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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Dinamisia: Jurnal Pengabdian Kepada Masyarakat Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Linguistik Komputasional Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Jurnal Bumigora Information Technology (BITe) Jurnal Teknik Informatika (JUTIF) JINAV: Journal of Information and Visualization Jurnal Pengabdian Masyarakat Indonesia Journal La Multiapp eProceedings of Engineering Eduvest - Journal of Universal Studies Jurnal INFOTEL
Yuliant Sibaroni
Telkom University
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Mathematics Neuroscience Public Health Social Sciences Other

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Journal : Building of Informatics, Technology and Science

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Air Pollution Classification Prediction Model with Deep Neural Network based on Time-Based Feature Expansion and Temporal Spatial Analysis Muldani, Muhamad Dika; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5675

Abstract

− Air pollution is one of the most significant global challenges, with serious impacts on the health of living beings. In Indonesia, particularly in major cities such as Jakarta and Surabaya, the increase in the Air Quality Index (AQI) over the past few years indicates worsening air quality conditions. This decline in air quality is caused by increased industrial activities, motor vehicle emissions, and deforestation. Rising AQI levels pose severe health risks, including respiratory and cardiovascular diseases, and present major challenges for urban planning, public health management and environmental policy. Addressing this issue requires concerted efforts to implement sustainable practices, reduce emissions, and improve air quality management. The increasing air pollution level indicate the need for a more effective approach to identify and classify air quality index results with relevant success rates without using relatively expensive air quality index detection tools. This research aims to classify the air quality index using a Deep Neural Network model based on time-based feature expansion and spatial-temporal analysis. The Deep Neural Network model is used to extract complex patterns and hidden features in the data and help generate more accurate air pollution classifications. Meanwhile, time-based feature expansion is useful for extending the time representation in the data. The results of this research are expected to make a significant contribution in improving the global understanding of air pollution. By providing a cost-effective and efficient method for air quality monitoring, this study can lead to better pollution control measures. Furthermore, the insight gained from this research can help policymakers develop strategies to mitigate the adverse effects of air pollution on public health and the environment.
Multi-Aspect Sentiment Analysis Using Elman Recurrent Neural Network (ERNN) Method for TripAdvisor App User Reviews Ridho, Fahrul Raykhan; Sibaroni, Yuliant; Puspandari, Dyas
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5746

Abstract

TripAdvisor is the world's largest travel platform that assists 463 million travelers each month in making their trips the best they can be. Users of TripAdvisor can provide reviews, comments, and ratings of travel destinations. However, reviews on TripAdvisor are considered insufficient in helping prospective travelers understand the strengths and weaknesses of a hotel. Therefore, a multiaspect sentiment analysis of TripAdvisor reviews on hotels was conducted to identify commonly discussed rating aspects among visitors and to determine specific evaluations. In this study, the Elman Recurrent Neural Network (ERNN) method was employed to build a classification system for multiaspect sentiment analysis of user reviews on the TripAdvisor application. The aspects examined in this research include Service, Cleanliness, Location, Value, Rooms, and Overall Experience, aiming to provide insights into the hotels under consideration. The results indicate that the ERNN method can deliver superior outcomes in multiaspect sentiment analysis of TripAdvisor hotel reviews. The ERNN model's performance in multiaspect sentiment analysis shows optimal accuracies: 81.35% for Service, 98.71% for Cleanliness, 74.87% for Location, 93.84% for Value and 71.52% for Rooms. These findings can assist travelers in better understanding the strengths and weaknesses of accommodations.
Application of Support Vector Machine and Kriging Interpolation for Rainfall Prediction in Java Island Purwanto, Brian Dimas; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Rainfall is one of the crucial meteorological elements that can significantly impact human life. Accurate rainfall prediction is essential for effective natural resource planning and management across various regions, especially in Java Island, which is one of the most densely populated areas in Indonesia. This study aims to develop a rainfall distribution prediction model for Java Island using Support Vector Machine (SVM). The scenario developed involves time-based feature expansion implemented in SVM. This method is combined with Kriging interpolation to obtain the rainfall distribution classification on Java Island. The results show that the model's performance, exceeding 90%, is effective in predicting future rainfall distribution classifications on Java Island. The contribution of this research lies in providing insights into feature expansion techniques in machine learning to refine predictive models applied in meteorology and environmental management.
The Comparison RNN and Maximum Entropy on Aspect-Based Sentiment Analysis of Gojek Application Umulhoir, Nida; Sibaroni, Yuliant; Fitriyani, Fitriyani
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5767

