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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Journal of Information Systems Engineering and Business Intelligence JURNAL NASIONAL TEKNIK ELEKTRO JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Information Technology and Computer Engineering Jurnal Hilirisasi IPTEKS (JHI) Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Manajemen Transportasi & Logistik (JMTRANSLOG) Warta Pengabdian Andalas: Jurnal Ilmiah Pengembangan Dan Penerapan Ipteks International Journal of Basic and Applied Science Andalasian International Journal of Applied Science, Engineering, and Technology Andalas Journal of Electrical and Electronic Engineering Technology Jurnal Andalas: Rekayasa dan Penerapan Teknologi Kreativitas Pada Pengabdian Masyarakat (Krepa)
Rusydi, Muhammad Ilhamdi
Universitas Andalas
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Veterinary Other

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Estimation of the Shoulder Joint Angle using Brainwaves Minoru Sasaki; Takaaki Iida; Joseph Muguro; Waweru Njeri; Pringgo Widyo Laksono; Muhammad Syaiful Amri bin Suhaimi; Muhammad Ilhamdi Rusydi
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 1 No. 1 (2021): May 2021
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ajeeet.v1i1.5

Abstract

This paper presents the angle of the shoulder joint as basic research for developing a machine interface using EEG. The raw EEG voltage signals and power density spectrum of the voltage value were used as the learning feature. Hebbian learning was used on a multilayer perceptron network for pattern classification for the estimation of joint angles 0o, 90o and 180o of the shoulder joint. Experimental results showed that it was possible to correctly classify up to 63.3% of motion using voltage values of the raw EEG signal with the neural network. Further, with selected electrodes and power density spectrum features, accuracy rose to 93.3% with more stable motion estimation.
The Use of Artificial Neural Networks in Agricultural Plants Roza Susanti; Riko Nofendra; Zaini Zaini; Muhammad Syaiful Amri bin Suhaimi; Muhammad Ilhamdi Rusydi
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 2 No. 2 (2022): November 2022
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ajeeet.v2i2.32

Abstract

Artificial Neural Networks use high-performance computing and big data technology, opportunities for science to create new opportunities in agriculture. The purpose of writing this article is to analyze the use of artificial neural networks on (a) plant diseases based on plant leaf diseases, (b) plant pests, (c) growth or quality, and (d) agricultural products. The writing method used is a literature study of the research that has been done. The keywords used in the search for references include ANN, plant, diseases, pests, growth or quality, and agricultural products. Publishers for the reference in this article are ScienceDirect and IEEE. The years of publication of the references are restricted from 2015 to 2022. Based on the literature study results, it was concluded that Artificial Neural Networks' deep learning models are accurate for detecting and classifying leaf diseases and pests, detecting growth, and application to agricultural plant products.
Electroencephalography on Controlling Assistive Device: A Systematic Literature Review Salisa 'Asyarina Ramadhani; Muhammad Ilhamdi Rusydi; Andrivo Rusydi; Minoru Sasaki; Luxfy Roya Azmi
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 4 No. 2 (2024): November 2024
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ajeeet.v4i2.42

Abstract

The present article delves into the practical applications of electroencephalography (EEG) in assistive devices. The article thoroughly summarizes the current state of the art, research trends, methods, and implementation. The focus is primarily on how EEG can operate various assistive devices effectively, incorporating artificial intelligence, machine learning, and several computing methods. The authors emphasize the importance of conducting more research and development in the field and offer valuable insights into its prospective directions. A complete search of the Scopus database from 2017 to 2022, including journals and proceedings such as IEEE Xplore, MDPI, Springer, Frontiers, and ScienceDirect, was conducted to ensure the findings are as comprehensive as possible. Conferring to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, 4397 metadata were transformed into 45. Based on the data synthesis, the following study execution must prioritize determining whether the observed signals are attributable to EEG artifacts or actual EEG signals. The derivation of input signals for controlling helpful devices can be enhanced by utilizing familiar activities, such as facial muscle movements, and employing various machine-learning techniques to ensure high levels of accuracy.
Perancangan Platform Pengaduan Perundungan Berlandasarkan Bukti menggunakan Metode Agile Muhammad Ilhamdi Rusydi; Yoan Winata; Dhiny Yurichy Putri; Budi Agung Santoso; Nurul Azizah Dhuha; Muhammad Khalish; Ikhwan Arief; Hermawan Nugroho
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 2 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i2.1547

