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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 Pendidikan dan Teknologi Indonesia 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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Clustering Content Types and User Roles Based on Tweet Text Using K-Medoids Partitioning Based Raisa Benaya; Yuliant Sibaroni; Aditya Firman Ihsan
Journal of Computer System and Informatics (JoSYC) Vol 4 No 4 (2023): August 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i4.3751

Abstract

In this modern era, the spread of information occurs rapidly through social media. One of the channels for disseminating information is through the Twitter platform. Many Twitter users respond to existing content with positive, negative and neutral responses. One of the hot content to respond to is political content. This content is currently being discussed considering the approaching election of the 2024 Presidential Candidate of the Republic of Indonesia. One of the candidate pairs discussed was Anies Baswedan. With so many responses from Twitter users, it will be difficult to track whether users support Anies Baswedan to run as a presidential candidate due to the large number of responses. This study aims to determine the response of twitter users to the advancement of Anies Baswedan as a presidential candidate. The method used in this study is the K-Medoids Partitioning-Based algorithm based on twitter user text. This algorithm was chosen because it is easy to implement considering the basis of K-Medoids development is the K-Means algorithm but the K-Medoids algorithm can overcome the shortcomings of the K-Means algorithm which is sensitive to outliners. The evaluation will be done using Silhouette Score which produces a value of 0.35 with the number of clusters is 2. Then an analysis of each cluster is carried out by looking at the words in the cluster. As a result, from the two clusters formed, both clusters contain positive content and show that Twitter users support Anies Baswedan to run as a 2024 presidential candidate.
Multi Aspect Sentiment Analysis of Mutual Funds Investment App Bibit Using BERT Method Serly Setyani; Yuliant Sibaroni
International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 1 (2023): June 2023
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v9i1.718

Abstract

With the rapid development of technology, an investor no longer needs to visit investment companies to make investments. Investors can conduct all investment transactions through their smartphone screens. Bibit is one investment application that can help investors invest in mutual funds. There are many reviews given by users every day, therefore, aspect-based sentiment analysis is needed to identify the aspects and sentiments of users from each review. BERT is one popular text classification method that currently has good performance. Therefore, aspect-based sentiment analysis will be carried out in this study using the BERT method with pre-trained IndoBERT on Bibit application reviews. From the multi-aspect sentiment analysis classification results, the service aspect had the highest average accuracy score of 0.92, the user satisfaction aspect had an average accuracy score of 0.87, and the system aspect had the lowest average accuracy score of 0.75. From the sentiment analysis results, the company can improve the system and service aspects of the Bibit application to provide better service & functionality.
Deep Learning for Multi-Aspect Sentiment Analysis of TikTok App using the RNN-LSTM Method Wahyudi, Diki; Sibaroni, Yuliant
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (574.398 KB) | DOI: 10.47065/bits.v4i1.1665

Abstract

Applications built expressly for consumers to communicate online are known as social media apps. Social media applications are utilized for enjoyment as well as for interacting. For Android users, applications may be found in the Google Play Store, while for iOS users, they can be found in the Apple App Store. The site offers a collection that is a big resource-rich in thoughts, opinions, and feelings, notably on Google Playstore. Each user's review has an aspect value. Due to a large number of reviews, sentiment analysis is tough. The author proposes to do an Aspect-Based Sentiment Analysis (ABSA) utilizing TikTok app reviews on the Google Play Store in this paper. Currently, there are 65.2 million active users of the Tik Tok program, including 8.5 million users from Indonesia, there are still a few studies that use the TikTok application dataset. In this study, sentiment classification is carried out on each aspect that has been determined, namely, aspects of features, business, and content, the method used is deep learning Recurrent Neural Network with the Long Short-Term Memory (RNN – LSTM) model and the addition of word embedding BERT. The results showed that the classification of sentiment in the business aspect showed the highest score, namely 0.94, the sentiment classification in the aspect received an accuracy of 0.91 while the feature aspect got the lowest accuracy, which was 0.85.
Optimal Number Data Trains in Hoax News Detection of Indonesian using SVM and Word2Vec Asramanggala, Muhammad Sulthon; Prasetyowati, Sri Suryani; Sibaroni, Yuliant
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3516