Abstract

Nowadays, mobile applications can help a person to carry out daily activities. The use of mobile applications is also increasingly in demand by the public. One of the most popular online transportation applications in Indonesia is Gojek, with the top level of the most downloads in Indonesia. However, Gojek also experienced a significant decline from the previous download results. This is used as sentiment analysis by the author to find out how users rated Gojek application reviews from various points of view. This research compares two methods, namely Maximum Entropy and Recurrent Neural Network (RNN) using Chi-Square as feature selection and TF-IDF as feature extraction for each aspect of Availability, System, Comfort, and Transaction. As for the results of user analysis of four aspects with positive and negative sentiment, it is carried out with a 70:30 comparison ratio because it gets a better accuracy result value. The results show that the RNN method gets a better accuracy value than the Maximum Entropy method, with an accuracy value in the accessibility aspect of 90%, system aspect of 89%, comfort aspect of 80%, and comfort aspect of 80%.
Spatio-temporal COVID-19 Spread Prediction: Comparing SVM with Time-Expanded Features and RNN Models Gusti Aji, Raden Aria; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6548

Abstract

Covid-19 which spread in early 2020, still needs to be observed, considering the high growth rate of the pandemic at that time. The right prediction model is needed, because it can estimate the speed and extent of its spread for some time to come. This study develops a prediction model for the classification of the spread of Covid-19 in the future using SVM with time-based feature expansion and RNN. The scenario developed to determine the effect of time-based feature expansion and kernel function on classification performance using time series and spatial data. The results obtained show that SVM with time-based feature expansion achieves the most optimal performance using a polynomial kernel with an accuracy of 96.23%, a precision of 96.48%, a recall of 96.23%, and an F1-score of 96.21%. The performance of the SVM is superior to RNN which achieves an accuracy of 93.55%, a precision of 87.51%, a recall of 93.55%, and an F1-score of 90.43. Spatial prediction using Kriging interpolation can provide an overview of the spread of COVID-19 in all villages in Bandung City. The contribution of this research can provide much-needed information for policy makers and the community in managing future pandemic predictions and management strategies in the field of public health.
A Optimizing Word2Vec Dimensions for Sentiment Analysis of Photomath Reviews using Random Forest and SVM Varissa Azis, Diva Azty; Sibaroni, Yuliant
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6616

Abstract

Technology in the Industrial Revolution 4.0 era supports modern learning through apps like Photomath, simplifying math problem-solving for users. However, diverse user reviews highlight the need for sentiment analysis to evaluate app quality. This research analyzes 9,059 reviews of Photomath collected from the Google Play Store using Python. Word2Vec is used in the study to compare Random Forest and Support Vector Machine (SVM) classifiers for feature extraction. To ensure clean and consistent data, preprocessing techniques such as stemming, tokenization, and stopword removal were used. Text with rich semantic aspects was mathematically represented using Word2Vec. The findings show that SVM using an RBF kernel performed better than Random Forest, with an F1-score of 88.5%, 88.5% accuracy, 88.7% precision, and 88.5% recall. Performance was effectively improved by combining 300-dimensional Word2Vec with stemming algorithms. While Random Forest achieved slightly lower accuracy, it shows promise for specific use cases. This study offers practical insights for improving Photomath by tailoring updates based on user sentiment. The findings emphasize the importance of preprocessing, dimensional optimization, and classifier selection in developing accurate sentiment analysis models. Limitations include the dataset size and the use of classical machine learning models. Future research could address these by exploring larger datasets or deep learning techniques to further improve performance.
Comparison of RoBERTa and IndoBERT on Multi-Aspect Sentiment Analysis of Indonesian Hotel Reviews with Tuning Optimization Syarif, Rizky Ahsan; Sibaroni, Yuliant
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7523

