sentimen ulasan pada e-commerce shopee menggunakan algoritma naive bayes dan support vector machine,†INFOTECH J. Inform. Teknol., vol. 4, no. 1, pp. 16–26, 2023.

C. Schröer, F. Kruse, and J. M. Gómez, “A systematic literature review on applying CRISP-DM process model,†Procedia Comput. Sci., vol. 181, pp. 526–534, 2021.

D. Ruswanti, D. Susilo, and R. Riani, “Implementasi Crisp-Dm Pada Data Mining Untuk Melakukan Prediksi Pendapatan Dengan Algoritma C. 45,†Go Infotech J. Ilm. Stmik Aub, vol. 30, no. 1, pp. 111–121, 2024.

S. Navisa, L. Hakim, and A. Nabilah, “Komparasi Algoritma Klasifikasi Genre Musik pada Spotify Menggunakan CRISP-DM,†J. Sist. Cerdas, vol. 4, no. 2, pp. 114–125, 2021.

D. Setiadi, S. Sasmita, and M. Yolanda, “Penerapan Algoritma Regresi Linier Berganda Untuk Memprediksi Hasil panen Padi Di Kota Pagar Alam,†Kesatria J. Penerapan Sist. Inf. (Komputer dan Manajemen), vol. 5, no. 2, pp. 337–438, 2024.

D. Setiadi, S. Sasmita, and Y. I. Mukti, “Optimization Of Agricultural Production In South Sumatera Using Multiple Linear Regression Algorithm,†Knowbase Int. J. Knowl. Database, vol. 4, no. 2, pp. 168–179, 2024.

M. R. Muttaqin, T. I. Hermanto, and M. A. Sunandar, “Penerapan K-Means Clustering dan Cross-Industry Standard Process For Data Mining (CRISP-DM) untuk Mengelompokan Penjualan Kue,†Komputasi J. Ilm. Ilmu Komput. dan Mat., vol. 19, no. 1, pp. 38–53, 2022.

M. A. Hasanah, S. Soim, and A. S. Handayani, “Implementasi CRISP-DM Model Menggunakan Metode Decision Tree dengan Algoritma CART untuk Prediksi Curah Hujan Berpotensi Banjir,†J. Appl. Informatics Comput., vol. 5, no. 2, pp. 103–108, 2021.




DOI: http://dx.doi.org/10.30811/jaise.v5i1.6456

Refbacks

  • Saat ini tidak ada refbacks.


Indexing :

Creative Commons License
Journal of Artificial Intelligence and Software Engineering (JAISE) licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.