Optimization of Diabetes Disease Classification Using Learning Vector Quantization Algorithm(LVQ)
DOI:
https://doi.org/10.55537/cosie.v4i2.1121Keywords:
Teknik Informatika, Klasifikasi, Diabetes, Learning Vector Quantization, AlgorithmAbstract
Diabetes is a chronic disease that attacks humans. Diabetes is caused by high levels of sugar in the human body. Diabetes attacks the metabolic function of the body where the body cannot digest or use high levels of sugar in the human body. Diabetes is classified as a dangerous chronic disease because it has a very fatal impact on humans, especially if complications of the disease have occurred. This study aims to develop a method for classifying diabetes using the Learning Vector Quantization (LVQ) method. Clinical data from patients diagnosed with diabetes and patients who are not diagnosed with diabetes will be analyzed to identify patterns related to this disease. Attribute data such as pregnancies, glucose, blood pressure, skin thickness, insulin, BMI, diabetes pedigree function and age will be used as variables in this analysis. The calculation results show that the Winning Class (Lowest value from the weighted results). So the classification results show an assessment to class 2 because the closest value is 70.002265497656 while the furthest value is 182.52975490778 in class 1
Downloads
References
[1] Amrullah, A., Nugroho, A., & Ramadhan, Z. (2023). Perbandingan Kinerja Web Server Pada Penyedia Layanan Cloud Microsoft Azure Dan Amazon Web Services. Jurnal Informatika Teknologi Dan Sains, 5(1), 92–97. https://doi.org/10.51401/jinteks.v5i1.2487
[2] Arifah, D., Isyriyah, L., Artikel, R., & Kunci, K. (2021). Jurnal Teknologi dan Manajemen Informatika Rancang Model Expert System Pada Diagnosa Penyakit Diabetes Melitus Dengan Metode Forward Chaining Info Artikel ABSTRAK. 7(1), 51–61. http://http//jurnal.unmer.ac.id/index.php/jtmi
[3] Aristanto, R., & Chandra, A. Y. (2022). Sistem Pakar Diagnosis Penyakit Diabetes Mellitus Dengan Menggunkan Metode Naive Bayes Berbasis Web (Studi Kasus : Puskesmas Kecamatan Sambit Ponorogo). 2(1).
[4] Budiman, A. P., Budianita, E., Yanti, N., & Candra, R. M. (2022). Implementasi Metode Learning Vector Quantization (LVQ) Untuk Sentimen Analisis Terhadap Aplikasi Go-Jek Pada Playstore. Jurnal Nasional Komputasi Dan Teknologi Informasi (JNKTI), 5(3), 364–373. https://doi.org/10.32672/jnkti.v5i3.4287
[5] Elgamar, E. (2020). Buku Ajar Konsep Dasar Pemrograman Website dengan PHP. CV Multimedia Publihser.
[6] Eyni Alfia, N., & Waseso, B. (2020). Perancangan Aplikasi Retensi Data Pada Database MySQL (Studi Kasus: PT. Telkomsigma). Maret, 2(3), 2655–7541. https://jurnal.ikhafi.or.id/index.php/jusibi/364
[7] Heliyanti Susana. (2022). Penerapan Model Klasifikasi Metode Naive Bayes Terhadap Penggunaan Akses Internet. Jurnal Riset Sistem Informasi Dan Teknologi Informasi (JURSISTEKNI), 4(1), 1–8. https://doi.org/10.52005/jursistekni.v4i1.96
[8] Kamil, M., Rahmat, B., & Primadianti, O. (2022). Perancangan Dan Implementasi Web Server Untuk Pemantauan Kualitas Air Berbasis Iot. E-Proceeding of Engineering, 8(6), 3515.
[9] Mukhlis, I. R., Irmawati, Sabur, F., Farkhan, M., Gunawan, P. W., Adhicandra, I., & Eldo, H. (2023). Buku Ajar Pemrograman Web 1. In Efitra (Ed.), PT. Sonpedia Publishing Indonesia (cetak pert, Issue Oktober). PT. Sonpedia Publishing Indonesia.
[10] Niqotaini, Z. (2023). Analisa Dan Perancangan Sistem Informasi. In Universitas Mercu Buana (Issue 18).
[11] Putro, H. F., Vulandari, R. T., & Saptomo, W. L. Y. (2020). Penerapan Metode Naive Bayes Untuk Klasifikasi Pelanggan. Jurnal Teknologi Informasi Dan Komunikasi (TIKomSiN), 8(2). https://doi.org/10.30646/tikomsin.v8i2.500
[12] Rahmat, A. ., Ladjamuddin, M. ., & Awaludin, T. . (2023). Perbandingan Algoritma Decision Tree, Random Forest Dan Naive Bayes Pada Prediksi Penilaian Kepuasan Penumpang Maskapai Pesawat Menggunakan Dataset Kaggle. Jurnal Rekayasa Informasi, 12(2), 150–159. www.kaggle.com,
[13] Tawakal, F., & Azkiya, A. (2020). Diagnosa Penyakit Demam Berdarah Dengue (DBD) menggunakan Metode Learning Vector Quantization (LVQ) Firman Tawakal (1) , Ahmedika Azkiya (2) Jurusan Teknik Informatika (1) , Manajemen Informatika (2) (1) Sekolah Tinggi Manajemen Informatika dan Komputer (. Januari, 4(3), 56–64.
[14] Tomasouw, B. P., Aulele, S. N., & Rijoly, M. E. (2021). Penerapan Metode Learning Vector Quantization (LVQ) untuk Mendeteksi Penyalahgunaan Narkoba. Contemporary Mathematics and Applications (ConMathA), 3(1), 36. https://doi.org/10.20473/conmatha.v3i1.26940
[15] Wali, M. (2020). Modul Praktikum Rekayasa Perangkat Lunak (A. Dwi (ed.); cetak 1, Issue July). Bandung.
[16] Wijaya, Y. F., & Triayudi, A. (2023). Perbandingan Algoritma Klasifikasi Data Mining Pada Prediksi Penyakit Diabetes. Journal of Computer System and Informatics (JoSYC), 5(1), 165–174. https://doi.org/10.47065/josyc.v5i1.4614
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Eska Avelina Sianipar, Muhammad Yasin S
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
