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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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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%.
Classification of Public Sentiment on Fuel Price Increases Using CNN Maharani, Anak Agung Istri Arinta; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12609

Abstract

The government's policy of changing fuel prices is carried out every year. The public gave responses to this policy categorized as positive, negative, or neutral sentiments. The community's response was conveyed through tweets on the Twitter application. Based on the public's response to the policy, sentiment classification can be done using data mining classification techniques. Some research has been carried out on classification techniques using deep learning and machine learning methods. In general, deep learning methods get better results, and this research will be approached using the CNN method. The system stages start from crawling data, labeling, and preprocessing, which consists of cleaning, case folding, tokenization, normalization, removing stopwords and stemming, classification using CNN, and evaluation using 10-Cross Validation. The dataset used is 17.270. The results show that the developed classification system is relatively high, with the highest accuracy of 87%, 93% recall, 93% precision, and 90% F1 score. An in-depth analysis of the classification results and an understanding of sentiment toward rising fuel prices can also provide valuable insights.
Analysis Content Type and Emotion of the Presidential Election Users Tweets using Agglomerative Hierarchical Clustering Sujadi, Cika Carissa; Sibaroni, Yuliant; Ihsan, Aditya Firman
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12616

Abstract

Over the past few years, social media has become essential for getting up-to-date information and interacting online. During presidential elections in Indonesia, Twitter has grown as a crucial platform for expressing opinions and sharing information. This study focuses on analyzing the content types and emotions of tweets related to Anies Baswedan, one of the presidential candidates. The results show a variety of discussions, including support, criticism, and discussion of policies for the 2024 presidential candidate. Clustering enables meaningful information extraction from vast Twitter data. Data were clustered using Agglomerative Hierarchical Clustering, which resulted in the identification of 10 clusters. With 4 clusters containing opinion content and 6 clusters containing information content. In addition, 6 clusters reflect excitement, 3 reflect expectations, and 1 reflect doubt. This research provides insights into the Twitter conversation around the 2024 presidential election, providing an understanding of content and emotions expressed by users.
Best Word2vec Architecture in Sentiment Classification of Fuel Price Increase Using CNN-BiLSTM Aqilla, Livia Naura; Sibaroni, Yuliant; Prasetiyowati, Sri Suryani
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12639

Abstract

The policy of increasing fuel prices has been carried out frequently in recent years, due to the instability of international price fluctuations. This study uses sentiment analysis to examine fuel price increases and their impact on public sentiment. Sentiment analysis is a data processing method to obtain information about an issue by recognizing and extracting emotions or opinions from existing texts. The method used is Word2vec Continuous Bag of Words (CBOW) and Skip-gram. Testing uses different vector dimensions in each architecture and uses a CNN-BiLSTM deep learning hybrid which performs better on sizable datasets for sentiment categorization. The results showed that the CBOW model with 300 vector dimensions produced the best performance with 87% accuracy, 87% recall, 89% precision and 88% F1 score.
The Performance of the Equal-Width and Equal-Frequency Discretization Methods on Data Features in Classification Process Putri, Pramaishella Ardiani Regita; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12730

Abstract

The classification process often needs help with suboptimal accuracy values, which can be attributed to various factors, including the dataset's wide range of attribute values. Discretization methods offer a solution to address these issues. This study aims to compare the effectiveness of Equal-Width and Equal-Frequency discretization methods in enhancing accuracy during the classification process using datasets with varying sizes. The research employs Naïve Bayes, Decision Tree, and Support Vector Machine as classification models, with three datasets utilized: Bandung City Traffic data (3804 records), Bandung City COVID-19 cases data (2718 records), and Bandung City Dengue Fever Disease Index data (150 records). Three experimental scenarios are executed to assess the impact of the two discretization methods on accuracy. The first scenario involves no discretization, the second employs Equal-Width, and the third applies Equal-Frequency discretization. Experimental results indicate significant accuracy improvements post-discretization. The Naïve Bayes model achieved 94% accuracy for the Traffic dataset, while the Decision Tree achieved 71% accuracy for the COVID-19 dataset and an impressive 98% for the Dengue Fever Disease dataset. These outcomes demonstrate that applying Equal-Width and Equal-Frequency discretization methods addresses the challenge of wide attribute value ranges in the classification process.
Deep Learning and Imbalance Handling on Movie Review Sentiment Analysis Utami, Sri; Lhaksmana, Kemas Muslim; Sibaroni, Yuliant
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12770

Abstract

Before watching a movie, people usually read reviews written by movie critics or regular audiences to gain insights about the movie’s quality and discover recommended films. However, analyzing movie reviews can be challenging due to several reasons. Firstly, popular movies can receive hundreds of reviews, each comprising several paragraphs, making it time-consuming and effort-intensive to read them all. Secondly, different reviews may express varying opinions about the movie, making it difficult to draw definitive conclusions. To address these challenges, sentiment analysis using CNN and LSTM models, known for their effectiveness in classifying text in various datasets, was performed on the movie reviews. Additionally, techniques such as TF-IDF, Word2Vec, and data balancing with SMOTEN were applied to enhance the analysis. The CNN achieved an impressive sentiment analysis accuracy of 98.56%, while the LSTM achieved a close 98.53%. Moreover, both classifiers performed well in terms of the F1-score, with CNN obtaining 77.87% and LSTM achieving 78.92%. These results demonstrate the effectiveness of the sentiment analysis approach in extracting valuable insights from movie reviews and helping people make informed decisions about which movies to watch.
Sentiment Classification of Fuel Price Rise in Economic Aspects Using Lexicon and SVM Method Alfauzan, Muhammad Fikri; Sibaroni, Yuliant; Fitriyani
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12851

