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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences JPTK: Jurnal Pendidikan Teknologi dan Kejuruan Jurnal Teknik IPTEK The Journal for Technology and Science TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Jurnal Edukasi dan Penelitian Informatika (JEPIN) ELINVO (Electronics, Informatics, and Vocational Education) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Electrical Engineering and Computer Science Lontara: Journal of Health Science and Technology International Journal of Community Service (IJCS) International Journal of Scientific Multidisciplinary Research (IJSMR) Lontara Journal Of Health Science And Technology Journal of Information Engineering and Technology
Fatchul Arifin
Universitas Negeri Yogyakarta
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Pengembangan Media Pembelajaran Kendali Fuzzy Logic Berbasis Arduino Nano Pada Mata Kuliah Praktik Sistem Kendali Cerdas Prabowo, Hernawan; Arifin, Fatchul
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 3 No. 1 (2018): May 2018
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (277.929 KB) | DOI: 10.21831/elinvo.v3i1.19739

Abstract

Dewasa ini berbagai aplikasi dalam bidang industri yang canggih banyak menggunakan kecerdasan buatan. Mata kuliah praktik Sistem Kendali Cerdas di Jurusan Pendidikan Teknik Elektronika & Informatika FT UNY memerlukan sebuah media pembelajaran yang terintegrasi antara hardware dan software agar kualitas pembelajaran sesuai dengan tuntutan dunia Industri. Hasil penelitian ini untuk menghasilkan media pembelajaran kendali fuzzy logic yang teruji kinerja dan kelayakannya pada mata kuliah praktik Sistem Kendali Cerdas. Pendekatan penelitian menggunakan Research and Development dengan 9 tahapan prosedur pengembangan meliputi: (1) potensi dan masalah, (2) pengumpulan data, (3) desain produk, (4) validasi desain, (5) revisi desain, (6) ujicoba produk, (7) revisi produk, (8) ujicoba pemakaian, dan (9) revisi produk. Hasil penelitian diketahui bahwa: (1) media pembelajaran kendali fuzzy logic yang dirancang terdiri dari hardware berbasis Arduino Nano dilengkapi jobsheet dengan pemrograman menggunakan Arduino IDE dan library yang mudah untuk dipelajari, (2) Unjuk kerja media pembelajaran sudah berfungsi dengan stabil baik pada setiap bagian maupun secara keseluruhan, (3) Tingkat kelayakan media dan materi memperoleh nilai 86% dan 96%. Tingkat kelayakan pemakaian berdasarkan uji pemakaian kepada 15 mahasiswa diperoleh nilai 85% termasuk dalam kategori sangat layak. Hal ini berarti media pembelajaran ini sangat layak digunakan pada mata kuliah praktik Sistem Kendali Cerdas.
ASRO (Amphibious Spy Robot): Prototipe Robot Amfibi Pengintai dengan First Person View dan Sistem Navigasi berbasis Sensor Kompas Rajif, R. Amirur; Arifin, Fatchul
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 4 No. 2 (2019): November 2019
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (589.323 KB) | DOI: 10.21831/elinvo.v4i2.26689

Abstract

Robots have an important role in all aspects of life, including the military field. The purpose of making this final project are building hardware and software of robot and to know the performance of robots. The method used in making the final project consists of identifying and analyzing requirements, designing and manufacturing hardware and software, and testing. The result of the performance of ASRO is that, the buoyancy force of the robot is greater than the weight of the object, namely Fa = 22,808 N and W = 15,696 N or Fa> W which makes the robot float while operate in the water field. The maximum range of control system robot is as far as 0-30 meters without obstacles and 0-15 meters with obstacles, while the monitoring system is as far as 0-75 meters without obstacles and 0-30 meters with obstacles. The Robot navigation system has a percentage of accuracy of reading 93.3% and the percentage response of the average robot when rotating 90⁰ is 100%, rotating 180⁰ is 100%, and rotating 270⁰ is 100%.
Prototipe Kursi Roda Elektrik dengan Kendali Joystick dan Smartphone Junior, Andy Sadewa; Arifin, Fatchul
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 4 No. 1 (2019): May 2019
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (339.053 KB) | DOI: 10.21831/elinvo.v4i1.28259

