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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Buana Informatika SemanTIK : Teknik Informasi IT JOURNAL RESEARCH AND DEVELOPMENT 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) International Journal of Informatics Engineering and Computing
Ordiyasa, I Wayan
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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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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.
Digital Democracy: Analyzing Political Sentiments through Multinomial Naive Bayes in Election Campaign Ads DIQI, MOHAMMAD; RAHMAYANTI, DIAN RHESA; HISWATI, MARSELINA ENDAH; ORDIYASA, I WAYAN; HAFIZAH, IDA
Jurnal Sistem Cerdas Vol. 7 No. 2 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i2.379

Abstract

This research delves into sentiment analysis for digital election campaign advertisements using the Multinomial Naive Bayes approach. The study addresses the limitations of standard sentiment analysis methodologies in capturing the intricacies of public sentiments toward political ads. The dataset, sourced from Kaggle, encompasses 3000 records with sentiments categorized as positive, neutral, and negative. The Multinomial Naive Bayes model demonstrated a substantial accuracy increase from 92% to 96%, outperforming the standard Naive Bayes model. Precision, recall, and F1-score metrics consistently improved across sentiment categories. While dataset representativeness and cultural specificity pose limitations, the research contributes significantly to sentiment analysis methodologies in politically charged digital environments. Future research recommendations include exploring advanced NLP techniques, incorporating real-time data from diverse social media platforms, and addressing ethical considerations in political sentiment analysis. The outcomes emphasize the importance of tailored methodologies for enhanced accuracy in understanding sentiments expressed in digital election campaign advertisements.
Advancing Natural Gas Price Predictions with ConcaveLSTM Diqi, Mohammad; Wanda, Putra; Hamzah; Ordiyasa, I Wayan; Fathinah, Azzah
Techné : Jurnal Ilmiah Elektroteknika Vol. 23 No. 1 (2024)
Publisher : Fakultas Teknik Elektronika dan Komputer Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31358/techne.v23i1.456

Abstract

This study investigates the application of the ConcaveLSTM model, a novel machine learning approach combining the strengths of Stacked Long Short-Term Memory (LSTM) and Bidirectional LSTM, for predicting natural gas prices. Given the inherent volatility and complexity of energy markets, accurate forecasting models are crucial for effective decision-making. The research employs a comprehensive dataset from 1997 to 2020, focusing on the daily price of natural gas in US Dollars per Million British thermal units (Btu). Through rigorous testing across various model configurations, the study identifies optimal settings for the ConcaveLSTM model that significantly improve prediction accuracy. Specifically, configurations utilizing 50 input steps with neuron counts of 100 and 300 exhibit superior performance, as evidenced by lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), alongside higher R-squared (R2) values. These findings validate the ConcaveLSTM model's potential in financial forecasting and highlight the importance of parameter tuning in enhancing model efficacy. Despite certain limitations regarding dataset scope and market variability, the results offer promising insights into developing advanced forecasting tools. Future research directions include expanding the dataset, incorporating additional market influencers, and conducting comparative analyses with other forecasting models. This study contributes to the evolving field of machine learning applications in financial market predictions, offering a foundation for further exploration and practical implementation in the energy sector.
Smart Fire Safety: Analyzing Radial Basis Function Kernel in SVM for IoT-driven Smoke Detection Ordiyasa, I Wayan; Diqi, Mohammad; Lustiyati, Elisabeth Deta; Hiswati, Marselina Endah; Salsabela, Marcella
SemanTIK : Teknik Informasi Vol 10, No 1 (2024):
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55679/semantik.v10i1.47433

Abstract

This research explores the application of Support Vector Machine (SVM) with the Radial Basis Function (RBF) kernel in smoke detection using a dataset collected from Internet of Things (IoT) devices, specifically Photoelectric Smoke Detectors. With 62,630 records and 16 attributes, the study aims to address limitations in smoke detection technology that may impact system accuracy. Through RBF kernel analysis, the SVM model demonstrates the capability to recognize complex patterns related to smoke presence, achieving an accuracy rate of 96.85%. The Classification Report reveals high precision, recall, and f1-score for both "No Fire" and "Fire" detection classes. Despite encountering some false positives, particularly in specific environmental conditions, the evaluation underscores the effectiveness of the model. Recommendations include integrating the model into security systems and further exploring model development by considering environmental factors. This research provides profound insights into smoke detection and affirms its relevance in advancing superior artificial intelligence solutions. In conclusion, the SVM model with the RBF kernel proves reliable for smoke detection with broad potential applications in fire risk mitigation. Keywords; Smoke Detection, Support Vector Machine (SVM), Radial Basis Function (RBF) Kernel, IoT Devices, Classification Report
Comparative Analysis of Kidney Disease Detection Using Machine Learning DIQI, MOHAMMAD; ORDIYASA, I WAYAN; HISWATI, MARSELINA ENDAH
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 15, No 2 (2023): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v15i2.21468