Abstract

The hospitality industry heavily relies on online reviews as a crucial source of information that influences potential guests' decisions. However, conducting sentiment analysis on hotel reviews can be challenging due to the complexity of language and contextual diversity, especially in Indonesian. This study aims to develop and optimize a RoBERTa-based sentiment analysis model to improve the accuracy of sentiment classification in Indonesian hotel reviews, focusing on the aspects of facilities, cleanliness, location, price, and service. The methodology includes data collection through web scraping from the Traveloka platform, manual labeling, and text pre-processing. The RoBERTa model was trained and optimized using fine-tuning techniques and evaluated using metrics such as accuracy, precision, recall, F1-score, and AUC. The results show that the optimized RoBERTa model achieves competitive performance, although the IndoBERT model with Bayesian Optimization demonstrates superior performance, particularly in terms of accuracy and efficiency in identifying positive and negative sentiments. This study is expected to contribute to the development of more effective and accurate aspect-based sentiment analysis (ABSA) for Indonesian-language hotel reviews. It also opens opportunities for applying NLP technology in the hospitality industry and across other review platforms, thereby improving sentiment analysis quality and assisting hotel managers in enhancing service and customer experience.
Fake News Detection with Hybrid CNN-SVM on Data AI and Technology Lesmana, Aditya; Sibaroni, Yuliant; Prasetyowati, Sri Suryani
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7871

Abstract

The spread of fake news or hoaxes in this digital era, especially related to the issue of intelligence (AI) and Technology, is increasingly unsettling because it can trigger public misunderstanding and reduce trust in technological developments. News such as the claim that AI will lead to mass unemployment is a clear example of the spread of misleading information. Therefore, a system that can accurately detect fake news is needed. The purpose of this research is to develop a fake news detection system that is able to accurately identify hoaxes on topics related to AI and Technology. This study proposes a hybrid deep learning method that combines Convolutional Neural Network (CNN) and Support Vector Machine to improve the accuracy of hoax news detection. CNN is used to extract complex news text features, whereas SVM is used as a classifier because of its advantage of being able to separate classes within optimal margins. The selection of this method is based on the results of previous research which shows that each method has good performance, but has certain limitations. By combining the two, it is hoped that more optimal results can be obtained in detecting fake news, especially the topic of AI and Technology. The evaluation was carried out using news datasets related to AI and Technology that have gone through a process of preprocessing, feature extraction with TF – IDF, and feature expansion using Glove Embedding. The results obtained showed that the CNN-SVM hybrid model provided increased accuracy compared to using a single method.
Sentiment Analysis of Wondr by BNI App Reviews on Play Store using the CNN-LSTM Method Putra, Ihsanudin Pradana; Sibaroni, Yuliant; Prasetiyowati, Sri Suryani
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7477

Abstract

As the use of digital applications in banking services increases, user opinions about these applications become an important source of data to study Wondr by BNI, which receives many user reviews, is one of the applications studied in this research. This research aims to build an accurate sentiment classification model and compare the effectiveness of two word representation methods, Word2Vec and FastText, to automatically classify sentiment into two classes, positive and negative, from unstructured review text using informal language. The data was processed through pre-processing, labeling, and processing stages using a hybrid CNN-LSTM model with 20,000 reviews available on the Google Play Store. The training process is carried out using 5-fold cross-validation and hyperparameter optimization using the random search method. The results show that the model with FastText has an accuracy of 86.38%, precision of 86.82%, recall of 86.46%, and F1-score of 86.46%. In contrast, the model with Word2Vec has an accuracy of 85.90%, precision of 86.38%, recall of 85.80%, and F1-score of 85.87%. These results show that FastText is better in accuracy and performance consistency compared to Word2Vec. This research provides a better understanding of how word representation methods affect sentiment analysis in app reviews and is expected to be a reference for future development of similar models.
Multi-Aspect Sentiment Analysis of Movie Reviews Using BiLSTM on Platform X Data Sinaga, Astria M P; Sibaroni, Yuliant; Prasetyowati, Sri Suryani
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7509

