Clustering Data Penduduk Desa Menggunakan Algoritma Mean Shift
- Tedi Maulani Universitas Islam Negeri Sultan Syarif Kasim, Pekanbaru, Indonesia
-
Elin Haerani
*
Universitas Islam Negeri Sultan Syarif Kasim, Pekanbaru,
Indonesia
- Fitri Wulandari Universitas Islam Negeri Sultan Syarif Kasim, Pekanbaru, Indonesia
- Lola Oktavia Universitas Islam Negeri Sultan Syarif Kasim, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
Social welfare remains a serious challenge in Indonesia, including in Riau Province, which, despite its abundant natural resources, still struggles with unequal distribution of welfare. One of the government’s efforts to address this issue is through social assistance programs. However, identifying the right beneficiaries remains problematic. This study aims to cluster residents of Desa Bina Baru using the Mean Shift algorithm to support more targeted social aid distribution. The clustering results were evaluated using the Silhouette Score to measure their quality. The optimal clustering was achieved at a quantile of 0.9, with the highest Silhouette Score of 0.5747, producing nine clusters with varying socioeconomic characteristics. Based on the analysis, clusters 2, 1, 5, and 6/7 were identified as the most eligible groups to receive government aid due to economic pressure, high number of dependents, and inadequate housing conditions. This prioritization is crucial for more accurate, data-driven distribution of aid and provides valuable insights to support sustainable poverty alleviation strategies in Desa Bina Baru.
Downloads
References
Aldisun, F., Ukas, U., & Zuhdi Arman, Z. A. (2022). Analisis Yuridis Pengelolaan Sumber Daya Alam Kelautan-Perikanan Berdasarkan Undang-Undang Nomor 32 Tahun 2014 Di Kepulauan Riau. Jurnal Cahaya Keadilan, 10(1), 1–12. https://doi.org/10.33884/jck.v10i1.5511
Aldoseri, A., Al-Khalifa, K. N., & Hamouda, A. M. (2023). Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges. Applied Sciences, 13(12), 7082. https://doi.org/10.3390/app13127082
Althobaiti, K., Wolters, M. K., Alsufyani, N., & Vaniea, K. (2023). Using Clustering Algorithms to Automatically Identify Phishing Campaigns. IEEE Access, 11, 96502–96513. https://doi.org/10.1109/ACCESS.2023.3310810
Andrei, A.-T., & Grigore, O. (2023). Mean Shift Clustering with Bandwidth Estimation and Color Extraction Module Used in Forest Segmentation. 2023 13th International Symposium on Advanced Topics in Electrical Engineering (ATEE), 1–6. https://doi.org/10.1109/ATEE58038.2023.10108106
Aranguren, I., Valdivia, A., & Navarro, J. (2025). Mean-Shift Clustering for Failure Detection in Quadcopter Unmanned Aerial Vehicles. In D. Oliva, A. Valdivia, S. J. Mousavirad, & K. Kalita (Eds.), Advances in Optimization Algorithms for Multidisciplinary Engineering Applications: From Classical Methods to AI-Enhanced Solutions (pp. 785–806). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-78440-8_35
Cariou, C., Le Moan, S., & Chehdi, K. (2022). A Novel Mean-Shift Algorithm for Data Clustering. IEEE Access, 10, 14575–14585. https://doi.org/10.1109/ACCESS.2022.3147951
Chakraborty, S., Paul, D., & Das, S. (2021). Automated Clustering of High-dimensional Data with a Feature Weighted Mean Shift Algorithm. Proceedings of the AAAI Conference on Artificial Intelligence, 35(8), 6930–6938. https://doi.org/10.1609/aaai.v35i8.16854
Cinderatama, T. A., Alhamri, R. Z., & Yunhasnawa, Y. (2022). Implementasi Metode K-Means, Dbscan, Dan Meanshift Untuk Analisis Jenis Ancaman Jaringan Pada Intrusion Detection System. INOVTEK Polbeng - Seri Informatika, 7(1), 169. https://doi.org/10.35314/isi.v7i1.2336
Fan, C., Chen, M., Wang, X., Wang, J., & Huang, B. (2021). A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data. Frontiers in Energy Research, 9. https://doi.org/10.3389/fenrg.2021.652801
Farhan Nugraha, M., Martano, M., & Hayati, U. (2024). Clustering Data Indonesian Food Delivery Menggunakan Metode K-Means Pada Gofood Product List. JATI (Jurnal Mahasiswa Teknik Informatika), 8(3), 3484–3492. https://doi.org/10.36040/jati.v8i3.9727
Ghassabeh, Y. A., & Rudzicz, F. (2021). Modified Subspace Constrained Mean Shift Algorithm. Journal of Classification, 38(1), 27–43. https://doi.org/10.1007/s00357-019-09353-1
Hendrastuty, N. (2024). Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Dalam Evaluasi Hasil Pembelajaran Siswa. Jurnal Ilmiah Informatika Dan Ilmu Komputer (JIMA-ILKOM), 3(1), 46–56. https://doi.org/10.58602/jima-ilkom.v3i1.26
Lidya Musaffak, A., Maulida Hindrayani, K., & Idhom, M. (2025). Penerapan Metode Mean Shift Clustering Untuk Mengelompokkan Wilayah Berdasarkan Pengelolaan Sampah. JATI (Jurnal Mahasiswa Teknik Informatika), 9(3), 4790–4797. https://doi.org/10.36040/jati.v9i3.13777
