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All Journal Cakrawala Pendidikan Harmonia: Journal of Research and Education Knowledge Engineering and Data Science ILKOM Jurnal Ilmiah Letters in Information Technology Education (LITE) Journal of Applied Data Sciences Science in Information Technology Letters International journal of education and learning Bulletin of Social Informatics Theory and Application
Dwiyanto, Felix Andika
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Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Languange, Linguistic, Communication & Media Social Sciences

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PROCENTAGE OF E-COMMERCE UTILIZATION AMONG UIN MAULANA MALIK IBRAHIM MALANG STUDENTS Hidhayat, Ahmad Rizkyka Agung; Yudistira, Setya; Dwiyanto, Felix Andika
Letters in Information Technology Education (LITE) Vol 2, No 2 (2019)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (531.176 KB) | DOI: 10.17977/um010v2i22019p040

Abstract

 This study aims to discover the influence of e-commerce on student life, both in terms of understanding and its use. The variables analyzed include the level of understanding, the experience of a transaction through e-commerce, reasons for choosing e-commerce, e-commerce knowledge sources, and a list of e-commerce sites they have used. Respondents were students of the State Islamic University of Maulana Malik Ibrahim Malang. A simple random sampling technique used in this study. As for the data collection, using a questionnaire technique. The obtained data will be processed and analyzed. Generally, the results of the study explain that most students know ecommerce, but the use is still not optimal, even very less.
Indonesian online learning system evaluation framework based on UTAUT 2.0 Dwiyanto, Felix Andika; Elmunsyah, Hakkun; Yoto, Yoto
Bulletin of Social Informatics Theory and Application Vol. 4 No. 2 (2020)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v4i2.287

Abstract

This study aims to propose an evaluation of Indonesian online learning system which known as SPADA. This system accommodate an online learning for teacher profession education program which known as PPG. The system evaluation is using unified theory of acceptance and use of technology 2 (UTAUT2) model with a few adjustments. This study provide an information such as required variables to evaluate the system and research design on SPADA. The proposed evaluation model using seven main constructs of UTAUT 2.0 and its influence on behavioral intention and use behavior. The data obtained from several questionnaires related to the variable and analyzed with path analysis technique. Moreover, this proposed study is expected can be as reference to conduct a similar study related to the system evaluation.
Deep learning in education: a bibliometric analysis Wibawa, Aji Prasetya; Dwiyanto, Felix Andika; Utama , Agung Bella Putra
Bulletin of Social Informatics Theory and Application Vol. 6 No. 2 (2022)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v6i2.596

Abstract

This study investigates the application and development of deep learning in educational settings. Based on the statistics of scientific papers, analysis done using bibliometrics demonstrates the rise of deep learning in educational settings. Deep learning is having a transformative effect on all aspects of education and learning, as well as research. These findings could pave the way for more investigation into deep learning, particularly in education. According to the bibliometric results, the Netherlands, China, the United States of America, India, and Norway are the five countries that have contributed the most to deep learning in education. Norway came in fifth place. In addition, some of the possible directions that research could go in the future concerning deep learning in education include online, machine, blended, remote, informal, and deep reinforcement learning.
Mapping crime determinants in Central Java: an in-depth exploration through local spatial association and regression analysis Humairoh, Nanda Lailatul; Purwaningsih, Tuti; Saifullah, Shoffan; Dwiyanto, Felix Andika; Rabbimov, Ilyos
Science in Information Technology Letters Vol 3, No 1 (2022): May 2022
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v3i1.1212

Abstract

Economic development often brings prosperity to communities, but it can also be accompanied by growing disparities that, when unaddressed, lead to increased crime rates. Central Java, an Indonesian province, has been grappling with a persistent high crime rate, necessitating an in-depth examination of the factors underlying this phenomenon. In this study, we employ a rigorous research methodology, incorporating data sources from the Central Java Central Statistics Agency (BPS) and utilizing key independent variables, including population, unemployment, poverty, Age-Dependency Ratio (APS), and Relative Location Quotient (RLS). Through the application of advanced spatial analysis techniques such as the Local Indicator of Spatial Association (LISA) and the Spatial Autoregressive Model (SAR), this research offers a nuanced exploration of the spatial relationships and regression analysis of these variables. Notably, the study presents a tree map highlighting crime distribution in Central Java's districts and cities. The findings reveal that these five variables exhibit a 75.48% accuracy in predicting crime in Central Java. Through this comprehensive analysis, our research aims to provide valuable insights for policymakers, law enforcement, and the community at large, enabling informed strategies for crime reduction and the promotion of a safer, more prosperous Central Java
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.
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.
Immersive learning through virtual reality for civil engineering education Kuncoro, Tri; Ichwanto, Muhammad Aris; Dwiyanto, Felix Andika
Jurnal Cakrawala Pendidikan Vol. 44 No. 1 (2025): Cakrawala Pendidikan (February 2025)
Publisher : LPMPP Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/cp.v44i1.50351