Abstract

The film industry generates scores of movie reviews annually, reflecting viewer opinion towards various aspects of movies such as story, music, performances, and so on. They are a good source to publicly analyze opinion automatically. Aspect-based and sentiment analysis of movie reviews based on a multitask classification model rooted in the Bidirectional Long Short-Term Memory (BiLSTM) structure is the theme of this study. The objective of this research is to develop and evaluate a multitask BiLSTM-based model capable of simultaneously classifying sentiment polarity and movie review aspects to enhance fine-grained opinion mining. Data was collected from Platform X through web crawling and subjected to various text preprocessing steps before feeding them into the model. Unlike traditional approaches that treat sentiment and aspect classification as independent operations, the method proposed in this work is performing both simultaneously—sentiment prediction (positive, neutral, negative) and aspect categories (plot, music, actors, others). The model was compared between three different sizes of BiLSTM layers—32, 64, and 128 units—to investigate the influence of model capacity on performance. A 10-fold cross-validation scheme also implemented to confirm the reliability and robustness of results. Experiment findings reveal that the 128-unit BiLSTM model outperformed other models across the board, particularly at picking up subtle contextual relationships, to achieve the highest accuracy score in both tasks. Although this model significantly longer to train, its improved generalization—most notably for difficult sentiment- aspect pairs such as neutral or low-resource categories—validated the trade-off. The findings validate the effectiveness of BiLSTM-based multitask learning for comprehensive movie review analysis, demonstrating the importance of model capacity in tackling complex language patterns and fine-grained opinion identification.
Co-Authors Abduh Salam Adhe Akram Azhari Aditya Andar Rahim Aditya Firman Ihsan Aditya Gumilar Aditya Iftikar Riaddy Adiwijaya Agi Maulana Al Ghazali, Nabiel Muhammad Alfauzan, Muhammad Fikri Alya, Hasna Rafida Andrew Wilson Angger Saputra, Revelin Annisa Aditsania Apriani, Iklima Aqilla, Livia Naura Ardana, Aulia Riefqi Arista, Dufha Arminta, Adisaputra Nur Arya Pratama Anugerah Asramanggala, Muhammad Sulthon Atikah, Balqis Sayyidahtul Attala Rafid Abelard Aufa, Rizki Nabil Aulia Rayhan Syaifullah Aurora Az Zahra, Elita Azmi Aulia Rahman Bunga Sari Chamadani Faisal Amri Chindy Amalia Claudia Mei Serin Sitio Damar, Muhammad Damarsari Cahyo Wilogo Delvanita Sri Wahyuni Derwin Prabangkara Desianto Abdillah Devi Ayu Peramesti Dhina Nur Fitriana Dhina Nur Fitriana Diyas Puspandari Ekaputra, Muhammad Novario Ellisa Ratna Dewi Ellisa Ratna Dewi Elqi Ashok Erwin Budi Setiawan Fadhilah