IMPLEMENTASI ALGORITMA K-MEANS CLUSTERING PADA SISTEM PENENTUAN ZONA RAWAN KEBAKARAN HUTAN DAN LAHAN BERBASIS WEBSITE DI DINAS KEHUTANAN PROVINSI SUMATERA SELATAN

Melindasari, Melsi (2026) IMPLEMENTASI ALGORITMA K-MEANS CLUSTERING PADA SISTEM PENENTUAN ZONA RAWAN KEBAKARAN HUTAN DAN LAHAN BERBASIS WEBSITE DI DINAS KEHUTANAN PROVINSI SUMATERA SELATAN. Diploma thesis, Politeknik Negeri Sriwijaya.

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Abstract

Salah satu masalah yang paling sering terjadi di Provinsi Sumatera Selatan adalah kebakaran hutan dan lahan, yang berdampak pada lingkungan, kesehatan, dan aktivitas masyarakat. Proses pencarian data, pengelolaan laporan, dan analisis tingkat kerawanan menjadi kurang efisien karena data laporan kebakaran hutan dan lahan masih diurus melalui rekapitulasi spreadsheet. Tujuan penelitian ini adalah untuk mengidentifikasi zona rawan kebakaran hutan dan lahan melalui penggunaan algoritma clustering K-Means di website Dinas Kehutanan Provinsi Sumatera Selatan. Analisis kebutuhan, perancangan, implementasi, dan pengujian adalah tahap dari metode pengembangan sistem Waterfall. Sistem dibangun dengan framework Laravel dan basis data MySQL. Selain itu, hasil clustering ditampilkan dalam peta interaktif dengan Leaflet.js. Jumlah hotspot dan luas area kebakaran digunakan sebagai dasar clustering, yang menghasilkan tiga kategori zona: Sangat Rawan, Rawan, dan Tidak Rawan. Semua fungsi sistem pada akses admin, PBPH/PPKH, dan Kabid telah berjalan sesuai dengan kebutuhan fungsional, menurut pengujian sistem dengan metode Black Box. Hasil penelitian menunjukkan bahwa sistem dapat membantu pelaporan kebakaran hutan dan lahan bulanan, mengelola data laporan, dan menampilkan hasil clustering. Selain itu, sistem dapat membantu Dinas Kehutanan Provinsi Sumatera Selatan dalam menentukan zona yang paling mungkin terjadi kebakaran hutan dan lahan. ABSTRACT Forest and land fires are among the most frequent issues in South Sumatra Province, impacting the environment, public health, and community activities. Data retrieval, report management, and vulnerability analysis are currently inefficient because fire report data is handled via spreadsheet-based aggregation. This study aims to identify forest and land fire-prone zones using the K-Means clustering algorithm within a web-based system for the South Sumatra Provincial Forestry Agency. The system was developed using the Waterfall methodology, comprising stages of requirements analysis, design, implementation, and testing. It was built using the Laravel framework and a MySQL database, with clustering results visualized on an interactive map via Leaflet.js. The number of hotspots and the burned area served as the basis for clustering, resulting in three zone categories: Highly Prone, Prone, and Not Prone. Black Box testing confirmed that all system functions—accessible to administrators, PBPH/PPKH users, and division heads—operate according to functional requirements. The results demonstrate that the system facilitates monthly fire reporting, data management, and the visualization of clustering outcomes. Furthermore, the system assists the South Sumatra Provincial Forestry Agency in identifying zones with the highest risk of forest and land fires.

Item Type:	Thesis (Diploma)
Uncontrolled Keywords:	K-Means Clustering, Zona Rawan Kebakaran Hutan dan Lahan, Laravel, Leaflet.js, Website. K-Means Clustering, Forest and Land Fire-Prone Zones, Laravel, Leaflet.js, Website.
Subjects:	T Technology > T Technology (General)
Divisions:	Informatics Management > Undergraduate Theses
Depositing User:	Pustaka Manajemen Informatika
Date Deposited:	24 Jul 2026 07:17
Last Modified:	24 Jul 2026 07:17
URI:	http://eprints.polsri.ac.id/id/eprint/23211

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