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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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Journal : international journal of informatics engineering and computing

 1 
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.
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