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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jupiter Coding Jurnal Komputer dan Aplikasi Techno.Com: Jurnal Teknologi Informasi CESS (Journal of Computer Engineering, System and Science) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Jurnal Informatika Upgris Jurnal Khatulistiwa Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI CYBERNETICS JURIKOM (Jurnal Riset Komputer) Journal of Computer System and Informatics (JoSYC) KLIK: Kajian Ilmiah Informatika dan Komputer Indonesian Journal of Computer Science
Rahmi Hidayati
Universitas Tanjungpura
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science

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Journal : Indonesian Journal of Computer Science

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Penentuan Pencemaran Air Menggunakan Metode Self Organizing Maps (SOM) Deni; Dwi Marisa Midyanti; Rahmi Hidayati
Indonesian Journal of Computer Science Vol. 11 No. 1 (2022): Indonesian Journal of Computer Science Volume 11. No. 1 (2022)
Publisher : STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (472.099 KB) | DOI: 10.33022/ijcs.v11i1.3036

Abstract

Water pollution is the entry of a component or substance into water, which causes water quality to decrease. The decrease in water quality causes the water to be unfit and affects the community's health. Therefore, an application for determining the water quality level building using the SOM algorithm with several data input parameters: Fluoride, Hardness, and Nitrite. These parameters use for data clustering in determining clean and polluted water. The SOM algorithm arranges SOM neurons based on the data input values ​​in a SOM cluster. For testing the SOM method on 114 data, the Silhouette Coefficient value was used to find the best number of clusters. Silhouette Coefficient will evaluate clustering the proximity of the distance between data. The test was carried out 27 times with variations of the experiment with the learning rate starting from 0.1 to 0.9 and the number of clusters from 2 to 4 to get the best Silhouette Coefficient value. The result of clustering the best silhouette coefficient value obtained is 0.7276473444141 with 3 clusters and the learning rate of 0.2.
Co-Authors Agus Harjoko Ariansyah Sudarsono Arif Rahmawan Aris Fajrianto Ayu Lathalia Lestari CHANDRA MONICA PERMADANI Cucu Suhery Darmawansyah Darmawansyah Dea Rizki Febrinamas Dedi Triyanto Deni Destia Arini Hairunnisa Dwi Marisa Midyanti Dwi Marisa Midyanti Ellif Ellif Fajar Mu'alim Fransiskus Julian Adresman Frasila Frasila Hafiz Adlan Afrigi Subri Ika Nurul Hidayah Ikhwan Ruslianto Ilhamsyah Indah Advia Lestarry Irma Nirmala Irwan Guntoro Irwan Guntoro Ita Permata Sari Jaka Jaka Kasliono Kasliono Kresna Satya Nugroho Krisna Madani Maulana Basuki Ilham Michael Michael Muhamad Reksy Mulia Nur Fitriana Putri Nurul Fajri 'Aini Firdaus Nurul Mutiah Rahmanita Widiyanti Rizky Risan R Sampe Hotlan Sitorus Suci Junianti Suhardi Suhardi Suhardi Suhardi Suhardi Sy Kamal Baraqbah Syamsul Bahri Sylviana Kusuma Tedy Rismawan Tika Suci Pania Uray Ristian