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All Journal JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics International Journal of Visual and Performing Arts Frontier Energy System and Power Engineering Food ScienTech Journal Indonesian Journal of Data and Science
Agung Bella Putra Utama
Universitas Negeri Malang
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Immunology & microbiology Mathematics

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An integrative review of computational methods for vocational curriculum, apprenticeship, labor market, and enrollment problems Ahmad Dardiri; Felix Andika Dwiyanto; Agung Bella Putra Utama
International Journal of Advances in Intelligent Informatics Vol 6, No 3 (2020): November 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v6i3.581

Abstract

Computational methods have been used extensively to solve problems in the education sector. This paper aims to explore the computational method's recent implementation in solving global Vocational education and training (VET) problems. The study used a systematic literature review to answer specific research questions by identifying, assessing, and interpreting all available research shreds of evidence. The result shows that researchers use the computational method to predict various cases in VET. The most popular methods are ANN and Naïve Bayes. It has significant potential to develop because VET has a very complex problem of (a) curriculum, (b) apprenticeship, (c) matching labor market, and (d) attracting enrollment. In the future, academics may have broad overviews of the use of the computational method in VET. A computer scientist may use this study to find more efficient and intelligent solutions for VET issues.
Gated Recurrent Unit (GRU) for Forecasting Hourly Energy Fluctuations Aji Prasetya Wibawa; Alfiansyah Putra Pertama Triono; Andien Khansa’a Iffat Paramarta; Faradini Usha Setyaputri; Ade Kurnia Ganesh Akbari; Akhmad Fanny Fadhilla; Agung Bella Putra Utama; Leonel Hernandez
Frontier Energy System and Power Engineering Vol 5, No 1 (2023): January
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um049v5i1p16-25

Abstract

In the current digital era, energy use undeniably supports economic growth, increases social welfare, and encourages technological progress. Energy-related information is often presented in complex time series data, such as energy consumption data per hour or in seasonal patterns. Deep learning models are used to analyze the data. The right choice of normalization method has great potential to improve the performance of deep learning models significantly. Deep learning models generally use several normalization methods, including min-max and z-score. In this research, the deep learning model chosen is Gated Recurrent Unit (GRU) because the computational load on GRU is lighter, so it doesn't require too much memory. In addition, the GRU data is easier to train, so that it can save training time. This research phase adopts the CRISP-DM methodology in data mining as a solution commonly used in business and research. This methodology involves six stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. In this research, the model was obtained using five attribute selection, which applied 2 normalization methods: min-max and z-score. With this normalization, the GRU model produces the best MAPE of 3.9331%, RMSE of 0.9022, and R2 of 0.9022. However, when using z-score normalization, the model performance decreases with MAPE of 10.4332%, RMSE of 0.7602, and R2 of 0.4213. Overall, min-max normalization provides better performance in multivariate time series data analysis.
Forecasting Hourly Energy Fluctuations Using Recurrent Neural Network (RNN) Aji Prasetya Wibawa; Ade Kurnia Ganesh Akbari; Akhmad Fanny Fadhilla; Alfiansyah Putra Pertama Triono; Andien Khansa’a Iffat Paramarta; Faradini Usha Setyaputri; Agung Bella Putra Utama; Jehad A.H. Hammad
Frontier Energy System and Power Engineering Vol 5, No 2 (2023): July
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um049v5i2p50-57

Abstract

Energy forecasting is currently essential due to its various benefits. Energy data analysis for forecasting requires a functional method due to the complexity of the observed data. This forecasting study used the Recurrent Neural Networks (RNN) method. Parameters used include batch size, epoch, hidden layers, loss function, and optimizer obtained from hyperparameter tuning grid search. A comparison of different normalization methods, namely min-max, and z-score, was conducted. Using min-max normalization yielded the best performance with MAPE of 3.9398%, RMSE of 0.0630, and R2 of 0.8996. In testing with z-score normalization, it showed a performance of MAPE of 10.6120%, RMSE of 0.7648, and R2 of 0.4142.
Mining the public sentiment for wayang climen preservation and promotion Aji Prasetya Wibawa; Adjie Rosyidin; Fitriana Kurniawati; Gwinny Tirza Rarastri; Ilham Ari Elbaith Zaeni; Suyono Suyono; Agung Bella Putra Utama; Felix Andika Dwiyanto
International Journal of Visual and Performing Arts Vol 5, No 2 (2023)
Publisher : ASSOCIATION FOR SCIENTIFIC COMPUTING ELECTRICAL AND ENGINEERING (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/viperarts.v5i2.1163

