Perbandingan Teknik Penyeimbang Kelas Pada Multi-Layer Perceptron (MLP) Berbasis Backpropagation Untuk Klasifikasi Diabetes Mellitus
DOI:
https://doi.org/10.47065/bulletincsr.v5i6.804Keywords:
BPNN; Class Imbalance; Classification; RUS; SMOTEAbstract
Diabetes Mellitus (DM) is a chronic disease that can lead to serious complications if not detected early; therefore, early diagnosis is highly important. One of the methods that can be applied for early diagnosis is the classification technique in data mining. However, the classification process often faces challenges due to class imbalance, which can reduce model performance. This study aims to analyze the effect of class balancing techniques on the performance of the Backpropagation Neural Network (BPNN) in classifying DM cases. BPNN is a form of Multi-Layer Perceptron (MLP) with a simple structure and the ability to solve complex problems with good accuracy. The dataset used in this study is the Pima Indians Diabetes Dataset, consisting of 768 instances, including 500 non-diabetic and 268 diabetic cases. The research was conducted using three scenarios: without balancing, Synthetic Minority Over-sampling Technique (SMOTE), and Random Under Sampling (RUS). The BPNN model was designed with two architectural variations (one hidden layer and two hidden layers), three learning rate values (0.1, 0.01, and 0.001), and a varying number of neurons. The dataset was divided using the 10-Fold Cross Validation technique. The results show that applying SMOTE achieved the best performance, with an average accuracy of 90.89%, precision of 91.22%, recall of 90.89%, and F1-score of 90.89% on the BPNN architecture with one hidden layer. Furthermore, the single hidden layer architecture proved more stable than the two hidden layers, especially when the dataset size decreased due to RUS. Therefore, the combination of SMOTE and BPNN with one hidden layer provides better performance in classifying Diabetes Mellitus cases.
Downloads
References
F. Marwati and R. Fauzi, “Prediksi Penyakit Diabetes Melitus Menggunakan Jaringan Syaraf Tiruan Dengan Metode Backpropagation,” Jitu J. Inform. Utama Hal, vol. 2, no. 1, pp. 26–34, 2024.
Z. Mutaqin, C. Rozikin, and Y. A. Tomo, “Klasifikasi Penyakit Diabetes Menggunakan Algoritma Logistic Regression,” J. Sist. dan Teknol. Inf., vol. 06, no. 3, pp. 320–329, 2024, [Online]. Available: https://journalpedia.com/1/index.php/jsti
S. Delfina, I. Carolita, S. Habsah, and S. Ayatillahi, “Analisis Determinan Faktor Risiko Kejadian Diabetes Mellitus Tipe 2 Pada Usia Produktif,” J. Kesehat. Tambusai, vol. 2, no. 4, pp. 141–151, 2021, doi: 10.31004/jkt.v2i4.2823.
M. K. Murtiningsih, K. Pandelaki, and B. P. Sedli, “Gaya Hidup sebagai Faktor Risiko Diabetes Melitus Tipe 2,” e-CliniC, vol. 9, no. 2, p. 328, 2021, doi: 10.35790/ecl.v9i2.32852.
D. N. S. Purqoti, Z. Arifin, D. Istiana, Ilham, B. R. Fatmawati, and H. P. Rusiana, “Sosialisasi konsep penyakit Diabetes Mellitus untuk meningkatkan pengetahuan Lansia tentang Diabetes Mellitus,” ABSYARA J. Pengabdi. Pada Masy., vol. 3, no. 1, pp. 71–78, 2022, doi: 10.29408/ab.v3i1.5771.
R. P. Fadhillah, R. Rahma, A. Sepharni, R. Mufidah, B. N. Sari, and A. Pangestu, “Klasifikasi Penyakit Diabetes Mellitus Berdasarkan Faktor-Faktor Penyebab Diabetes menggunakan Algoritma C4.5,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 7, no. 4, pp. 1265–1270, 2022, doi: 10.29100/jipi.v7i4.3248.
A. Prastyo, S. Sutikno, and K. Khadijah, “Improving support vector machine and backpropagation performance for diabetes mellitus classification,” Comput. Sci. Inf. Technol., vol. 5, no. 2, pp. 140–149, 2024, doi: 10.11591/csit.v5i2.pp140-149.
W. Guswanti, I. Afrianty, E. Budianita, and F. Syafria, “Perbandingan Inisialisasi Bobot Random dan Nguyen-Widrow Pada Backpropagation Dalam Klasifikasi Penyakit Diabetes,” J. Inform. J. Pengemb. IT, vol. 10, no. 2, pp. 323–332, 2025, doi: 10.30591/jpit.v9ix.xxx.