Abstract

This research aimed to compare the performance of ten machine learning algorithms for detecting kidney disease, utilizing data from UCI Machine Learning Repository. The algorithms tested included K-Nearest Neighbour, RBF SVM, Linear SVM, Neural Net, Decision Tree, Naïve Bayes, AdaBoost, Random Forest, Gaussian Process, and QDA. The evaluation metrics used were accuracy, precision, recall, and F1-score. The findings revealed that AdaBoost was the most effective algorithm for all evaluation metrics, achieving an accuracy, precision, recall, and F1-score of 1.00. Random Forest and RBF followed closely, while Naïve Bayes and QDA had the lowest performance. These results suggest that machine learning algorithms, especially ensemble methods such as AdaBoost, can significantly improve the accuracy and efficiency of detecting kidney disease. This can lead to better patient outcomes and reduced healthcare costs.
Enhancing Stock Price Prediction Using Stacked Long Short-Term Memory Diqi, Mohammad; Ordiyasa, I Wayan; Hamzah, Hamzah
IT Journal Research and Development Vol. 8 No. 2 (2024)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2023.13486

Abstract

This research explores the Stacked Long Short-Term Memory (LSTM) model for stock price prediction using a dataset obtained from Yahoo Finance. The main objective is to assess the effectiveness of the model in capturing stock price patterns and making accurate predictions. The dataset consists of stock prices for the top 10 companies listed in the Indonesia Stock Exchange from July 6, 2015, to October 14, 2021. The model is trained and evaluated using metrics such as RMSE, MAE, MAPE, and R2. The average values of these metrics for the predictions indicate promising results, with an average RMSE of 0.00885, average MAE of 0.00800, average MAPE of 0.02496, and an average R2 of 0.9597. These findings suggest that the Stacked LSTM model can effectively capture stock price patterns and make accurate predictions. The research contributes to the field of stock price prediction and highlights the potential of deep learning techniques in financial forecasting.
Improving Urban Air Quality Prediction Using Bidirectional GRU: A Case Study of CO Concentration to Support Education in Yogyakarta Ordiyasa, I Wayan; Sriwidodo, Sriwidodo; Wiratma, Harits Dwi; Diqi, Mohammad; Hiswati, Marselina Endah; Noverianus, Noverianus; Syihab, Namira Anjani Rahmadina
Letters in Information Technology Education (LITE) Vol 7, No 2 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um010v7i22024p63-69

Abstract

Urban air pollution, particularly carbon monoxide (CO), poses serious health risks, emphasizing the need for accurate prediction models to support real-time monitoring and timely responses. This study explores the use of a Bidirectional Gated Recurrent Unit (Bi-GRU) model to improve CO concentration forecasts, capturing intricate temporal patterns in air quality data. The model, optimized for varying input-output sequences, contributes to advancements in air quality prediction by enhancing accuracy with extended historical data. Using hourly CO data from Yogyakarta, Indonesia, the Bi-GRU model was evaluated across input lengths of 48, 96, and 144 hours with prediction outputs of 24 and 48 hours. Results show high prediction accuracy, with the best performance at 144-hour inputs, achieving an R² of 0.99 and minimal error metrics. These findings underscore the model's reliability and precision in capturing CO fluctuations, making it a promising tool for urban environmental management. This research offers a foundation for further refinement and broader applications in air quality monitoring systems.
Optimizing Breast Cancer Detection: A Comparative Study of SVM and Naive Bayes Performance Diqi, Mohammad; Hiswati, Marselina Endah; Hamzah, Hamzah; Ordiyasa, I Wayan; Mulyani, Sri Hasta; Wijaya, Nurhadi; Wanda, Putra
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 7 No 1 (2024): June
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study evaluates the performance of Support Vector Machine (SVM) and Naive Bayes algorithms in classifying breast cancer using the Breast Cancer Wisconsin dataset. Both models exhibited high accuracy, with Naive Bayes achieving a slightly higher overall accuracy of 97% and demonstrating a balanced performance between precision and recall. The SVM model showed strong proficiency in detecting positive cases, with an overall accuracy of 95%, though it faced minor challenges in recall for negative cases. These results highlight the effectiveness of both algorithms in breast cancer detection, emphasizing the significance of model selection based on specific diagnostic requirements. Although there are limitations, such as the small sample size and assumptions made in the model, the findings provide useful insights into the use of machine learning in medical diagnostics. This supports the idea that these models have the potential to enhance early detection and treatment results. Future research should focus on utilizing larger, more diverse datasets, exploring advanced feature processing techniques, and integrating additional algorithms to enhance further the accuracy and reliability of breast cancer detection systems.
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.
Optimizing Sunspot Forecasts: An In-Depth Analysis of the ConcaveLSTM Model Ordiyasa, I Wayan; Diqi, Mohammad; Hiswati, Marselina Endah; Wandani, Aulia Fadillah Wani
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.
Co-Authors Dian Rhesa Rahmayanti Elisabeth Deta Lustiyati Evrita Lusiana Utari, Evrita Lusiana Fathinah, Azzah HAFIZAH, IDA Hamzah Hamzah Hamzah Marselina Endah Hiswati Martin, Benedicto Reynaka Filio Meliala, Dyan Avando Mohammad Diqi Noverianus, Noverianus Putra Wanda, Putra Sahal, Ahmad Salsabela, Marcella Sri Hasta Mulyani Sriwidodo Sriwidodo, Sriwidodo Sugeng Winardi Sugiarto, Raden Bagus Nurhadi Wijaya Syihab, Namira Anjani Rahmadina Wandani, Aulia Fadillah Wani Wijaya, Nurhadi Wiratma, Harits Dwi