Abstract

Penelitian ini bertujuan untuk membuat sebuah platform pengaduan perundungan berlandaskan bukti yang terhubung dengan institusi terkait. Orang ketiga dapat menggunakan platform ini untuk melaporkan kejadian perundungan. Platform ini juga dapat digunakan untuk mengetahui kesehatan mental penggunanya. Platform memiliki fitur konsultasi dalam jaringan melalui fitur chat serta artikel edukasi psikologi dengan berbasis Progressive Web App. Laporan dapat dilakukan oleh korban ataupun pihak ketiga. Laporan perundungan akan masuk ke sekolah korban dan diproses melalui admin sekolah. Pelapor dapat memantau status dari kasusnya. Sekolah dapat merekap laporan kasus dalam rentang waktu tertentu. Pengujian telah dilakukan bersama siswa SMP dan SMA, guru BK, mahasiswa, bagian kemahasiswaan perguruan tinggi dan masyarakat umum. Pengujian dilakukan dengan peran admin institusi, admin pengaduan dan pengguna dengan total responden sebanyak 81 orang. Pengujian dilakukan terkait fungsional menggunakan blackbox test dan uji performa dari platform berdasarkan beberapa aspek. Berdasarkan pengujian yang telah dilakukan, platform ini sudah berfungsi dengan baik. Nilai rata-rata pengujian untuk semua fungsi adalah 351 dari maksimal 400 poin. Pengujian memberikan nilai rentang kelompok tinggi terhadap performa platform karena sudah sesuai dengan kebutuhan pengguna dan sudah memiliki tampilan yang mudah dimengerti, nyaman digunakan. Platform ini bisa menjadi alternatif solusi untuk menyelesaikan kasus perundungan terutama di sekitar kita.
Negative Content Detection Model to Classify Text of a Website Using Machine Learning Method Budi Sunaryo; Muhammad Ilhamdi Rusydi; Ariadi Hazmi; Oluwarotimi Williams Samuel
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7119

Abstract

Harmful online content can negatively influence users and create social risks. This study develops a machine learning model to detect harmful website content using Naïve Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The analysis focuses on website text extracted from the HTML Document Object Model (DOM). Four classes were used: gambling, pornography, phishing, and whitelist content. The dataset consisted of 2911 URLs collected from the UT1 Blacklist repository. Text preprocessing and TF-IDF feature extraction with unigram and bigram representations produced 71,967 tokens. Experimental results show that SVM achieved the best performance with 90.50% accuracy on 2821 active URLs. A real-time Flask-based web application was also developed to classify URLs from user input. The findings demonstrate that combining NLP and machine learning provides an effective and practical solution for harmful website content detection.
Smart System for Mushroom Identification and Classification: A Systematic Literature Review Indra Laksmana; Muhammad Ilhamdi Rusydi; Feskaharny Alamsjah
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 2 (2026): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.2.489-506

Abstract

Background: Mushrooms play an essential role in ecological balance and significantly contribute to economic activities. However, the close visual resemblance between edible and toxic species continues to cause fatal poisoning incidents worldwide. Conventional identification approaches rely heavily on expert judgment, making them subjective, time-consuming, and unsuitable for large-scale or real-time use. Recent developments in artificial intelligence (AI), particularly deep learning techniques, have created new opportunities for developing automated and reliable identification systems. Objective: This study systematically reviews recent research on intelligent technologies for mushroom identification and classification. This study maps the main research objectives, data types, morphological features, technologies, algorithms, evaluation practices, and remaining challenges, with particular attention to toxicity classification and safety-critical decision support. Methods: A systematic literature review was conducted following the PRISMA 2020 protocol and the PICOC framework. We screened publications indexed in Scopus between 2021 and 2025. From 1,308 initial records, 90 high-quality studies were selected for detailed analysis. Results: The analysis of the selected studies reveals that the majority of research focuses on discriminating between edible and poisonous mushrooms (52 studies), predominantly using visual characteristics related to shape (64 studies) and color (59 studies). Deep learning techniques, especially convolutional neural networks (CNNs) (40 studies) and vision transformers (11 studies), dominate the field and frequently report classification accuracies exceeding 95%. Despite these achievements, several challenges persist, including the difficulty of fine-grained classification among visually similar species, limited dataset availability, and performance degradation in complex natural environments. Conclusion: Although AI-based approaches have considerable potential to support mushroom identification, their reliability is limited by reliance on visual data alone when species are morphologically similar. Future studies should place greater emphasis on lightweight models for field deployment, multimodal sensing, risk-aware evaluation, and Explainable AI (XAI) so that intelligent systems can be used more safely and transparently in real-world contexts.   Keywords: Mushroom Identification, Artificial Intelligence, Deep Learning, Food Safety, Systematic Literature Review
PENINGKATAN KETERTARIKAN DAN PENGETAHUAN SISWA/I DALAM MEMPELAJARI BAM MELALUI PENGEMBANGAN MEDIA AJAR INTERAKTIF Muhammad Ilhamdi Rusydi; Pepi Putri Utami; Muhammad Fikri; Agung Wibowo Ardiyanta Surbakti; Pratama Halim; Aulia Rahman
Jurnal Hilirisasi IPTEKS Vol. 2 No. 4.b (2019)
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jhi.v2i4.b.313