P. M. S. Tarigan, J. T. Hardinata, H. Qurniawan, M. Saffi, and R. Winanjaya, “Implementasi Data Mining Menggunakan Algoritma Apriori Dalam Menentukan Persediaan Barang (Studi Kasus?: Toko Sinar Harahap),” Just IT J. Sist. Informasi, Teknol. Inf. dan Komput., vol. 12, no. 2, pp. 51–61, 2022, doi: 10.35316/justify.v3i1.5335.
P. Rosyani, S. Saprudin, and R. Amalia, “Klasifikasi Citra Menggunakan Metode Random Forest dan Sequential Minimal Optimization (SMO),” J. Sist. dan Teknol. Inf., vol. 9, no. 2, p. 132, 2021, doi: 10.26418/justin.v9i2.44120.
N. Nurhadi, S. Defit, and G. W. Nurcahyo, “Model Deep Learning Berbasis Multilayer Perceptron untuk Identifikasi Demam Berdarah Dengue dan Tifus,” Bull. Comput. Sci. Res., vol. 5, no. 5, pp. 1095–1102, 2025, doi: 10.47065/bulletincsr.v5i5.754.
R. Ramadani, E. Budianita, F. Yanto, and S. K. Gusti, “Klasifikasi Penyakit Jantung Koroner Menggunakan Metode Backpropagation Neural Network,” Semin. Nas. Teknol. Informasi, Komun. dan Ind., pp. 192–200, 2024.
F. K. Wardhana et al., “Penerapan backpropagation jaringan saraf tiruan untuk prediksi diabetes menggunakan dataset pima indians,” Semin. Nas. AMIKOM Surakarta, no. November, pp. 331–344, 2024.
L. A. Ma’rifah, I. Afrianty, E. Budianita, and F. Syafria, “Klasifikasi Tulang Tengkorak Berdasarkan Jenis Kelamin Menggunakan Correlation-Based Feature Selection ( CFS ) dengan Backpropagation Neural Network ( BPNN ),” J. Inform. J. Pengemb. IT, vol. 10, no. 2, pp. 333–347, 2025, doi: 10.30591/jpit.v9ix.xxx.
I. S. Ramadhan and A. Salam, “Teknik Random Undersampling untuk Mengatasi Ketidakseimbangan Kelas pada CT Scan Kista Ginjal,” Techno.Com, vol. 23, no. 1, pp. 20–28, 2024, doi: 10.62411/tc.v23i1.9738.
A. Muhidin, M. Danny, and N. Surojudin, “Prediksi Kegagalan Perangkat Industri Menggunakan Random Forest dan SMOTE untuk Pemeliharaan Preventif,” Bull. Comput. Sci. Res., vol. 5, no. 5, pp. 1089–1094, 2025, doi: 10.47065/bulletincsr.v5i5.745.
M. P. Pulungan, A. Purnomo, and A. Kurniasih, “Penerapan SMOTE untuk Mengatasi Imbalance Class dalam Klasifikasi Kepribadian MBTI Menggunakan Naive Bayes Classifier,” J. Teknol. Inf. dan Ilmu Komput., vol. 10, no. 7, pp. 1493–1502, 2023, doi: 10.25126/jtiik.1077989.
L. Pasiolo, I. Afrianty, E. Budianita, and R. Abdillah, “Penerapan Teknik Smote Pada Klasifikasi Penyakit Stroke Dengan Algoritma Support Vector Machine,” J. Sist. Inf., vol. 7, no. 1, pp. 61–73, 2025, [Online]. Available: https://www.kaggle.com/fedesoriano/stroke-prediction-dataset.
L. Qadrini, H. Hikmah, and M. Megasari, “Oversampling, Undersampling, Smote SVM dan Random Forest pada Klasifikasi Penerima Bidikmisi Sejawa Timur Tahun 2017,” J. Comput. Syst. Informatics, vol. 3, no. 4, pp. 386–391, 2022, doi: 10.47065/josyc.v3i4.2154.
M. Sulistiyono, Y. Pristyanto, S. Adi, and G. Gumelar, “Implementasi Algoritma Synthetic Minority Over-Sampling Technique untuk Menangani Ketidakseimbangan Kelas pada Dataset Klasifikasi,” Sistemasi, vol. 10, no. 2, p. 445, 2021, doi: 10.32520/stmsi.v10i2.1303.
E. Saputro and D. Rosiyadi, “Penerapan Metode Random Over-Under Sampling Pada Algoritma Klasifikasi Penentuan Penyakit Diabetes,” Bianglala Inform., vol. 10, no. 1, pp. 42–47, 2022, doi: 10.31294/bi.v10i1.11739.