Abstract

After being hit by COVID-19 for a long time around the world which resulted in the paralysis of all countries, especially the economic aspects of all countries that dropped dramatically, the world was again shocked by the conflict between Russia and Ukraine which resulted in an increase in world oil prices including in Indonesia, many people complained and opposed the government's policy of increasing fuel prices because fuel affects various aspects, including economic aspects. Based on these problems, researchers use sentiment analysis methods that aim to find out people's opinions on issues that are being discussed throughout Indonesia and this research focuses on comparing the SVM algorithm with TF-IDF feature extraction then using K-Fold Cross Validation after that it is compared with the Lexicon Inset dictionary, in this case the model with Lexicon Inset which contains weighting on each word. In this study, it was found that the dataset model using the SVM algorithm with TF-IDF feature extraction and then using K-Fold Cross Validation obtained an average accuracy of 0.85 using the SVM algorithm. While the model using the automatic labeling dataset using the Indonesian sentiment Lexicon (Lexicon Inset) obtained an average accuracy of 0.68. Classification using SVM with TF-IDF feature extraction is superior to using Lexicon Inset.
Effect of Epoch Value on the Performance of the RNN-LSTM Algorithm in Classifying Lazada App Review Sentiments Putra, Maswan Pratama; Yuliant Sibaroni
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13368

Abstract

In today's development, the process of buying and selling transactions between sellers and buyers is so developed. not only done directly but can also be done online or can be called e-commerce. Which is where the development of technology is so fast that it indirectly encourages entrepreneurs to develop through e-commerce. Lazada is one of the online stores in Indonesia that has many users and Lazada makes it easy to shop without the need to come to the place or directly. However, purchasing goods using e-commerce has problems regarding the quality of the goods you want to buy, therefore purchasing goods can be seen through reviews of each one you want to buy. Sentiment analysis is carried out using the Recurrent Neural Network (RNN) method with Long Short Term Memory (LSTM). And using the Epoch value as a parameter in processing validation data and test data to produce the best accuracy value
Whoosh User Sentiment Analysis on Social Media Using Word2Vec and the Best Naïve Bayes Probability Model Islamanda, Muhammad Dinan; Yuliant Sibaroni
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13742

Abstract

By using the Twitter microblogging feature, users can post short tweets with limited characters that express their thoughts and opinions regarding a matter. The newest transportation in Indonesia, a high-speed train namely Whoosh is one of the things that Twitter users responded to. This latest transportation has led to the emergence of opinions from the Indonesian people which are shared publicly in various media, one of which is social media. Therefore, to make it easier for business people or companies to understand public opinion regarding service improvements in the future, sentiment analysis on social media is needed to determine user opinions regarding high-speed train transportation. In this research, sentiment analysis of high-speed train users will be carried out on social media Twitter using Word2Vec and Naïve Bayes as classification methods. In this research, a comparison of Naïve Bayes models will also be carried out to find out the best Naïve Bayes method opportunity model. Simultaneously, the Word2vec feature extraction method was chosen because Word2Vec can be used to improve model performance and increase the accuracy of sentiment classification. This research found that the Word2Vec Skip-Gram model outperformed the Word2Vec CBOW model. The best model obtained was the use of the Gaussian Naïve Bayes and Word2Vec Skip-Gram models with an accuracy score of 77.18%, precision 70.35%, recall 76.09%, and f1-score 73.10%.
Public Sentiment Dynamics: Analysis of Twitter/X Data on the 2024 Indonesian Election with NB-SVM Satyananda, Karuna Dewa; Sibaroni, Yuliant
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7710

Abstract

This research analyzes the dynamics of public sentiment towards three pairs of presidential candidates in the 2024 Indonesian Election. This research was conducted using Twitter data as a source of information to gain a deeper understanding of the pattern of public sentiment during six crucial phases in the context of the election. The data is analyzed periodically during the election period. Sentiment analysis was carried out using the Naïve Bayes-Support Vector Machine classification approach to understand the sentiment patterns that emerged in each phase. NB-SVM utilizes class frequency information from NB to weight features, then trains separate SVMs for each class using these weighted features, improving classification accuracy. Models using NB-SVM classification produce better accuracy than models using NB and SVM classification, with an average accuracy of 76%. In Pair 01, a dynamic pattern was formed, namely a decrease in the level of positive sentiment during the debate and increasing again at a later time. Meanwhile, for Pair 02 and 03, a pattern was not formed for different reasons, namely sentiment that was too stable for Pair 02, and unstable sentiment for Pair 03. While Pair 01 obtained the most positive sentiment, Pair 02 received the most negative, with an average of 65.19% during the election process. This research proves that the results of sentiment analysis on Twitter/X contradict the official results by KPU of the general election in Indonesia.
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