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All Journal 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 Indonesian Language Education and Literature 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 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
Herman Dwi Surjono
Faculty Of Engineering, Universitas Negeri Yogyakarta
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts 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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Implementation of Flipped Classroom Model in Vocational High School: A Systematic Literature Review Cobena, Desy Yanty; Surjono, Herman Dwi
JTP - Jurnal Teknologi Pendidikan Vol. 24 No. 1 (2022): Jurnal Teknologi Pendidikan
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat, Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/jtp.v24i1.25185

Abstract

This research aims to provide a comprehensive overview of flipped classroom implementation in Indonesian Vocational High School (VHS). Articles were searched using Google Scholar and selected using the PRISMA (Preferred Reporting Items for Systematic reviews dan Meta-Analyses) method. Only articles indexed by Sinta (a scientific database designed by the Ministry of Researcher, Technology and Higher Education of The Republic of Indonesia) or Scopus were used. Twenty-nine articles were included, published from 2017 to 2021. The results revealed that flipped classroom in Indonesian VHS impacts students' interests and motivation. Moreover, it impacts students' affective, cognitive, and psychomotor competencies and personal skills in the form of self-efficacy, critical thinking, problem-solving, communication, and resilience. Various learning media and tools can be integrated into out-of-class and in-class activities, embodied in learning design. As the topic of future research, the authors recommended investigating the impact of the flipped classroom on other research areas, integrating flipped classroom with other learning models and evaluating the effectiveness, also developing learning media to improve the learning outcomes.
Innovative E-Learning Strategies in Mathematics Education: Enhancing Self-Directed Learning and Student Motivation Khairah, Nurul; Surjono, Herman Dwi; Nurmiati, Euis
Media Pendidikan Matematika Vol. 13 No. 1 (2025)
Publisher : Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/mpm.v13i1.15412

Abstract

This study employs the ADDIE model (Analysis, Design, Development, Implementation, Evaluation) within a Research and Development (R&D) framework to develop a Moodle-based e-learning platform for enhancing mathematics learning motivation and self-directed among 9th-grade students at SMPN 1 Kota Bima. The research addresses two key issues: (1) Indonesia's low math competency (PISA score: 366) and (2) local findings showing 60% of target students scoring below minimum standards with observed low learning autonomy.Using the ADDIE model, the platform was developed with interactive features (structured materials, automated quizzes, discussion forums, and progress dashboards). Evaluation instruments included pre-post motivation questionnaires (Likert scale), self-regulated learning rubrics, and math achievement tests. Validation by material and media experts confirmed high feasibility (88% and 82%, respectively). Quantitative analysis revealed statistically significant improvements in the experimental class: motivation increased by 13.83 points (paired t-test, p < 0.01, g = 0.72 [high]), and self-directed learning by 17.66 points (p < 0.01, g = 0.64 [moderate]), surpassing the control class. The results demonstrate Moodle’s efficacy in fostering 21st-century skills (4C) through hybrid learning, aligned with Vygotsky’s constructivism (social interaction scaffolding) and Keller’s ARCS theory (attention-relevance-confidence-satisfaction). The study contributes actionable insights for technology-mediated math education, emphasizing structured, feedback-driven, and student-centered design.
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)
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.
Co-Authors Abdul Haris Munandar Afdal, Zul Aji Purnomo, Fendi Akrom, Akrom 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 Deni Hardianto Dian Wahyuningsih Disa Hediansah Dwi Kurniawan, Prabowo Dwi Widjanarko Dyah Respati Dyah Respati Suryo Sumunar Eko Marpanaji Fatchul Arifin Fendi Aji Purnomo, Fendi Aji Gilang Nugraha Putu Pratama Gunawan, Rohmat Habib Hambali Handaru Jati Heni Rita Susila Herminarto Sofyan Heru Amrul Mu&#039;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 Mardian, Ari Martin Hartmann Marwanto, Eko Mashoedah Mashoedah Muhammad Ahmad Jumasa Muhammad Kholil Muhammad Rizky Putra Wadhana Muhyadi Muhyadi 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