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All Journal Harmonia: Journal of Research and Education Jurnal Pendidikan: Teori, Penelitian, dan Pengembangan JOIV : International Journal on Informatics Visualization 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
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 Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Social Sciences

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Natural Language Processing in Higher Education Putri , Nastiti Susetyo Fanany; Widiharso , Prasetya; Utama, Agung Bella Putra; Shakti, Maharsa Caraka; Ghosh , Urvi
Bulletin of Social Informatics Theory and Application Vol. 6 No. 1 (2022)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v6i1.593

Abstract

The application of Natural Language Processing (NLP) in an educational institution is still quite broad in its scope of use, including the use of NLP on chatterbots for academic consultations, handling service dissatisfaction, and spam email detection. Meanwhile, other uses that have not been widely used are the combination of NLP and Global Positioning Satellite (GPS) in finding the location of lecture buildings and other facilities in universities. The combination of NLP and GPS is expected to make it easier for new students, as well as visitors from outside the university, to find the targeted building and facilities more effectively.
Perspektif Global terhadap Upskilling and Reskilling Pendidikan Vokasi : Sebuah Studi Literatur Ramadhan, Sany Putra; Dwiyanto, Felix Andika; Utama, Agung Bella Putra; Sutadji, Eddy
Jurnal Pendidikan: Teori, Penelitian, dan Pengembangan Vol 8, No 8: AUGUST 2023
Publisher : Graduate School of Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/jptpp.v8i8.25117

Abstract

Abstract: The purpose of this scientific study is to find out the perspective of the upskilling and reskilling program in preparing skilled, relevant, work-ready and productive human resources in vocational education. In vocational education, most are focused on graduates who have a special expertise. The role of the teacher is the key to the successful implementation of the activities to be achieved. The right program to prepare is by upskilling and reskilling industry standards. The technique used is a literature study using the PICOC method (problem, intervention, comparison, outcome, context). The results of this literature study reveal a global perspective, application and urgency regarding industry-standard upskilling and reskilling programs for vocational education teachers.Abstrak: Tujuan dari kajian ilmiah ini untuk mengetahui prespektif program  upskilling dan reskilling dalam menyiapkan sumber daya manusia yang terampil, relevan, siap bekerja dan produktif pada Pendidikan vokasi. Pada Pendidikan vokasi, sebagian besar difokuskan dengan lulusan yang memiliki suatu keahlian khusus. Peran dari guru merupakan kunci dari keberhasilan terimplementasinya kegiatan yang akan dicapai. Program yang tepat untuk menyiapkan yakni dengan upskilling dan reskilling standar industri. Teknik yang digunakan yakni studi literature dengan metode PICOC (problem, intervention, comparation, outcome, context). Hasil dari studi literatur ini mengetahui perspektif global, penerapan dan urgensi terkait program upskilling dan reskilling berstandar industri bagi guru pendidikan vokasi.
Sentiment analysis of wayang climen using naive bayes method Kurniawati, Fitriana; Wibawa, Aji Prasetya; Utama, Agung Bella Putra
Science in Information Technology Letters Vol 3, No 2 (2022): November 2022
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v3i2.1220

Abstract

This research focuses on sentiment analysis of Wayang Climen performances in Indonesia using the Naïve Bayes algorithm. Wayang, a traditional puppet show, holds cultural significance and has persisted alongside modern entertainment options. The study collected public comments from Dalang Seno and Ki Seno Nugroho's YouTube channels, classified them into positive, negative, and neutral sentiments, and employed a translation process to align comments with program language objectives. Preprocessing steps included case folding, removing punctuation, tokenizing, stopword removal, and post-tagging. To address data class imbalances, resampling was performed using the Synthetic Minority Oversampling Technique (SMOTE). The Naïve Bayes algorithm was utilized for data classification, exploring various translation scenarios. Evaluation involved the confusion matrix method and metrics like accuracy, precision, recall, and f-measure. Results demonstrated that the Dalang Seno train data scenario outperformed Ki Seno Nugroho's, with higher precision, recall, accuracy, and f-measure values. Additionally, the translation scenario from Indonesian to English yielded the most effective results. In conclusion, this study highlights the suitability of the Naïve Bayes algorithm for sentiment analysis in the context of Wayang Climen performances, with practical implications for understanding public sentiment in the digital age.
Mean-Median Smoothing Backpropagation Neural Network to Forecast Unique Visitors Time Series of Electronic Journal Wibawa, Aji Prasetya; Utama, Agung Bella Putra; Lestari, Widya; Saputra, Irzan Tri; Izdihar, Zahra Nabila; Pujianto, Utomo; Haviluddin, Haviluddin; Nafalski, Andrew
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.97

