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All Journal Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Syukirman Amir
Universitas AMIKOM Yogyakarta
Computer Science & IT

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Sistem Pendukung Keputusan Kelayakan Mendirikan Bangunan Menggunakan MOORA Rafli Junaidi Kasim; Samsul Bahri; Syukirman Amir; Rudi Prietno; Rahim Jamal; Andi Sunyoto; M. Syukri Mustafa
Prosiding SISFOTEK Vol 5 No 1 (2021): SISFOTEK V 2021
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (447.938 KB)

Abstract

The selection of the feasibility of building a building permit is one of the tasks of the Ternate City Investment and One Stop Integrated Service. In building feasibility selection is still done manually by going down a location survey assessing the criteria needed one by one for each proposal without a method that can provide an assessment priority with various criteria including type of building, foundation, building level, building area and walls, there are many criteria for proposals that are submitted to DPMPTSP. Decision Support System (SPK) is needed to facilitate the Investment and One Stop Integrated Service. The system that was built was web-based, in this research it was carried out through literature reviews and direct interviews with DMPTSP employees. The model used is the method of Multy Objective Optimization On The Basis Of Ratio Analysis. The results of the Multy Objective Optimization Method On The Basis Of Ratio Analysis are considered to have a good level of selectivity in determining an alternative, easy to understand and flexible in separating objects to the evaluation process of the decision weight criteria.
Implementasi Metode K-Means Untuk Clustering Data Penduduk Miskin Dengan Systematic Random Sampling Rafli Junaidi Kasim; Samsul Bahri; Syukirman Amir
Prosiding SISFOTEK Vol 5 No 1 (2021): SISFOTEK V 2021
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (544.044 KB)

Abstract

The poor population grouping aims to differentiate the population with the highest or the most appropriate level of poverty to get assistance specifically for the population with the highest level of poverty. Grouping is done by using the k-means method. Grouping with the k-means method will increase the level of similarity in groups and reduce the level of similarity between groups. Random grouping on k-means will be applied systematic random sampling methods that will influence and narrow down the possibility of many initial centroid values ??to be generated, while speeding up the computation process for random grouping. Furthermore, the silhouette coefficient is validated to determine the best group in grouping the poor population. The number of groups determined is 2 clusters, 3 clusters, and 4 clusters. The results obtained are the number of groups of 2 clusters is better than 3 clusters and 4 clusters with a value of 2 clusters namely 0.435489, while in 3 clusters 0.434857 and 4 clusters 0.30832.
Co-Authors Andi Sunyoto M. Syukri Mustafa Rafli Junaidi Kasim Rahim Jamal Rudi Prietno Samsul Bahri