PENERAPAN ALGORITMA K-MEANS DALAM PENGELOMPOKKAN KAWASAN PEMUKIMAN BERDASARKAN KELAYAKAN HUNIAN DI DINAS PERUMAHAN DAN KAWASAN PEMUKIMAN

MISDAWATI RAMBE, NPM 2108100065 (2025) PENERAPAN ALGORITMA K-MEANS DALAM PENGELOMPOKKAN KAWASAN PEMUKIMAN BERDASARKAN KELAYAKAN HUNIAN DI DINAS PERUMAHAN DAN KAWASAN PEMUKIMAN. Skripsi thesis, Universitas Labuhanbatu.

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Abstract

Permukiman yang layak huni merupakan salah satu indikator penting dalam pembangunan wilayah yang berkelanjutan. Oleh karena itu, diperlukan analisis berbasis data untuk mengidentifikasi tingkat kelayakan suatu kawasan permukiman. Clustering merupakan salah satu teknik data mining yang digunakan untuk mengelompokkan data berdasarkan kemiripan karakteristik, dan dalam penelitian ini digunakan algoritma K-Means. Metode ini memungkinkan pemetaan kawasan berdasarkan tiga variabel utama, yaitu infrastruktur dasar, aksesibilitas, dan kondisi sosial ekonomi. Penelitian ini menggunakan pendekatan kuantitatif dengan jumlah data sebanyak 60 entri, yang kemudian diproses melalui tahapan normalisasi dan clustering menggunakan metode K-Means. Hasil clustering menunjukkan bahwa objek data dapat dikelompokkan menjadi tiga cluster yang mencerminkan tingkat kelayakan hunian berbeda, yaitu layak huni, kurang layak, dan tidak layak huni. Analisis terhadap jarak rata-rata antar data dengan centroid dalam tiap cluster (Average Within Centroid Distance) juga dilakukan untuk melihat kualitas pengelompokan. Berdasarkan hasil tersebut, dapat disimpulkan bahwa pendekatan clustering mampu menyederhanakan kompleksitas data permukiman dan membantu dalam pengambilan keputusan yang lebih tepat sasaran. Selain itu, temuan ini dapat menjadi dasar bagi dinas terkait dalam menyusun strategi pengembangan kawasan secara lebih terarah. Kata kunci: Clustering, K-Means, Permukiman, Layak Huni, Data Mining ================================================================================ Livable settlements are an important indicator of sustainable regional development. Therefore, data-driven analysis is needed to identify the level of livability of a residential area. Clustering is a data mining technique used to group data based on similar characteristics, and in this study the K-Means algorithm was used. This method allows mapping of areas based on three main variables: basic infrastructure, accessibility, and socioeconomic conditions. This study used a quantitative approach with 60 data entries, which were then processed through normalization and clustering stages using the K-Means method. The clustering results showed that the data objects could be grouped into three clusters reflecting different levels of livability: livable, less livable, and unlivable. An analysis of the average distance between data and the centroid in each cluster (Average Within Centroid Distance) was also conducted to assess the quality of the clustering. Based on these results, it can be concluded that the clustering approach can simplify the complexity of settlement data and assist in more targeted decision-making. In addition, these findings can serve as a basis for relevant agencies in developing more targeted regional development strategies. Keywords: Clustering, K-Means, Settlements, Livability, Data Mining

Item Type:	Thesis (Skripsi)
Uncontrolled Keywords:	Clustering, K-Means, Permukiman, Layak Huni, Data Mining=============Clustering, K-Means, Settlements, Livability, Data Mining
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases
Divisions:	Fakultas Sains Dan Teknologi > Teknologi Informasi
Depositing User:	Unnamed user with email repository@ulb.ac.id
Date Deposited:	29 Oct 2025 07:38
Last Modified:	29 Oct 2025 07:38
URI:	http://repository.ulb.ac.id/id/eprint/1885

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