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All Journal Harmonia: Journal of Research and Education JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research Knowledge Engineering and Data Science ILKOM Jurnal Ilmiah Journal of Applied Data Sciences Science in Information Technology Letters International Journal of Robotics and Control Systems International journal of education and learning Applied Engineering and Technology Bulletin of Social Informatics Theory and Application JMMR (Jurnal Medicoeticolegal dan Manajemen Rumah Sakit)
Utama, Agung Bella Putra
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Arts Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Medicine & Pharmacology Nursing Public Health Social Sciences

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Journal : knowledge engineering and data science

 1 
Optimized Three Deep Learning Models Based-PSO Hyperparameters for Beijing PM2.5 Prediction Pranolo, Andri; Mao, Yingchi; Prasetya Wibawa, Aji; Utama, Agung Bella Putra; Dwiyanto, Felix Andika
Knowledge Engineering and Data Science
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Deep learning is a machine learning approach that produces excellent performance in various applications, including natural language processing, image identification, and forecasting. Deep learning network performance depends on the hyperparameter settings. This research attempts to optimize the deep learning architecture of Long short term memory (LSTM), Convolutional neural network (CNN), and Multilayer perceptron (MLP) for forecasting tasks using Particle swarm optimization (PSO), a swarm intelligence-based metaheuristic optimization methodology: Proposed M-1 (PSO-LSTM), M-2 (PSO-CNN), and M-3 (PSO-MLP). Beijing PM2.5 datasets was analyzed to measure the performance of the proposed models. PM2.5 as a target variable was affected by dew point, pressure, temperature, cumulated wind speed, hours of snow, and hours of rain. The deep learning network inputs consist of three different scenarios: daily, weekly, and monthly. The results show that the proposed M-1 with three hidden layers produces the best results of RMSE and MAPE compared to the proposed M-2, M-3, and all the baselines. A recommendation for air pollution management could be generated by using these optimized models.
Deep Learning Approaches with Optimum Alpha for Energy Usage Forecasting Wibawa, Aji Prasetya; Utama, Agung Bella Putra; Akbari, Ade Kurnia Ganesh; Fadhilla, Akhmad Fanny; Triono, Alfiansyah Putra Pertama; Paramarta, Andien Khansa’a Iffat; Setyaputri, Faradini Usha; Hernandez, Leonel
Knowledge Engineering and Data Science
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Energy use is an essential aspect of many human activities, from individual to industrial scale. However, increasing global energy demand and the challenges posed by environmental change make understanding energy use patterns crucial. Accurate predictions of future energy consumption can greatly influence decision-making, supply-demand stability and energy efficiency. Energy use data often exhibits time-series patterns, which creates complexity in forecasting. To address this complexity, this research utilizes Deep Learning (DL), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU) models. The main objective is to improve the accuracy of energy usage forecasting by optimizing the alpha value in exponential smoothing, thereby improving forecasting accuracy. The results showed that all DL methods experienced improved accuracy when using optimum alpha. LSTM has the most optimal MAPE, RMSE, and R2 values compared to other methods. This research promotes energy management, decision-making, and efficiency by providing an innovative framework for accurate forecasting of energy use, thus contributing to a sustainable and efficient energy system.
A Comparative Study of Machine Learning Models for Javanese Wuku Classification: Exploring SVM, Naïve Bayes, and CNN for Cultural Texts Sulistyo, Danang Arbian; Prasetya Wibawa, Aji; Prasetya, Didik Dwi; Ahda, Fadhli Almu'iini; Utama, Agung Bella Putra
Knowledge Engineering and Data Science
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This study rigorously evaluates machine learning models for classifying culturally significant Javanese Wuku texts from the “Keagamaan atau Spiritual” category, a domain challenged by unique linguistic nuances and limited digitized resources. We compared Support Vector Machine (SVM), Naïve Bayes, and Convolutional Neural Network (CNN) on texts from five pivotal Wuku types (Sinta, Galungan, Kuningan, Sungsang, Warigalit) sourced from sastra.org, aiming to identify the most effective computational approach. The dataset comprises N = 1419 documents (T = 751.290 tokens), with per-class document counts reported for all five Wuku types. Our evaluation uses accuracy, precision, recall, F1-score, and Area Under the Curve (AUC) under repeated stratified 5-fold cross-validation (10 repeats; 50 runs) to ensure robust estimates. CNN achieved the best performance with Accuracy = 0.92 ± [SD], Macro-F1 = 0.90 ± [SD], and AUC = 0.93 ± [SD], outperforming SVM (Accuracy: 0.87; F1-score: 0.84) and Naïve Bayes (Accuracy: 0.82; F1-score: 0.78). The results underscore CNN’s strong effectiveness for nuanced, context-rich text classification, offering a vital contribution to cultural heritage preservation and advancing Natural Language Processing (NLP) for under-resourced languages. From a knowledge-engineering perspective, predicted Wuku labels can serve as structured metadata to support computational indexing and retrieval of Wuku narratives in cultural information systems. Methodologically, our CNN is a lightweight, small-corpus design that uses tuned regularization (dropout/early stopping) and multi-scale convolution to capture culturally salient n-gram cues, rather than relying on a fixed default TextCNN configuration. Future work involves expanding the dataset and exploring advanced deep learning architectures.
