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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Buana Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SemanTIK : Teknik Informasi IT JOURNAL RESEARCH AND DEVELOPMENT Indonesian Journal of Artificial Intelligence and Data Mining Techne : Jurnal Ilmiah Elektroteknika Jurnal Sistem Cerdas PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Applied Technology and Computing Science Journal Letters in Information Technology Education (LITE) Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) International Journal of Informatics Engineering and Computing
I Wayan Ordiyasa
Universitas Respati Yogyakarta
Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Pelatihan Penggunaan LMS untuk Peningkatan Kualitas Layanan Perkuliahan di Fakultas Sains dan Teknologi, Universitas Respati Yogyakarta: Training on Using LMS to Improve the Quality of Lecture Services at the Faculty of Science and Technology, Universitas Respati Yogyakarta Ordiyasa, I Wayan; Sugiarto, Raden Bagus Nurhadi Wijaya; Winardi, Sugeng; Meliala, Dyan Avando; Utari, Evrita Lusiana; Sahal, Ahmad
PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Vol. 10 No. 2 (2025): PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/pengabdianmu.v10i2.8500

Abstract

Training on the Use of Learning Management Systems (LMS) is essential for enhancing the quality of academic services in an era of increasingly adopting technology. The integration of LMS with conventional methods, known as blended learning, which combines distance learning, regular classes, and LMS, results in a more effective and efficient learning process. With the shift towards digital learning, LMS use becomes crucial for improving the efficiency, accessibility, and quality of academic services. Through e-learning, students not only listen to lectures but also actively observe, perform, demonstrate, and more. Teaching materials can be virtualized in various formats to create more engaging and dynamic content, motivating students to delve deeper into the learning process. This training aims to equip educators and administrative staff with knowledge of LMS features and potential, enabling them to maximize its use for content delivery, facilitating teacher-student interaction, and enhancing course management and evaluation. The training methods include presentations on basic LMS concepts, demonstrations of key features, and hands-on practice sessions that allow participants to actively engage in the learning process. Additionally, interaction between participants and facilitators is enhanced through discussions and Q&A sessions, ensuring deep understanding and practical skills in LMS usage to improve academic service quality. Consequently, this training is expected to provide a solid foundation for educational institutions to meet challenges and leverage the opportunities offered by the digital era in providing quality academic services.
Machine Learning for Environmental Health: Optimizing ConcaveLSTM for Air Quality Prediction Diqi, Mohammad; Hamzah; Ordiyasa, I Wayan; Wijaya, Nurhadi; Martin, Benedicto Reynaka Filio
Jurnal Buana Informatika Vol. 15 No. 01 (2024): Jurnal Buana Informatika, Volume 15, Nomor 01, April 2024
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v15i1.8707

Abstract

This study investigates the optimization of the ConcaveLSTM model for air quality prediction, focusing on the interplay between input sequence lengths and the number of LSTM units to enhance forecasting accuracy. Through the evaluation of various model configurations against performance metrics such as RMSE, MAE, MAPE, and R-squared, an optimal setup featuring 50 input steps and 300 neurons was identified, demonstrating superior predictive capabilities. The findings underscore the critical role of model parameter tuning in capturing temporal dependencies within environmental data. Despite limitations related to dataset representativeness and environmental variability, the research provides a solid foundation for future advancements in predictive environmental modeling. Recommendations include expanding dataset diversity, exploring hybrid models, and implementing real-time data integration to improve model generalizability and applicability in real-world scenarios.
Geometric Structured Trend Tunneling: A Hybrid VARIMA-SVR Model for Synthetic Stock Time Series Generation I Wayan Ordiyasa; Ahmad Sahal; Gladies Serren Kutani
International Journal of Informatics Engineering and Computing Vol. 3 No. 1 (2026): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/g9r7y321

Abstract

This study presents a novel hybrid framework, Geometric Structured Trend Tunneling (GSTT), for generating synthetic multivariate time series data, specifically applied to stock price data of Medco Energi Internasional (MEDC), a major player in Indonesia’s energy sector. The proposed model integrates the statistical power of Vector Autoregressive Integrated Moving Average (VARIMA) with the nonlinear pattern-capturing capability of Support Vector Regression (SVR), enabling high-fidelity reconstruction of temporal structures and feature dependencies in financial datasets. The dataset used spans over two decades (2003–2024) and includes core trading indicators such as Open, High, Low, and Close prices. Experimental results demonstrate that GSTT achieves excellent performance across multiple evaluation metrics, including MAE, RMSE, R², and KS tests, while preserving inter-feature correlations and distributional fidelity. Visual comparisons and descriptive statistics further confirm the model’s ability to replicate realistic market behavior. Unlike deep generative models such as GANs or VAEs, GSTT offers a more interpretable, stable, and computationally efficient alternative for financial data augmentation, simulation, and robust AI training. This work contributes a scalable solution for addressing data scarcity in financial modeling, with potential applications in backtesting, risk analysis, and algorithmic trading simulations.
Optimizing Sunspot Forecasts: An In-Depth Analysis of the ConcaveLSTM Model I Wayan Ordiyasa; Mohammad Diqi; Marselina Endah Hiswati; Aulia Fadillah Wani Wandani
International Journal of Informatics Engineering and Computing Vol. 2 No. 1 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v2i1.103

