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All Journal Teknotan: Jurnal Industri Teknologi Pertanian Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer JURNAL KESEHATAN LINGKUNGAN (Journal of Environmental Health) Edu Komputika Journal Jurnal Teknologi dan Sistem Komputer International Journal of Artificial Intelligence Research JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Teknologi Informasi dan Multimedia Jurnal Eduscience (JES) Jurnal Abdi Insani Jurnal Bumigora Information Technology (BITe) Journal of Applied Data Sciences Journal of Community Development Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Bakti Sekawan : Jurnal Pengabdian Masyarakat Paradharma: Jurnal Aplikasi IPTEK Jurnal INFOTEL International Journal Research on Metaverse Indenesian Journal of Tropical Biology Indenesian Journal of Tropical Biology Journal of Biology, Environment, and Edu-Tourism Jurnal Elektronika dan Telekomunikasi
Syahroni Hidayat
Universitas Negeri Semarang
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Classification of Game Genres Based on Interaction Patterns and Popularity in the Virtual World of Roblox Hasanah, Uswatun; Sunarko, Budi; Hidayat, Syahroni; Rachmawati, Rina
International Journal Research on Metaverse Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i3.30

Abstract

The rapid growth of user-generated virtual environments has elevated the importance of understanding player behavior and content dynamics in metaverse platforms. This study investigates the relationship between game genres and user engagement in Roblox, one of the largest and most interactive virtual worlds. Utilizing a dataset of over 300 game entries, we analyzed engagement metrics including visits (ranging from thousands to over 2.8 billion), likes (up to 1,000,000), favorites (up to 3.4 million), and active user counts (as high as 22,155). Descriptive statistics and correlation analysis revealed that action-oriented genres—particularly Action, Shopping, and Obby & Platformer—consistently outperform others in attracting and retaining users. The strong positive correlation between likes and favorites (r = 0.95) indicates that user satisfaction strongly predicts long-term interest, while negative feedback (dislikes) shows minimal correlation with other variables. In contrast, genres such as Education and Entertainment demonstrated significantly lower averages, with visits below 1 million, and active user counts typically under 1,000. These findings provide practical insights for developers and platform administrators seeking to optimize content strategies and offer a foundation for future research involving clustering analysis, sentiment mining, and temporal behavior modeling to enhance recommendation systems and genre personalization within metaverse ecosystems.
Optimalisasi Model Ensemble Learning dengan Augmentasi dan SMOTE pada Sistem Pendeteksi Kualitas Buah Syahroni Hidayat; Taofan Ali Achmadi; Hanif Ardhiansyah; Hanif Hidayat; Rian Febriyanto; Abdulloh Abdulloh; Intan Ermawati
Jurnal Teknologi Informasi dan Multimedia Vol. 6 No. 1 (2024): May
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v6i1.406

Abstract

Fruit quality is an important factor in selecting fruit for consumption because it affects consumer health and satisfaction. Identification of fruit quality has become the focus of research, and one of the approaches used is a non-destructive approach through measuring the gases produced by the fruit. Machine learning can be used to process this gas data and build system models that can classify fruit quality. This research discusses the application of the DCS-OLA and Stacking dynamic ensemble learning algorithms to build a fruit quality detection system model. The basic methods used to build models are Logistic Regression, Decision Tree, Gaussian Naïve Bayes, and Mul-ti-Layer Perceptron. The fruit used is mango with a shelf life of 7 days and Srikaya (sugar apple) with a shelf life of 4 days. The condition of the initial dataset is unbalanced. The research results show that trimming the mango dataset to only 4 days according to the shelf life of sugar apple helps reduce the difference in shelf life between the two. Then jittering and balancing techniques are used to increase and balance the number of datasets between the two types of fruit. High accuracy is achieved by the DCS-OLA ensemble and stacking ensemble by combining the basic methods of Logistic Regression and Decision Tree, especially in balanced dataset conditions. In conclusion, the use of ensemble learning in detecting fruit quality has great potential for real-world applications. However, further validation is needed with larger datasets and a wider variety of conditions.
Wavelet-Based MFCC and CNN Framework for Automatic Detection of Cleft Speech Disorders Muhammad Hilmy Herdiansyah; Syahroni Hidayat; Nur Iksan
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.780