Abstract

Along with the development of the era of technological development also has an increase. Information dissemination occurs very quickly on social media, especially Twitter. On Twitter, only some news circulating is necessarily accurate information. Lots of information that is spread is hoax news that irresponsible individuals apply. In this research, the author will build a system to determine the optimal amount of data trained in the hoax news classification process. In this study, the authors will use the support vector machine and word2vec algorithms to classify hoax and non-hoax news on the system to be created. In this study, five experiments were carried out with the number of train data used as many as 5000, 10000, 15000, 20000, and 25000. 5000 data train results in an accuracy of 77.28%, 10000 data train produce an accuracy of 79.68%, data 15,000 trains produce an accuracy of 79.892%, 20,000 data trains produce an accuracy of 80,416%, and 25,000 data trains produce an accuracy of 81,184%, by using a combination of unigram with token full token selection. This research aims to build a hoax detection system that can determine the optimal amount of data training to use. Also, this research is used to see the performance of the Support Vector Machine algorithm with Word2Vec in detecting hoax news
The Effect of Feature Weighting on Sentiment Analysis TikTok Application Using The RNN Classification Aufa, Rizki Nabil; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3597

Abstract

Social media is a medium used by people to express their opinions. In its development, social media has become a necessity in social life. One of the most popular social media applications since 2020 is TikTok. Short videos with an average duration of 60 seconds can entertain the community so that they don't feel isolated. There are 17 million TikTok application reviews in the Google Play store in Indonesia from various user ages. The rapid development of information and technology has led to the pros and cons of this application. Freedom of expression without specific restrictions on content publication negatively impacts the user's mentality. Based on this, sentiment analysis is very important to reveal trends in opinions about applications that are useful for the community in increasing awareness of whether the application is good before use. Proper feature weighting is required to improve the sentiment analysis results' accuracy. More optimal results can be obtained by determining the appropriate weight for different feature weighting. This study compares the TF IDF, TF RF, and Word2Vec feature weighting methods with the RNN classifier on the TikTok app review. The experiment shows that TF RF is superior to TF IDF, with successive feature weighting accuracy with TF RF of 87,6%, TF IDF of 86%, and Word2Vec of 80%. The contribution of this research lies in its exploration of different feature weighting methods to enhance sentiment analysis accuracy and provide valuable insights for decision-making processes.
Hate Speech Classification in Tiktok Reviews using TF-IDF Feature Extraction, Differential Evolution Optimization, and Word2Vec Feature Expansion in a Classification System using Recurrent Neural Network (RNN) Fatha, Rizkialdy; Sibaroni, Yuliant; Prasetyowati, Sri Suryani
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

In the ever-evolving digital era, social media, especially platforms like TikTok, has become a primary channel for users to share opinions, experiences, and expressions. However, the increasing prevalence of hate speech in reviews on the Google Play Store for the TikTok app indicates the need for a sophisticated approach to identify and classify harmful content. This research is aimed to optimize the classification of hate speech in Google Play reviews of the TikTok app by integrating Term Frequency-Inverse Document Frequency (TF-IDF), Differential Evolution, and Word2Vec within a Recurrent Neural Network (RNN) model. The TF-IDF technique will be used to extract relevant features from a review, while Differential Evolution will be applied to efficiently optimize the model parameters. The use of Word2Vec will enhance the representation of words in the context of app reviews, whereas the RNN model will enable the recognition of temporal patterns in hate speech. The results of this research are expected to contribute significantly to the improvement of hate speech classification on digital platforms focused on app reviews.
Multi-aspect Sentiment Analysis of Shopee Application Reviews using RNN Method and Query Expansion Ranking Novitasari, Ariqoh; Sibaroni, Yuliant; Puspandari, Diyas
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.v6i2.5605

Abstract

Online shopping using e-commerce is a common activity society does in this digital era. Shopee is one of the well-known e-commerce in Indonesia. There are a lot of e-commerce platforms that can easily be accessed through mobile applications like Google Play Store. Users are allowed to review and rate the application they have downloaded. The reviews from the users become an opportunity for e-commerce companies to advance their performances and services. To enhance the understandability of user reviews, a system that can efficiently analyze the sentiment is needed. This study aims to design and establish a system that can perform sentiment analysis on the selected aspects. Sentiment classification is implemented by using the Recurrent Neural Network (RNN) algorithm and Query Expansion Ranking feature selection to classify Shopee application reviews into two classes, which are positive and negative. Feature selection is used to reduce less useful features so that the classification model conducts the classification process optimally and more efficiently. In conclusion, the evaluation results based on an 80:20 data split ratio indicate that the RNN achieves the highest accuracy of 95% in the delivery cost aspect, 93% in the delivery speed aspect, and 86% in the application access aspect.
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.
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 Mauluvy Senjaya, Argya 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