Abstract

Sessions or unique visitors is the number of visitors from one IP who accessed a journal portal for the first time in a certain period of time. The large number of unique daily average subscriber visits to electronic journal pages indicates that this scientific periodical is in high demand. Hence, the number of unique visitors is an important indicator of the accomplishment of an electronic journal as a measure of the dissemination in accelerating the journal accreditation system. Numerous methods can be used for forecasting, one of which is the backpropagation neural network (BPNN). Data quality is very important in building a good BPNN model, because the success of modeling at BPNN is very dependent on input data. One way that can be carried out to improve data quality is by smoothing the data. In this study, the forecasting method for predicting time series data for unique visitors to electronic journals employed three models, respectively BPNN, BPNN with mean smoothing, and BPNN with median smoothing. Based on the findings, the results of the smallest error were obtained by the BPNN model with a mean smoothing with MSE 0.00129 and RMSE 0.03518 with a learning rate of 0.4 on 1-2-1 architecture which can be used as a forecast for unique visitors of electronic journals.
Exploring Visitor Sentiments: A Study of Nusantara Temple Reviews on TripAdvisor Using Machine Learning Hariyono, Hariyono; Wibawa, Aji Prasetya; Noviani, Erina Fika; Lauretta, Giovanny Cyntia; Citra, Hana Rachma; Utama, Agung Bella Putra; Dwiyanto, Felix Andika
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.208

Abstract

This study examines the mood of tourist evaluations for the Nusantara Temples, such as Borobudur, Prambanan, Ijo, Plaosan, and Mendut Temples, on TripAdvisor using Stochastic Gradient Descent (SGD), Logistic Regression (LR), and Support Vector Machine (SVM) classification techniques. The study examines the viewpoints and encounters of tourists from different nations on Indonesia's cultural legacy through English-language evaluations. The evaluation findings show that LR achieves the highest performance in sentiment classification, with an accuracy rate of 91.66%. The research offers valuable insights but has limits in portraying local visitors and relies heavily on the English language. Future studies might focus on doing sentiment analysis on more historical tourism sites in Indonesia, integrating multilingual data, and experimenting with novel categorization methods. This study significantly enhances our understanding of how technology and social media impact tourists' impressions of cultural heritage in the digital age via strengthening analytical methodologies and investigating alternative destinations.
Performance analysis of random forest on quartile classification journal Sucahyo, Cornaldo Beliarding; Rizqini, Fajriwati Qoyyum; Naufal, Ayyub; Yandratama, Hengky; Shiddiqy, Jabar Ash; Utama, Agung Bella Putra; Putri, Nastiti Susetyo Fanany; Wibawa, Aji Prasetya
Applied Engineering and Technology Vol 3, No 1 (2024): April 2024
Publisher : ASCEE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/aet.v3i1.1189

Abstract

Journals play a pivotal role in disseminating scientific knowledge, housing a multitude of valuable research articles. In this digital age, the evaluation of journals and their quality is essential. The SCImago Journal Rank (SJR) stands as one of the prominent platforms for ranking journals, categorizing them into five index classes: Q1, Q2, Q3, Q4, and NQ. Determining these index classes often relies on classification methodologies. This research, drawing inspiration from the Cross-Industry Standard Process for Data Mining (CRISP-DM), seeks to employ the Random Forest method to classify journals, thus contributing to the refinement of journal ranking processes. Random Forest stands out as a robust choice due to its remarkable ability to mitigate overfitting, a common challenge in machine learning classification tasks. In the context of approximating SJR index classes, Random Forest, when utilizing the Gini index, exhibits promise, albeit with an initial accuracy rate of 62.12%. The Gini index, an impurity measure, enables Random Forest to make informed decisions while classifying journals into their respective SJR index classes. However, it is worth noting that this accuracy rate represents a starting point, and further refinement and feature engineering may enhance the model's performance. This research underscores the significance of machine learning techniques in the domain of journal classification and journal-ranking systems. By harnessing the power of Random Forest, this study aims to facilitate more accurate and efficient categorization of journals, thereby aiding researchers, academics, and institutions in identifying and accessing high-quality scientific literature.
Modelling Naïve Bayes for Tembang Macapat Classification Wibawa, Aji Prasetya; Ningtyas, Yana; Atmaja, Nimas Hadi; Zaeni, Ilham Ari Elbaith; Utama, Agung Bella Putra; Dwiyanto, Felix Andika; Nafalski, Andrew
Harmonia: Journal of Arts Research and Education Vol 22, No 1 (2022): June 2022
Publisher : Department of Drama, Dance and Music, FBS, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/harmonia.v22i1.34776

Abstract

The tembang macapat can be classified using its cultural concepts of guru lagu, guru wilangan, and guru gatra. People may face difficulties recognizing certain songs based on the established rules. This study aims to build classification models of tembang macapat using a simple yet powerful Naïve  Bayes classifier. The Naive Bayes can generate high-accuracy values from sparse data. This study modifies the concept of Guru Lagu by retrieving the last vowel of each line. At the same time, guru wilangan’s guidelines are amended by counting the number of all characters (Model 2) rather than calculating the number of syllables (Model 1). The data source is serat wulangreh with 11 types of tembang macapat, namely maskumambang, mijil, sinom, durma, asmaradana, kinanthi, pucung, gambuh, pangkur, dandhanggula, and megatruh. The k-fold cross-validation is used to evaluate the performance of 88 data. The result shows that the proposed Model 1 performs better than Model 2 in macapat classification. This promising method opens the potential of using a data mining classification engine as cultural teaching and preservation media.
Enhanced Multivariate Time Series Analysis Using LSTM: A Comparative Study of Min-Max and Z-Score Normalization Techniques Pranolo, Andri; Setyaputri, Faradini Usha; Paramarta, Andien Khansa’a Iffat; Triono, Alfiansyah Putra Pertama; Fadhilla, Akhmad Fanny; Akbari, Ade Kurnia Ganesh; Utama, Agung Bella Putra; Wibawa, Aji Prasetya; Uriu, Wako
ILKOM Jurnal Ilmiah Vol 16, No 2 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i2.2333.210-220