Nadia Puteri Fadli Fauzi Zain Fairuz, Mitha Putrianty Faiza Aulia Rahma Putra Farizi, Azziz Fachry Al Fatha, Rizkialdy Fathin, Muhammad Ammar Fatihah Rahmadayana Fatri Nurul Inayah Fauzaan Rakan Tama Feby Ali Dzuhri Fery Ardiansyah Effendi Ferzi Samal Yerzi Fhira Nhita Fitriansyah, Alam Rizki Fitriyani Fitriyani F. Fitriyani Fitriyani Fitriyani Fitriyani Gilang Brilians Firmanesha Gusti Aji, Raden Aria Gutama, Soni Andika Hanif, Ibrahim Hanurogo, Tetuko Muhammad Hanvito Michael Lee Hawa, Iqlima Putri Haziq, Muhammad Raffif I Gusti Ayu Putu Sintha Deviya Yuliani I Putu Ananda Miarta Utama Ibnu Muzakky M. Noor Indra Kusuma Yoga Indwiarti irbah salsabila Irfani Adri Maulana Irma Palupi Islamanda, Muhammad Dinan Izzan Faikar Ramadhy Izzatul Ummah Janu Akrama Wardhana Jauzy, Muhammad Abdurrahman Al Kemas Muslim Lhaksmana Kinan Salaatsa, Titan Ku Muhammad Naim Ku Khalif Lanny Septiani Laura Imanuela Mustamu Lesmana, Aditya Lintang Aryasatya Lisbeth Evalina Siahaan Made Mita Wikantari Mahadzir, Shuhaimi Maharani, Anak Agung Istri Arinta Mahmud Imrona Maulida , Anandita Prakarsa Mitha Putrianty Fairuz Muhamad Agung Nulhakim Muhammad Arif Kurniawan Muhammad Damar Muhammad Ghifari Adrian Muhammad Hadyan Baqi Muhammad Ikram Kaer Sinapoy Muhammad Kiko Aulia Reiki Muhammad Novario Ekaputra Muhammad Rajih Abiyyu Musa Muhammad Reza Adi Nugraha Muldani, Muhamad Dika Nanda Ihwani Saputri Naufal Alvin Chandrasa Ni Made Dwipadini Puspitarini Niken Dwi Wahyu Cahyani Novitasari, Ariqoh Nuraena Ramdani Okky Brillian Hibrianto Okky Brillian Hibrianto Pernanda Arya Bhagaskara S M Pilar Gautama, Hadid Prasetiyowati, Sri Prasetyo, Sri Suryani Prasetyowati, Sri Sulyani Prawiro Weninggalih Priyan Fadhil Supriyadi Purwanto, Brian Dimas Puspandari, Dyas Putra, Daffa Fadhilah Putra, Ihsanudin Pradana Putra, Maswan Pratama Putri, Dinda Rahma Putri, Pramaishella Ardiani Regita Rachmadania Irmanita Rafik Khairul Amin Rafika Salis Rahmanda, Rayhan Fadhil Raisa Benaya Revi Chandra Riana Rian Febrian Umbara Rian Putra Mantovani Ridha Novia Ridho Isral Essa Ridho, Fahrul Raykhan Rifaldy, Fadil Rifki Alfian Abdi Malik Riski Hamonangan Simanjuntak Rizki Annas Sholehat Rizky Fauzi Ramadhani Rizky Yudha Pratama Rizky, Muhammad Zacky Faqia Salis, Rafika Salsabila, Syifa Saniyah Nabila Fikriyah Saragih, Pujiaty Rezeki Satyananda, Karuna Dewa Septian Nugraha Kudrat Septian Nugraha Kudrat Serly Setyani Shyahrin, Mega Vebika Sinaga, Astria M P Siti Inayah Putri Siti Uswah Hasanah Sri Suryani Prasetiyowati Sri Suryani Prasetyowati Sri Suryani Sri Suryani Sri Utami Sujadi, Cika Carissa Suryani Prasetyowati, Sri Syarif, Rizky Ahsan Umulhoir, Nida Varissa Azis, Diva Azty Viny Gilang Ramadhan Vitria Anggraeni WAHYUDI, DIKI Widya Pratiwi Ali Winico Fazry Wira Abner Sigalingging Zaenudin, Muhammad Faisal Zaidan, Muhammad Naufal Zain, Fadli Fauzi ZK Abdurahman Baizal