Exploring LSTM-based Attention Mechanisms with PSO and Grid Search under Different Normalization Techniques for Energy demands Time Series Forecasting Pranolo, Andri; Zhou, Xiaofeng; Mao, Yingchi; Pratolo, Bambang Widi; Wibawa, Aji Prasetya; Utama, Agung Bella Putra; Ba, Abdoul Fatakhou; Muhammad, Abdullahi Uwaisu
Knowledge Engineering and Data Science
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Advanced analytical approaches are required to accurately forecast the energy sector's rising complexity and volume of time series data. This research aims to forecast the energy demand utilising sophisticated Long Short-Term Memory (LSTM) configurations with Attention mechanisms (Att), Grid search, and Particle Swarm Optimization (PSO). In addition, the study also examines the influence of Min-Max and Z-Score normalization approaches in the preprocessing stage on the accuracy performances of the baselines and the proposed models. PSO and Grid Search techniques are used to select the best hyperparameters for LSTM models, while the attention mechanism selects the important input for the LSTM. The research compares the performance of baselines (LSTM, Grid-search-LSTM, and PSO-LSTM) and proposes models (Att-LSTM, Att-Grid-search-LSTM, and Att-PSO-LSTM) based on MAPE, RMSE, and R2 metrics into two scenarios normalization: Min-Max, and Z- Score. The results show that all models with Min-Max normalization have better MAPE, RMSE, and R2 than those with Z-Score. The best model performance is shown in Att-PSO-LSTM MAPE 3.1135, RMSE 0.0551, and R2 0.9233, followed by Att-Grid-search-LSTM, Att-LSTM, PSO-LSTM, Grid-search-LSTM, and LSTM. These findings emphasize the effectiveness of attention mechanisms in improving model predictions and the influence of normalization methods on model performance. This study's novel approach provides valuable insights into time series forecasting in energy demands.
Performance of Ensemble Classification for Agricultural and Biological Science Journals with Scopus Index Putri, Nastiti Susetyo Fanany; Wibawa, Aji Prasetya; Rosyid, Harits Ar; Utama, Agung Bella Putra; Uriu, Wako
Knowledge Engineering and Data Science
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The ensemble method is considered an advanced method in both prediction and classification. The application of this method is estimated to have a more optimal output than the previous classification method. This article aims to determine the ensemble's performance to classify journal quartiles. The subject of agriculture was chosen because Indonesia is an agricultural country, and the interest of researchers in this field shows a positive response. The data is downloaded through the Scimago Journal and Country Rank with the accumulation in 2020. Labels have four classes: Q1, Q2, Q3, and Q4. The ensemble applied is Boosting and Bagging with Decision Tree (DT) and Gaussian Naïve Bayes (GNB) algorithms compiled from 2144 instances. The Boosting meta-ensembles used are Adaboost and XGBoost. From this study, the Bagging Decision Tree has the highest accuracy score at 71.36, followed by XGBoost Decision Tree with 69.51. The third is XGBoost Gaussian Naïve Bayes with 68.82, Adaboost Decision Tree with 60.42, Adaboost Gaussian Naïve Bayes with 58.2, and Bagging Gaussian Naïve Bayes with 56.12 results. This paper shows that the Bagging Decision Tree is the ensemble method that works optimally in this subject classification. This result suggests that the ensemble method can still fail to produce an ideal outcome that approaches the SJR system.
Co-Authors Aji Prasetya Wibawa Akbari, Ade Kurnia Ganesh Alamsyah, David Satria Andri Pranolo Anik Nur Handayani Atmaja, Nimas Hadi Ba, Abdoul Fatakhou Bambang Widi Pratolo Chuttur, Mohammad Yasser Citra, Hana Rachma Dewandra, Aderyan Reynaldi Fahrezza Didik Dwi Prasetya Dwiyanto, Felix Andika Eddy Sutadji Fadhilla, Akhmad Fanny Fadhli Almu’iini Ahda Ghosh , Urvi Harits Ar Rosyid Hariyono Hariyono Haviluddin Haviluddin Haviluddin, - Hery Widijanto Izdihar, Zahra Nabila Kurniawati, Fitriana Lauretta, Giovanny Cyntia Leonel Hernandez, Leonel Mao, Yingchi Muhammad, Abdullahi Uwaisu Musyaffa', Ahmad 'Ammar Nabila, Khurin Nafalski, Andrew Naufal, Ayyub Ningtyas, Yana Noviani, Erina Fika Nurhalifah, Siti Paramarta, Andien Khansa’a Iffat Purnomo Purnomo Putri , Nastiti Susetyo Fanany Putri, Nastiti Susetyo Fanany Ramadhan, Sany Putra Rizqini, Fajriwati Qoyyum Saputra, Irzan Tri Setyaputri, Faradini Usha Shakti, Maharsa Caraka Shiddiqy, Jabar Ash Sucahyo, Cornaldo Beliarding Sulistyo, Danang Arbian Trahutomo, Dinnuhoni Triono, Alfiansyah Putra Pertama Uriu, Wako Utomo Pujianto Widiharso , Prasetya Yandratama, Hengky Yulianto, Aldy Rahmat Zaeni, Ilham Ari Elbaith Zakaria, Adil Zhou, Xiaofeng