Analisis Data Jumlah Pemohon Perpanjangan SIM Dengan Metode Crisp-DM Sebagai Rekomendasi Penjadwalan Layanan SIM Keliling (Studi Kasus: Polres Metro Depok)
Keywords:
SIM Keliling, CRISP-DM, K-Means Clustering, Penjadwalan Dinamis, Polres Metro DepokAbstract
The Mobile SIM extension service at Polres Metro Depok faces challenges related to long queues and static operational scheduling that does not fully accommodate fluctuations in applicant demand. This study aims to analyze the pattern of SIM extension applicants as a basis for recommending a more flexible and effective Mobile SIM service scheduling strategy. The study applies a descriptive quantitative approach using the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. The dataset consists of secondary historical data covering 11,756 SIM A and SIM C applicants recorded during January–June 2025 across 131 operational days. Data processing includes data understanding, data preparation, K-Means clustering, model evaluation, and deployment of the analysis results into a web-based dashboard prototype. K-Means was applied with three clusters representing low, medium, and high service density. The cluster quality was evaluated using the Davies-Bouldin Index (DBI) through RapidMiner and Python. RapidMiner produced a DBI value of 0.583, while Python produced 0.520. The results indicate substantial fluctuations in applicant demand, with the lowest monthly average occurring in April at 36.76 applicants and the highest in June at 166.68 applicants. The clustering results were subsequently transformed into an adaptive operational scheduling recommendation and implemented in a Python-Flask web dashboard prototype.
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