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All Journal Jurnal Buana Informatika Telematika : Jurnal Informatika dan Teknologi Informasi Wikrama Parahita : Jurnal Pengabdian Masyarakat Indonesian Journal of Artificial Intelligence and Data Mining PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Applied Technology and Computing Science Journal Community Development Journal International Journal of Informatics and Computation Prosiding Seminar Nasional Multidisiplin Ilmu Respati Jurnal Computer Science and Information Technology (CoSciTech) Journal of Computer, Electronic, and Telecommunication (COMPLETE) Journal of Soft Computing Exploration Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Jurnal Pengabdian Pada Masyarakat
Wijaya, Nurhadi
Universitas Respati Yogyakarta
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Enhancing Heart Disease Detection Using Convolutional Neural Networks and Classic Machine Learning Methods Mulyani, Sri Hasta; Wijaya, Nurhadi; Trinidya, Fike
Journal of Computer, Electronic, and Telecommunication (COMPLETE) Vol. 4 No. 2 (2023): December
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/complete.v4i2.394

Abstract

This study addresses the problem of heart disease detection, a critical concern in public health. The research aims to compare the performance of Convolutional Neural Networks (CNN) with conventional machine learning algorithms in diagnosing heart disease using a dataset comprising 14 features. The primary objective is to determine whether CNNs can provide more accurate and reliable results than traditional techniques. The research employs rigorous preprocessing, normalizing relevant features, and splits the dataset into an 80-20 training-testing split. The model is trained for 300 epochs with a batch size of 64, and performance evaluation is conducted using confusion matrices and classification reports. The results reveal that the CNN model achieved a remarkable accuracy of 100%, demonstrating its potential to outperform conventional machine learning algorithms. These findings emphasize the significance of deep learning techniques in improving heart disease diagnostics, although further research is needed to optimize CNN models and address interpretability concerns for practical implementation in healthcare settings.
Stacked Gated Recurrent Units and Indonesian Stock Predictions: A New Approach to Financial Forecasting DIQI, MOHAMMAD; HISWATI, MARSELINA ENDAH; WIJAYA, NURHADI
Jurnal IT UHB Vol 5 No 1 (2024): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v5i1.1106

Abstract

This research paper introduces a novel approach to predicting stock prices using a Stacked Gated Recurrent Unit (GRU) model. The model was trained on historical data from the top 10 companies listed on the Indonesia Stock Exchange, covering the period from July 6, 2015, to October 14, 2021. The performance of the model was evaluated using key metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-squared (R2). The results demonstrated promising performance, with average RMSE, MAE, and MAPE values of 0.00592, 0.00529, and 0.01654, respectively, indicating a high level of accuracy in the model's predictions. The average R2 value of 0.97808 further suggests a high degree of predictive power, with the model able to explain a significant proportion of the variance in the stock prices. These findings highlight the effectiveness of the Stacked GRU model in capturing stock price patterns and making accurate predictions. The practical implications of this research are significant, as the model provides a powerful tool for forecasting future stock price trends, which can be utilized in investment decision-making, financial analysis, and risk management. Future research could explore other deep learning architectures, incorporate additional features, or consider different evaluation metrics to enhance the model's performance further.
Identifying Types of Waste as Efforts in Plastic Waste Management Based on Deep Learning Buyung, Irawadi; Munir, Agus Qomaruddin; Wijaya, Nurhadi; Listyalina, Latifah
Telematika Vol 20 No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.10804

Abstract

Purpose: This research aims at designing a computer algorithm for automatic waste sorting.Design/methodology/apprach: This research is quantitative and uses secondary data, specifically images of various types of waste. The images will be classified into organic and inorganic waste types with the assistance of a deep learning model. In this research, we propose the EfficientNet method for Waste Type Identification as an Effort in Plastic Waste Management. Experiments were conducted on a secondary dataset from Kaggle.com, which involved classifying various types of waste into 'Plastic' and 'Non-Plastic' categories, showing the effectiveness of the proposed method.Findings/result: The measurement is performed to compute the accuracy of the designed deep learning model in classifying waste images into the appropriate waste types. Based on the research results, our system achieved the highest accuracy of 97% during testing.Originality/value/state of the art: The designed method can perform fast and automatic waste sorting, which is useful in reducing the increasing amount of waste accumulating each year. 
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.
Peningkatan Literasi Gizi Atlet Sepak Bola Remaja melalui Edukasi Berbasis Aplikasi Smartwebcalc di Maguwoharjo Football Academy: Improving Nutritional Literacy of Young Football Athletes through Smartwebcalc Application-Based Education at Maguwoharjo Football Academy Yuni Afriani; Desty Ervira Puspaningtyas; Siska Puspita Sari; Nurhadi Wijaya; Jessica Paskalina; Reggy Chrismanta
PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Vol. 11 No. 6 (2026): 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.v11i6.11785

Abstract

Balanced nutrition plays a vital role in supporting the performance of young football athletes, especially adolescents who are experiencing rapid growth and intensive training demands. This community engagement program aimed to strengthen the nutritional literacy and meal-planning skills of remaja football athletes by using the Smartwebcalc web-based application at Maguwoharjo Football Academy, Sleman. The program included sessions on balanced nutrition, hands-on training in application, guided menu-planning simulations, and evaluations using pre- and post-tests. A total of 22 U-15 athletes took part in the activities. The pre-test results showed that 54.54% of participants scored ≥70, while only 9.09% achieved a perfect score of 100, indicating that although basic nutritional knowledge was present, it was not yet evenly distributed, particularly in understanding energy needs and selecting appropriate foods for training. In the post-test, the proportion of athletes scoring ≥70 remained at 54.54%, while the number of perfect scores increased to 13.64%, suggesting improved comprehension among some participants. Overall, the program contributed positively to enhancing nutritional awareness, promoting healthier eating habits, and encouraging the use of digital tools among young athletes. The Smartwebcalc application proved an effective medium for learning about nutrition and holds strong potential for replication in other sports academies.
Co-Authors Agus Qomaruddin Munir Ahmad Wakhid Anang Faktchur Rachman Anggie Puspita Dewi Desty Ervira Puspaningtyas Endah H, Marselina Evrita Lusiana Utari, Evrita Lusiana Hamzah Hamzah Hamzah Hamzah Herliana Riska I Wayan Ordiyasa Ikhwan Mustiadi Imam Mahfudz I'tisyam Irawadi Buyung, Irawadi Jessica Paskalina Joko Aryanto kasmawaru kasmawaru Khotibul Ahsan, Hilmi Listiawan, Indra Listyalina, Latifah Marselina Endah Hiswati Martin, Benedicto Reynaka Filio Meliala, Dyan Avando Mohammad Diqi Muliani, Sri Hasta Nurain, Maisarah Nurkhasanah Putra Wanda, Putra Reggy Chrismanta Rike Pradila Rodiyah Sahal, Ahmad Siska Puspita Sari Siska Puspita Sari Sri Hasta Mulyani Sugeng Winardi Suwarto Trinidya, Fike Windi Vensia Putri Wita Adelia Yuni Afriani Yuni Afriani, Yuni