Abstract

Indonesia is a country that has a variety of cultural arts, one of which is shadow puppetry (Wayang). Wayang, in a staged, simple, and minimalist manner, is called Wayang Climen. Wayang Climen has been performed since the COVID-19 pandemic as a solution to keep working while still complying with health protocols. Utilization through YouTube social media attracts people to watch and provide opinions through comments. This opinion is beneficial and can be used as a feasibility study through sentiment analysis information classified as positive, negative, and neutral opinions. Sentiment analysis determines a person's opinion and tendency to opinionated sentences. The methods used are Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB). The dataset comes from YouTube comments of Dalang Seno and Ki Seno Nugroho. The best accuracy is generated by SVM (70.29%). The positive sentiment shows the public's appreciation for the Wayang Climen performance, which ultimately represents the performance even though it is staged densely. This research contributes to effectively utilizing digital platforms for cultural preservation and audience engagement during challenging times, demonstrating the potential for innovative solutions in traditional arts and entertainment.
Fixed sherwood duel optimization for time series imputation Agung Bella Putra Utama; Aji Prasetya Wibawa; Anik Nur Handayani; Andrew Nafalski
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2396

Abstract

Missing values remain a persistent challenge in time-series data, particularly within large-scale monitoring systems where reliable forecasting and evaluation are essential. Incomplete records often arise from irregular reporting, infrastructure limitations, or system failures, leading to biased analyses and inaccurate predictions. Traditional imputation methods, such as mean, median, and mode substitution, provide computational efficiency but oversimplify temporal structures. At the same time, more advanced approaches, including Multiple Imputation by Chained Equations (MICE) and K-Nearest Neighbors (KNN), offer improvements yet remain sensitive to data distribution and model configuration. To address this gap, this study introduces Sherwood Duel Optimization (SDO). This socio-inspired framework reconceptualizes imputation as a deterministic duel-based optimization problem. In its fixed form, SDO generates multiple candidate imputations and selects the most robust replacement value using a composite multi-metric scoring mechanism that integrates forecasting accuracy and explanatory power. The framework was evaluated using multivariate educational time-series data and further validated across heterogeneous SDG-related domains, and compared against classical and advanced baselines across three forecasting models. Experimental results demonstrate that SDO consistently outperforms existing methods, reducing forecasting error (MAPE) by more than 40%, achieving the lowest RMSE, and producing R² values exceeding 0.95. Statistical testing confirms that these improvements are significant across experimental configurations. These findings highlight the potential of SDO as a reliable, interpretable, and computationally efficient optimization-based imputation framework. By strengthening data reliability at the reconstruction stage, SDO enhances the credibility of downstream forecasting and decision-making in institutional and sustainability-oriented monitoring systems.
Allergen Detection with Optimized Logistic Regression in Indonesian Cuisine to Enhance Food Safety Ahmad 'Ammar Musyaffa'; Aji Prasetya Wibawa; David Satria Alamsyah; Aldy Rahmat Yulianto; Adil Zakaria; Agung Bella Putra Utama
Food ScienTech Journal Vol 8, No 1 (2026)
Publisher : University of Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33512/fsj.v8i1.36462

Abstract

Traditional Indonesian dishes often contain peanuts, coconut milk, and shrimp which are common allergens. However, allergen information is frequently absent from food vendors and digital recipe sites, posing potential health risks for individual with food allergies. This study presents an automated allergen detection system in Indonesian cuisine that uses a Logistic Regression model and has been trained on 14 primary allergen categories defined by the European Union. Each recipe is converted into a fixed-dimension binary vector using a bag-of-ingredients feature representation. As the evaluation results, the hyperparameter tuning approach significantly improved the model's performance. The model that was not fine-tuned only performed well in Scenario 1 (0 and 1) where it achieved an accuracy of 0.9995. In the Scenario 2 (0 - 3) Grid Search CV improved accuracy to 0.9997. In the Scenario 3 (0 - 14) Random Search achieved the best values with an accuracy of 0.9990 and a balanced precision-recall rate of over 0.97. Compared to the other methods, Random Search appears to be more adaptable to complex data distributions as these results show. Furthermore, this method has the potential to be widely applied to various culinary contexts like oriental and continental cuisines, which often uses high-allergen ingredients such as fermented soy products and dairy-gluten rich dishes. This system contributes to the advancement of food safety and public health through the integration of artificial intelligence in allergen detection.
The Performance of the XGBOOST-LSTM and CNN-LSTM Algorithms in the Analysis of Stock Price Prediction Models for the Indonesian Banking Sector M. Zainal Arifin; Filbert Chaitra Bessel Kristianto; Fadia Irsania Putri; Agung Bella Putra Utama
Indonesian Journal of Data and Science Vol. 7 No. 2 (2026): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i2.459

