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All Journal Indonesian Journal of Disability Studies Indonesian Journal of Human Nutrition Jurnal Penelitian Pendidikan IPA (JPPIPA) JUTIK : Jurnal Teknologi Informasi dan Komputer Jurnal Agrotek Tropika Akrab Juara : Jurnal Ilmu-ilmu Sosial G-Couns: Jurnal Bimbingan dan Konseling International Journal of Educational Review, Law And Social Sciences (IJERLAS) JURNAL INOVASI DAN PENGABDIAN MASYARAKAT INDONESIA Journal of Artificial Intelligence and Engineering Applications (JAIEA) International Journal of Accounting, Management, Economics and Social Sciences (IJAMESC) Intelmatics Teknomatika: Jurnal Informatika dan Komputer Ngabdimas The Indonesian Journal of Computer Science Tanggap Masyarakat untuk Aksi dan Sinergi JMAEKA: Jurnal Manajemen Ekonomi Akuntansi Prawara Jurnal Pengabdian Masyarakat
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Journal : The Indonesian Journal of Computer Science

 1 
Pemilihan Bibit Padi yang Tepat untuk Musim Hujan Menggunakan Algoritma MOORA dalam Sistem Pendukung Keputusan Muhammad Alwi Badal; Supriatin
The Indonesian Journal of Computer Science Vol. 12 No. 5 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i5.3462

Abstract

The optimal selection of rice seedlings is a key factor in enhancing rice farming productivity during the rainy season. To achieve this goal, this research utilizes a Decision Support System (DSS) based on the MOORA Algorithm (Multi-Objective Optimization by Ratio Analysis). This study explores the selection of the best rice seedlings for the rainy season using the MOORA Algorithm. Evaluation criteria include plant height, harvesting time, tolerance to waterlogging, resistance to pests, disease resistance, and average yield per hectare. The research results identify Inpari 30 Ciherang Sub-1 (BP07) as the top choice for the rainy season with a Yi (max) value of 0.2998. This is followed by Inpara 4 (BP03) in second place with a Yi (max) value of 0.2913, and Ciherang (BP02) in third place with a Yi (max) value of 0.2849. We hope that the research findings will provide a positive contribution to improving agricultural productivity in the future and assisting farmers in better facing the challenges of the rainy season.
Supervised Machine Learning Model untuk Prediksi Penyakit Hepatitis Putra, Andriyan Dwi; Nurani, Dwi; Dewi, Melany Mustika; Rahmi, Alfie Nur; Supriatin
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3817

Abstract

Hepatitis menjadi salah satu penyakit mematikan yang diakibatkan karena peradangan yang terjadi pada organ hati manusia. Hepatitis seringkali disebabkan karena infeksi virus dan gaya hidup yang tidak sehat. Hepatitis bahkan bisa menular apabila dikaitkan dengan infeksi dari adanya virus tertentu. Hepatitis perlu dideteksi secara dini dan diantisipasi sedini mungkin sehingga tidak mengakibatkan adanya penyakit komplikasi yang lebih serius yang bahkan dapat mengakibatkan terjadinya kematian. Perkembangan teknologi informasi dan komunikasi yang terus berkembang hingga saat ini memungkinkan penyakit hepatitis untuk dapat dikenali dan diprediksi. Salah satunya menggunakan teknologi pembelajaran mesin. Pada penelitian ini, metode supervised learning yang menerapkan algoritma Naïve Bayes dan KNearest Neighbor digunakan untuk memprediksi adanya penyakit hepatitis. Dengan menggunakan dataset yang diunduh secara langsung dari halaman website UCI Machine Learning Repository, Naïve Bayes menghasilkan nilai akurasi sebesar 91.67% dengan nilai presisi dan recall mencapai 95%, Sedangkan penggunaan K-Nearest Neighbor menghasilkan nilai akurasi sebesar 95.8%, dengan adanya perbedaan nilai presisi dan recall sebesar 1%, menunjukkan bahwa penggunaan pervised machine learning model berdasarkan algoritma Naïve Bayes dan K-Nearest Neighbor memiliki potensi untuk digunakan dalam pengembangan berbagai sistem terutama untuk prediksi penyakit hepatitis.
Implementasi Algoritma ARAS Pada SPK Untuk Menentukan Peringkat Dosen Terbaik Alex Rizky Saputra; Supriatin
The Indonesian Journal of Computer Science Vol. 11 No. 2 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i2.3057