M. Azhima, I. Afrianty, E. Budianita, and S. Kurnia Gusti, “Penerapan Metode Backpropagation Neural Network untuk Klasifikasi Penyakit Stroke,” KLIK Kaji. Ilm. Inform. dan Komput., vol. 4, no. 6, pp. 3013–3021, 2024, doi: 10.30865/klik.v4i6.1956.
M. C. Untoro and M. A. N. M. Yusuf, “Evaluate of Random Undersampling Method and Majority Weighted Minority Oversampling Technique in Resolve Imabalanced Dataset,” IT J. Res. Dev., vol. 8, no. 1, pp. 1–13, 2023, doi: 10.25299/itjrd.2023.12412.
M. Karmila and I. Nirmala, “Prediksi Jumlah Produksi Kebutuhan Air Pada Perumda Air Minum Tirta Khatulistiwa Pontianak Menggunakan Metode Extreme Learning Machine (Elm),” Coding J. Komput. dan Apl., vol. 11, no. 1, p. 137, 2023, doi: 10.26418/coding.v11i1.58052.
F. Alghifari and D. Juardi, “Penerapan Data Mining Pada Penjualan Makanan Dan Minuman Menggunakan Metode Algoritma Naïve Bayes,” 2021.
A. J. P. Sibarani, “Implementasi Data Mining Menggunakan Algoritma Apriori Untuk Meningkatkan Pola Penjualan Obat,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 7, no. 2, pp. 262–276, 2020, doi: 10.35957/jatisi.v7i2.195.
R. S. Pradana and R. Nooraeni, “Penerapan SMOTE pada Data Tidak Seimbang dalam Pemodelan Status NEET Penduduk Usia Muda di Provinsi Banten Tahun 2022,” J. Kebijak. Pembang., vol. 18, no. 1, pp. 91–104, 2023.
I. Kurniawan, D. C. P. Buani, A. Abdussomad, W. Apriliah, and E. Fitriani, “Penerapan Teknik Random Undersampling untuk Mengatasi Imbalance Class dalam Prediksi Kebakaran Hutan Menggunakan Algoritma Decision Tree,” Acad. J. Comput. Sci. Res., vol. 5, no. 1, p. 1, 2023, doi: 10.38101/ajcsr.v5i1.617.
W. Wijiyanto, A. I. Pradana, S. Sopingi, and V. Atina, “Teknik K-Fold Cross Validation untuk Mengevaluasi Kinerja Mahasiswa,” J. Algoritm., vol. 21, no. 1, pp. 239–248, 2024, doi: 10.33364/algoritma/v.21-1.1618.
H. Hafid, “Penerapan K-Fold Cross Validation untuk Menganalisis Kinerja Algoritma K-Nearest Neighbor pada Data Kasus Covid-19 di Indonesia,” J. Math., vol. 6, no. 2, pp. 161–168, 2023, [Online]. Available: http://www.ojs.unm.ac.id/jmathcos
I. Saluza, L. Widya Astuti, and E. Yulianti, “Ensemble Backpropagation Neural Network Dalam Memprediksi Inflasi,” J. JUPITER, vol. 15, no. 1, pp. 732–741, 2023.
B. F. K. Lestari and L. A. Kusnaraharja, “Peran Ilmu Forensik Dalam Memecahkan Kasus Kriminalitas: Studi Di Rumah Sakit Bhayangkara Mataram,” UnizarLawReview, vol. 4, no. 1, pp. 117–6, 2021.
F. Ramadhan and J. Hernadi, “Evaluasi Optimizer Adam dan RMSProp pada Arsitektur VGG-19 Klasifikasi Ekpresi Wajah Manusia,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 10, no. 2, pp. 1414–1426, 2025, [Online]. Available: https://doi.org/10.29100/jipi.v10i2.6197
P. Prihandoko and P. Alkhairi, “Optimasi JST Backpropagation dengan Adaptive Learning Rate Dalam Memprediksi Hasil Panen Padi,” Jurasik (Jurnal Ris. Sist. Inf. dan Tek. Inform., vol. 10, no. 1, p. 441, 2025, doi: 10.30645/jurasik.v10i1.887.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Perbandingan Teknik Penyeimbang Kelas Pada Multi-Layer Perceptron (MLP) Berbasis Backpropagation Untuk Klasifikasi Diabetes Mellitus
ARTICLE HISTORY
How to Cite
Issue
Section
Copyright (c) 2025 Robby Azhar, Siska Kurnia Gusti, Iis Afrianty, Elvia Budianita
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