Abstract

This work examines how effectively the ConcaveLSTM model can forecast sunspot numbers, recognizing their importance in space weather. The model addresses the complex and changing sunspot characteristics to improve forecasting accuracy. By comparing different model variations, this research identifies optimal combinations of input steps and LSTM units that enhance forecast performance while avoiding overfitting. The study showcases the capability of specific architectures concerning detail versus computational cost, using evaluation metrics such as RMSE, MAE, MAPE, and R2. Considering factors like limited data availability and the complexity of solar phenomena, the ConcaveLSTM model could be a valuable tool for predicting solar activity. This research advances understanding of space weather forecasting through machine learning and offers guidance for further model development and future investigations.
Monitoring System for Sugar Storage using DHT22, Ultrasonic, and Light Sensors Moh. Izzurohman; Sri Hasta Mulyani; I Wayan Ordiyasa
International Journal of Informatics Engineering and Computing Vol. 2 No. 2 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/c3d6kr84

Abstract

This study develops an Internet of Things (IoT)-based monitoring system designed to maintain stable environmental conditions in palm sugar storage warehouses. The system integrates a NodeMCU ESP8266 microcontroller, a DHT22 temperature and humidity sensor, an OLED display, and a relay-controlled exhaust fan to monitor and regulate environmental parameters. Experimental evaluation was conducted using 30 measurement samples collected at 15-minute intervals in a simulated warehouse environment. The accuracy of the DHT22 sensor was assessed by comparing its readings with calibrated digital instruments. The results show that the average temperature measurement error was 0.3923°C, while the humidity error reached approximately 2.1%. The monitoring system successfully displayed real-time environmental conditions and automatically activated the exhaust fan when the temperature exceeded 30°C or the humidity surpassed 67.89%. Telegram notifications were delivered with an average latency of approximately 1–2 seconds after threshold detection, demonstrating near real-time system responsiveness. Overall, the proposed IoT-based monitoring system demonstrates reliable performance in monitoring and managing environmental conditions in palm sugar storage facilities. The integration of automated control, remote notification, and web-based data visualization provides a practical and cost-effective solution for warehouse monitoring.
Log-Scale Correlation Classifier for Mushroom Identification in Agricultural Internet of Things Systems I Wayan Ordiyasa; Mohammad Diqi; Marselina Endah Hiswati; Dian Rhesa Rahmayanti; Umar Basuki; Ida Hafizah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.6841

Abstract

Classifying edible and poisonous mushrooms is crucial to food safety, as misidentification can pose severe toxicological risks. Conventional probabilistic classifiers, such as Naïve Bayes and Logistic Regression, often underperform on categorical datasets with correlated attributes and skewed distributions. This study introduces the Log-Scale Feature Correlation Classifier, a novel probabilistic framework that integrates logarithmic transformation and correlation-weighted probability estimation to address these challenges. Using the UCI Mushroom dataset and a 10-fold cross-validation scheme, LSFCC was benchmarked against standard models. The results demonstrate that LSFCC achieved consistently superior accuracy (0.99), precision, and recall, significantly outperforming both Logistic Regression and Naïve Bayes, as confirmed by statistical tests (p<0.01). Its lightweight design and interpretability make it highly suitable for real-time deployment on resource-constrained IoT devices, particularly within Agricultural IoT systems for autonomous mushroom identification. Future research will explore LSFCC’s adaptability to noisy, multimodal data and hybrid architectures, ensuring broader applicability in real-world bioinformatics and food safety domains.
Co-Authors Ahmad Wakhid Aulia Fadillah Wani Wandani Dian Rhesa Rahmayanti Dian Rhesa Rahmayanti Elisabeth Deta Lustiyati Evrita Lusiana Utari, Evrita Lusiana Fathinah, Azzah Gladies Serren Kutani HAFIZAH, IDA Hamzah Hamzah Hamzah Ida Hafizah Lila Retnani Utami Marselina Endah Hiswati Marselina Endah Hiswati Martin, Benedicto Reynaka Filio Meliala, Dyan Avando Moh. Izzurohman Mohammad Diqi Noverianus, Noverianus Putra Wanda Putra Wanda, Putra Rainbow Tambunan Sahal, Ahmad Salsabela, Marcella Sri Hasta Mulyani Sri Hasta Mulyani Sriwidodo Sriwidodo Sugeng Winardi Supardi RS Syihab, Namira Anjani Rahmadina Umar Basuki Wijaya, Nurhadi Wiratma, Harits Dwi