Ling, L. S., & Weiling, C. T. (2025). Enhancing Segmentation: A Comparative Study of Clustering Methods. IEEE Access, 13, 47418–47439. https://doi.org/10.1109/ACCESS.2025.3550339
Lisnawati Sopiah, & Ria Haryatiningsih. (2023). Karakteristik Penduduk Miskin dan Penyebab Kemiskinan di Desa Sukagalih. Jurnal Riset Ilmu Ekonomi Dan Bisnis, 69–74. https://doi.org/10.29313/jrieb.v3i1.1977
Maharana, K., Mondal, S., & Nemade, B. (2022). A review: Data pre-processing and data augmentation techniques. Global Transitions Proceedings, 3(1), 91–99. https://doi.org/10.1016/j.gltp.2022.04.020
Punhani, A., Faujdar, N., Mishra, K. K., & Subramanian, M. (2022). Binning-Based Silhouette Approach to Find the Optimal Cluster Using K-Means. IEEE Access, 10, 115025–115032. https://doi.org/10.1109/ACCESS.2022.3215568
Purba, W., Siawin, W., & . H. (2019). Implementasi Data Mining Untuk Pengelompokkan Dan Prediksi Karyawan Yang Berpotensi Phk Dengan Algoritma K-Means Clustering. Jurnal Sistem Informasi Dan Ilmu Komputer Prima(JUSIKOM PRIMA), 2(2), 85–90. https://doi.org/10.34012/jusikom.v2i2.429
Ren, Y., Pu, J., Yang, Z., Xu, J., Li, G., Pu, X., Yu, P. S., & He, L. (2025). Deep Clustering: A Comprehensive Survey. IEEE Transactions on Neural Networks and Learning Systems, 36(4), 5858–5878. https://doi.org/10.1109/TNNLS.2024.3403155
Rianti, R., Andarsyah, R., & Awangga, R. M. (2024). Penerapan PCA dan Algoritma Clustering untuk Analisis Mutu Perguruan Tinggi di LLDIKTI Wilayah IV. NUANSA INFORMATIKA, 18(2), 67–77. https://doi.org/10.25134/ilkom.v18i2.211
Rizuan, R., Haerani, E., Jasril, J., & Oktavia, L. (2023). Penerapan Algoritma Mean-Shift Pada Clustering Penerimaan Bantuan Pangan Non Tunai. Journal of Computer System and Informatics (JoSYC), 4(4), 1019–1027. https://doi.org/10.47065/josyc.v4i4.3876
Sangadah, S. K., Laut, L. T., & Jalunggono, G. (2020). Pengaruh faktor-faktor penyebab kemiskinan di Kabupaten Kebumen tahun 2009-2018. DINAMIC: Directory Journal of Economic, 2(1), 229–243.
Sari, L. P., Nurjannah, N., & Yovita, I. (2022). Analysis Of The Effect Of Population, Human Development Index And Open Unemployment Rate On Poverty In Riau Province, 2002-2021. Eqien - Jurnal Ekonomi Dan Bisnis, 11(1), 567 – 574. https://doi.org/10.34308/eqien.v11i1.721
Setiawan, Y. (2023). Data Mining berbasis Nearest Neighbor dan Seleksi Fitur untuk Deteksi Kanker Payudara. Jurnal Informatika: Jurnal Pengembangan IT, 8(2), 89–96. https://doi.org/10.30591/jpit.v8i2.4994
Sinurat, R. P. P. (2023). Analisis Faktor-Faktor Penyebab Kemiskinan Sebagai Upaya Penanggulangan Kemiskinan Di Indonesia. Jurnal Registratie, 5(2), 87–103. https://doi.org/10.33701/jurnalregistratie.v5i2.3554
Surbakti, N. K. (2021). Data Mining Pengelompokan Pasien Rawat Inap Peserta BPJS Menggunakan Metode Clustering (Studi Kasus : RSU.Bangkatan). Journal of Information and Technology, 1(2), 47–53. https://doi.org/10.32938/jitu.v1i2.1470
Tipwong, W., Sirikham, A., Konpang, J., Kirdpipat, P., Chongjarearn, Y., & Preutisrunyanont, O. (2025). Comparison of Unsupervised Learning Algorithms for K-Means Clustering Efficiency in IoE Sensor Detection Systems for Smart Agriculture. 2025 13th International Electrical Engineering Congress (IEECON), 1–5. https://doi.org/10.1109/iEECON64081.2025.10987590
Utari, R. F., Insani, F., Agustian, S., & Afriyanti, L. (2024). Pengelompokan Data Pendistribusian Listrik Menggunakan Algoritma Mean Shift. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 4(3), 1015–1023. https://doi.org/10.57152/malcom.v4i3.1428
Wang, J., Alroobaea, R., Baqasah, A. M., Althobaiti, A., & Kansal, L. (2023). Study on Library Management System Based on Data Mining and Clustering Algorithm. Informatica, 46(9). https://doi.org/10.31449/inf.v46i9.3858
Zamri, N., Pairan, M. A., Azman, W. N. A. W., Abas, S. S., Abdullah, L., Naim, S., Tarmudi, Z., & Gao, M. (2022). A comparison of unsupervised and supervised machine learning algorithms to predict water pollutions. Procedia Computer Science, 204, 172–179. https://doi.org/10.1016/j.procs.2022.08.021
Zhao, M., Jha, A., Liu, Q., Millis, B. A., Mahadevan-Jansen, A., Lu, L., Landman, B. A., Tyska, M. J., & Huo, Y. (2021). Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking. Medical Image Analysis, 71, 102048. https://doi.org/10.1016/j.media.2021.102048
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Clustering Data Penduduk Desa Menggunakan Algoritma Mean Shift
Pages: 15-23
Copyright (c) 2025 Tedi Maulani, Elin Haerani, Fitri Wulandari, Lola Oktavia
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).
