Article Per Year (5 Year)
p-Index From 2020 - 2025
2.218
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization JOURNAL OF APPLIED INFORMATICS AND COMPUTING Communications in Science and Technology Building of Informatics, Technology and Science Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) JURNAL PAI: Jurnal Kajian Pendidikan Agama Islam Intechno Journal : Information Technology Journal
Agastya, I Made Artha
Unknown Affiliation
Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Other

Published : 15 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 15 Documents
Search

 1   2 
Enhancing COVID-19 forecasting through deep learning techniques and fine-tuning López, Alba Puelles; Martínez-Béjar, Rodrigo; Kusrini, Kusrini; Setyanto, Arief; Agastya, I Made Artha
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp934-943

Abstract

In this study, a comprehensive analysis of classical linear regression forecasting models and deep learning techniques for predicting coronavirus disease of 2019 (COVID-19) pandemic data was presented. Among the deep learning models, the long short-term memory (LSTM) neural network demonstrated superior performance, delivering accurate predictions with minimal errors. The neural network effectively addressed overfitting and underfitting issues through rigorous tuning. However, the diversity of countries and dataset attributes posed challenges in achieving universally optimal predictions. The current study explored the application of the LSTM in predicting healthcare resource demand and optimizing hospital management to provide potential solutions for overcrowding and cost reduction. The results showed the importance of leveraging advanced deep learning techniques for improved COVID-19 forecasting and extending the application of the models to address broader healthcare challenges beyond the pandemic. To further enhance the model performance, future work needed to incorporate additional attributes, such as vaccination rates and immune percentages.
Sistem Inventaris Stok Obat Menggunakan Metode Exponential Moving Average Sukaria, Petra Nugra; Muzakki, Mohammad Haris; Adhani, Muhammad Azmi; Kusrini, K; Agastya, I Made Artha
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 9, No 2 (2024): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v9i2.828

Abstract

The management of drug inventory in hospitals is a crucial aspect that affects the quality of healthcare services and patient safety. Uncertain drug demand can lead to overstock, resulting in wastage due to expiration, or understock, endangering patient safety. This study aims to develop a drug inventory system using the Exponential Moving Average (EMA) method to forecast drug sales. Historical sales and purchase data from Betang Pambelum Hospital, Palangka Raya, were used for forecasting. The implementation of the EMA method proved to provide accurate forecasting results, with the Mean Absolute Percentage Error (MAPE) falling into good to very accurate categories. This system not only reduces the risks of drug overstock and understock but also helps hospitals in more efficient inventory management. The adoption of this system is expected to enhance the quality of healthcare services through better drug inventory management
Comparison of Distributed K-Means and Distributed Fuzzy C-Means Algorithms for Text Clustering Agastya, I Made Artha; Adji, Teguh Bharata; Setiawan, Noor Akhmad
Communications in Science and Technology Vol 2 No 1 (2017)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.2.1.2017.46

Abstract

Text clustering has been developed in distributed system due to increasing data. The popular algorithms like K-Means (KM) and Fuzzy C-Means (FCM) are combined with MapReduce algorithm in Hadoop Environment to be distributable and parallelizable. The problem is performance comparison between Distributed KM (DKM) and Distributed FCM (DFCM) that use Tanimoto Distance Measure (TDM) has not been studied yet. It is important because TDM’s characteristics are scale invariant while allowing discrimination collinear vectors. This work compared the combination of TDM with DKM (DKM-T) and TDM with DFCM (DFCM-T) to acquire performance of both algorithms. The result shows that DFCM-T has better intra-cluster and inter-cluster densities than those of DKM-T. Moreover, DFCM-T has lower processing time than that of DKM-T when total nodes used are 4 and 8. DFCM-T and DKM-T could perform clustering of 1,400,000 text files in 16.18 and 9.74 minutes but the preprocessing times take hours.
Comparison of Parametric and Nonparametric Forecasting Methods for Daily COVID-19 Cases in Malaysia Agastya, I Made Artha; Aminuddin, Afrig
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1819

Abstract

Numerous research studies are currently examining various measures to control the transmission of COVID-19. One essential task in this regard is predicting or forecasting the number of infected individuals. This predictive capability is crucial for governments to allocate resources effectively. However, the most effective approach to handling time series problems between the parametric and non-parametric methods is unclear. The parametric method utilizes a fixed number of parameters to calculate the value. On the other hand, the non-parametric method increases its parameters along with the number of observations. To address the issue, we conducted a study comparing parametric and non-parametric models for time series forecasting, specifically using Malaysia's daily confirmed COVID-19 cases from 18/3/2020 to 30/12/2020. Since there have been limited comparisons of these models in time series forecasting, we believe our study is beneficial. We considered various models, including persistence, autoregression, ARIMA, SARIMA, single, double, and triple exponential smoothing, multi-linear regression, support vector regression, artificial neural networks (ANN), K-nearest neighbor regression, decision trees regression, random forest regression, and Gaussian processes regression models. Our study revealed significant characteristics of these methods, and we found that exponential smoothing methods were the most effective in capturing the level and trend of the data compared to other methods. Additionally, ANN had the least forecasting error among the machine learning methods. In conclusion, non-parametric methods are not suitable for predicting daily cases of Covid-19 in Malaysia. Enhancing the parametric methods will be preferable in the future.  
EEG Signal Classification Using Low Temporal Information in Virtual Reality Environments Agastya, I Made Artha; Marco, Robert; Firdaus, Mohamad
Intechno Journal : Information Technology Journal Vol. 6 No. 1 (2024): July
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2024v6i1.1698