Abstract

One indicator that can be used to gauge a country’s economic performance is its stock market, as it reflects investors’ expectations when buying or selling shares in line with the economic conditions of companies within that country. Stock price forecasting is necessary as it is in the interests of various parties, including both investors and the companies receiving investment funds. This study compares the XGBoost-LSTM and CNN-LSTM algorithms. The dataset used consists of daily closing prices over a period of 1,000 days for shares in the banking sector, specifically PT Bank Central Asia Tbk, PT Bank Rakyat Indonesia (Persero) Tbk, and PT Bank Mandiri (Persero) Tbk. Each model makes a prediction for the next day’s data based on the previous four days’ historical data. A comparison of the results from the CNN-LSTM and XGBoost-LSTM models was carried out using predefined evaluation metrics, namely RMSE, RMAE, R², and MAPE. The results showed that the CNN-LSTM model generally performed better across most evaluation metrics.
Pengaruh Metode Imputasi terhadap Kelayakan Model LSTM dalam Peramalan Deret Waktu Klimatologi Dhia Rafifah Thifal; Aji Prasetya Wibawa; Adelia Desyana Eka Putri; Adelia Khansa Ristiaputri; Adhelia Wida Khaidir; Agung Bella Putra Utama
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.29185

Abstract

Missing values substantially degrade the reliability of environmental time-series forecasting; however, prior studies largely evaluate imputation methods in isolation without systematically linking missingness mechanisms to deep learning forecasting performance. To address this gap, this study proposes a mechanism-aware comparative framework that evaluates deletion and six imputation methods (Mean, Median, Mode, LOCF, KNN, and MICE) across three environmental time-series datasets with naturally occurring missing values, using LSTM as the forecasting model. The novelty lies in jointly analyzing statistical error (MAPE, RMSE), goodness-of-fit (R²), and statistical significance to identify structurally aligned imputation strategies under different missingness patterns. Experimental results show that deletion as baseline consistently produces the worst performance (MAPE: 5.91429; 7.35000; 2.84881), whereas imputation reduces proportional error by more than 70% on average (p < 0.05). LOCF performs best under temporal dependency (MAPE 0.73959; R² 0.92757), KNN achieves the most balanced performance under MCAR-like behavior (R² 0.94086), and Mean imputation yields the lowest error in MAR-structured data (MAPE 0.41560; R² 0.97077). These findings demonstrate that imputation effectiveness depends on alignment with missingness structure rather than methodological complexity, providing evidence-based guidance for robust environmental.
Co-Authors Ade Kurnia Ganesh Akbari Adelia Desyana Eka Putri Adelia Khansa Ristiaputri Adhelia Wida Khaidir Adil Zakaria Adjie Rosyidin Ahmad &#039;Ammar Musyaffa&#039; Ahmad Dardiri Aji Prasetya Wibawa Akhmad Fanny Fadhilla Aldy Rahmat Yulianto Alfiansyah Putra Pertama Triono Andien Khansa’a Iffat Paramarta Andrew Nafalski Anik Nur Handayani David Satria Alamsyah Dhia Rafifah Thifal Fadia Irsania Putri Faradini Usha Setyaputri Felix Andika Dwiyanto Felix Andika Dwiyanto Filbert Chaitra Bessel Kristianto Fitriana Kurniawati Gwinny Tirza Rarastri Harits Ar Rosyid Jehad A.H. Hammad Leonel Hernandez M. Zainal Arifin Nastiti Susetyo Fanany Putri Suyono Suyono Wako Uriu Zaeni, Ilham Ari Elbaith