Article Per Year (5 Year)
p-Index From 2021 - 2026
3.734
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) JPTK: Jurnal Pendidikan Teknologi dan Kejuruan 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
Fatchul Arifin
Universitas Negeri Yogyakarta
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 34 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 34 Documents
Search

 1   2   3   4 
Emotions and gesture recognition using affective computing assessment with deep learning Herjuna Artanto; Fatchul Arifin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1419-1427

Abstract

Emotions have an important role in education. Affective development, attitudes, and emotions in learning are measured using affective assessment. This method is the right way to determine the student’s affective development. However, the process did not run optimally because the teacher found it difficult to collect student’s affective data. This paper describes the development of a system that can assist teachers in carrying out affective assessment. The system was developed using a v-model that aligns the verification phase with the validation. The use of the system is carried out during learning activities. The emotion detection system detects through body gestures using PoseNet to generate emotional data for each student. The detection results are then processed and displayed on an information system in the form of a website for affective assessment. The accuracy of emotion detection got validation values of 84.4% and 80.95% after being tested at school. In addition, the acceptance test with the usability aspect of the system by the teacher got a score of 77.56% and a score of 79.85% by the students. Based on several tests carried out, this developed system can assist the process of implementing affective assessment. 
Enhanced Emotion Recognition in Videos: A Convolutional Neural Network Strategy for Human Facial Expression Detection and Classification Arselan Ashraf; Teddy Surya Gunawan; Fatchul Arifin; Mira Kartiwi; Ali Sophian; Mohamed Hadi Habaebi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 1: March 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i1.4449

Abstract

The human face is essential in conveying emotions, as facial expressions serve as effective, natural, and universal indicators of emotional states. Automated emotion recognition has garnered increasing interest due to its potential applications in various fields, such as human-computer interaction, machine learning, robotic control, and driver emotional state monitoring. With artificial intelligence and computational power advancements, visual emotion recognition has become a prominent research area. Despite extensive research employing machine learning algorithms like convolutional neural networks (CNN), challenges remain concerning input data processing, emotion classification scope, data size, optimal CNN configurations, and performance evaluation. To address these issues, we propose a comprehensive CNN-based model for real-time detection and classification of five primary emotions: anger, happiness, neutrality, sadness, and surprise. We employ the Amsterdam Dynamic Facial Expression Set – Bath Intensity Variations (ADFES-BIV) video dataset, extracting image frames from the video samples. Image processing techniques such as histogram equalization, color conversion, cropping, and resizing are applied to the frames before labeling. The Viola-Jones algorithm is then used for face detection on the processed grayscale images. We develop and train a CNN on the processed image data, implementing dropout, batch normalization, and L2 regularization to reduce overfitting. The ideal hyperparameters are determined through trial and error, and the model's performance is evaluated. The proposed model achieves a recognition accuracy of 99.38%, with the confusion matrix, recall, precision, F1 score, and processing time further quantifying its performance characteristics. The model's generalization performance is assessed using images from the Warsaw Set of Emotional Facial Expression Pictures (WSEFEP) and Extended Cohn-Kanade Database (CK+) datasets. The results demonstrate the efficiency and usability of our proposed approach, contributing valuable insights into real-time visual emotion recognition.
Development and Evaluation of a High-Performance Electrochemical Potentiostat-Based Desktop Application for Rapid SARS-CoV-2 Testing Faisal Ahmed Assaig; Teddy Surya Gunawan; Anis Nurashikin Nordin; Rosminazuin Ab. Rahim; Zainihariyati Mohd Zain; Rozainanee Mohd Zain; Fatchul Arifin
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 2: June 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i2.4645

