RANCANG BANGUN SISTEM INFORMASI PENGELOMPOKAN CALON PENERIMA BANTUAN SOSIAL MENGGUNAKAN K-MEANS CLUSTERING DI DESA SRIBASUKI KECAMATAN BATANGHARI

AUFAA, DAFFA FADHILAH (2026) RANCANG BANGUN SISTEM INFORMASI PENGELOMPOKAN CALON PENERIMA BANTUAN SOSIAL MENGGUNAKAN K-MEANS CLUSTERING DI DESA SRIBASUKI KECAMATAN BATANGHARI. Diploma thesis, UIN Raden Intan Lampung.

	PDF Download (2MB)
	PDF Restricted to Repository staff only Download (5MB)

Abstract

ABSTRAK RANCANG BANGUN SISTEM INFORMASI PENGELOMPOKAN CALON PENERIMA BANTUAN SOSIAL MENGGUNAKAN K-MEANS CLUSTERING DI DESA SRIBASUKI KECAMATAN BATANGHARI Oleh Aufaa Daffa Fadhilah Desa Sribasuki Kecamatan Batanghari memiliki 2.315 jiwa atau sekitar 831 kepala keluarga, dengan 83 kepala keluarga tercatat sebagai penerima bantuan PKH. Namun proses penentuan calon penerima bantuan masih dilakukan secara manual sehingga berpotensi menimbulkan ketidaktepatan sasaran, subjektivitas penilaian, dan kesulitan verifikasi data. Penelitian ini merancang dan membangun sistem informasi pengelompokan calon penerima bantuan sosial berbasis website menggunakan metode Research and Development (R&D) dengan model pengembangan Waterfall. Sistem dibangun menggunakan bahasa pemrograman PHP, database MySQL, serta library PhpSpreadsheet untuk proses import data Excel. Algoritma K-Means Clustering diterapkan berdasarkan kriteria usia, pendidikan, pekerjaan, pendapatan, jenis aset, dan jumlah tanggungan. Sistem berhasil dikembangkan dengan fitur login, pengelolaan data penduduk, import data Excel, proses K-Means Clustering, evaluasi cluster, riwayat penerima bantuan dan cetak laporan. Hasil pengujian Black Box Testing menunjukkan seluruh fitur berjalan valid. Perhitungan manual K=3 menggunakan 4 data sampel menghasilkan nilai Elbow Method atau SSE 0.1250, Davies Bouldin Index 0.1680, dan Silhouette Score 0.3146. Sistem ini dapat membantu perangkat desa mengelompokkan calon penerima bantuan secara lebih objektif, terstruktur, dan efisien. Kata kunci: Sistem Informasi, Bantuan Sosial, K-Means Clustering, Davies Bouldin Index, Website. ABSTRACT DESIGN AND DEVELOPMENT OF A SOCIAL ASSISTANCE RECIPIENT GROUPING INFORMATION SYSTEM USING KMEANS CLUSTERING IN SRIBASUKI VILLAGE, BATANGHARI DISTRICT By Aufaa Daffa Fadhilah Sribasuki Village, Batanghari District, has a population of 2,315 people or approximately 831 households, with 83 households registered as recipients of the Family Hope Program (PKH) assistance. However, the process of determining prospective assistance recipients is still carried out manually, which has the potential to cause inaccurate targeting, subjective assessment, and difficulties in data verification. This study designs and develops a website-based information system for grouping prospective social assistance recipients using the Research and Development (R&D) method with the Waterfall development model. The system was built using the PHP programming language, a MySQL database, and the PhpSpreadsheet library for Excel data import processes. The K-Means Clustering algorithm was applied based on criteria including age, education, occupation, income, type of assets, and number of dependents. The system was successfully developed with features including login, resident data management, Excel data import, KMeans Clustering processing, cluster evaluation, and report printing. Black Box Testing results indicate that all features function as valid. Manual calculation with K=3 using 4 sample data points yielded an SSE value of 0.1250, a Davies Bouldin Index of 0.1680, and a Silhouette Score of 0.3146. This system can assist village officials in grouping prospective assistance recipients in a more objective, structured, and efficient manner.

Actions (login required)

View Item