Abstract

Purpose: This research systematically compares the performance of K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM) in recognizing emotional and cognitive states from EEG data in a virtual reality (VR) environment. It aims to identify the model with the highest accuracy for each participant. Methods: EEG data were collected from four channels (TP9, AF7, AF8, TP10) with a data range of 0.0 - 1682.815 µV and a sampling rate of 2 Hz. The sampling rate is shallow compared to the standard EEG datasets. Features extracted included statistical measures (mean, standard deviation, skewness, kurtosis) and Hjorth parameters (activity, mobility, complexity), classifier (SVM, RF, KNN). Each classifier’s performance was evaluated using accuracy, indicating the proportion of correctly classified instances. Result: RF achieved the highest average accuracy but showed more significant variability. SVM demonstrated a high median accuracy with consistent performance, as indicated by a narrow interquartile range (IQR) and few outliers. KNN exhibited the lowest median accuracy and highest variability, suggesting sensitivity to data characteristics and parameters. These findings highlight RF’s potential for consistent performance with careful tuning and SVM’s reliability. Novelty: The research’s novelty lies in its personalized performance analysis, evaluating each model’s accuracy individually for participants. This tailored approach reveals the best-performing model for each person, emphasizing customized machine-learning applications in VR-EEG systems. The study’s detailed, participant-specific evaluation enhances emotion and cognitive state recognition precision, advancing individualized VR therapeutic interventions and cognitive research methodologies.
EEG Emotion Recognition using Deep Neural Network (DNN) in Virtual Reality Environments Agastya, I Made Artha; Marco, Robert; Handayani, Dini Oktarina Dwi
Intechno Journal : Information Technology Journal Vol. 6 No. 2 (2024): December
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2024v6i2.1903

Abstract

Purpose: The purpose of this study is to explore the integration of EEG technology with virtual reality (VR) systems to enhance therapeutic interventions, improve cognitive state recognition, and develop personalized immersive experiences. Specifically, it investigates the classification of EEG signals in a VR environment using machine learning models and identifies the most effective methods for individual-level analysis.Methods: The study utilized EEG data collected from 31 participants using the Muse 2016 headset, with electrodes positioned according to the 10-20 international system. EEG signals were analyzed for features such as statistical metrics (mean, median, standard deviation, skewness, and kurtosis) and Hjorth parameters (activity, mobility, complexity). Machine learning models, including K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM), were evaluated for their performance in classifying emotional and cognitive states in a VR environment. Result: The results indicate that the Deep Neural Network (DNN) outperformed SVM and KNN models, achieving the highest average classification accuracy. SVM demonstrated consistent performance, with accuracy values consistently above 0.8 across subjects, while KNN showed greater variability and lower overall performance. DNN's architecture, incorporating two hidden layers with ReLU activation and a softmax output layer, demonstrated superior capability in modeling complex EEG patterns. The findings emphasize the effectiveness of DNN in handling high-dimensional and non-linear data, particularly for multi-class classification tasks.Novelty: This study is novel in its focus on personalized machine learning model performance in a VR-EEG setup. Instead of a one-size-fits-all approach, it emphasizes individualized analysis, identifying the most effective model for each participant.
Penerapan Algoritma Machine Learning Untuk Sistem Prediksi Penyakit Osteoporosis Wiryawan Sujana, Rajendra Artanto; Agastya, I Made Artha
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8448

Abstract

Osteoporosis is a condition characterized by decreased bone density, leading to fragile and easily fractured bones. This disease is a significant concern as it can cause disability, fractures, and death, particularly in the elderly population. Early detection of osteoporosis is crucial to prevent disease progression through timely interventions. This study aims to develop a machine learning-based prediction system capable of detecting osteoporosis using three different algorithms, Random Forest, Support Vector Machine (SVM), and Gradient Boosting. The study involves analyzing and comparing the performance of these algorithms based on evaluation metrics such as Accuracy, Precision, Recall, and F1 Score. The data used is processed in two formats, namely ordinal and one-hot encoding, to assess the impact of encoding techniques on model performance. The results show that the Gradient Boosting algorithm performs the best on both types of data, with the highest Accuracy of 91.07% on the one-hot encoded data. Meanwhile, SVM and Random Forest also demonstrate competitive performance but with slightly lower results. This study concludes that Gradient Boosting is the most effective algorithm for osteoporosis prediction in this research. These findings can serve as a foundation for further development in the early detection of osteoporosis and support more effective and efficient prevention and treatment efforts.
Betta Fish Identification System Based On Convolutional Neural Network Saputra, Gilang Ardhi; Agastya, I Made Artha
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8449

