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All Journal Scientific Journal of Informatics Jurnal Teknologi Informasi MURA Jurnal Teknologi Jurnal Mantik Journal of Intelligent Decision Support System (IDSS) Pena : Jurnal Ilmu Pengetahuan dan Teknologi Jurnal Cahaya Mandalika Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Jurnal Mandiri IT Jurnal Minfo Polgan (JMP) Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Masyarakat Madani Indonesia Jurnal Ekonomi, Teknologi dan Bisnis Innovative: Journal Of Social Science Research Jurnal Teknik Indonesia Vertex Jurnal Teknologi Informasi Mura IKRAM: Jurnal Ilmu Komputer Al Muslim ALMUISY: Journal of Al Muslim Information System Jurnal Pengabdian Masyarakat Terapan
Andriani, Wresti
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Aerospace Engineering Humanities Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Transportation Other

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Journal : Journal of Intelligent Decision Support System (IDSS)

 1 
Application of machine learning for election data classification in Tegal city based on political party support Andriani, Wresti; Gunawan, Gunawan; Naja, Naella Nabila Putri Wahyuning; Anandianskha, Sawaviyya
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 4 (2024): December: Intelligent Decision Support System
Publisher : Institute of Computer Science (IOCS)

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Abstract

Elections are a critical aspect of democracy, where voter sentiment and political party support significantly influence outcomes. This study aims to predict election results in Tegal City using machine learning models, specifically Neural Networks, Random Forest, and Naive Bayes. Each algorithm was applied to a dataset containing demographic, polling, and Sentiment data to analyze political party support. The research revealed that Neural Networks outperformed other models in terms of accuracy (92%) and F1 scores for both positive (91%) and negative sentiments (92%). Random Forest and Naive Bayes, while effective, displayed lower overall performance. The findings highlight the value of utilizing advanced algorithms for local election sentiment analysis to help candidates adjust campaign strategies. This approach enhances understanding of voter behavior and supports more informed decision-making processes for the public and policymakers
Decision Support System to assess customer satisfaction using Analytical Hierarchy Process Andriani, Wresti; Gunawan, Gunawan; Anandianskha, Sawaviyya
Journal of Intelligent Decision Support System (IDSS) Vol 6 No 4 (2023): December: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v6i4.163

Abstract

Transportation is an important aspect of mobility or global movement and activities. As public transportation that can be accessed online by the public, Gojek and Grab types of transportation provide transportation services and are growing rapidly. At the time of Covid 19 around 2020, online transportation was very important and much sought after. More and more online transportation companies are appearing, especially in Tegal City, so that there are more service offerings that consumers can use. User or consumer satisfaction measurements were carried out using Fuzzy Logic Method Analytical Hierarchy Process (AHP) on 200 consumers who used Gojek or Grab or other online transportation for 3 to 4 months in 2022 in Tegal City. The results obtained by customers or consumers were satisfied with Gojek transportation at 45%, with male consumers at 67%, and Grab at 37%, with male consumers at 65%, followed by other online transportation (X and Y). These results can be used as an option for consumers who expect the best service.
Machine learning algorithm-based decision support system for prime bank stock trend prediction Gunawan, Gunawan; Budiono, Wahyu; Andriani, Wresti; Naja, Naella Nabila Putri Wahyuning
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i1.207

Abstract

In the complex landscape of financial markets, predicting bank stock trends is a critical aspect that supports more accurate investment decision-making. This study aims to develop and evaluate machine learning algorithms—Random Forest, Support Vector Machine (SVM), and Artificial Neural Network (ANN)—for predicting the trends of major bank stocks in Indonesia using the IDX-PEFINDO dataset from January 1, 2020, to December 31, 2023. The adopted methodology includes collecting historical data, initial processing, feature selection, and training and validating models using evaluation metrics such as Accuracy, Precision, Recall, F1-Score, MAE, and RMSE. Results indicate that although no single algorithm is dominant, SVM and ANN perform better within the given data context. This research underscores the importance of a tailored approach to maximize the potential of machine learning algorithms in stock prediction, providing new insights into developing decision support systems for bank stock investments. This study implies that it recommends the integration of broader economic indicators and the exploration of advanced machine-learning techniques to enhance stock prediction accuracy in the future.
Identification of vacant land in Tegal Regency using cnn algorithm based on goolge earth imagery Andriani, Wresti; Fatkhurrohman, Fatkhurrohman; Gunawan, Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 2 (2024): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i2.243

Abstract

This research developed a Convolutional Neural Network (CNN) algorithm to identify vacant land in Tegal Regency using imagery from Google Earth. By utilizing labeled imagery datasets, CNN models are optimized to recognize texture characteristics, colors, and distribution patterns of vacant land. Preprocessing and image sharing techniques are applied to improve model quality. The results of this study offer a new methodology in visual data processing for accurate and efficient identification of vacant land, providing a solid basis for more sustainable and efficient land use policies. This research contributes significantly to the scientific literature and field practice, particularly in natural resource management and regional planning
Application of deep neural network with stacked denoising autoencoder for ECG signal classification Gunawan, Gunawan; Aimar Akbar, Aminnur; Andriani, Wresti
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 2 (2024): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i2.247

Abstract

Applying deep neural networks with stacked denoising autoencoders (SDAEs) for ECG signal classification presents a promising approach for improving the accuracy of arrhythmia diagnosis. This study aims to develop a robust model that enhances the classification of ECG signals by effectively denoising the input data and extracting rich feature representations. The research employs a method involving data preprocessing, feature extraction using SDAEs, and classification with a deep neural network (DNN) validated on the MIT-BIH Arrhythmia Database. The results demonstrate that the proposed model achieves an impressive accuracy of 98.91%, significantly outperforming traditional machine learning methods. The implications of this research are substantial, offering a reliable and automated tool for arrhythmia diagnosis that can be utilized in clinical settings to improve patient care. The study highlights the model's potential for real-time clinical application, although further validation on more extensive and diverse datasets is necessary to confirm its generalizability and robustness. This research contributes to the field by integrating advanced SDAEs with deep learning, paving the way for more accurate and efficient ECG signal classification systems
Co-Authors Aang Alim Murtopo Aimar Akbar, Aminnur Al Fattah, Muhammad Raikhan Almujahidah, Lutfiatun Rahma Anandianskha, Sawaviyya Arianti, Tezya Sekar Arif, Zaenul Arrohman, Zidni Dlia Aslam, Muhammad Nur Bangkit Indarmawan Nugroho Budiono, Wahyu Dari, Mayang Melan Fatkhurrohman Fatkhurrohman, Fatkhurrohman Ferdyansyah, Muhammad Rio Firmansyah, Hasbi Gunawan Gunawan Gunawan Gunawan Gunawan, Gunawam Harefa, Reyvan Sinatria Hidayatullah, Fikri Zain Limaknun, Lulu Maryanto, Sesilia Putri Maulana, M Taufik Fajar Mustaqiim, Restu Abi Naja, Naella Nabila Putri Wahyuning Nasichatun, Nasichatun Putra, Alif Sya’Bani Ramadhani, Ivan Rizky Santoso, Nugroho Adh Sarif Surorejo Septian Ari Wibowo Setiawati, Windi Syefudin, Syefudin Ujianto, Nur Tulus W.N, Naella Nabila Putri Wahyuning Naja, Naella Nabila Putri Yan Kurniawan, Yan