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All Journal ComEngApp : Computer Engineering and Applications Journal JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Edukasi dan Penelitian Informatika (JEPIN) Annual Research Seminar E-Dimas: Jurnal Pengabdian kepada Masyarakat JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal ULTIMA InfoSys Sriwijaya Journal of Informatics and Applications Jurnal Generic Jurnal Mandiri IT Jurnal Medika: Medika
Buchari, Muhammad Ali
Program Studi Teknik Informatika Universitas Sriwijaya Palembang, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Health Professions Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Perancangan Kunci Pintu otomatis dengan Personal Identification Number (PIN) Berbasis Mikrokontroler ATMega8535 untuk Siswa SMA Negeri 2 Banyuasin I Sarmayanta Sembiring; Hadir Kaban; Jorena Bangun; Beta Susanto Barus; Muhammad Ali Buchari
E-Dimas: Jurnal Pengabdian kepada Masyarakat Vol 13, No 4 (2022): E-DIMAS
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/e-dimas.v13i4.5902

Abstract

Telah dilaksanakan pelatihan perancangan kunci pintu otomatis dengan personal identification number (PIN) berbasis Mikrokontrler  ATMega8535 pada hari sabtu, tanggal tanggal 2 november tahun 2019 yang dilaksanakan di SMA Negeri 2 Banyuasin I. Kegiatan pelatihan ini bertujuan untuk menambah wawasan, minat, kreativitas dan inovasi kepada siswa dan menjadikan kegiatan pelatihan ini sebagai pemicu bagi siswa untuk berpartisipasi dalam kompetisi inovasi di bidang teknologi menggunakan mikrokontroler. Perangkat keras yang digunakan dalam pelatihan ini terdiri dari solenoid door lock, relay, keypad 3x4, LCD 16 x 2, push button switch, buzzer dan mikrokontroler ATMega8535. Perangkat lunak yang digunakan dalam pelatihan ini menggunakan Basic Compiler AVR. Pelatihan ini menghasilkan prototipe kunci pintu otomatis menggunakan PIN dan meningkatkan pemahaman siswa tentang aplikasi mikrokontroler. Peningkatan kemampuan siswa untuk menjawab pertanyaan dengan benar pada post test 45,5% dari sebelumnya hanya mampu menjawab dengan benar 21,5% pada pre test menunjukkan peningkatan pemahaman siswa tentang mikrokontroler dan aplikasinya.
Optimization of Tsukamoto FIS Using Genetic Algorithm for Rainfall Prediction in Banyuasin Regency Akbar, Muhammad Rafi; Miraswan, Kanda Januar; Rodiah, Desty; Buchari, Muhammad Ali; Marjusalinah, Anna Dwi
Sriwijaya Journal of Informatics and Applications Vol 5, No 2 (2024)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v5i2.118

Abstract

Indonesia, as a tropical country with high rainfall, heavily relies on accurate rainfall predictions for various critical purposes, including water resource management and extreme weather impact mitigation. One commonly used method is the Tsukamoto Fuzzy Inference System (FIS). However, implementing the Tsukamoto FIS often leads to high error rates. This is attributed to the difficulty in determining the boundaries of fuzzy variable membership functions. To address this issue, this research proposes an innovative approach by optimizing the boundaries of fuzzy membership functions using Genetic Algorithms (GA). The study resulted in a 49.02% reduction in the error rate, decreasing from 76.82% to 27.8%. This method significantly enhances rainfall prediction accuracy and contributes to the advancement of more sophisticated prediction methods. The optimization method proposed in this study also holds potential for application across various atmospheric science contexts.
Pendampingan Inovasi Kecerdasan Buatan dalam Pengembangan Asesmen Pembelajaran untuk Mendukung Literasi Digital bagi Guru SMP Buchari, Muhammad Ali; Sukemi, Sukemi; Oktadini, Nabila Rizky; Marjusalinah, Anna Dwi; Simarmata, Ruth Helen; Afif, Hasnan
Jurnal Medika: Medika Vol. 4 No. 3 (2025)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/7xep0n45