Abstract

This study developed an automated identification system based on the Convolutional Neural Network (CNN) to classify Betta Splendens, a fish species with high economic value in Indonesia. The system aims to improve accuracy and efficiency in the identification process. The research was divided into several experiments, where the data was split into 320 images for training, 80 for validation, and 100 for testing. We used two optimizers, Adam and RMSprop. The Adam optimizer experiments conducted two stages with learning rates of 0.0001 and 0.001, each with 100 and 200 epochs. The results showed that a lower learning rate (0.0001) with 200 epochs yielded the best test accuracy of 71%, while a learning rate of 0.001 caused accuracy to stagnate at 66%, indicating potential overfitting. The RMSprop optimizer with a learning rate of 0.00001 demonstrated good stability, though with slightly lower accuracy than Adam. This study highlights the importance of selecting the appropriate learning rate and number of epochs to achieve an optimal balance between training, validation, and testing accuracy, ensuring the model generalizes well to new data.
Enhancing fire detection capabilities: Leveraging you only look once for swift and accurate prediction Nugroho, Agung; Agastya, I Made Artha; Kusrini, Kusrini
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1326-1334

Abstract

Detecting fires is crucial to prevent potentially catastrophic outcomes. Traditional fire detection methods, relying on electronic, chemical, or mechanical sensors, often suffer from time delays in activation due to threshold parameters. An emerging alternative utilizes artificial intelligence, particularly image-based fire detection, using convolutional neural networks (CNNs). You only look once (YOLO) is a state-of-the-art object detection framework prized for speed and real-time capabilities. In our research, we conducted multiple training experiments employing various deep neural network (DNN) architectures as feature extractors for object detection within the YOLOv5 framework. These architectures included MobileNetV3, ResNet, and CSP-Darknet53. Among these configurations, YOLOv5 with CSP-Darknet53 (scale s) emerged as the most accurate, boasting mAP@50 of 0.88 and an impressive FPS of 73, with training model size of 14.50 MB. Furthermore, we integrated the selected model with the streamlit package to create a user-friendly web application interface for fire detection testing. The resulting model demonstrates remarkable efficiency, detecting fires within 0.01 seconds. This research represents a significant advancement in fire detection technology, offering both rapid detection and enhanced accuracy, with potential applications in various settings, from indoor facilities to outdoor environments.
Perbandingan Algoritma Support Vector Machine, Decision Tree, Naïve Bayes, dan Neural Network dalam Klasifikasi Email Wicaksono, Dika; Agastya, I Made Artha
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6949

Abstract

This study aims to compare the effectiveness of four machine learning models in email classification, namely Support Vector Machine (SVM), Decision Tree, Naive Bayes, and Neural Network. This research uses datasets obtained from the Kaggle website. The first dataset contains 18,650 phishing emails (7,328 phishing and 11,322 non-phishing). The second dataset is the result of merging two different datasets containing Indonesian spam emails, resulting in a total of 4,681 emails (2,670 spam and 2,011 non-spam). The merging was done to obtain a more representative amount of data for model evaluation. The results of the study of the two datasets above showed that the Neural Network achieved the highest accuracy with an average of 96.60%. Then, followed by SVM with an average accuracy of 96.43%. Meanwhile, Decision Tree has a fairly high accuracy with an average of 92.38%. In contrast, Naive Bayes recorded the lowest performance with an average accuracy of 90.22%. Although Neural Network has the highest accuracy, other models may be more suitable depending on the needs of the system. Models with lower accuracy, such as Naive Bayes, can be more useful in systems with computational limitations due to their efficiency. SVM offers a balance between high accuracy and computational efficiency, making it an ideal choice for systems that require optimal performance without too much computational burden. Decision Tree is superior in result interpretation, making it suitable for applications that require transparency in decision making.
Co-Authors Adhani, Muhammad Azmi Afrig Aminuddin Arief Setyanto Dini Oktarina Dwi Handayani Kusrini, K Kusrini, Kusrini López, Alba Puelles Martínez-Béjar, Rodrigo Mohamad Firdaus, Mohamad Mohammad Haris Muzakki, Mohammad Haris Noor Akhmad Setiawan Nugroho, Agung Prasetyo, Martinus Juan Pratama, Muhammad Egy Ramadhan, Akhdan Ferdiansyah Robert Marco, Robert Rohman, Wahyutri Nur Saputra, Gilang Ardhi Sukaria, Petra Nugra Teguh Bharata Adji Wicaksono, Dika Wiryawan Sujana, Rajendra Artanto