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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences International Journal of Evaluation and Research in Education (IJERE) Jurnal Pendidikan Vokasi Cakrawala Pendidikan Jurnal Kependidikan: Penelitian Inovasi Pembelajaran Jurnal Penelitian Saintek JPTK: Jurnal Pendidikan Teknologi dan Kejuruan Jurnal Pendidikan Indonesia Bulletin of Electrical Engineering and Informatics Jurnal Pendidikan Teknik Mesin Jurnal Inovasi Teknologi Pendidikan Indonesian Green Technology Journal Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Educational Science and Technology ELINVO (Electronics, Informatics, and Vocational Education) PYTHAGORAS: Jurnal Program Studi Pendidikan Matematika Jurnal Pendidikan Teknik Mesin Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Pendidikan (Teori dan Praktik) REiD (Research and Evaluation in Education) Script Journal: Journal of Linguistic and English Teaching Jurnal Penelitian Pendidikan IPA (JPPIPA) JKTP: Jurnal Kajian Teknologi Pendidikan Journal of Education Technology JTP - Jurnal Teknologi Pendidikan COMPTON: Jurnal Ilmiah Pendidikan Fisika Jurnal Eduscience (JES) Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Pedagogi dan Pembelajaran Letters in Information Technology Education (LITE) Ideguru: Jurnal Karya Ilmiah Guru Jurnal Pendidikan Vokasi Otomotif JURNAL TEKNOLOGI INFORMASI & KOMUNIKASI DALAM PENDIDIKAN JP (Jurnal Pendidikan) : Teori dan Praktik International Journal of Technology and Education Research Journal of Information Engineering and Technology Al-Hayat: Journal of Islamic Education Journal of Information Technology and Education (JITED) Jurnal Pendidikan Teknik Mesin Media Pendidikan Matematika Indonesian Language Education and Literature
Herman Dwi Surjono
Faculty Of Engineering, Universitas Negeri Yogyakarta
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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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.
Algorithms Green AI: An Artificial Intelligence Algorithms Review for Hate Speech Videos Ari Setyawan, Ryan; Surjono, Herman Dwi; Wardani, Ratna
Indonesian Green Technology Journal Vol. 13 No. 2 (2024): Indonesian Green Technology Journal
Publisher : Sekolah Pascasarjana, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.igtj.2024.013.02.03

Abstract

Hate speech is a form of abusive language and toxic language aimed at individuals, groups, races or ethnicities, genders and certain religions. The problem that occurs is the absence of a good filtering process on social media. For this reason, a filtering process is needed that aims to distinguish between video content that contains hate speech or not. The filtering process can use a classification model. This classification model uses an artificial intelligence algorithm. The purpose of this study is to conduct a literature review on algorithms that can be proposed for detecting hate speech videos. The algorithm used refers to the concept of green technology, with low energy resource consumption and minimizing negative environmental impacts. The literature study conducted obtained the CNN, BERT and LSTM algorithms for the hate speech video classification model. The three algorithms can be used as a reference to obtain a Green AI model by considering the low performance indicator parameters on the CPU, or using a fusion level that can reduce CPU performance. This is in accordance with the concept of green technology, reducing the use of computing processes that absorb large amounts of electrical energy.
Pengembangan Model Hybrid Arima-Machine Learning untuk Prediksi Harga Saham BCA Dwi Kurniawan, Prabowo; Dwi Surjono, Herman; Jati, Handaru
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 6: Desember 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025126