Abstract

In the world of higher education, lecturers are one of the main components in building quality and quantity. Good quality will give good results as well, to improve the quality of each lecturer, it is necessary to have an award given to lecturers from the campus so that it becomes a motivation for lecturers to improve the quality given to students and the community. Amik Mitra Gama is a private campus located in the Duri Riau area, in this case to improve the quality of education one of the steps taken is to give awards and appreciation to the best lecturers who will be selected every year. To realize this, we need an easy calculation system that is carried out in the form of ranking according to the final value, therefore a decision support system using the ARAS method is chosen because it is very appropriate for the selection process and provides convenience in the calculations which are determined based on ranking. The decision support system using the ARAS method uses 8 criteria that are set as a reference in determining the best lecturers, namely Recent Education, Lecturer Functional Position, Lecturer Certification, Number of Journal Publications, Roles in Research, Journal Publication History, Research Grants, and Community Service. There are 10 lecturers in the field of computers who will be used as alternative data with lecturer codes D01, D02, D03, D04, D05, D06, D07, D08, D09, D10. The results obtained from this study are the lecturer code D04 = 0.0974, D06 = 0.0965, D09 = 0.0932, D07 = 0.0903, D03 = 0.0901 was selected as the best lecturer in 2021/2022. So that the results of this study can help the campus to determine the best lecturers every year fairly and be selected based on rankings.
Penerapan Algoritma MOORA dalam Menentukan Sekolah Dasar Terbaik Athif Fauzan; Supriatin
The Indonesian Journal of Computer Science Vol. 11 No. 3 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i3.3114

Abstract

Elementary school is the first level of education for students who have an important role in the development of behavior, skills and talents. Thus, the selection of the best primary school becomes something important for parents, because this will affect the future of their children in the future. In this regard, parents are faced with many choices of primary schools, especially in the Purworejo area for their children. This study aims to assist parents in determining the best primary school that is right for their children by using a Decision Support System. The method used in this research is MOORA. Multi Objective Optimization On The Basic Of Ratio Analysis (MOORA) is a method that can filter the best alternatives because this method is able to determine goals based on conflicting criteria on several constraints, so the use of this method is very appropriate to solve existing problems. This study uses 6 criteria that have been determined as a form of reference in determining the best elementary school, namely Accreditation, School Location, School Facilities, School Achievement, Professional HR (Teachers), and Number of Excellent Programs. There are 7 elementary schools that will be used as alternative data, all of which are taken from elementary schools in Purworejo. Of the 7 elementary schools taken, 3 elementary schools with school codes SK03 = 0.3382 , SK01 = 0.2922 , and SK02 = 0.2538 were selected to be the best elementary schools in 2022.
Co-Authors Abdul Kadir Salam Alex Rizky Saputra Alfie Nur Rahmi Andi Halimah Anwari Arif, M.A Syamsul Asep Dony Suhendra Asharudin, ⁠Firman Asriani Natong Athif Fauzan Awis Hamid Dhani Charien Rahayu Lestari Dede Suleman Dedi Prasetyo Desi Andalasari Dewi Yuliandari Dian Ardiansyah Dina, Nur Rahma Dwi Nurani Dwi Nurani, Dwi Emigawaty Enggar Widianingrum Erawan, Tiar Etik Dwi Styaningrum Hayati, Nung Helmy Ivan Taruna Herawati, Cucu Iin Kristanti Ikmah Indra, Indra Riswanto Indragiri, Suzana Jamalam Lumbanraja Joesah , Nurzalinar Laili Nurjannah Yulistiyana Leksonowati, Sri Saadiyah LILIS DWI FARIDA Lukman Mardianto Mareanus Lase Masruro, Ahlihi Melany Mustika Dewi Moh. Badri Tamam Muhammad Alwi Badal Nicodias Palasara Nugroho, Agung Nurhayaty, Ety Puspasari, Aprilia Putra, Andriyan Dwi Rahmi, Alfie Nur Rofiuddin Rohani Lestari Napitupulu Roydawaty Bunga Septiana, Liska Wahyudin Nur Nasution, Wahyudin Nur Wahyuni, Nuniek Tri Walim Widiyanti Kurnianingsih Wiwin Wianti, Wiwin Yohanes Totok Suyoto Yudistira Yuli Astuti Yuli Astuti