IDHAYATUN, TASYA (2026) PENERAPAN K-MEANS CLUSTERING PADA APLIKASI DINASTIK UNTUK MENDUKUNG SEGMENTASI ANGGARAN (STUDI KASUS: DISKOMINFO KABUPATEN INDRAMAYU). S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
The management of official travel administration at the Department of Communication and Informatics of Indramayu Regency generates budget data that should be utilized to support the monitoring and evaluation of budget absorption across sub-activities. The current monitoring process still relies on administrative data presentation, which does not provide sufficient information regarding the budget absorption characteristics of each sub-activity. As a result, identifying sub�activities based on their budget absorption levels has not been carried out optimally. This study aims to apply the K-Means Clustering algorithm to the DINASTIK (Digitalization of Electronic Official Travel Administration) application to support budget segmentation based on budget allocation and budget absorption percentage. This research employed an applied quantitative approach using the Waterfall software development model. The research stages consisted of the Extract, Transform, and Load (ETL) process, Min-Max Normalization, determining the optimal number of clusters using the Elbow Method, and clustering using the K-Means Clustering algorithm with Euclidean Distance calculations. The research dataset consisted of sub-activity budget allocation data and budget realization data obtained from Official Travel Orders (SPD) and Fund Disbursement Notes (NPD) for the 2025 fiscal year. The data were then processed through the ETL stage, resulting in a dataset of 16 sub-activities as the clustering objects. The system functionality was evaluated using the Black Box Testing method to ensure that all application features operated according to the specified requirements. The results show that the K-Means Clustering algorithm successfully grouped the data into three clusters, consisting of 7 sub-activities in the High Cluster, 3 sub-activities in the Medium Cluster, and 6 sub-activities in the Low Cluster. The segmentation results were then integrated into the DINASTIK application through a dashboard that visualizes the clustering results, providing information on the budget absorption characteristics of each sub-activity to support budget monitoring and evaluation. Keywords: K-Means Clustering, Budget Segmentation, Official Travel Administration, Waterfall Model. Pengelolaan administrasi perjalanan dinas di Dinas Komunikasi dan Informatika Kabupaten Indramayu menghasilkan data anggaran yang perlu dimanfaatkan sebagai informasi untuk mendukung proses monitoring dan evaluasi terhadap tingkat penyerapan anggaran pada setiap subkegiatan. Proses monitoring yang dilakukan masih mengandalkan penyajian data administratif sehingga belum mampu memberikan gambaran mengenai karakteristik penyerapan anggaran pada setiap subkegiatan. Kondisi tersebut menyebabkan identifikasi subkegiatan berdasarkan tingkat penyerapan anggaran belum dapat dilakukan secara optimal. Penelitian ini bertujuan menerapkan algoritma K-Means Clustering pada aplikasi DINASTIK (Digitalisasi Administrasi Perjalanan Dinas Elektronik) untuk mendukung segmentasi anggaran berdasarkan pagu anggaran dan persentase penyerapan anggaran. Penelitian menggunakan pendekatan kuantitatif terapan dengan model pengembangan Waterfall. Tahapan penelitian meliputi proses Extract, Transform, dan Load (ETL), Min-Max Normalization, penentuan jumlah cluster menggunakan Metode Elbow, serta proses clustering menggunakan algoritma K-Means Clustering dengan perhitungan Euclidean Distance. Dataset penelitian menggunakan data pagu anggaran subkegiatan dan data realisasi anggaran yang berasal dari Surat Perjalanan Dinas (SPD) serta Nota Pencairan Dana (NPD) tahun anggaran 2025. Data tersebut kemudian diproses melalui tahapan ETL sehingga menghasilkan dataset sebanyak 16 subkegiatan sebagai objek clustering. Fungsionalitas sistem kemudian diuji menggunakan metode Black Box Testing untuk memastikan seluruh fitur aplikasi berjalan sesuai dengan kebutuhan yang telah ditetapkan. Hasil penelitian menunjukkan bahwa algoritma K-Means Clustering berhasil mengelompokkan data ke dalam tiga cluster, yaitu Cluster Tinggi sebanyak 7 subkegiatan, Cluster Sedang sebanyak 3 subkegiatan, dan Cluster Rendah sebanyak 6 subkegiatan. Hasil segmentasi kemudian diintegrasikan pada aplikasi DINASTIK dalam bentuk dashboard yang menyajikan visualisasi hasil clustering sehingga dapat memberikan informasi mengenai karakteristik penyerapan anggaran pada setiap subkegiatan sebagai pendukung proses monitoring dan evaluasi anggaran. Kata Kunci: K-Means Clustering, Segmentasi Anggaran, Administrasi Perjalanan Dinas, Model Waterfall.
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