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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Buana Informatika Jurnal Transformatika Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) SemanTIK : Teknik Informasi Jurnal CoreIT IT JOURNAL RESEARCH AND DEVELOPMENT Indonesian Journal of Artificial Intelligence and Data Mining Techne : Jurnal Ilmiah Elektroteknika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Compiler MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Sistem Cerdas Applied Technology and Computing Science Journal JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Teknologi Informasi dan Terapan (J-TIT) International Journal of Informatics and Computation Jurnal Informatika dan Rekayasa Perangkat Lunak Respati Letters in Information Technology Education (LITE) Jurnal Teknik Informatika (JUTIF) Teknika Jurnal Computer Science and Information Technology (CoSciTech) Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) International Journal of Informatics Engineering and Computing
Mohammad Diqi
Universitas Respati Yogyakarta
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Mechanical Engineering Social Sciences Other

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Enhancing Weather Prediction Using Stacked Long Short-Term Memory Networks MOHAMMAD DIQI; HAMZAH HAMZAH; SRI HASTA MULYANI
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 10 No 3 (2023): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v10i3.5376

Abstract

Weather prediction is crucial in various domains, such as agriculture, transportation, and disaster management. This research investigates the Stacked Long-Short Term Memory (LSTM) for weather prediction using the Denpasar Weather Data spanning 20 years from January 1, 1990, to January 7, 2020. The dataset contains hourly weather data, including temperature, pressure, humidity, and wind speed. Our Stacked LSTM model consists of multiple LSTM layers that capture temporal dependencies and patterns in the data. Evaluating the model's performance using metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-squared (R2), we obtain an average RMSE of 0.03471, an average MAE of 0.02718, an average MAPE of 0.05572, and an average R2 of 0.87087. These results demonstrate the effectiveness of the Stacked LSTM model in accurately predicting weather conditions. The findings have practical implications for weather forecasting applications and suggest avenues for future research, such as exploring different deep learning architectures and incorporating additional features to improve weather prediction accuracy further.
Waste Classification using CNN Algorithm Mohammad Diqi
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 1 No. 1 (2022): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v1i1.4

Abstract

One of the cornerstones to efficient waste management is proper and accurate waste classification. However, people find it challenging to categorize such a big and diverse amount of waste. As a result, we employ deep learning to classify waste efficiently. This paper uses the CNN algorithm to provide a problem-solving strategy to waste classification. The model achieves an accuracy of 0.9969 and a loss of 0.0205. As a result, we argue that employing CNN algorithms to categorize waste yields better results and reduces losses efficiently.
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 Stock Price Prediction Accuracy with StacBi LSTM Diqi, Mohammad; Hamzah, Hamzah
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 1 (2024): Januari 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.1.10-26

Abstract

This research aimed to enhance stock price prediction accuracy using the Stacked Bidirectional Long Short-Term Memory (StacBi LSTM) model. The study addressed the challenge of capturing long-term dependencies and temporal patterns inherent in stock price data. The research objectives were to evaluate the model's performance across different input sequence lengths and identify the optimal length for prediction. Leveraging a dataset from the Indonesian Stock Exchange, the model's predictions were evaluated using key metrics such as RMSE, MAE, MAPE, and R2. Results indicated that the StacBi LSTM model excelled in capturing stock price trends and demonstrated strengths over traditional methods. The optimal input sequence length was identified, balancing computational efficiency and prediction accuracy. This research contributes valuable insights into improving stock price prediction techniques and offers practical implications for traders and investors. Future research directions encompass hybrid models and integrating external factors to enhance predictive capabilities further.
Precision in Obstetric Care: A Machine Learning Approach with CatBoost and Grid Search Optimization Hiswati, Marselina Endah; Diqi, Mohammad; Azijah, Izattul; Subandi, Yeyen; Fathinah, Azzah; Ariani, Rahayu Cahya
Teknika Vol. 13 No. 3 (2024): November 2024
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v13i3.1010

Abstract

This study focuses on improving how we classify fetal health using machine learning by fine-tuning the CatBoostClassifier with Grid Search. Our main achievement in this research is significantly boosting the accuracy of fetal health classification based on Cardiotocogram (CTG) data. Finding the best hyperparameters has created a more precise and reliable diagnostic tool for making informed prenatal care decisions. The model reached an impressive overall accuracy of 96%, especially excelling in identifying Normal and Pathological cases. However, it faced some challenges in classifying Suspect cases, suggesting room for further improvement. These results highlight the potential of machine learning to enhance the reliability of fetal health assessments, which could lead to better outcomes in clinical settings. The success of Grid Search in this study is evident, as the optimized parameters led to the highest accuracy and lowest loss values, proving its effectiveness in fine-tuning the model.
Co-Authors Ahmad Wakhid Ariani, Rahayu Cahya Arum Kurnia Sulistyawati Arum Kurnia Sulistyowati Azijah, Izattul Bagus Subekti Nuswantoro Basuki, Umar Dian Rhesa Rahmayanti Dyan Avando Meliala Eka Damayanti Elisabeth Deta Lustiyati Ema Utami Erizal Erizal Farida Nur Aini Fathinah, Azzah FAUZIYYAH, ANNUR Ferry Wahyu Wibowo HAFIZAH, IDA Hamzah Hamzah Hamzah Husna Arwa Salsabil I Wayan Ordiyasa Ikhwan Mustiadi Indra Listiawan Jelina Imlabla Kristian, Tadem Vergi Kusrini, Kusrini Lestari, Sri Listiawan, Indra Marselina Endah Hiswati Martin, Benedicto Reynaka Filio Meliala, Dyan Avando Mujastia Muhdalifah Noverianus, Noverianus nur aini, farida Ordiyasa, I Wayan Putra Wanda Putra Wanda, Putra Romana Herlinda Sahal, Ahmad Salsabela, Marcella Sri Hasta Mulyani Sri Lestari Sriwidodo Sriwidodo, Sriwidodo Subandi, Yeyen Sugeng Winardi Suwarto Suwarto Syafitri, Endang Nurul Syihab, Namira Anjani Rahmadina Wandani, Aulia Fadillah Wani Widyaningrum, Rini Wijaya, Nurhadi Wiratma, Harits Dwi Zaidir