Abstract

Penelitian ini bertujuan untuk menganalisis kinerja metode hybrid antara algoritma machine learning dan model ARIMA dalam memprediksi harga saham Bank BCA selama lima tahun terakhir. Data yang digunakan berasal dari saham Bank BCA periode 13 November 2019 hingga 12 November 2024, diperoleh melalui Yahoo Finance. Dataset ini terdiri dari 1210 record dengan tujuh variabel: Date, Open, Close, High, Low, Volume, dan Adj Close. Pengujian dilakukan memodelkan data linier menggunakan ARIMA, kemudian memprediksi residual menggunakan algoritma machine learning yaitu KNN, Naïve Bayes, Logistic Regression, SVM, Random Forest, dan Gradient Boost. Selanjutnya Prediksi Akhir didapatkan dari penjumlahan Prediksi ARIMA dengan Prediksi Residual oleh Machine Learning. Hasil evaluasi menunjukkan bahwa model hybrid ARIMA–SVM memberikan performa terbaik dengan nilai MSE sebesar 13.341,72, MAE sebesar 89,69, dan MAPE sebesar 0,9078%. Model ini juga memiliki nilai korelasi (R) tertinggi sebesar 0,9785. Sementara itu, model ARIMA–Gradient Boosting juga menunjukkan performa yang kompetitif dengan MSE sebesar 14.126,60 dan MAPE sebesar 0,9434%. Temuan ini menunjukkan bahwa pendekatan hybrid efektif dalam meningkatkan akurasi dan kestabilan prediksi saham, serta dapat dijadikan alternatif yang unggul dalam analisis pasar keuangan berbasis data historis.   Abstract This study aims to analyze the performance of a hybrid method combining machine learning algorithms and the ARIMA model in predicting the stock prices of Bank BCA over the past five years. The data used were obtained from Yahoo Finance, covering the period from November 13, 2019, to November 12, 2024. The dataset consists of 1,210 records and includes seven variables: Date, Open, Close, High, Low, Volume, and Adjusted Close. The testing procedure involved modeling the linear component of the data using ARIMA, followed by predicting the residuals with machine learning algorithms, namely K-Nearest Neighbors (KNN), Naïve Bayes, Logistic Regression, Support Vector Machine (SVM), Random Forest, and Gradient Boosting. The final prediction was obtained by summing the ARIMA forecast with the residual predictions from the machine learning models. Evaluation results show that the hybrid ARIMA–SVM model delivered the best performance with an MSE of 13,341.72, MAE of 89.69, and MAPE of 0.9078%, along with the highest correlation (R) value of 0.9785. The ARIMA–Gradient Boosting model also demonstrated competitive performance with an MSE of 14,126.60 and a MAPE of 0.9434%. These findings indicate that the hybrid approach is effective in enhancing the accuracy and stability of stock price predictions and can serve as a promising alternative in historical data-based financial market analysis.
Development of Interactive Learning Media for Fundamental Pencak Silat Techniques in Junior High School Pradisetya, Bagus Yoga; Herman Dwi Surjono
International Journal of Technology and Education Research Vol. 4 No. 01 (2026): January- March, International Journal of Technology and Education Research (IJ
Publisher : International journal of technology and education research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63922/ijeter.v4i01.2934

Abstract

This study aims to develop, validate, and test the effectiveness of interactive learning media for fundamental Pencak Silat techniques for junior high school students. The background of this research is the difficulty students face in visualizing complex motor movements in Pencak Silat through conventional methods. This research employed the Research and Development (R&D) method with the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation). The study was conducted at SMP Muhammadiyah 4 Yogyakarta with 8th-grade students as the subjects. Data were collected using validation sheets from material and media experts, as well as student response questionnaires. The results showed that the developed media achieved a validation score of 83.9% from both material and media experts, placing it in the "Very Feasible" category. Furthermore, the media proved to be effective in improving student learning outcomes, evidenced by an average post-test score of 84.8% and an N-Gain score of 0.757, which is categorized as "High Effectiveness." This study concludes that the interactive learning media created with Adobe Flash CS6 is a viable and effective tool for enhancing the mastery of fundamental Pencak Silat techniques in physical education.
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.
DEVELOPMENT OF A MOODLE-BASED LEARNING MANAGEMENT SYSTEM (LMS) TO IMPROVE STUDENT MOTIVATION AND LEARNING OUTCOMES Mukhadzib, Izzudin; Surjono, Herman Dwi
JURNAL EDUSCIENCE Vol 13, No 2 (2026): Jurnal Eduscience (JES), (Authors from Malaysia and Indonesia)
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jes.v13i2.9068

