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All Journal JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science Jurnal Sistem Komputer dan Informatika (JSON)
Umbar Riyanto
Universitas Muhammadiyah Tangerang, Tangerang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Journal : Building of Informatics, Technology and Science

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Klasifikasi Citra Jenis Tanaman Jamur Layak Konsumsi Menggunakan Algoritma Multiclass Support Vector Machine Nuke L. Chusna; Mohammad Imam Shalahudin; Umbar Riyanto; Allan Desi Alexander
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): Juni 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (483.267 KB) | DOI: 10.47065/bits.v4i1.1624

Abstract

Mushrooms are plants that have high nutritional content and have various benefits for the health of the human body. However, not everyone knows the types of mushrooms that are suitable for consumption. The types of mushrooms have their own characteristics when viewed from the image. For this reason, a system is needed by utilizing digital image processing to classify types of mushrooms suitable for consumption, so that people can find out which types of mushrooms are suitable for consumption. This research is to classify types of mushrooms suitable for consumption using the Multiclass SVM algorithm with first-order feature extraction, which performs feature extraction based on the characteristics of the image histogram. The result of feature extraction is used as input for classification in Multiclass SVM. Multiclass SVM can map data points to dimensionless space to obtain hyperplane linear separation between each class. The developed method is implemented in Matlab, in order to produce a system in the form of a GUI so that it can be used by general users easily. Based on the test results, the average accuracy is 83%.
Sistem Pendukung Keputusan Pemilihan Platform Investasi P2P Lending Menggunakan Metode Complex Proportional Assessment (COPRAS) Muhammad Bagir; Umbar Riyanto; Rini Nuraini; Dedi Kustiawan
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): Maret 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3246

Abstract

Through technological developments, many fintech P2P lending have emerged which are competing to offer convenience in transactions and offer fast processes. To determine a P2P lending platform as a place to invest, one must know in advance about the company profile or the application and programs offered as a whole. This of course will take a long time to select a P2P lending platform. If you choose an inappropriate P2P lending platform, it will result in losses. The purpose of this research is to build a Decision Support System (DSS) for choosing a P2P lending platform by implementing the Complex Proportional Assessment (COPRAS) approach in order to get the right decision and not take a long time. The COPRAS approach has the ability to produce the best alternative which is limited to alternative analysis through alternative assumptions by providing utility judgment so that the attributes of each alternative are arranged based on intervals. Based on the results of the case studies conducted, the highest utility score was Danamas Lender with a score of 100, then followed by Alami Funding Sharia with a score of 99.2338, Accelerant with a score of 89.8827 and Amartha Microfinance with a score of 83.4988. In addition, based on the results of black box testing, it shows that the software can run as it should.
Co-Authors Alexander, Allan D Amat Damuri Chusna, Nuke L Dedi Kustiawan Hengki Rusdianto Mohammad Aminudin Mohammad Imam Shalahudin Muhammad Bagir Nurdiana Handayani Rini Nuraini