Abstract

The COVID-19 pandemic has necessitated the development of rapid and trustworthy diagnostic tools. Reverse transcription-polymerase chain reaction (RT-PCR) is the gold standard for detecting SARS-CoV-2 but has cost and time constraints. The sensitivity, specificity, and low cost of electrochemical biosensors make them an attractive alternative for virus detection. This study aims to develop and evaluate a high-performance desktop application for an electrochemical potentiostat-based SARS-CoV-2 test device, with a user-friendly interface that automatically interprets results, to expedite the testing process and improve accessibility, particularly in resource-limited settings. The application was built with the Electron framework and the HTML, CSS, and JavaScript programming languages. Our findings indicate that the developed electrochemical potentiostat-based desktop application demonstrates high accuracy compared to commercial software, achieving rapid detection within 30 seconds. The graphical user interface was found to be straightforward and user-friendly, requiring minimal training for efficient system operation. Our electrochemical potentiostat-based desktop application represents a valuable tool for rapid SARS-CoV-2 testing, particularly in settings with limited resources. This research contributes to developing rapid and reliable diagnostic tools for SARS-CoV-2 and potentially other pandemic-causing viruses, addressing the pressing need for improved public health surveillance and response strategies.
Pengembangan Aplikasi Evaluasi Tingkat Penguasaan Praktikum Aircraft Electrical Berbasis Fuzzy Expert System Stand Alone Application Richard Sambera Sagala; Fatchul Arifin
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 9, No 2 (2023): Volume 9 No 2
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v9i2.64239

Abstract

Penelitian ini dilatarbelakangi oleh kebutuhan akan sebuah aplikasi yang dapat mengevaluasi tingkat penguasaan praktik siswa. Tujuan penelitian adalah menghasilkan aplikasi untuk kebutuhan evaluasi tingkat penguasaan praktik siswa, yang dapat dioperasikan tanpa harus menjalankan aplikasi editor utama terlebih dahulu. Metode penelitian yang digunakan adalah penelitian pengembangan dengan tahapan 4D. Batasan masalah penelitian terletak pada pengembangan aplikasi hanya untuk pengolahan nilai praktik siswa kelas XI jurusan Electrical Avionics pada mata pelajaran Aircraft Electrical. Berdasarkan pengembangan yang dilakukan, aplikasi dapat dijalankan tanpa harus menjalankan aplikasi editor utama terlebih dahulu, mampu memproses input dan mengeluarkan output, serta dapat mencetak tangkapan layar aplikasi. Melalui pemrosesan data nilai-nilai praktik siswa, didapatkan hasil bahwa tingkat penguasaan praktik siswa menggunakan aplikasi (TPF) adalah 75% - 80%. Berbeda halnya dengan tingkat penguasaan praktik siswa bermetode manual (TPM) adalah 80% s/d 87%. Walaupun terdapat perbedaan hasil antara 2 cara tersebut, hubungan antara TPF dan TPM adalah kuat. Uji korelasi Spearmen menunjukkan, nilai korelasi antara TPF dan TPM adalah 0,609 (60,9%) dan berkategori “tinggi”. Hal ini menunjukkan bahwa aplikasi yang telah dibangun mampu menentukan tingkat penguasaan praktikum siswa. Akan tetapi, untuk menyatakan apakah aplikasi layak digunakan dalam skala kecil maupun skala besar, masih diperlukan penelitian dan pengembangan lebih lanjut, seperti uji validasi oleh ahli dan uji penerimaan oleh pengguna. Sehingga diharapkan kedepannya, aplikasi tersebut siap didistribusikan secara luas dan dimanfaatkan oleh berbagai sekolah yang memiliki jurusan Electrical Avionic dalam mengevaluasi kemampuan praktik siswa-siswanya. Evaluasi berdampak signifikan dalam meningkatkan kualitas dan mutu pembelajaran.
Klasifikasi Indeks Kedalaman Kemiskinan Provinsi Sulawesi Selatan Berbasis Decision Tree, K-Nearest Neighbor, Naive Bayes, Neural Network, dan Random Forest Muhammad Faozan Mulad Khalik; Fatchul Arifin
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 9, No 2 (2023): Volume 9 No 2
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v9i2.67492