Abstract

Wheelchairs are one of the walking aids for people with disabilities and also for people who are unable to move from one place to another. The purpose of this research was to build hardware and software into a wheelchair prototype that can be controlled with joysticks and smartphones and to know its performance. The method in making the final project consists of the stages of need identification, requirements analysis, system design, tool making, tool testing and data collection. Based on the testing that has been done, the results are obtained that the control input from the smartphone through the application and also the joystick produces the output of the wheelchair prototype movement according to the instructions that have been set as input. The maximum distance of the control system from the smartphone is 0-10 meters either with obstacles or without obstacles. The response of the wheelchair prototype device has an average error of 0.024. Smartphone applications that are used as controls can be installed on the latest Android version "Nougat" and 4 versions of Android below. The speed of the wheelchair prototype on the joystick control matches the value of the resistance issued by the joystick module.
Pengembangan Media Pembelajaran Berbasis Role Playing Game (RPG) Untuk Siswa Kelas X SMK Negeri 3 Yogyakarta Swadyaya, Putu Yana; Arifin, Fatchul
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 4 No. 2 (2019): November 2019
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1172.792 KB) | DOI: 10.21831/elinvo.v4i2.28323

Abstract

The purpose of this study is to develop a game like learning media program about resistor's color code reading and simple resistor circuits made in RPG Maker MV game engine for grade X vocational school student. The developed learning media includes: learning media program, student workbook, and a complementary module book. The method of research and development is used in this study. Descriptive analysis was used on the feasibility analysis of learning media. Result of experts and users test validation stated that the developed learning media is in the feasible category based on all validated and tested aspects. First educational expert's assessment shows feasibility level of 93.04% (Very Feasible). Second educational expert's assessment shows feasibility level of 72.17% (Feasible). First media expert's assessment shows feasibility level of 86.65% (Very Feasible). Second media expert's assessment shows feasibility level of 89.29% (Very Feasible). User trial assessment shows feasibility level of 78.52% (Feasible)
Classification of Organic and Inorganic Waste Types Based on Neural Networks Arifin, Fatchul; Habiburrahman, M.; Gusti, Wahyu Ramadhani
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 8 No. 1 (2023): Mei 2023
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v8i1.53284

Abstract

Garbage is the   residue of unused industrial production and household consumption. In Indonesia, waste is divided into 2 types, namely organic and inorganic waste. The two types of waste can be recycled in diverse ways, so they must be separated. So far, it is often difficult for the community to sort waste. This paper presents the process of recognizing and sorting waste automatically by utilizing Artificial Intelligence technology, especially Artificial Neural Networks (ANN). The ANN architecture used in this study consists of 4 layers. The number of neurons in each layer consists of 3 neurons in the input layer, 4 neurons in the hidden layer-1, 4 neurons in the hidden layer-2 and 1 neuron in the output layer. The ANN model that has been designed is trained, so that the best weight and bias model will be obtained, which in turn gives the ANN the ability to be able to sort waste properly. The best weights and biases will then be implanted into the Arduino UNO Microcontroller hardware. In this developed system, the microcontroller is given input obtained from 3 kinds of sensors, namely capacitive proximity, inductive proximity, and photodiode. While the input consists of 2 pieces of organic or in organic waste conditions. From the test results, it was found that the system has 100% training accuracy and 100% test accuracy.  
Komparasi Beberapa Algoritma Machine Learning untuk Klasifikasi Sentimen Pengguna X (Twitter) Terkait Isu Kabur Aja Dulu Ramadhan, Rayhan Dwi; Arifin, Fatchul
Journal of Information Engineering and Technology Vol. 3 No. 2 (2025): September 2025
Publisher : Department of Electronics and Informatics Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jiety.v3i2.2160

