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All Journal Journal of Computing and Informatics Research Bulletin of Information System Research
Dedy Hartama
STIKOM Tunas Bangsa, Pematang Siantar
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Penerapan K-Means Clustering Dalam Menentukan Banyaknya Desa/Kelurahan Menurut Keberadaan dan Jenis Industri Kecil dan Mikro (Desa) Fira Fania; Agus Perdana Windarto; Dedy Hartama
Bulletin of Information System Research Vol 1 No 1 (2022): Desember 2022
Publisher : Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/bios.v1i1.24

Abstract

Processing Industry is an economic activity that carries out activities to change a basic item mechanically, chemically, or by hand so that it becomes finished / semi-finished goods, and or goods of less value to goods of higher value, and which are closer to the end user. This study aims to model the grouping in determining the Number of Villages / Villages According to the Existence and Types of Small and Micro Industries (Villages). This research is a reference, especially for the government, so that the potential for employment in this industry group can continue to be developed and optimized. Government contributions can be realized through the creation of stable social, economic and political conditions and through the policy of determining the direction of business development of Micro and Small Industries. The data from this study were taken from a government statistical data provider website, BPS (Statistics Indonesia) www.bps.go .id. This research uses the K-Mens method and is tested with RapidMiner software to create 3 clusters, namely high, medium and low level clusters and see what the contents of the cluster are. From the research results obtained by high cluster data centroids namely ((2151.79), ( 1494.34), (1135.76), moderate clusters ((406.64), (525.06), (616,218), and low clusters ((455,361), (345,523), (1074.09), (176,434), (1410,34), (243,749), (295,151), (463,266), (5868,13), (9344.07), (170,925), (8818,85), (1031,65), (433,61), (5985,505), (1630,75), (367,928), (119,082), (560,907), (172,333), (545,342), (226,174), (776,643), (1880,857), (172,333), (545,342), (226,174), (776,643), (1880,853), (1880,853), (18,80,853), (1880,853), (1880,853), (1880,853), (1880,853), (1880,853) ), (1482.39), (115,573), (232,734), (187.04), (142,884), (455,674), (441,934) With this analysis expected to be input and information for the government of each region to pay more attention to regions micro / small industrial areas occupying low clushter (C1) positions in order to improve industrial quality in the region.
Sistem Pendukung Keputusan Pemilihan Merek Body Lotion Lokal Terbaik untuk Mencerahkan Kulit dengan Menggunakan Metode MAUT Nurul Aisyah; Selly Andari; Ririn Nadya Utari; Nadya; Dedy Hartama; Putrama Alkhairi
Journal of Computing and Informatics Research Vol 5 No 2 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v5i2.2636

Abstract

This study aims to determine the best local body lotion brand for skin brightening using the Multi-Attribute Utility Theory (MAUT) method. MAUT was chosen for its capability to process data based on various criteria such as benefits, quality, effectiveness, price, and brand reputation. Data were collected through online questionnaires distributed via Google Forms and shared on social media. From 36 alternatives, five local brands were selected for analysis: Marina, Citra, Scarlett, Natur-e, and Herborist. The analysis process involved determining the weight of each criterion, matrix normalization, utility evaluation, and alternative ranking. The results indicate that Marina ranks first with a score of 19, followed by Scarlett (8.7) and Citra (8.1). The MAUT method has proven effective in supporting decisions regarding the selection of the best local body lotion brand, providing objective and structured guidance for consumers.
Co-Authors Agus Perdana Windarto Fira Fania Nadya Nurul Aisyah Putrama Alkhairi Ririn Nadya Utari Selly Andari