Abstract

Purpose – This research and development aims to 1 develop a Moodle-based LMS system for Geography that is feasible, 2 determine the practicality of LMS in learning, and 3 test the effectiveness of LMS in improving learning outcomes and motivation of grade XII students at SMAN 1 Tanjunganom.Methodology – The research used the Alessi and Trollip (2001) development model with three stages: planning, design, and development. This study employs a descriptive mixed-methods analysis (qualitative and quantitative). Product testing included alpha testing by 4 experts and beta testing in a small group (5 students and 1 teacher) and a large group (31 students and 1 teacher). Effectiveness data were obtained from the control class XII-9 and the experimental class XII-8, each with 35 students.Findings – The results showed that 1. the LMS was very feasible based on the assessments of subject matter experts 4.45 and media experts 4.54, 2. the LMS was practical based on the responses of students 2.60 and teachers 4.69, 3 LMS is effective in improving learning outcomes with a higher n-gain in the experimental class than in the control class 0.760.47 and a Sig. With a value of 0.001, 4 LMS is effective in increasing learning motivation, with a higher n-gain in the experimental class (0.510.36) and a Sig. value of 0.013.Contribution – Thus, the Moodle-based LMS effectively improves Geography learning outcomes and motivation for grade XII students at SMAN 1 Tanjunganom. This study offers a replicable model for Indonesian high schools implementing the Merdeka Curriculum, providing edtech evidence and practical guidelines for integrating digital maps and simulations in resource-limited settings.
Drone-assisted deep learning weed detection for sustainable agriculture and environmental resilience Latif, Agustan; Jati, Handaru; Surjono, Herman Dwi; Yusuf, Mani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1428-1440

Abstract

Effective weed detection plays a crucial role in sustainable agriculture, boosting crop productivity and supporting environmental conservation. This study compares three deep learning models—YOLOv5, YOLO-NAS, and mask region-based convolutional neural network (Mask R-CNN)-against traditional methods in terms of accuracy, processing speed, and adaptability in tropical agricultural conditions, with Merauke, Indonesia, as the case study. The results show that YOLO-NAS delivers the highest accuracy at 96% with a processing time of 25 ms per image, making it suitable for high precision applications. YOLOv5 balances strong accuracy (94%) with faster processing at 12 ms per image, establishing it as the most effective for real time scenarios. Mask R-CNN also achieves 94% accuracy and provides advanced segmentation capabilities, but its slower processing speed of 31 ms limits large-scale implementation. Traditional methods perform poorly in comparison, with only 85% accuracy and processing time above 50 ms per image. These findings highlight the transformative potential of artificial intelligence (AI)-based weed detection for precision agriculture, particularly in tropical regions like Merauke. Adoption of models such as YOLOv5 reduces manual labor dependence while advancing efficient, eco-friendly weed management. Future research should expand datasets and explore newer models like YOLOv8, YOLO-NAS, vision transformers (ViTs), and hybrid approaches.
ENHANCEMENT OF CRITICAL THINKING SKILLS VOCATIONAL STUDENTS IN INDONESIA USING PROBLEM-BASED LEARNING-STEM BY E-LEARNING Farhan Yadi; Herminarto Sofyan; Herman Dwi Surjono
Jurnal Pendidikan Teknik Mesin Vol. 10 No. 1 (2023): Jurnal Pendidikan Teknik Mesin
Publisher : Program Studi Pendidikan Teknik Mesin Fakultas Keguruan dan Ilmu Pendidikan Universitas Sriwiajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/jptm.v10i1.144

Abstract

This study aims to develop a problem-based learning model with the Science, Technology, Engineering, and Mathematics (STEM) approach through e-learning in vocational high schools (SMK) to enhance learning outcomes and critical thinking skills of the students of light vehicle electrical maintenance, which is valid, practical, and effective. The design phase began with determining the fundamental philosophy, theoretical background, hypothetical model, and evaluation aspects, while exhaustive tests were conducted to validate the developed system during the development phase. Based on practical testing in four vocational high schools in Indonesia, the developed PBL-STEM e-learning system is considered valid and effective for enhancing the quality of vocational education. The critical thinking skills of experimental class students have increased, both in groups and individually, as indicated by the progressive percentage increase of 38.32%. The application of this model has proven to be effective in enhancing critical thinking, skills.
Stimulus Variation Strategies and Audiovisual Media for Indonesian for Foreign Speakers Students (Strategi Variasi Stimulus dan Media Audiovisual bagi Pemelajar Bahasa Indonesia bagi Penutur Asing) Dadi Satria; Sulis Triyono; Herman Dwi Surjono
Indonesian Language Education and Literature Vol. 8 No. 2 (2023)
Publisher : Jurusan Tadris Bahasa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/ileal.v8i2.8670

