ANALISIS POLA GEJALA PCOS MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING
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Abstract
Penelitian ini menerapkan algoritma K-Means untuk mengelompokkan pasien PCOS berdasarkan intensitas gejala. PCOS adalah gangguan hormonal yang sulit didiagnosa karena gejalanya beragam. Dengan menggunakan algoritma K-Means, hasil clustering menunjukkan tiga kategori utama: ringan, sedang, dan berat. Setiap klaster mencerminkan kombinasi gejala seperti siklus menstruasi tidak teratur, pertumbuhan rambut berlebih, dan perubahan suasana hati. Pendekatan ini efektif dalam membantu pemahaman pola distribusi gejala PCOS serta mendukung pengambilan keputusan medis yang lebih tepat.
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References
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Oktavia, R., Lanita, U., Siregar, S. A., Reskiaddin, L. O., & Perdana, S. M. (2024). Efektivitas Edukasi Kesehatan Tentang Polycystic Ovary Syndrome (PCOS) Melalui Media Sosial Terhadap Pengetahuan dan Sikap Remaja Putri di Madrasah Aliyah Laboratorium Jambi. Jurnal Kesmas Jambi, 8(2), 81–93. https://doi.org/10.22437/jkmj.v8i2.32755
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Sari, D. A., Kurniawati, E. Y., & Ashari, M. A. (2023). SKRINING DAN DETERMINAN KEJADIAN SINDROM OVARIUM POLIKISTIK (SOPK) PADA REMAJA. Jurnal Ilmu Kebidanan, 9(2), 102–106. https://doi.org/10.48092/jik.v9i2.211
Sheikdavood, K., & Bala, M. P. (2023). Polycystic Ovary Cyst Segmentation Using Adaptive K-means with Reptile Search Algorith. Information Technology and Control, 52, 85–99.
Sugianto, C. A., Rahayu, A. H., & Gusman, A. (2020). Algoritma K-Means untuk Pengelompokkan Penyakit Pasien pada Puskesmas Cigugur Tengah. Journal of Information Technology, 2(2), 39–44. https://doi.org/10.47292/joint.v2i2.30
Tanjung, N. N. M., & Achmad Fauzi. (2023). Hubungan Antara Kejadian Policystic Ovarium Syndrome Dengan Akne Pada Wajah Di Nu Beauty Medical Aestetics Jonggol TAHUN 2022. Jurnal Ilmiah Keperawatan (Scientific Journal of Nursing), 9(3), 74–83. https://doi.org/10.33023/jikep.v9i3.1592
WHO. (2025, February 7). Polycystic ovary syndrome. World Health Organization. https://www.who.int/news-room/fact-sheets/detail/polycystic-ovary-syndrome
Wibowo, A. S., & Mulyastuti, I. D. (2022). Penerapan Algoritma K-Means Clustering Pada Jumlah Fasilitas Kesehatan Menurut Pemerintah Provinsi DKI Jakarta. Tekinfo: Jurnal Bidang Teknik Industri Dan Teknik Informatika, 23(2), 116–122. https://doi.org/10.37817/tekinfo.v23i2.2603
Dapas, M., & Dunaif, A. (2022). Deconstructing a Syndrome: Genomic Insights Into PCOS Causal Mechanisms and Classification. In Endocrine Reviews (Vol. 43, Issue 6, pp. 927–965). Endocrine Society. https://doi.org/10.1210/endrev/bnac001
Elmannai, H., El-Rashidy, N., Mashal, I., Alohali, M. A., Farag, S., El-Sappagh, S., & Saleh, H. (2023). Polycystic Ovary Syndrome Detection Machine Learning Model Based on Optimized Feature Selection and Explainable Artificial Intelligence. Diagnostics, 13(8). https://doi.org/10.3390/diagnostics13081506
Emanuel, R. H. K., Docherty, P. D., Lunt, H., Murray, R., & Campbell, R. E. (2024). Clustering polycystic ovary syndrome laboratory results extracted from a large internet forum with machine learning. Intelligence-Based Medicine, 9, 100135. https://doi.org/10.1016/j.ibmed.2024.100135
Faris, N. N., & Miften, F. S. (2022). An intelligence model for detection of PCOS based on k‐means coupled with LS‐SVM. Concurrency and Computation: Practice and Experience, 34(21). https://doi.org/10.1002/cpe.7139
Faris, N., Sahi, A., Diykh, M., Abdulla, S., & Siuly, S. (2025). Enhanced Polycystic Ovary Syndrome diagnosis model leveraging a K-means based genetic algorithm and ensemble approach. Intelligence-Based Medicine, 11. https://doi.org/10.1016/j.ibmed.2025.100253
Hoeger, K. M., Dokras, A., & Piltonen, T. (2021). Update on PCOS: Consequences, Challenges, and Guiding Treatment. In Journal of Clinical Endocrinology and Metabolism (Vol. 106, Issue 3, pp. E1071–E1083). Endocrine Society. https://doi.org/10.1210/clinem/dgaa839
Karadayı Ataş, P. (2024). Exploring the Molecular Interaction of PCOS and Endometrial Carcinoma through Novel Hyperparameter-Optimized Ensemble Clustering Approaches. Mathematics, 12(2), 295. https://doi.org/10.3390/math12020295
Kholil, K. A., Rahaningsih, N., & Danar Dana, R. (2024). Penerapan Data Mining Untuk Clustering Penyakit Diare Menggunakan Algoritma K-Means. JATI (Jurnal Mahasiswa Teknik Informatika), 8(3), 3124–3131. https://doi.org/10.36040/jati.v8i3.9616
Koli, S., Tayde, J., & Khan, F. (2023). PCOS 2023 Dataset. Kaggle. https://www.kaggle.com/datasets/sahilkoli04/pcos2023/suggestions?status=pending
Kurniawati, E. Y., Hutabarat, N. C., & Noviasari, E. (2023). Status Gizi dan Gaya Hidup Wanita dengan Sindrom Ovarium Polikistik (PCOS) di Yogyakarta. JURNAL KESEHATAN PERINTIS (Perintis’s Health Journal), 10(1), 74–82. https://doi.org/10.33653/jkp.v10i1.971
Mantik, J., & Hidayati, N. (2022). Implementation of data mining to determine stock inventory at kenza grocery stores using the k-means clustering method. In Jurnal Mantik (Vol. 6, Issue 3). Online.
