Comparative Analysis of Naïve Bayes and K-NN in Determining Location of Mobile Population Services
Keywords:
method comparison, Naïve bayes, K-nn, mobile civil service
Abstract
Tantangan geografis dan jarak antar desa menjadi kendala dalam pemerataan pelayanan kependudukan dan pencatatan sipil. Hal ini memerlukan intervensi program jemput bola atau layanan keliling. Permasalahannya adalah tidak semua desa dapat terlayani layanan keliling, sehingga perlu dilakukan pemetaan desa-desa yang memenuhi syarat menjadi lokasi layanan keliling. Penelitian ini menjelaskan teknik pembelajaran mesin, khususnya algoritma K-NN dan Naïve Bayes, untuk mengatasi masalah pemilihan lokasi yang memenuhi syarat. Hasil percobaan menunjukkan kedua metode mempunyai tingkat akurasi yang cukup baik, dengan K-NN mencapai tingkat akurasi tertinggi sebesar 97,14% pada dataset yang dinormalisasi dengan metode Normaliasi Min-Max (NMM). Sebaliknya, Naïve Bayes menunjukkan nilai akurasi yang tinggi pada seluruh dataset. Oleh karena itu, penelitian ini merekomendasikan penggunaan algoritma K-NN dengan nilai K=2 untuk menentukan lokasi yang layak menerima layanan kependudukan bergerak.
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References
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[14] K. Polat, S. Şahan, and S. Güneş, “Automatic detection of heart disease using an artificial immune recognition system (AIRS) with fuzzy resource allocation mechanism and k-nn (nearest neighbour) based weighting preprocessing,” Expert Syst. Appl., vol. 32, no. 2, pp. 625–631, 2007, doi: 10.1016/j.eswa.2006.01.027.
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[18] M. P. Vaishnnave, K. Suganya Devi, P. Srinivasan, and G. Arutperumjothi, “Detection and classification of groundnut leaf diseases using KNN classifier,” 2019 IEEE Int. Conf. Syst. Comput. Autom. Networking, ICSCAN 2019, pp. 1–5, 2019, doi: 10.1109/ICSCAN.2019.8878733.
[19] Marisa F et al., “Pengukuran Tingkat Kematangan Kopi Arabika Menggunakan Algoritma K-Nearest Neighbor,” JIMPJurnal Inform. Merdeka Pasuruan, vol. 6, no. 3, pp. 1–5, 2021, [Online]. Available: http://ejurnal.unmerpas.ac.id/index.php/informatika/article/view/280/122
[20] A. Yudhana, D. Sulistyo, and I. Mufandi, “GIS-based and Naïve Bayes for nitrogen soil mapping in Lendah, Indonesia,” Sens. Bio-Sensing Res., vol. 33, pp. 100435:1-100435:11, 2021, doi: 10.1016/j.sbsr.2021.100435.
[21] A. Yudhana, A. D. Cahyo, L. Y. Sabila, A. C. Subrata, and I. Mufandi, “Spatial distribution of soil nutrient content for sustainable rice agriculture using geographic information system and Naïve Bayes classifier,” Int. J. Smart Sens. Intell. Syst., vol. 16, no. 1, pp. 1–14, 2023, doi: 10.2478/ijssis-2023-0001.
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[25] A. Yudhana, I. Riadi, and M. R. Djou, “Pengembangan Layanan Kependudukan Dan Pencatatan Sipil Menggunakan Algoritma Naïve Bayes,” JURIKOM (Jurnal Ris. Komputer), vol. 9, no. 4, p. 1062, 2022, doi: 10.30865/jurikom.v9i4.4515.
[26] A. Yudhana, I. Riadi, and M. R. Djou, “Determining eligible villages for mobile services using k- NN algorithm,” vol. 15, no. 1, pp. 11–20, 2023, doi: http://dx.doi.org/10.30865/jurikom.v9i4.4515.
[27] N. Dengen, “Comparison Performance of C4.5, Naïve Bayes And K-Nearest Neighbor in Determination Drug Rehabilitation,” Int. Conf. Sci. Inf. Technol., pp. 112–117, 2019, doi: DOI:10.1109/ICSITech46713.2019.8987455.
