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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer
Alwis Nazir
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Pengelompokan Wilayah Bencana Banjir di Indonesia Menggunakan Algoritma K-Means Wenny Tarisa Oktaviany; Fitri Insani; Alwis Nazir; Pizaini Pizaini
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.608

Abstract

Floods are one of the natural disasters that often occur in Indonesia, especially during the rainy season. This disaster is caused by various factors, both natural and caused by human activities, such as high rainfall, poor drainage systems, land conversion, and suboptimal spatial planning. The impact of floods is very detrimental, both physically and psychologically, including loss of life and damage to property. Therefore, a method is needed to group areas based on their level of vulnerability to flooding. This study aims to group flood disaster areas in Indonesia using the K-Means algorithm. The data used comes from the BNPB Geoportal covering flood events from January 2020 to December 2024, with a total of 7,487 events from 498 areas. Based on the test results obtained using the Silhouette Coefficient, it shows that 2 clusters were selected as the best number of clusters with a Silhouette Coefficient value of 0.8461 which is included in the strong clustering structure. Of the 2 clusters obtained, cluster 1 is a high-risk category consisting of 35 areas, while cluster 2 is a low-risk category consisting of 463 areas. The results of this study can provide information for related parties to improve the efficiency of flood disaster management.
Analisis Algoritma Fuzzy C-Means Untuk Pengelompokan Data Keluarga Wahyu Cahyadi; Elin Haerani; Alwis Nazir; Iwan Iskandar
TIN: Terapan Informatika Nusantara Vol 6 No 7 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i7.8981

Abstract

Mapping the socio-economic conditions of the community plays a crucial role in supporting targeted development planning at the village level. This study aims to apply the Fuzzy C-Means (FCM) algorithm to cluster families in Bina Baru Village based on social, economic, and household environmental indicators. The variables used include family size, income sources, physical condition of the house, basic facilities, as well as monthly expenditure and income levels. This study uses population data from Bina Baru Village, consisting of 1,000 entries with 16 variables. The FCM algorithm was chosen for its ability to accommodate multiple degrees of membership (fuzzy membership), making it more adaptable in capturing the diversity and ambiguity of socio-economic characteristics. The results show that FCM produces two main clusters: Cluster 0, with 440 members, reflects families with middle to lower economic conditions, permanent housing, and adequate basic facilities; and Cluster 1, with 560 members, represents families with lower economic conditions, semi-permanent housing, and relatively smaller family sizes. Evaluation using the Xie–Beni index (35.4976), Fuzzy Partition Entropy (0.6843), and Fuzzy Cluster Index (0.4468) indicates that the two-cluster model has the best clustering quality compared to other numbers of clusters. Overall, the Fuzzy C-Means algorithm is effective in mapping variations in family welfare and can be used as a basis for formulating development policies and data-driven community empowerment programs in Bina Baru Village.
Co-Authors Adrian Maulana Agung Surya Maulana Ana Komaria Baskara Beni Basuki Eka Pandu Cynthia Elin Haerani Elvia Budianita Fadhilah Syafria Ferdian Arya Dinata Fitri Insani Gusti, Siska Kurnia Ihda Syurfi Iis Afrianty Iwan Iskandar Lestari Handayani Lili Rahmawati Lola Oktavia Muhammad Irsyad Muhammad Rifaldo Al Magribi Nining Nur Habibah Nur Iza Nurafni Syahfitri Pizaini Pizaini Qurrata A'yuni Reski Mai Candra Reski Mei Candra Said Nurfan Hidayad Tillah Sephia Pratista Suwanto Sanjaya Syahbudin Hamwar Teddie Darmizal Trisia Intan Berliana Wahyu Cahyadi Wenny Tarisa Oktaviany Yusra Yusra Yusril Hidayat