Abstract

Penelitian ini bertujuan untuk mendeskripsikan strategi keterampilan variasi stimulus dengan menggunakan media audio visual bagi mahasiswa yang mengambil program Bahasa Indonesia bagi Penutur Asing (BIPA). Jenis penelitian ini adalah studi kasus proses di Universitas Negeri Padang. Data penelitian berupa catatan lapangan yang dikumpulkan melalui observasi langsung dan observasi partisipan. Hasil penelitian menunjukkan bahwa pembelajaran BIPA dapat dilakukan dengan memanfaatkan media audio visual untuk merangsang penglihatan, pendengaran, dan gerak. Penggunaan rekaman dan lagu dapat merangsang pendengaran. Hal ini untuk mempelajari bunyi bahasa dan struktur kalimat dalam bahasa Indonesia. Gambar dapat membantu siswa untuk menguasai berbagai macam objek. Video berupa lagu mampu merangsang gerakan melalui proses menirukan. Dengan demikian, strategi ini dapat digunakan oleh para pengajar BIPA untuk menciptakan pembelajaran yang aktif, kreatif, komunikatif, dan kolaboratif.This study aims to describe the strategy of stimulus variation skills using audio-visual media for BIPA students. This type of research is a case study at Universitas Negeri Padang. Research data in the form of field notes is collected through direct observation and participant observation. The results of the study found that learning Indonesian for early speakers through a variety of stimulus strategies in the form of sight, hearing, and movement by utilizing audio-visual media formed active and communicative learning. The use of audio media in the form of recordings and songs can stimulate auditory stimulation to learn the sounds of language and sentence structure in Indonesian. Furthermore, visual media in the form of images can help students to master various kinds of objects and objects. Meanwhile, video playback in the form of songs is able to stimulate movement stimuli through the process of imitating the movements in the video song being played. Thus, this strategy can be used by BIPA teachers to create active, creative, communicative, and collaborative learning.
Co-Authors Abdul Haris Munandar Afdal, Zul Agustan Latif Alam Rahmatulloh Ali Muhtadi Ali Mustadi Almadina, Nazela Ulhaqy Anak Agung Adi Wiryya Putra Anang Fathoni Anik Ghufron Ari Setyawan, Ryan Arif Rahman Ariyanto S. Helianak Aurum, Emma Valensia Ayu Nuswantari Ayu Sandra Dewi Ayuriyanti, Siswi Dwi Bashir, Ashadi Bekti Wulandari Cahyandaru, Pramudya Cobena, Desy Yanty Dadi Satria Deni Hardianto Dian Wahyuningsih Disa Hediansah Dwi Kurniawan, Prabowo Dwi Widjanarko Dyah Respati Dyah Respati Suryo Sumunar Eko Marpanaji Fatchul Arifin Fendi Aji Purnomo Gilang Nugraha Putu Pratama Gunawan, Rohmat Habib Hambali Handaru Jati Heni Rita Susila Herminarto Sofyan Heru Amrul Mu'arif Hilyatush Shofa Huzaima Mas’ud Ibnu Siswanto Indra Hidayatulloh Irfan Adi Nugroho Jati, Prasetyawan Caesar Wijaya Ju-Ling Shih Kadarisman Tejo Yuwono Kadek Cahya Dewi Karuniawan, Yoga Khairah, Nurul Mahmud Al Haq Patwary, Mahmud Al Haq Mani Yusuf Mardian, Ari Martin Hartmann Marwanto, Eko Mashoedah Mashoedah Muhammad Ahmad Jumasa Muhammad Kholil Muhammad Rizky Putra Wadhana Muhyadi Muhyadi Mukhadzib, Izzudin Munandar, Abdul Haris Niluh Putu Puri Palupi Sukenasa Novi Trilisiana Nurmiati, Euis Nuswantari, Ayu Pradisetya, Bagus Yoga Priyanto Priyanto Priyanto, Priyanto Puput Tri Anggara Purmadi, Ary Putu Indah Ciptayani R. Hafid Hardyanto, R. Hafid Raekha Azka Rama Faiz Pangestu Randy Irawan Ratna Wardani Restu Widiatmono Rifkisyahputra Rizal, Randi Rukiyati Rukiyati Safitri, Ria Rochmi Satria, Dadi Satrio Dwi Ananda Setyabudi Indartono Sigit Pambudi Siti Sumiyati Sri Sumardiningsih Sulis Triyono Suprapto Suprapto Surahman, Ence Sutirman Sutirman Totok Sukardiyono Tsabita, Latifah Victor Novianto Wahyu Kurniawati Wu, Ying-Tien Yadi, Farhan Yassin, Abdulnassir Ying-Tien Wu Zainal Arifin Zamroni Zamroni Ziaurrahman Ziaurrahman