Abstract

Penelitian ini bertujuan untuk mendapatkan klasifikasi indeks kedalaman kemiskinan dengan metode terbaik untuk kabupaten/kota di Provinsi Sulawesi Selatan dengan membandingkan metode Decision Tree, K-Nearest Neighbor, Naïve Bayes, Neural Network, dan Random Forest. Penelitian ini menggunakan metode kuantitatif dengan menggunakan data sekunder yang diperoleh dari situs resmi Badan Pusat Statistik Provinsi Sulawesi Selatan. Pada penelitian ini digunakan 168 data latih yang bersumber dari data tahun 2014 sampai dengan data tahun 2021, kemudian untuk data uji yang digunakan yaitu 24 data yang bersumber dari data tahun 2022. Hasil dari penelitian ini menunjukkan bahwa metode K-NN dan Neural Network memperoleh performa paling tinggi dibandingkan dengan metode lain tingkat akurasi 79,17%, precission 85,71%, recall 80%. Namun pada penilaian parameter AUC, metode Neural Network lebih unggul dibandingkan metode K-NN dengan skor AUC 0,837. Sehingga dapat disimpulkan bahwa metode Neural Network ini dapat dijadikan sebagai metode untuk melakukan klasifikasi indeks kedalaman kemiskinan kabupaten/kota Provinsi Sulawesi Selatan.
Perancangan Prototipe Pendiagnosa Penyakit Jantung Koroner Dengan Metode Backpropagation Ranu Iskandar; Prasetyo Prasetyo; Muhammad Rofiq Banu Alfath; Fatchul Arifin
Lontara Journal of Health Science and Technology Vol 2 No 1 (2021): Ilmu dan Teknologi Kesehatan
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Politeknik Kesehatan Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53861/lontarariset.v2i1.81

Abstract

Penyakit jantung koroner adalah suatu kelainan yang disebabkan oleh penghambatan pembuluh arteri yang mengalirkan darah ke otot jantung. Penyakit ini merupakan salah satu penyakit tidak menular yang kerap mengakibatkan kematian secara langsung pada para korbannya. Tujuan penulisan artikel ini adalah merancang sebuah arsitektur jaringan syaraf tiruan menggunakan metode backpropagation yang dapat memprediksi seseorang terkena penyakit jantung koroner dengan input kadar kolesterol, tekanan darah, dan kadar gula darah, dan indeks masa tubuh. Penelitian ini merupakan penelitian dan pengembangan. Metode penelitian yang digunakan pada pembuatan prototipe ini, yaitu: (1) analisa masalah, (2) analisa kebutuhan, (3) studi pustaka, (4) perancangan prototipe, dan (5) pengujian prototipe. Data pasien yang digunakan untuk menguji prototipe sejumlah 20. Hasil menunjukkan model jaringan syaraf tiruan yang digunakan memiliki nilai rata-rata kesalahan sebesar 0,792% dengan 5000 kali training. Prototipe diagnosa penyakit jantung koroner menggunakan backpropagation berjalan berhasil dibangun dengan hasil baik.
Implementation of FDSS (Fuzzy Decision Support System) Sugeno Model in Optimizing Bandwidth Requirement Management of Web-Based Networks Lahiya, Indah Wardati; Arifin, Fatchul
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 8 No. 2 (2023): November 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.v8i2.57557

Abstract

To increase the efficacy of bandwidth allocation at PT. Digdaya Monokrom Group, this study describes the development of a Fuzzy Decision Support System (FDSS) utilizing the Sugeno methodology. The Waterfall development process is employed for the purposes of system planning, construction, and maintenance. The study consists of three primary stages: the creation of fuzzy sets, the development of fuzzy rules, and the process of defuzzification. The study findings demonstrate that the utilization of FDSS has effectively improved the distribution of bandwidth. The distribution has shifted from a uniform one to a more optimized allocation, focusing on the Execution, Content Creator, Administration, and Research Team departments. During a four-week monitoring period, modifications were implemented to distribute bandwidth based on the preferences and needs of various departments, while adhering to the limitations of the current broadband subscription. This has enhanced the efficient exploitation of network resources. The research findings highlight the efficacy of FDSS in prioritizing resource allocations according to specific departmental requirements, consequently improving service quality and maximizing bandwidth subscription capacity. This demonstrates the implementation of strategic management methodologies to optimize the allocation of network resources, resulting in enhanced organizational efficiency and production.
Machine Learning System Implementation of Education Podcast Recommendations on Spotify Applications Using Content-Based Filtering and TF-IDF Raharjo, Muhammad Mukti; Arifin, Fatchul
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 8 No. 2 (2023): November 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.v8i2.58014