Abstract

The Merdeka curriculum formulates learning that refers to the abilities needed in the era of industrial revolution 4.0 and society 5.0. The assessment is carried out as an effort to measure the level of achievement of learning indicators and collect information on student learning progress in various aspects.  Facts in the field show problems in implementing independent curriculum cognitive assessments in learning. This indicates the high need for preparing independent curriculum assessments, namely diagnostic assessments, formative assessments and summative assessments. Artificial intelligence or Artificial Intelligence (AI) is part of the industrial revolution 4.0 and society 5.0 so that integrating society and technology cannot be avoided.  Artificial Intelligence in mathematics learning has great potential to support learning effectiveness and efficiency. This service is a lecture activity that integrates artificial intelligence evaluation courses into service activities that are integrated with community service. This initiative was implemented to support digital literacy and increase teacher competency in utilizing modern technology, in accordance with the principles of the Independent Curriculum. This activity includes training, mentoring, and evaluation of the use of AI in learning assessment. As a result of this activity, teachers are able to understand the basic concepts of AI, innovation in assessment development, and its implementation in the classroom. Through various face-to-face and online meetings, this activity succeeded in providing new insights for teachers regarding the use of AI technology in learning, although there were challenges related to initial understanding and availability of supporting facilities. With intensive assistance, teachers are able to prepare assessments that are more adaptive and appropriate to student needs, so they are expected to be able to support improving the quality of education in the digital era.
Performance analysis of MobileNetV2 based automatic waste classification using transfer learning Firnando, Ricy; Buchari, Muhammad Ali; Marjusalinah, Anna Dwi; Willy; Abdurahman; Isnanto, Rahmat Fadli
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.451

Abstract

The significant increase in global waste requires innovative and accessible solutions, which aligns with Sustainable Development Goal (SDG) 12, which focuses on reducing the environmental impact of human activities. Automatic waste sorting using Computer Vision and Deep Learning offers a promising alternative to labor-intensive and risky manual methods. This study presents the design, implementation, and comprehensive performance analysis of an automated waste classification system, with a specific focus on evaluating its feasibility on hardware without specialized GPU accelerators. By leveraging transfer learning on a lightweight Convolutional Neural Network (CNN) architecture, MobileNetV2, a model was trained to classify six common waste categories: cardboard, glass, metal, paper, plastic, and other waste. The public “Garbage Classification” dataset from Kaggle, consisting of 2,527 images, was used as the basis for training and validation. The experiment was conducted using the tensorflow-cpu library, which does not require a dedicated GPU accelerator. After 10 training epochs, the model achieved a significant validation accuracy of 86.73%. Computational performance analysis showed an efficient average training time of 31.17 seconds per epoch and a fast average inference time of 14.47 milliseconds per image (~69 FPS) on the validation dataset. These findings demonstrate the feasibility of developing an effective AI-based waste classification system on hardware without a GPU accelerator, providing a realistic performance benchmark for the development of low-cost smart bins with embedded waste sorting solutions in the future, thereby contributing to sustainable waste management practices.
Co-Authors A.A. Ketut Agung Cahyawan W Abdurahman Abdurahman Afif, Hasnan Akbar, Muhammad Rafi Anna Dwi Marjusalinah Ayu Ferdiyanti Beta Susanto Barus Desty Rodiah Dita Zhapira Firdaus Firdaus Gilbert Frans Wijaya Hadir Kaban Huda Ubaya Jambak, Muhammad Ihsan Jorena Bangun Kanda Januar Miraswan Kartika Haritami M Aldi Ariqi Mahendra, Calvin Marjusalinah, Anna Dwi Mastura Diana Marieska Muhammad Rafly Hafizin Oktadini, Nabila Rizky Osvari Arsalan Putri Eka Sevtiyuni Rahmat Fadli Isnanto Ricy Firnando Rossi Passarela Samsuryadi Samsuryadi Sarmayanta Sembiring Simarmata, Ruth Helen Sukemi Sukemi Willy