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All Journal International Journal of Advances in Intelligent Informatics Knowledge Engineering and Data Science International Journal of Visual and Performing Arts Frontier Energy System and Power Engineering
Agung Bella Putra Utama
Universitas Negeri Malang
Arts Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering

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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.
Performance of Ensemble Classification for Agricultural and Biological Science Journals with Scopus Index Nastiti Susetyo Fanany Putri; Aji Prasetya Wibawa; Harits Ar Rosyid; Agung Bella Putra Utama; Wako Uriu
Knowledge Engineering and Data Science Vol 5, No 2 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v5i22022p137-142

Abstract

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.
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.
Co-Authors Ade Kurnia Ganesh Akbari Adjie Rosyidin Ahmad Dardiri Aji Prasetya Wibawa Akhmad Fanny Fadhilla Alfiansyah Putra Pertama Triono Andien Khansa’a Iffat Paramarta Faradini Usha Setyaputri Felix Andika Dwiyanto Felix Andika Dwiyanto Fitriana Kurniawati Gwinny Tirza Rarastri Harits Ar Rosyid Jehad A.H. Hammad Leonel Hernandez Nastiti Susetyo Fanany Putri Suyono Suyono Wako Uriu Zaeni, Ilham Ari Elbaith