Abstract

Media ajar memiliki peranan dalam peningkatan prestasi belajar siswa. SDN No. 14 Pauh, Padang, mengalami permasalahan dalam pelaksanaan kelas Budaya Alam Minangkabau (BAM). Mata pelajaran yang merupakan muatan lokal daerah Sumatera Barat ini tidak hanya kekurangan referensi namun juga kesulitan dalam mengembangkan media ajar. Hal tersebut berdampak terhadap ketertarikan siswa/i di dalam kelas. Tulisan ini menjelaskan pengaruh media ajar interaktif yang dirancang terhadap ketertarikan siswa/i dalam mengikuti pelajaran BAM. Sampel sebanyak 22 siswa kelas 4. Materi ajar yang dibahas adalah pakaian adat, randai, silek, rumah gadang dan seni ukir Minangkabau. Kegiatan dilaksanakan pada semester genap tahun ajaran 2018/2019. Terdapat dua media ajar utama yang dikembangkan, yaitu laman www.sibuyuang.com dan museum mini yang disebut Pojok Minangkabau. Laman website memiliki beberapa fitur selain materi ajar berupa teks, yaitu video interaktif, permainan edukatif dan buku pop-up ber-teknologi Augmented Reality. Pojok Minangkabau menawarkan konsep museum mini dengan beberapa koleksi khas minangkabau seperti ukiran, peralatan musik dan pakaian. Implementasi media ajar telah dilakukan sebanyak enam kali. Empat pertemuan pertama digunakan untuk mengevaluasi ketertarikan siswa menggunakan strategi hand signal sedangkan dua pertemuan terakhir berhubungan dengan pengetahuan siswa berdasarkan ujian lisan dan ujian tertulis. Uji T digunakan untuk mengevaluasi hipotesis dengan tingkat keyakinan 5%. Dari empat kali pertemuan pertama, didapatkan ketertarikan siswa/i setelah menggunakan media ajar interaktif lebih besar daripada sebelum menggunakan. Hal yang sama juga ditemukan pada evaluasi terhadap pengetahuan siswa pada dua pertemuan terakhir. Hasil tersebut memperlihatkan bahwa media ajar yang dikembangkan telah berhasil meningkatkan ketertarikan dan pengetahuan siswa/i dalam mempelajari pelajaran BAM.
Plant Disease Identification Using Image Processing: A Systematic Literature Review Minarni Minarni; Muhammad Ilhamdi Rusydi; Darwison Darwison; Hermawan Nugroho; Budi Sunaryo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7171

Abstract

This article is a literature review focusing on plant disease identification using image processing techniques. This review aims to provide a comprehensive analysis of dataset sources, preprocessing methodologies, segmentation techniques, feature extraction processes, and various classification methods, along with their associated accuracies. It also discusses challenges encountered and potential future research directions. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, a literature search was conducted in the Scopus database to obtain primary studies. The search covered Scopus-indexed journals and proceedings published by IEEE, Elsevier, Springer, MDPI, and ACM between 2019 and 2025. The initial identification phase yielded 9,286 studies screened. Further screening was performed based on specific eligibility criteria, including relevance to the topic, year of publication, subject area, document type, and articles written in English, resulting in the selection of 82 studies for the review. The findings indicate that the most commonly used dataset is PlantVillage, followed by field data. The dominant preprocessing techniques include image enhancement and augmentation. For segmentation and feature extraction, the most frequently used methods were k-means and CNN, respectively. Sixty-one studies achieved an accuracy exceeding 90%. However, several key challenges remain: data limitations, methodological issues, and practical constraints. Future research should focus on developing more representative datasets, hybrid approaches that integrate classical and deep learning methods, and lightweight, adaptive decision support systems suitable for real-world agricultural applications. This review supports continued progress in this field by providing valuable insights for researchers developing image-based methods for identifying plant diseases.
Electrooculography Based Control of a Robotic Manipulator with Dual Cameras for Object Retrieval Muhammad Ilhamdi Rusydi; Andre Paskah Gultom; Adam Jordan; Rahmad Novan Nurhadi; Darwison Darwison
International Journal of Basic and Applied Science Vol. 14 No. 4 (2026): March: Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v14i4.798