Abstract

This academic work focuses on creating and assessing a Virtual Reality (VR) learning tool designed for civil engineering education. The tool aims to help students understand complex construction phenomena better by providing laboratory and real-world simulation-based construction scenarios. With the aid of immersive learning offered by VR, the gap between theoretical and practical knowledge is narrowed, and issues such as limited site access, safety requirements, and logistical challenges are mitigated. The study employs a systematic approach to development, including needs identification, tool creation, and evaluation after the tool is developed. The use of paired t-tests enables quantitative analysis of the data. It reveals improved learning outcomes among students utilizing the developed VR tool compared to traditional methods. In addition, students with VR perception reported high ease of use and satisfaction scores. The results emphasize how VR technology can cultivate critical 21st-century skills like problem-solving, collaboration, creativity, and constructive technological engagement. VR can deliver students with safe, inexpensive, and easy-to-scale solutions that offer a wider range of construction learning that was impossible to provide in the past. This study aids a growing understanding of innovations in teaching and learning technology in civil engineering education, especially the importance of VR in preparing them for Industry 4.0. More research should investigate the sustainability of the impacts of the VR environment on learning among engineers from different specialities.
Social informatics and CDIO: revolutionizing technological education Wibawa, Aji Prasetya; Nabila, Khurin; Utama, Agung Bella Putra; Purnomo, Purnomo; Dwiyanto, Felix Andika
International Journal of Education and Learning Vol 5, No 2: August 2023
Publisher : Association for Scientific Computing Electrical and Engineering(ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijele.v5i2.1175

Abstract

Social informatics is an interdisciplinary area that examines how information and communication technologies (ICT) and the complex web of social and cultural contexts interact and change over time. This study not only helps with the design and use of ICT but also shows how these technologies significantly affect society and culture. It encourages new ideas, collaborations between different fields, and policymaking insights, which drives technological innovation and a better knowledge of how ICT affects society. The Conceive, Design, Implement, operate (CDIO) educational system stands out as a new and innovative teaching method. It emphasizes active learning and gives engineering students both technical and social skills. Its use in social informatics ushers in a new era of education that combines innovation and technology to help students become strong and independent. Future study on CDIO programs in social informatics education has the potential to augment the technical proficiency and social consciousness of graduates, thereby rendering them significant contributors to the field.
Analisis Performa Metode Support Vector Regression (SVR) dalam Memprediksi Harga Bahan Sembako Nasional Azis, Huzain; Purnawansyah, Purnawansyah; Nirwana, Nirwana; Dwiyanto, Felix Andika
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1686.390-397

Abstract

Support Vector Regression (SVR) is a supervised learning algorithm to predict continuous variable values. The basic goal of the SVR algorithm is to find the most suitable decision line. SVR has been successfully applied to several issues in time series prediction. In this research, SVR is used to predict the price of staple commodity, which are constantly changing in price at any time due to several factors making it difficult for the public to get groceries that are easy to reach. National staple commodity data consisting of 17 commodities, including shallots, honan garlic, kating garlic, medium rice, premium rice, red cayenne peppers, curly red chilies, red chili peppers, meat of broiler chicken, beef hamstrings, granulated sugar, imported soybeans, bulk cooking oil, premium packaged cooking oil, simple packaged cooking oil, broiler chicken eggs, and wheat flour. With a data set for the last 3 years, including from January 1, 2020, to December 31, 2022. There are 3 variables in the data set, namely commodity, date, and price. This research divides the entire dataset into 80% training and 20% testing data. The results of this research show that SVR using the RBF kernel produces good forecasting accuracy for all datasets with an average Mean Square Error (MSE) training data of 6,005 while data testing is 6,062, Mean Absolute Deviation (MAD) of training data is 6,730 while data testing is 6.6831, Mean Absolute Percentage Error (MAPE) training data is 0.0148 while data testing is 0.0147, and Root Mean Squared Error (RMSE) training data is 7.772 while data testing is 7.746.
Sentiment analysis of Indonesian reviews using fine-tuning IndoBERT and R-CNN Jayadianti, Herlina; Kaswidjanti, Wilis; Utomo, Agung Tri; Saifullah, Shoffan; Dwiyanto, Felix Andika; Drezewski, Rafal
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1505.348-354

Abstract

Reviews are a form of user experience information on a product or service that can be used as a reference for potential consumers’ preferences to buy, use, or consume a product. They can be also used by business entities to find out public opinion about their product or the performance of their business products. It will be very difficult to process the review data manually and it will take a long time. Therefore, sentiment analysis automation can be used to get polarity information from existing reviews. In this study, IndoBERT with Recurrent Convolutional Neural Network (RCNN) was used to automate sentiment analysis of Indonesian reviews. The data used was a sentiment analysis dataset obtained from IndoNLU with sentiment consisting of negative sentiment, neutral sentiment, and positive sentiment. The results of the test showed that IndoBERT with the Recurrent Convolutional Neural Network (RCNN) had better results than the IndoBERT base. IndoBERT with Recurrent Convolutional Neural Network (RCNN) obtained 95.16% accuracy, 94.05% precision, 92.74% recall and 93.27% f1 score.
Co-Authors Aji Prasetya Wibawa Andri Pranolo Atmaja, Nimas Hadi Citra, Hana Rachma Drezewski, Rafal Eddy Sutadji Hakkun Elmunsyah Harits Ar Rosyid Hariyono Hariyono Herlina Jayadianti Hidhayat, Ahmad Rizkyka Agung Humairoh, Nanda Lailatul I Nyoman Gede Arya Astawa I Wayan Raka Ardana Irawan, Novta Danyel Lauretta, Giovanny Cyntia Mao, Yingchi Muhammad Aris Ichwanto Muhammad Iqbal Akbar Nabila, Khurin Nafalski, Andrew Ningtyas, Yana Nirwana, Nirwana Noviani, Erina Fika Purnama, Ida Bagus Irawan Purnawansyah Purnawansyah Purnomo Purnomo Putra, Aulia Yahya Harindra Rabbimov, Ilyos Radhitya, Made Leo Ramadhan, Sany Putra Saifullah, Shoffan Sutoko, Sutoko Tri Kuncoro Tuti Purwaningsih, Tuti Utama , Agung Bella Putra Utama, Agung Bella Putra Wilis Kaswidjanti Yoto Yoto, Yoto Yudistira, Setya Yusriel Ardian Zaeni, Ilham Ari Elbaith