Abstract

Cleft Lip and Palate (CLP) is a congenital condition that often results in atypical speech articulation, making automatic recognition of CLP speech a challenging task. This study proposes a deep learning-based classification system using Convolutional Neural Networks (CNN) and Wavelet-MFCC features to distinguish speech patterns produced by CLP individuals. Specifically, we investigate the use of two wavelet families Reverse Biorthogonal (rbio1.1) and Biorthogonal (bior1.1)—with three decomposition strategies: single-level (L1), two-level (L2), and a combined level (L1+2). Speech data were collected from 10 CLP patients, each pronouncing nine selected Indonesian words ten times, resulting in 900 utterances. The audio signals were processed using wavelet-based decomposition followed by Mel-Frequency Cepstral Coefficients (MFCC) extraction to generate time-frequency representations of speech. The resulting features were input into a CNN model and evaluated using 5-fold cross-validation. Experimental results show that the combined L1+2 decomposition yields the highest classification accuracy (92.73%), sensitivity (92.97%), and specificity (99.04%). Additionally, certain words such as “selam”, “kapak”, “baju”, “muka”, and “abu” consistently achieved recall scores above 0.94, while “lampu” and “lembab” proved more difficult to classify. The findings demonstrate that integrating multi-level wavelet decomposition with CNN significantly improves the recognition of pathological speech and offers promising potential for clinical diagnostic support.
Acoustic Analysis on Cleft Lip Speech Signal Yusuf, Sitti Agripina Alodia; Sulistianingsih, Nani; Dinata, Muhammad Imam; Hidayat, Syahroni; Darmawan, Joelianto
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 4 (2025): November
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i4.766

Abstract

Cleft conditions significantly disrupt phonetic articulation, leading to hypernasality and irregular resonance characteristics. In this study, the formant analysis of normal and cleft speech is presented, with the aim of investigating acoustic differences in formant frequencies between cleft and normal speech using real-word utterances, focusing on the articulation of plosive consonants and resonance variability.  The dataset consisted of 280 speech signals (140 cleft and 140 normal) uttering word /paku/. The speech signals were resampled to 16kHz and the silence in the speech was removed, next stage was followed by extracting the first three formants using the Burg algorithm. Statistical analysis revealed that the value of F1 and F2 in cleft speech were higher, alongside greater variability in formant distribution. Further analysis of plosive articulation highlighted irregular formant transition in cleft speech, indicating compromised intraoral pressure control. Additionally, a moderate negative correlation (r = -0.423, p<0.001) between F1 and F3 suggests a spectral pattern indicative of hypernasality. This finding underscores the potential of formant-based acoustic features as objective markers for early clinical assessment and provides a foundation for the development of diagnostic models in cleft speech research.
Oyster mushroom agrotourism development design in Kekeri, West Lombok Ida Ayu Widhiantari; Joko Sumarsono; Amrullah; Syahroni Hidayat; Astri Iga Siska
Journal of Biology, Environment, and Edu-Tourism Vol. 1 No. 3 (2025): December
Publisher : Yayasan Siti Widhatul Faeha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65622/jbee.v1i3.167

Abstract

The development of oyster mushroom agrotourism in Kekeri, West Lombok, offers a strategic solution to improve the sustainability of the rural economy. This study aims to formulate an integrated and applicable agrotourism development Design and determine priority strategies. The research method combines qualitative methods (surveys, observations, interviews, FGD) and uses the Analytical Hierarchy Process (AHP) to analyze stakeholder data. The results of the AHP analysis indicate that diversification of processed products is the top-priority strategy (weighted at 0.570), followed by infrastructure strengthening (0.198), digital marketing & collaboration (0.126), and educational tour packages (0.107). To address the technical challenges posed by temperature and humidity fluctuations, integrating IoT (Internet of Things) technology into the oyster mushroom cultivation system is recommended. In conclusion, the development of oyster mushroom agrotourism in Kekeri is highly feasible and supported by strong local potential. The implementation of priority strategies, supported by innovative technology, is expected to add value, increase farmers’ income, and enable sustainable agrotourism.
Ensemble Learning Approaches for Air Pollution Classification and Environmental Health Risk Assessment Budi Sunarko; Syahroni Hidayat; Uswatun Hasanah
JURNAL KESEHATAN LINGKUNGAN Vol. 18 No. 2 (2026): JURNAL KESEHATAN LINGKUNGAN
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jkl.v18i2.2026.159-170