Mohamed, A. H., Albasheer, O., Ghoniem, M. A., Abdalghani, N., Ayish, F., Abdelwahab, S. I., Abdelmageed, M. M., Hakami, A. M. S., Khormi, A. H., Altraifi, A. A., Medani, I., Chourasia, U., Ali, S. A., Abdelmola, A., & Ahmed, A. E. (2025). Impact of lifestyle interventions on reproductive and psychological outcomes in women with polycystic ovary syndrome: A systematic review. Medicine, 104(3), e41178. https://doi.org/10.1097/MD.0000000000041178
Nasution, A. R. S. (2021). Identifikasi Permasalahan Penelitian. ALACRITY : Journal of Education, 13–19. https://doi.org/10.52121/alacrity.v1i2.21
National Institutes of Health, (NIH). (2024, August 21). Polycystic Ovary Syndrome (PCOS). Eunice Kennedy Shriver National Institute of Child Health and Human Development. https://www.nichd.nih.gov/health/topics/pcos
Nurdiawan, O., Susana, H., & Faqih, A. (2024). Deep Learning For Polycystic Ovarian Syndrome Classification Using Convolutional Neural Network. JITK (Jurnal Ilmu Pengetahuan Dan Teknologi Komputer), 9(2), 218–226. https://doi.org/10.33480/jitk.v9i2.4575
Novalia, V., Tania, K. D., Meiriza, A., & Wedhasmara, A. (2024, September). Knowledge discovery of application review using word embedding’s comparison with CNN-LSTM model on sentiment analysis. In 2024 International Conference on Electrical Engineering and Computer Science (ICECOS) (pp. 234–238). IEEE. https://doi.org/10.1109/icecos63900.2024.10791113
Oktavia, R., Lanita, U., Siregar, S. A., Reskiaddin, L. O., & Perdana, S. M. (2024). Efektivitas Edukasi Kesehatan Tentang Polycystic Ovary Syndrome (PCOS) Melalui Media Sosial Terhadap Pengetahuan dan Sikap Remaja Putri di Madrasah Aliyah Laboratorium Jambi. Jurnal Kesmas Jambi, 8(2), 81–93. https://doi.org/10.22437/jkmj.v8i2.32755
Putri, S. N., Santoso, B., & Budiono, B. (2022). Sedentary Lifestyle and Overweight in Relation to the Risk of Polycystic Ovary Syndrome in Senior High School Students in Surabaya. JUXTA: Jurnal Ilmiah Mahasiswa Kedokteran Universitas Airlangga, 13(2), 57–61. https://doi.org/10.20473/juxta.V13I22022.57-61
Sari, D. A., Kurniawati, E. Y., & Ashari, M. A. (2023). SKRINING DAN DETERMINAN KEJADIAN SINDROM OVARIUM POLIKISTIK (SOPK) PADA REMAJA. Jurnal Ilmu Kebidanan, 9(2), 102–106. https://doi.org/10.48092/jik.v9i2.211
Sheikdavood, K., & Bala, M. P. (2023). Polycystic Ovary Cyst Segmentation Using Adaptive K-means with Reptile Search Algorith. Information Technology and Control, 52, 85–99.
Sugianto, C. A., Rahayu, A. H., & Gusman, A. (2020). Algoritma K-Means untuk Pengelompokkan Penyakit Pasien pada Puskesmas Cigugur Tengah. Journal of Information Technology, 2(2), 39–44. https://doi.org/10.47292/joint.v2i2.30
Tanjung, N. N. M., & Achmad Fauzi. (2023). Hubungan Antara Kejadian Policystic Ovarium Syndrome Dengan Akne Pada Wajah Di Nu Beauty Medical Aestetics Jonggol TAHUN 2022. Jurnal Ilmiah Keperawatan (Scientific Journal of Nursing), 9(3), 74–83. https://doi.org/10.33023/jikep.v9i3.1592
WHO. (2025, February 7). Polycystic ovary syndrome. World Health Organization. https://www.who.int/news-room/fact-sheets/detail/polycystic-ovary-syndrome
Wibowo, A. S., & Mulyastuti, I. D. (2022). Penerapan Algoritma K-Means Clustering Pada Jumlah Fasilitas Kesehatan Menurut Pemerintah Provinsi DKI Jakarta. Tekinfo: Jurnal Bidang Teknik Industri Dan Teknik Informatika, 23(2), 116–122. https://doi.org/10.37817/tekinfo.v23i2.2603
Published
2025-06-30
How to Cite
AULIA, Cantika et al.
ANALISIS POLA GEJALA PCOS MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING.
JOISIE (Journal Of Information Systems And Informatics Engineering), [S.l.], v. 9, n. 1, p. 91-99, june 2025.
ISSN 2527-3116.
Available at: <https://ejournal.pelitaindonesia.ac.id/ojs32/index.php/JOISIE/article/view/4939>. Date accessed: 14 july 2025.
doi: https://doi.org/10.35145/joisie.v9i1.4939.
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