Roihan, Faris Alif (2026) Penerapan Algoritma K-Means Clustering Berbasis Web Untuk Klasifikasi Penerima Bantuan Sosial Berdasarkan Data Sosial Ekonomi Warga Rt 12 Kelurahan Baru Tenggarong. S1 Teknik Informatika thesis, STMIK Widya Cipta Dharma.
![[img]](https://repository.wicida.ac.id/style/images/fileicons/text.png) |
Text
2243090-S1-Jurnal.pdf Download (238kB) |
|
Text
2243090-S1-Teknik Informatika.pdf Restricted to Repository staff only Download (4MB) | Request a copy |
Abstract
Penyaluran bantuan sosial sering kali menghadapi kendala terkait akurasi pendataan sasaran penerima akibat penilaian yang bersifat subjektif. Penelitian ini bertujuan untuk membangun sebuah Sistem Pendukung Keputusan berbasis web menggunakan algoritma K-Means Clustering guna mengklasifikasikan tingkat kelayakan penerima bantuan secara objektif dan transparan. Studi kasus dilaksanakan pada data sosial ekonomi warga di wilayah RT 12 Kelurahan Baru Tenggarong. Proses pengelompokan diukur berdasarkan empat kriteria utama, yaitu besaran penghasilan bulanan, jumlah beban tanggungan keluarga, status pekerjaan kepala keluarga, serta kualitas fisik kondisi tempat tinggal warga. Proses komputasi di dalam sistem diawali dengan tahapan normalisasi data menggunakan metode Min-Max Scaling untuk menyamakan skala seluruh variabel kriteria. Selanjutnya, mesin algoritma K-Means memproses data tersebut melalui perhitungan jarak Euclidean dan membaginya ke dalam tiga kelompok prioritas sasaran, yaitu status Layak, Dipertimbangkan, dan Tidak Layak menerima bantuan. Aplikasi ini juga dilengkapi dengan antarmuka yang memungkinkan warga melakukan pengajuan berkas secara mandiri serta memudahkan pengurus wilayah dalam memantau visualisasi hasil pengelompokan. Hasil pengujian fungsionalitas menggunakan metode Black Box Testing menunjukkan bahwa seluruh modul aplikasi berjalan dengan tingkat keberhasilan seratus persen tanpa adanya kegagalan fungsi. Selain itu, evaluasi tingkat akurasi menggunakan metode Silhouette Coefficient menghasilkan skor yang sangat positif dan mendekati angka satu. Capaian tersebut memvalidasi bahwa sistem komputasi yang dibangun mampu menghasilkan penempatan kelompok warga yang sangat presisi dan terhindar dari bias subjektif manusia. Sistem ini diharapkan dapat menjadi alat bantu bagi perangkat kewilayahan dalam mengambil keputusan pembagian kuota bantuan secara cepat dan tepat sasaran. ============================================================ The distribution of social assistance frequently encounters obstacles related to data collection accuracy for target recipients due to subjective assessments. This study aims to develop a web based Decision Support System using the K Means Clustering algorithm to classify the eligibility levels of assistance recipients objectively and transparently. The case study was conducted on the socio economic data of residents in the RT 12 area of Baru Tenggarong Urban Village. The clustering process was measured based on four primary criteria, namely monthly income amount, number of family dependents, employment status of the head of the household, and the physical quality of the residential housing conditions. The computational process within the system initiates with a data normalization phase using the Min Max Scaling method to balance the scale of all criteria variables. Subsequently, the K Means algorithm engine processes the data through Euclidean distance calculations and divides it into three target priority groups, namely Eligible, Under Consideration, and Ineligible statuses to receive assistance. This application is also equipped with an interface that enables residents to submit files independently and facilitates local administrators in monitoring the visualization of clustering results. The functionality testing results using the Black Box Testing method demonstrate that all application modules operate with a one hundred percent success rate without any functional failures. Furthermore, the accuracy evaluation using the Silhouette Coefficient method yields a highly positive score that approaches the absolute value of one. This achievement validates that the developed computational system is capable of producing highly precise resident group assignments and avoiding human subjective bias. This system is expected to serve as a supportive tool for local administrative authorities in making decisions regarding social assistance quota distribution quickly and accurately.
| Item Type: | Thesis (S1 Teknik Informatika) |
|---|---|
| Uncontrolled Keywords: | Kata Kunci: Algoritma K-Means, Bantuan Sosial, Klasifikasi Data, Min-Max Scaling, Silhouette Coefficient, Sistem Pendukung Keputusan. Keywords: K Means Algorithm, Data Classification, Decision Support System, Min Max Scaling, Silhouette Coefficient, Social Assistance. |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Teknik Informatika |
| Depositing User: | Mr Faris Alif Roihan . |
| Date Deposited: | 07 Aug 2026 05:42 |
| Last Modified: | 07 Aug 2026 05:42 |
| URI: | http://repository.wicida.ac.id/id/eprint/6429 |
Actions (login required)
 |
View Item |