Abstract

The primary objective of this study is to analyze multivariate time series data by employing the Long Short-Term Memory (LSTM) model. Deep learning models often face issues when dealing with multivariate time series data, which is defined by several variables that have diverse value ranges. These challenges arise owing to the potential biases present in the data. In order to tackle this issue, it is crucial to employ normalization techniques such as min-max and z-score to guarantee that the qualities are standardized and can be compared effectively. This study assesses the effectiveness of the LSTM model by applying two normalizing techniques in five distinct attribute selection scenarios. The aim of this study is to ascertain the normalization strategy that produces the most precise outcomes when employed in the LSTM model for the analysis of multivariate time series. The evaluation measures employed in this study comprise Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R-Squared (R2). The results suggest that the min-max normalization method regularly yields superior outcomes in comparison to the z-score method. Min-max normalization specifically resulted in a decreased mean absolute percentage error (MAPE) and root mean square error (RMSE), as well as an increased R-squared (R2) value. These improvements indicate enhanced accuracy and performance of the model. This paper makes a significant contribution by doing a thorough comparison analysis of normalizing procedures. It offers vital insights for researchers and practitioners in choosing suitable preprocessing strategies to improve the performance of deep learning models. The study's findings underscore the importance of selecting the appropriate normalization strategy to enhance the precision and dependability of multivariate time series predictions using LSTM models. To summarize, the results indicate that min-max normalization is superior to z-score normalization for this particular use case. This provides a useful suggestion for further studies and practical applications in the field. This study emphasizes the significance of normalization in analyzing multivariate time series and contributes to the larger comprehension of data preprocessing in deep learning models
Exploring the Role of Deep Learning in Forecasting for Sustainable Development Goals: A Systematic Literature Review Utama, Agung Bella Putra; Wibawa, Aji Prasetya; Handayani, Anik Nur; Chuttur, Mohammad Yasser
International Journal of Robotics and Control Systems Vol 4, No 1 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i1.1328

Abstract

This paper aims to explore the relationship between deep learning and forecasting within the context of the Sustainable Development Goals (SDGs). The primary objective is to systematically review 38 articles published between 2019 and 2023, following PRISMA guidelines, to understand the current landscape of deep learning forecasting for SDGs. Using data from 2019-2023 allows capturing the latest developments in deep learning forecasting for Sustainable Development Goals (SDGs), while excluding data before 2019 and after 2023 is based on the desire to avoid including potentially less relevant or unpublished research and to maintain focus on the most current and contextually relevant literature. The methodological approach involves analyzing the application of deep learning methods for forecasting within various SDG fields and identifying trends, challenges, and opportunities. The literature review results reveal the popularity of LSTM models, challenges related to data availability, and the interconnected nature of SDGs. Additionally, the study demonstrates that deep learning models enhance forecast accuracy and computational performance, as measured by Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R-squared (R2). The findings underscore the importance of advanced data preparation techniques and the integration of deep learning with SDGs to improve forecasting outcomes. The novelty of this research lies in its comprehensive overview of the current landscape and its valuable insights for researchers, policymakers, and stakeholders interested in advancing sustainable development goals through deep learning forecasting. Finally, the paper suggests future research directions, including exploring the potential of hybrid forecasting models and investigating the impact of emerging technologies on SDG forecasting methodologies. Innovative methods for imputing missing values in deep learning forecasting models could be further explored to enhance predictive accuracy and robustness.
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 Vol 6, No 2 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v6i22023p170-187

Abstract

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.
Co-Authors Aji Prasetya Wibawa Akbari, Ade Kurnia Ganesh Andri Pranolo Anik Nur Handayani Atmaja, Nimas Hadi Ba, Abdoul Fatakhou Bambang Widi Pratolo Chuttur, Mohammad Yasser Citra, Hana Rachma Dwiyanto, Felix Andika Eddy Sutadji Fadhilla, Akhmad Fanny Ghosh , Urvi Hariyono Hariyono Haviluddin Haviluddin Haviluddin, - Hery Widijanto Izdihar, Zahra Nabila Kurniawati, Fitriana Lauretta, Giovanny Cyntia Leonel Hernandez, Leonel Mao, Yingchi Muhammad, Abdullahi Uwaisu 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 Trahutomo, Dinnuhoni Triono, Alfiansyah Putra Pertama Uriu, Wako Utomo Pujianto Widiharso , Prasetya Yandratama, Hengky Zaeni, Ilham Ari Elbaith Zhou, Xiaofeng