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen pengguna media sosial X (Twitter) terhadap isu “Kabur Aja Dulu” serta membandingkan performa beberapa algoritma machine learning dalam mengklasifikasikan sentimen tersebut. Penelitian ini menggunakan pendekatan kuantitatif dengan kerangka kerja SEMMA (Sample, Explore, Modify, Model, Assess). Data dikumpulkan melalui teknik crawling menggunakan tools Tweet Harvest dan diproses melalui tahapan preprocessing seperti pembersihan data, normalisasi, tokenisasi, dan stemming. Pelabelan sentimen dilakukan dengan pendekatan lexicon-based menggunakan InSet Lexicon, sementara proses ekstraksi fitur menggunakan Term Frequency–Inverse Document Frequency (TF-IDF). Untuk mengatasi ketidakseimbangan kelas, diterapkan metode Synthetic Minority Oversampling Technique (SMOTE). Enam algoritma diuji, yaitu Support Vector Machine (SVM), Logistic Regression, Naive Bayes, Random Forest, Decision Tree, dan K-Nearest Neighbors (KNN). Hasil penelitian menunjukkan bahwa mayoritas sentimen bersifat negatif. Algoritma SVM menghasilkan akurasi terbaik sebesar 80%, diikuti oleh Logistic Regression (77%) dan Random Forest (75%). Temuan ini menunjukkan bahwa SVM paling efektif digunakan dalam klasifikasi sentimen terkait isu sosial, dan dapat menjadi acuan dalam pengembangan sistem pemantauan opini publik.
Optimizing VR-UX: analysis and adaptive recommendations for enhancing immersion and reducing motion sickness Aji Purnomo, Fendi; Arifin, Fatchul; Surjono, Herman Dwi
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1181-1191

Abstract

This study presents an adaptive recommendation framework to enhance comfort and immersion in virtual reality (VR) by actively reducing motion sickness. Unlike prior research that views VR user experience (UX) as static, this approach integrates statistical analysis with dynamic system design. Using a Kaggle dataset of 1,000 entries, we applied descriptive statistics, Spearman correlation, Kruskal-Wallis tests, and regression to explore relationships among session duration, motion sickness, immersion, headset type, and user demographics. Findings show that session duration alone does not significantly predict motion sickness or immersion (R²=0.00, p>0.05), but certain user profiles, such as individuals over 30 using PlayStation VR, are more prone to discomfort. These insights inform a four-module framework: user profiling, real-time duration monitoring, rule-based adaptation logic (such as slowing navigation speed or adding a virtual nose for visual stability), and personalized in-VR recommendations. The system is compatible with Unity and Unreal Engine and integrates with commercial headset software development kits (SDKs). Future validation will use A/B testing, standardized questionnaires, simulator sickness questionnaire /immersion presence questionnaire (SSQ/IPQ), and physiological metrics. This work shifts VR design toward personalized, responsive systems that prioritize user well-being and immersive engagement.
A System Identification of Diabetes Based on Ensemble Method: Bagging, Random Forest, and Extreme Gradient Boosting de Deus Guterres, Jonas; Arifin, Fatchul
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 10 No. 2 (2025): November 2025 (In-Press)
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v10i2.89649

Abstract

Diabetes is a prevalent chronic illness that is recognized worldwide, with an estimated prevalence in adults ranging from 42% to 170% globally. To reduce the likelihood of developing diabetes, it is vital for individuals at an increased risk to understand the importance of embracing healthy lifestyles and managing their consumption of foods that can potentially raise insulin levels in the body. Therefore, it is crucial to detect early pre-symptoms to minimize the incidence of individuals being afflicted by this condition without their awareness. Machine learning has emerged as a contemporary tool that aids in the prediction of various diseases, including diabetes, by analyzing patient data. Despite numerous research attempts using various machine learning techniques, achieving high accuracy in predicting diabetes has remained challenging. Therefore, this study implemented an ensemble approach that combined bagging, random forest, and Extreme Gradient Boost (XGBoost) algorithms to enhance the predictive performance for diabetes. This approach involved evaluating selected features based on their highest correlation and incorporating all available features in the analysis. Based on the results, the bagging technique demonstrated the highest accuracy of 0.83 in predicting model 6. Following closely behind was the random forest algorithm, which achieved an accuracy of 0.82, and XGBoost with an accuracy of 0.81.
Pengembangan Nutri-Bumil dengan Model APPED Berbasis Website Terintegrasi Chatbot sapina, Sapina; Mashoedah; Dwi Kurniawan, Prabowo; Arifin, Fatchul; Syaiful Rijal, Bait
Jurnal Teknik Vol 23 No 2 (2025): Jurnal Teknik
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37031/jt.v23i2.731