[28] S. Yonesa, S. M. Nasution, and R. A. Nugrahaeni, “Path Determination with Classification from Data Mining Using C4.5 Algorithm,” 2018 Int. Conf. Inf. Technol. Syst. Innov. ICITSI 2018 - Proc., pp. 22–25, 2018, doi: 10.1109/ICITSI.2018.8696020.
[29] S. Sunardi, A. Yudhana, and S. A. Wijaya, “Face Detection Analysis of Digital Photos Using Mean Filtering Method,” Int. J. Artif. Intell. Res., vol. 6, no. 2, 2022, doi: 10.29099/ijair.v6i2.307.
[30] C. Schröer, F. Kruse, and J. M. Gómez, “A systematic literature review on applying CRISP-DM process model,” Procedia Comput. Sci., vol. 181, no. 2019, pp. 526–534, 2021, doi: 10.1016/j.procs.2021.01.199.
[31] S. Huber, H. Wiemer, D. Schneider, and S. Ihlenfeldt, “DMME: Data mining methodology for engineering applications - A holistic extension to the CRISP-DM model,” Procedia CIRP, vol. 79, pp. 403–408, 2019, doi: 10.1016/j.procir.2019.02.106.
[32] J. A. Solano, D. J. Lancheros Cuesta, S. F. Umaña Ibáñez, and J. R. Coronado-Hernández, “Predictive models assessment based on CRISP-DM methodology for students performance in Colombia - Saber 11 Test,” Procedia Comput. Sci., vol. 198, no. 2020, pp. 512–517, 2021, doi: 10.1016/j.procs.2021.12.278.
[33] A. Morais, H. Peixoto, C. Coimbra, A. Abelha, and J. Machado, “Predicting the need of Neonatal Resuscitation using Data Mining,” Procedia Comput. Sci., vol. 113, pp. 571–576, 2017, doi: 10.1016/j.procs.2017.08.287.
[34] A. Abubakar and A. B. Garko, “A Predictive Model for Network Intrusion Detection System Using Deep Neural Network,” Dutse J. Pure Appl. Sci., vol. 7, no. 3a, pp. 113–128, 2022, doi: 10.4314/dujopas.v7i3a.12.
[35] Y. A. Setianto, K. Kusrini, and H. Henderi, “Penerapan Algoritma K-Nearest Neighbour Dalam Menentukan Pembinaan Koperasi Kabupaten Kotawaringin Timur,” Creat. Inf. Technol. J., vol. 5, no. 3, pp. 232–241, 2019, doi: 10.24076/citec.2018v5i3.179.
[36] D. Singh and B. Singh, “Investigating the impact of data normalization on classification performance,” Appl. Soft Comput., vol. 97, no. B, p. 105524, 2020, doi: 10.1016/j.asoc.2019.105524.
[37] A. Pérez, P. Larrañaga, and I. Inza, “Supervised classification with conditional Gaussian networks: Increasing the structure complexity from naive Bayes,” Int. J. Approx. Reason., vol. 43, no. 1, pp. 1–25, 2006, doi: 10.1016/j.ijar.2006.01.002.
[38] Rovidatul, Y. Yunus, and G. W. Nurcahyo, “Perbandingan algoritma c4.5 dan naive bayes dalam prediksi kelulusan mahasiswa,” J. CoSciTech (Computer Sci. Inf. Technol., vol. 4, no. 1, pp. 193–199, 2023, doi: 10.37859/coscitech.v4i1.4755.
[39] Fitri Wulandari, Elin Haerani, Muhammad Fikry, and Elvia Budianita, “Analisis sentimen larangan penggunaan obat sirup menggunakan algoritma naive bayes classifier,” J. CoSciTech (Computer Sci. Inf. Technol., vol. 4, no. 1, pp. 88–96, 2023, doi: 10.37859/coscitech.v4i1.4781.
[40] V. Tsenev and M. Ivanova, “Statistical and machine learning approach for evaluation of control systems for automatic production lines,” Bull. Electr. Eng. Informatics, vol. 11, no. 5, pp. 2527–2536, 2022, doi: 10.11591/eei.v11i5.3664.
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Published
2024-01-01
How to Cite
Riadi, I., Yudhana, A., & M. Rosyidi Djou. (2024). Comparative Analysis of Naïve Bayes and K-NN in Determining Location of Mobile Population Services . Jurnal CoSciTech (Computer Science and Information Technology), 4(3), 733-742. https://doi.org/10.37859/coscitech.v4i3.6543
Section
Articles
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