Abstract

Introduction: Conventional statistical models often struggle to represent complex interactions among multiple air pollutants and their non-linear associations with health outcomes. To address this limitation, this study evaluates the effectiveness of ensemble learning approaches for classifying air pollution exposure levels and predicting associated health risks across heterogeneous pollutant contexts. Methods: Two publicly accessible datasets were analyzed. The first dataset comprises toxic gas exposure measurements (CH₄, CO₂, and CO) annotated with short-term physiological health effect categories, reflecting acute exposure scenarios. The second dataset is the Jakarta Air Quality dataset (2021), which includes AQI-based criteria pollutants (PM10, PM2.5, SO₂, CO, O₃, and NO₂) representing urban ambient air quality conditions. Multiple base classifiers Decision Trees, Random Forests, Naïve Bayes, k-Nearest Neighbor, Logistic Regression, Support Vector Machines, AdaBoost, and Multi-Layer Perceptrons were implemented. Data preprocessing involved cleaning, normalization, and a 70:30 training-testing split. Ensemble strategies, particularly stacking, were developed to integrate complementary classifier strengths and improve predictive reliability. Results and Discussion: The stacking ensemble consistently outperformed individual base classifiers, achieving classification accuracies of 0.9993 for the toxic gas exposure dataset and 0.9816 for the Jakarta AQI dataset. These results indicate that ensemble learning enhances robustness, mitigates misclassification risks, and adapts effectively to variations in pollutant concentration patterns across different exposure contexts. Conclusion: Ensemble learning demonstrates strong potential as a reliable computational approach for environmental health risk assessment. Its high predictive performance supports its application in air quality management, early warning systems, and evidence-based policy development aimed at mitigating health risks associated with air pollution.
THE EFFECTIVENESS OF VIRTUAL REALITY IN VOCATIONAL EDUCATION FOR FASHION DESIGN AND PRODUCTION Irmayanti, Irmayanti; Hidayat, Syahroni; Budisantoso, Heri Tri Luqman; Khoiron, Ahmad Mustamil; Achmadi, Taofan Ali
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.v13i2.8267

Abstract

Purpose – The fashion industry must master the practical skills needed today to employ immersive technology like VR in Vocational Education and Training. Due to the cost and hazard of hands-on instruction, Indonesian vocational high schools have a skill gap. In order to deal with this problem, this study talks about the real-life outcomes of "Fashion Tech Edu-VR," an immersive learning tool that fits perfectly with the Indonesian national curriculum.Methodology – This study employs a quasi-experimental research approach with a pre-test and post-test Control Group Design. The research subjects consist of 90 grade XI (Phase F) students from the Fashion Design and Production expertise program at a Vocational High School (SMK) in Semarang City. The data collection techniques used were threefold: tests, observation, and questionnaires. To test the hypotheses in this study, a t-test (paired sample t-test) was utilized with the assistance of IBM SPSS Statistics 26, comparing the post-test scores between the control group and the experimental group.Findings – The findings show a statistically significant difference in learning outcomes between the control and experimental classes (t = -27.935). Student engagement in the control group was 3.31, compared to 4.62 in the experimental class after the Fashion Tech Edu-VR intervention. This study found that students who used 'Fashion Tech Edu-VR' achieved significantly higher learning gains compared to the control group. The platform also received excellent usability ratings and fostered much higher levels of student engagement, confirming its effectiveness as an educational tool.Contribution – The study concludes that "Fashion Tech Edu-VR" is a useful educational tool that solves real-world training problems and is a very effective teaching example
Pineapple Waste Processing Design as Functional Food to Support Agrotourism in East Lombok, Indonesia: Desain Pengolahan Limbah Nanas sebagai Makanan Fungsional untuk Mendukung Agrowisata di Lombok Timur, Indonesia Ayu, Hanifah; Wardatullatifah S, Ince Siti; Hidayat, Syahroni; Jannah, Mirriyadhil
Indonesian Journal of Tropical Biology Vol. 1 No. 3 (2025): December 2025
Publisher : Yayasan Siti Widhatul Faeha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65622/ijtb.v1i3.192