Abstract

This study presents an assistive control system for a four-degree-of-freedom (4-DoF) robotic manipulator that integrates image-based spatial perception with electrooculography (EOG)-based human–machine interaction for three-dimensional object retrieval. The system is motivated by the need for intuitive, non-contact assistive technologies to support individuals with severe motor impairments, such as tetraplegia, in performing basic manipulation tasks. The proposed framework employs an orthogonal dual-camera vision configuration to achieve explicit 3D target localization, where planar object positions on the XY plane and depth along the Z axis are estimated using focal length–based geometric modeling. User commands are generated through an EOG interface, in which eye movements and voluntary blinks are classified using a K-Nearest Neighbor (KNN) algorithm to control manipulator motion. Compared to conventional assistive robotic systems that rely on depth sensors or high-degree-of-freedom manipulators, the proposed approach utilizes asymmetric monocular viewpoints and a minimal 4-DoF architecture to reduce system complexity. Experimental results demonstrate high performance, achieving average localization accuracies of 99.52% on the XY plane and 95.88% along the Z axis, as well as an EOG classification accuracy of 94.38%. Manipulation experiments confirmed reliable operation with a 100% task success rate, while task completion time and positional error increased gradually with target distance. These findings validate the feasibility of the proposed system as a low-complexity, high-accuracy assistive robotic solution for rehabilitation and human–machine interaction applications.
Development of Electrical Laboratory Information System using Model View Controller Architecture Zen Resti Maulana; Eka Fitrianto; Muhammad Ilhamdi Rusydi; Asrul Azani Mahmood
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 6 No. 1 (2026): March 2026
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v6i1.319

Abstract

Effective and efficient laboratory inventory management is crucial to support academic and research activities in higher education. The manual system currently used to manage eight laboratories in the electrical engineering department often leads to various problems, such as recording errors, asset loss, and inaccuracy in reporting. This is the urgency of establishing an integrated information system to improve the accuracy and efficiency of laboratory inventory management. This research aims to digitize the laboratory inventory through the implementation of an application-based laboratory inventory management information system, namely Silab Elektro. In addition, this system also aims to increase transparency and accountability in laboratory asset management. This information system was developed using UML (Unified Modeling Language) modeling with MVC (Model-View-Controller) architecture. This research produces a laboratory information system that can manage equipment inventory more effectively and efficiently, as well as improve data accuracy.
Co-Authors . Darwison Adam Jordan Agung W. Setiawan Agung Wibowo Ardiyanta Surbakti Agung Wibowo Ardiyanta Surbakti Andi Pawawoi Andre Paskah Gultom Andrivo Rusydi Andrivo Rusydi Anton Hidayat Ariadi Hazmi Arrya Anandika Asrul Azani Mahmood Aulia Novira Aulia Rahman Aulia Rahman Avelia Fairuz Faadhilah Azmi, Luxfy Roya Budi Agung Santoso Budi Sunaryo Chatarina Umbul Wahyuni Daelita Berliana H. Devianda Ananta Sandri Dhiny Yurichy Putri Dhiny Yurichy Putri Eka Fitrianto Elmiyasna Kimin Febdian Rusydi Feskaharny Alamsjah Hafidzah Putri Rahmadani Haznam Putra Hermawan Nugroho Hermawan Nugroho Ikhwan Arief Indra Laksmana Jack Febrian Rusdi Joseph Muguro Joseph Muguro Khairunnisa Lizzikrillah Kojiro Matsushita Laksono, Heru Dibyo M. Farhan Minarni Minarni Minoru Sasaki Minoru Sasaki Minoru Sasaki Muhammad Dani Anwar muhammad fikri Muhammad Fikri Muhammad Fikri Muhammad Hadi Sucipto Muhammad Imran Hamid Muhammad Ismail Opera Muhammad Khalish Muhammad Naufalun Nabil Muhammad Syaiful Amri bin Suhaimi Muhammad Syaiful Amri bin Suhaimi Mumuh Muharram Mutia Firza Nadia Alfitri Nurul Azizah Dhuha Oluwarotimi Williams Samuel Oluwarotimi Williams Samuel Palman Pepi Putri Utami Pepi Putri Utami Pepi Putri Utami Pratama Halim Pratama Halim Prima Fithri Pringgo Widyo Laksono Rahdian Hadi Farhan Rahmad Novan Nurhadi Rahmadi Kurnia Ramadhani, Salisa 'Asyarina Ridho Tullah Syahputra Rifda Suriani Riko Chandra Riko Nofendra Riko Nofendra Rio Nakajima Rizka Hadelina Robbi Noberlam Dasyura Roza Susanti Roza Susanti Sandy Azizi Sunaryo, Budi Sunaryo, Budi Syafii . Syafril Daus Syamsul Huda Syarkawi Syamsuddin Takaaki Iida Takayuki Nakagome Taufiqurrahman Vinoza Shalsabila Waweru Njeri Waweru Njeri Yoan Winata Yoan Winata Zaini Zaini Zaini, Zaini Zen Resti Maulana