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All Journal Jurnal Sistem Teknik Industri Arus Jurnal Sosial dan Humaniora Journal of Informatics, Electrical and Electronics Engineering
Pradana, Ari
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Implementasi Metode MAUT Dalam Pemilihan Aplikasi Pembuatan Media Pembelajaran Dimasa Pandemi Covid 19 Pradana, Ari; Manik, Lastri; Syahrizal, Muhammad
Journal of Informatics, Electrical and Electronics Engineering Vol. 3 No. 1 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v3i1.1609

Abstract

Corona Virus disiase 2019 (covid 19) is an infectious disease caused by SARS-CoV-2 a type of Corona virus. This virus is transmitted through physical contact and causes symptoms of respiratory problems, colds, fever, dry cough, and attacks the human physical immunity. covid 19 has disrupted the activities of daily human life, including in the field of education. To prevent the spread of covid 19 the government proposes that during the covid 19 pandemic the teaching and learning process takes place online or online. In the online learning process so that students are more active, creative and enthusiastic, a system is needed decision support that can determine and choose the application of learning media during the covid 19 pandemic, a decision support system that can solve the problem using the MAUT (Multi Attribute Utility Theory) method and produce an appropriate decision based on the results of the data obtained such as alternatives and the criteria so as to produce a decision, namely the application of the best learning media in the era of covid 19, namely the Sony Vegas Pro application with a score of 0.9025.
Perancangan Game Edukasi Pengenalan Angka untuk Anak Berkebutuhan Khusus (Abk) Tunagrahita di Sekolah Luar Biasa Negeri 1 Palopo Pradana, Ari; Kasma, Safwan; Jumarniati
Arus Jurnal Sosial dan Humaniora Vol 5 No 2: Agustus (2025)
Publisher : Arden Jaya Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57250/ajsh.v5i2.1626

Abstract

Penelitian ini bertujuan untuk merancang dan mengembangkan game edukasi pengenalan angka yang ditujukan bagi anak berkebutuhan khusus (ABK) tunagrahita di Sekolah Luar Biasa (SLB) Negeri 1 Palopo. Anak tunagrahita cenderung memiliki keterbatasan dalam kemampuan kognitif dan daya konsentrasi, sehingga dibutuhkan media pembelajaran yang lebih visual, interaktif, dan menyenangkan. Game edukasi ini dikembangkan menggunakan metode Multimedia Development Life Cycle (MDLC) yang terdiri dari enam tahap, yaitu: concept, design, material collecting, assembly, testing, dan distribution. Aplikasi dikembangkan dengan menggunakan Unity dan dirancang untuk berjalan pada platform Android agar mudah diakses. Pengujian dilakukan dengan metode black box serta validasi oleh ahli media dan ahli materi. Hasil pengujian menunjukkan bahwa game edukasi ini layak digunakan sebagai media pembelajaran alternatif. Media ini tidak hanya membantu siswa dalam mengenal angka secara lebih menyenangkan, tetapi juga memudahkan guru dalam menyampaikan materi secara efektif. Dengan demikian, game edukasi ini diharapkan dapat menjadi solusi pembelajaran yang inklusif.
Production Process Quality Inspection with Machine Learning Approach Pradana, Ari; Matondang, Nazaruddin; Anizar, Anizar
Jurnal Sistem Teknik Industri Vol. 27 No. 4 (2025): JSTI Volume 27 Number 4 September 2025
Publisher : TALENTA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jsti.v27i4.21005

Abstract

Technological developments in the industrial world encourage innovation in the inspection process, one of which is the application of artificial intelligence with machine learning. CV. XYZ is a palm oil machine component fabrication workshop that still applies manual quality inspection. Manual inspections are prone to errors, depend on human skills, and take a long time. This research aims to develop an automated inspection system using the YOLO (You Only Look Once) model which is a convolutional neural network (CNN) based algorithm for product defect detection. The manual inspection used is considered inconsistent, error-prone, and time-consuming. The use of machine learning is able to identify product defects such as geometry defect, porous defect, and surface defect. Evaluation of model performance using confusion matrix, loss graph, and precision recall curve. The results obtained show that the model has detection accuracy with a mAP50-95 value of 74.5%, mAP50 of 88.5%, and detection time of 0.0084 seconds per image.
Co-Authors Anizar Anizar Jumarniati Kasma, Safwan Manik, Lastri Muhammad Syahrizal Nazaruddin M Yuslem, H. Nawir Zulkarnain Zulkarnain