Abstract

The abundant pineapple waste in East Lombok remains underutilized as a functional food resource and has not been integrated into sustainable agrotourism development, leading to environmental challenges and missed economic opportunities for local communities. This study aims to design a model for converting pineapple waste into functional food products within a zero-waste agrotourism and circular economy framework that supports community-based development. A descriptive qualitative literature review was conducted using reputable journals, BPS statistics, data from agricultural and tourism agencies, and regional planning documents. Thematic analysis identified the bioactive potential of pineapple waste, explored functional product innovations, and formulated integration schemes for agrotourism activities. The results show that pineapple peel, core, and crown contain bromelain, phenolic compounds, and dietary fiber that can be processed into fermented beverages, functional vinegar, peel tea, fiber flour, and high-fiber snacks suitable as agrotourism products. Integrating these products through workshops, demonstrations, tasting sessions, educational tours, and souvenir sales can enhance commodity value, strengthen green destination branding, and support the SDGs. Overall, the utilization of pineapple waste offers a synergistic strategy that links agriculture, food innovation, sustainability, and tourism. The study highlights the need to establish a circular economy–based pineapple agrotourism pilot model supported by product guidelines, food safety standards, innovation facilities, and collaboration among farmers, MSMEs, researchers, and local government.
PERFORMANCE EVALUATION OF RECENT YOLO VERSIONS FOR CLASSROOM STUDENT BEHAVIOR DETECTION Mahendra Adiastoro; Febry Putra Rochim; Syahroni Hidayat
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7773

Abstract

The increasing adoption of smart classroom systems underscores the need for automated, objective, and real-time  monitoring of student behavior to support effective teaching and learning. Computer vision–based object detection, particularly the You Only Look Once (YOLO) family, has shown strong potential for this task. However, existing studies predominantly evaluate YOLO models in isolation or across different frameworks, resulting in biased comparisons. To address this gap, this study presents a controlled intra-family comparative evaluation of four recent YOLO generations YOLOv8, YOLOv10, YOLOv11, and YOLOv12 across three weight variants (nano, small, and medium), yielding 12 model configurations. All experiments were conducted under a uniform training pipeline and computing environment using an NVIDIA T4 GPU to ensure fair benchmarking. Model performance was assessed using Precision, Recall, F1-Score, mean Average Precision (mAP), inference speed (FPS), and computational complexity. The results reveal a consistent trade-off between detection accuracy and inference speed: YOLOv12m achieves the highest detection accuracy but the lowest FPS due to increased architectural complexity. At the same time, YOLOv10n offers the fastest inference at the cost of reduced reliability for subtle behaviors. Within the scope of the evaluated dataset and controlled classroom setting, YOLOv8s and YOLOv11s demonstrate the most balanced accuracy–speed performance, making them suitable candidates for real-time  classroom monitoring under similar conditions. This study provides practical insights for researchers and developers by offering an objective benchmark and model-selection guidance tailored to smart classroom applications, while accounting for dataset and environmental constraints.
Pineapple Waste Processing Design as Functional Food to Support Agrotourism in East Lombok, Indonesia: Desain Pengolahan Limbah Nanas sebagai Makanan Fungsional untuk Mendukung Agrowisata di Lombok Timur, Indonesia Ayu, Hanifah; Wardatullatifah S, Ince Siti; Hidayat, Syahroni; Jannah, Mirriyadhil
Indonesian Journal of Tropical Biology Vol. 1 No. 3 (2025): December 2025
Publisher : Yayasan Siti Widhatul Faeha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65622/ijtb.v1i3.192