Abstract

This study aims to develop the Nutri-Bumil System, a web-based nutrition intake monitoring service for pregnant women integrated with a chatbot. The system development employed a Research and Development (R&D) approach using the APPED model (Analysis, Planning, Production, Evaluation, and Distribution). The research instruments included validation by content experts and media experts using a 4-point Likert scale, which was converted into percentages to assess the system’s feasibility based on the ISO/IEC 25010 software quality standards in the aspects of Usability, Functional Suitability, and Performance Efficiency. The validation results showed that content experts rated the system at 80.71% for Usability, 82.65% for Functional Suitability, and 76.78% for Performance Efficiency, which fall into the categories of Feasible and Fairly Feasible. Meanwhile, media experts provided scores of 80%, 85.41%, and 87.5%, all of which are categorized as Feasible. These findings indicate that the developed Nutri-Bumil system meets the software quality aspects in terms of ease of use, functional suitability, and performance efficiency. Overall, the system is declared Feasible for use as an adaptive, accurate, and efficient digital nutrition service supporting the needs of pregnant women and nutrition professionals.
Web-Based Deepfake Detection Using VERITAS: Integrating Vision-Based Excitation with Transformer-Driven Intelligence Alam Rahmatulloh; Surjono, Herman Dwi; Arifin, Fatchul; Gunawan, Rohmat; Rizal, Randi
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.v10i1.7320

Abstract

This study proposes a web-based deepfake detection system that integrates Vision-Based Excitation technology and Transformer-based intelligence, called VERITAS (Vision-based Excitation and Robust Intelligence for Transformer-Assisted Deepfake Detection). The system is designed to automatically detect manipulated images and videos by leveraging the Vision Transformer (ViT) model architecture, equipped with the Grad-CAM mechanism for interpretability of detection results. The study conducted a series of tests to measure the system's performance in various scenarios and ensure its reliability in dealing with various types of input. Load testing results showed that up to 30 simultaneous users, the system can operate with good responsiveness (average response time of 130 ms) without experiencing errors. However, when the number of users reaches 40 or more, the system performance drops drastically with a very high error rate, reflecting limitations in handling server load. Real-world testing showed the system can detect deepfakes with an accuracy of 73.61%, with results varying depending on the quality of the tested images. Furthermore, unit functional testing and coverage analysis demonstrated an excellent test pass rate (85%), with all major functions running smoothly and error handling needed to be fixed in some code sections. Overall, the VERITAS system demonstrates strong potential for web-based deepfake detection, with high reliability under low load and adequate performance in functional testing. However, further optimization is needed to handle higher user loads.
Co-Authors Agus Setyawan Aji Purnomo, Fendi Al Matiin, Muhammad Azril Haidar Alam Rahmatulloh Alfath, Muhammad Rofiq Banu Ali Sophian Ali Sophian Anggun Winursito Anis Nurashikin Nordin Ardy Seto Priambodo Aris Nasuha Arselan Ashraf Arselan Ashraf Bekti Wulandari Caryn, Femilia Hardina de Deus Guterres, Jonas Dessy Irmawati Dwi Kurniawan, Prabowo Dwi Marlina Dyah Muslihah Erman Agusta, Gino Faisal Ahmed Assaig Fauziah, Khasna Nur Fendi Aji Purnomo, Fendi Aji Gunawan, Rohmat Gunawan, Teddy Surya Gusti, Wahyu Ramadhani Habiburrahman, M. Hazlan, Mohamed Alfian Al-Zikry Herjuna Artanto Herman Dwi Surjono Indradewa, Rhian Iskandar, Ranu Junior, Andy Sadewa Kartiwi, Mira Lahiya, Indah Wardati Malik Arman Morshidi Mashoedah Mashoedah Mauridhy Hery Purnomo Mir Suhail Alam Mira Kartiwi Mira Kartiwi Mohamed Hadi Habaebi Mohamed Hadi Habaebi Muhammad Faozan Mulad Khalik Muhammad Rofiq Banu Alfath Muslikhin Ningrum Astriawati Nur Hasanah Nuri Aslami Pahtoni, Tri Yuli Prabowo, Hernawan Prasetyo Prasetyo Raharjo, Muhammad Mukti Rahmahwati, Febri Suci Rajif, R. Amirur Ramadhan, Rayhan Dwi Rashidah Funke Olanrewaju Richard Sambera Sagala Rizal, Randi Rosminazuin Ab. Rahim Rozainanee Mohd Zain sapina, Sapina Suprapto Suprapto Swadyaya, Putu Yana Syaiful Rijal, Bait Teddy Surya Gunawan Teddy Surya Gunawan Teddy Surya Gunawan Teddy Surya Gunawan Tri Arief Sardjono Yaacob, Mashkuri Zainal Arifin Zainihariyati Mohd Zain