Abstract

Spotify, this popular music and podcast streaming service, has a fundamental problem in assisting clients in finding podcasts that fit their interests. Thus, the goal of this project is to develop a podcast recommendation system that would enhance users' capacity to identify pertinent content, particularly in the educational genre. By using content-based filtration techniques, this system analyzes the user's listening preferences and interests before recommending educational podcasts. The podcast data source is Spotify, and the suggestions are produced using the TF-IDF and Cosine Similarity techniques. The recommendations provide a list of educational podcasts catered to the user's specific interests. The Confusion Matrix Classification Report was tested to assess system performance during the review phase. Precision values show how accurate the system was at recommending educational podcasts; on average, they range from 0.52 to 0.74. Additionally, the recall value showed a mean of 0.51 and a mean of 0.79, indicating that the algorithm successfully located the relevant content. To put it briefly, this custom recommendation engine enhances the listening experience for Spotify customers by suggesting educational podcasts based on their preferences. The system's ability to match users with material that aligns with their interests was demonstrated by the metrics used to assess its performance. With more user interactions with the system, it was anticipated by Cosine Similarity, a statistic used to determine the quality of recommendations, will continue to improve. To improve user experience and personalize the podcast listening experience on Spotify, this research addresses the challenge of locating suitable podcasts.
Performance Analysis of EMG Signal Classification Methods for Hand Gesture Recognition in Stroke Rehabilitation Winursito, Anggun; Arifin, Fatchul; Muslikhin, Muslikhin; Artanto, Herjuna; Caryn, Femilia Hardina
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 8 No. 2 (2023): November 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.v8i2.76811

Abstract

This study evaluates the performance of different classification methods in classifying healthy individuals and stroke patients. The hand gesture variations of the subjects were also analyzed based on electromyography (EMG) signals. Several classification methods were tested in this analysis to find out which method had the most suitable performance. The results showed that Decision Tree and Naive Bayes classifiers achieved the highest performance in classifying EMG signals from healthy individuals and stroke patients, with both methods showing high accuracy, precision, recall, and F1 score. Specifically, Decision Tree excelled in overall accuracy and recall, while Naive Bayes showed superior precision. For hand gesture recognition, SVM, KNN, and Random Forest classifiers showed similarly high performance, achieving accuracy, precision, recall, and F1 score above 82%. Naive Bayes also performed well, especially in precision, while Decision Tree performed poorly compared to other methods. This insight can form the basis for the development of more effective and personalized rehabilitation systems for stroke patients, by utilizing reliable and accurate EMG signal classification
Design and Development of Industrial Practice Monitoring and Assessment Systems using Tsukamoto Fuzzy Logic Pahtoni, Tri Yuli; Arifin, Fatchul
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 8 No. 2 (2023): November 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.v8i2.57669

Abstract

Vocational high schools are given flexibility for their students to carry out direct learning in the industry as part of the practical education activities of implementing student skills. The implementation of industrial practice requires a special way to find out and monitor each student's activities so that the achievements of the implementation of industrial practice can be carried out properly.  The implementation of industrial work practice assessment has several assessment criteria. These criteria include attendance, neatness, attitude, skills, and knowledge. The problems found in the assessment system are still done manually so that the effectiveness is minimal. This study aims to create a system that can monitor and assess the implementation of industrial practices.  The system developed will be tested as a medium for monitoring and assessing industrial practices.  This research uses Fuzzy Tsukamoto's logic approach as a scoring logic  model and  uses the waterfall method as a development model consisting of analysis, design, coding, and testing. The results of the research conducted resulted in a system that can monitor and assess the implementation of industry practices.  The test was carried out by 24 people consisting of guidance teachers and students. Testing is done by testing aspects of functionality and aspects of usability. Based on the test results, the functionality aspect scored 100% (very feasible) and the usage aspect got a score of 84.8% (very feasible)
Co-Authors Agus Setyawan Aji Purnomo, Fendi Al Matiin, Muhammad Azril Haidar 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 Dessy Irmawati Dwi Marlina Dyah Muslihah Erman Agusta, Gino Faisal Ahmed Assaig Fauziah, Khasna Nur Fendi Aji Purnomo, Fendi Aji Gunawan, Teddy Surya Gusti, Wahyu Ramadhani Habiburrahman, M. Herjuna Artanto Herman Dwi Surjono Indradewa, Rhian Iskandar, Ranu Junior, Andy Sadewa Lahiya, Indah Wardati Malik Arman Morshidi 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 Rashidah Funke Olanrewaju Richard Sambera Sagala Rosminazuin Ab. Rahim Rozainanee Mohd Zain Suprapto Suprapto Swadyaya, Putu Yana Teddy Surya Gunawan Teddy Surya Gunawan Teddy Surya Gunawan Teddy Surya Gunawan Tri Arief Sardjono Winursito, Anggun Zainal Arifin Zainihariyati Mohd Zain