Abstract

The abundant pineapple waste in East Lombok remains underutilized as a functional food resource and has not been integrated into sustainable agrotourism development, leading to environmental challenges and missed economic opportunities for local communities. This study aims to design a model for converting pineapple waste into functional food products within a zero-waste agrotourism and circular economy framework that supports community-based development. A descriptive qualitative literature review was conducted using reputable journals, BPS statistics, data from agricultural and tourism agencies, and regional planning documents. Thematic analysis identified the bioactive potential of pineapple waste, explored functional product innovations, and formulated integration schemes for agrotourism activities. The results show that pineapple peel, core, and crown contain bromelain, phenolic compounds, and dietary fiber that can be processed into fermented beverages, functional vinegar, peel tea, fiber flour, and high-fiber snacks suitable as agrotourism products. Integrating these products through workshops, demonstrations, tasting sessions, educational tours, and souvenir sales can enhance commodity value, strengthen green destination branding, and support the SDGs. Overall, the utilization of pineapple waste offers a synergistic strategy that links agriculture, food innovation, sustainability, and tourism. The study highlights the need to establish a circular economy–based pineapple agrotourism pilot model supported by product guidelines, food safety standards, innovation facilities, and collaboration among farmers, MSMEs, researchers, and local government.
Co-Authors Abdulloh Abdulloh Abdurahim, Abdurahim Achmadi, Taofan Ali Adam Bachtiar Maulachela Agung Budiwirawan Agus Ardiyanto Ahmad Zuli Amrullah Ahmat Adil Akbar Juliansyah Akmal Fikri Amrullah Anan Nugroho Anan Nugroho Ananda, Briska Putra Andi Sofyan Anas Ansar Ansar Ardiansyah, Muhammad Irfan Astri Iga Siska Ayu, Hanifah Baroroh, Luluk Taufiqul Budi Sunarko Budiarto, Jian Budisantoso, Heri Tri Luqman Danang Tejo Kumoro Danang Tejo Kumoro Darmawan, Joelianto Dian Syafitri Chani Saputri Dinata, Muhammad Imam Esa Apriaskar Febry Putra Rochim Feddy Setio Pribadi Habib Ratu Perwira Negara Haikal Abror Hakiki, Muhammad Khikam Hanif Ardhiansyah Hanif Hidayat Ida Ayu Widhiantari Intan Ermawati Irmayanti Irmayanti, Irmayanti Ismarmiaty Ismarmiaty, Ismarmiaty Jhonatur Stheven Simanjuntak Joko Sumarsono Khoiron, Ahmad Mustamil Khoirudin Fathoni, Khoirudin Kumoro, Danang Tejo Ledi Diyanasari Mahendra Adiastoro Mona Subagja Mona Subagja Muhammad Fathurrahman Muhammad Hilmy Herdiansyah Muhammad Muhammad MUHAMMAD TAJUDDIN Muhammad, Naufal Murad Murad Murad, Murad Ni Luh Putu Merawati Nur Azis Salim Nur Iksan Qudsi, Jihadil R Fanny Priniti Raden Fanny Printi Ardi Rahmat Sabani Rezky Ramdhaningsih Ria Rismayati Rian Febriyanto Rifki Lukman Satria Rina Rachmawati Risanuri Hidayat Rismayati, Ria Rizal, Ahmad Ashril Salim, Nur Azis Sandi Justitia Putra Satria, Rifki Lukman Sukmawaty Sukmawaty Sukmawaty Sukmawaty Sulistianingsih, Nani Sulistyawan, Vera Noviana Tajuddin, Muhammad Taofan Ali Achmadi Teguh Bharata Adji Tri Agus Wahyudi Uswatun Hasanah Uswatun Hasanah USWATUN HASANAH Vera Noviana Sulistyawan Wafi, Ahmad Zein Al Wardatullatifah S, Ince Siti Yusuf, Siti Agrippina Alodia Yusuf, Sitti Agripina Alodia Zaenal Abidin Zaurarista Dyarbirru Zidan Vieri Wijaya