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All Journal Eksplorasi JOIV : International Journal on Informatics Visualization Journal of Islamic Architecture Adi Widya : Jurnal Pengabdian Masyarakat Al Qalam: Jurnal Ilmiah Keagamaan dan Kemasyarakatan JISIP: Jurnal Ilmu Sosial dan Pendidikan Journal of Information Systems and Informatics Jurnal Mandala Pengabdian Masyarakat Wasana Nyata Indonesian Journal Computer Science (ijcs) Jurnal Pendidikan Sains Indonesia (Indonesian Journal of Science Education) WASANA NYATA Muara: Jurnal Manajemen Pelayaran Nasional Jurnal Ilmu-Ilmu Pertanian
Supriyanta Supriyanta
Fakultas Pertanian Universitas Gadjah Mada
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Veterinary Other

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Journal : JOIV : International Journal on Informatics Visualization

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Knowledge-Based Intelligent System for Diagnosing Three-Wheeled Motorcycle Engine Faults Ary Setyadi, Heribertus; Supriyanta, Supriyanta; Nurohim, Galih Setiawan; Widodo, Pudji; Sutanto, Yusuf
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2487

Abstract

Three-wheeled motor engine damage is one of the most serious problems with all motorcycles. When problems appear, it becomes difficult for users to repair and diagnose faults because knowledge about machine breakdown symptoms is minimal. Most motorcycle repair shops don’t have mechanics who understand tricycle motorbike engines, so they are less accurate in diagnosing damage symptoms, only based on estimates. Three-wheeled motorbikes have several differences in structure and spare parts compared to motorcycles because tricycle motorbikes have an axle like a car. For this problem, an information system is needed with a method that combines an expert's experience, expertise, and knowledge to develop expert system applications based on several cases that have been experienced and are known as case-based reasoning. This research aims to produce a web-based expert system to diagnose and solve tricycle motorbike engine damage problems. The case-based reasoning method with the K-Nearest Neighbor algorithm is used to assist in analyzing engine damage and give solutions to the issues in three-wheeled motorbike engines. Using two methods is appropriate because of the answers found and the similarities calculated by the cosine similarity method, which experts then review to get the proper solution. From testing using 20 samples of diagnostic data, an accuracy percentage of 85% was obtained. The calculation result for precision is 85%, and recall is 85%.
Multi Criteria Decision Making Method For Developing Smart Indonesia Program Scholarship Recipient Candidate System Supriyanta, Supriyanta; Sutanto, Yusuf; Susilo, Dahlan; Setyadi, Heribertus Ary; Syukron, Akhmad
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3706

Abstract

The Government of Indonesia is continuously striving to improve its education quality with the provision of scholarship programs, one of which is the Smart Indonesia Program (SIP). Students' interest in obtaining SIPs is increasing, but the selection process still relies on conventional methods. Without adequate IT support, the selection process for SIP scholarship candidates will be complex, less objective, and somewhat unfair. State Vocational High School (SVHS) 5 Surakarta was selected as a case study for this research to establish the selection process and the data collection methods used in previous years. The research aims to develop a Decision Support System (DSS) to assist in nominating students deemed eligible for SIP scholarship recommendations. The applied methods include Analytical Hierarchy Process (AHP) and Multi-Objective Optimization by Ratio Analysis (MOORA). Four criteria have been set in this DSS: card ownership status, total parental income, household income, and number of siblings. Each of which is further broken down into several sub-criteria and assigned a value for use in the AHP process. Upon comparing data from 2021 to 2023, it was found that the accuracy in 2021 was 92.9%, in 2022 it reached 94.7%, and in 2023 it recorded 92.3%. Based on the results of this system accuracy test, it can be concluded that the AHP and MOORA methods can be used to objectively produce recommendations for students eligible for SIP scholarships, based on the input criteria.
Co-Authors Akhmad Syukron Astuti, Sri Dweni Aziz Purwantoro BAMBANG ALI KUSUMO Dahlan Susilo eka rahmawati Eko Saputro, Eko Fauzi Ahmad Muda Fifaldyovan, Muhammad Ikhsan Galih Setiawan Nurohim Haryanta, Joko Tri Heribertus Ary Setyadi Himawan*, Nur Arviyanto Ibrahim Malik, Ibrahim Inayah, Suwaibah Ummul Maharani, Sonia Mulyanto, Dody Nugroho, Wawan Nur Fauzan, Haidar Nuroho, Wawan Permatasari, Evi Putri Pudji Widodo Putri, Pretty Oktianti Menur Kusuma Rahman, Zulfi Saipudin, Iqwan Sardiarinto Sardiarinto Setiaji Setiaji, Setiaji Setiawan, Mahardika Yudha Sihombing, Hotmanahan Sri Rejeki Susilowati, Yuni Sutikno Sutikno Vita Istihapsari, Vita Yusuf Sutanto