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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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FUEL INCREASE SENTIMENT ANALYSIS USING SUPPORT VECTOR MACHINE WITH PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM AS FEATURE SELECTION Laura Imanuela Mustamu; Yuliant Sibaroni
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 3 (2023): JUTIF Volume 4, Number 3, June 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.3.881

Abstract

BBM, or fuel oil, is one of the essential needs of the Indonesian people. The government's policy regarding the increase in fuel prices raises many opinions from the public. Twitter is one of the social media that Indonesian people often use to express opinions on a topic. In this study, sentiment analysis was carried out on public opinion regarding the fuel price increase policy from Twitter social media. This research is expected to help determine public opinion regarding the fuel price increase policy with positive, neutral and negative sentiments. The sentiment analysis method used is the Support Vector Machine (SVM) classification algorithm. The results of the accuracy of SVM were compared with accuracy by adding a feature selection process. The Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) algorithms are used for the feature selection method. After several experiments using the three methods, the SVM method with the Radial Basis Function (RBF) kernel produced the best accuracy of 71.2%. The combination of the SVM method with the RBF and PSO kernels obtained an accuracy of 68.84%, and the combination of the RBF and GA kernel SVM methods obtained an accuracy of 69.52%.
Effectiveness of Word Embedding GloVe and Word2Vec within News Detection of Indonesian uUsing LSTM Muhammad Ghifari Adrian; Sri Suryani Prasetyowati; Yuliant Sibaroni
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6411

Abstract

In recent years the use of social media platforms in Indonesia has continued to increase. The increasing use of social media has several advantages and disadvantages. The advantage is that the news is easily accessible by anyone, while the disadvantage is that much information that is spread is hoax news. Hoax news must be detected because hoax news spreads false and misleading information. This undermines the integrity of the information and needs to be clarified for the public. By detecting hoax news, we can ensure the information being disseminated is accurate and trustworthy. In this study, the author will detect hoax news on Indonesian news media on Twitter using LSTM with word embedding GloVe and Word2Vec and compare the two-word embeddings to find the best performance in the LSTM model. The reason for choosing the GloVe and Word2Vec extraction features to be compared is that both are useful for representing vectors of words. Their performance may vary. Word2Vec might better capture semantic relationships between words, whereas GloVe might better capture distributional relationships and word co-occurrence. This study shows that LSTM with Word2Vec performs better than LSTM and GloVe in detecting Indonesian language news. LSTM and Word2Vec produced an average accuracy value of 95%, while LSTM with GloVe produced an average accuracy value of 90%.
Hoax Detection of Indonesian News Media on Twitter Using IndoBERT with Word Embedding Word2Vec Pernanda Arya Bhagaskara S M; Sri Suryani Prasetiyowati; Yuliant Sibaroni
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6367

Abstract

Hoax is data that is added or deducted from the news that occurred. In the digital age, hoaxes are increasingly being spread, and people are very quickly affected by their spread, especially hoaxes circulating in Indonesian news media on social media. Disseminating information that has not been confirmed as accurate can cause public concern and anxiety. Virtual diversion has transformed into a correspondence key to begin thinking, talking, and moving around cordial issues. In this manner, exploration will be led by consolidating the IndoBERT model with the Word2Vec development highlight in arranging deception news in Indonesian news media. This model was constructed using K-Fold cross-validation to enhance model performance across extensive data sets. The information utilized comes from tweets shared on Twitter by the Indonesian public. The trials that have been carried out demonstrate that combining Word2Vec with IndoBERT is effective at detecting hoaxes, with an overall accuracy score of 88% for the entire dataset. This conclusion can be drawn from the classification results of Word2Vec with IndoBERT. Also, the best precision and incentive for every cycle is almost 99%. In addition, the study's objective is to identify hoax news in Indonesian news media disseminated via social media. This will encourage individuals to be more cautious when reading and disseminating news, as untrue information will significantly impact certain individuals.
Hate Speech Detection in Indonesia Twitter Comments Using Convolutional Neural Network (CNN) and FastText Word Embedding Fadhilah Nadia Puteri; Yuliant Sibaroni; Fitriyani F.
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6401

Abstract

Hate speech is a problem that is often present in Indonesia, including on social media platforms such as Twitter. Refers to any form of communication, whether oral, written, or symbolic, that may offend, threaten or insult an individual or group based on attributes such as religion, race, ethnicity, sexual orientation, or other characteristics. The existence of freedom of expression and communication on social media triggers the spread of hate speech quickly and widely. To avoid this, a system is needed that can detect hate speech on social media. Deep learning is potentially better at recognizing and analyzing language patterns that reflect hate speech in text. In the previous study, the accuracy obtained was 73.2% using the Convolutional Neural Network method. This study proposed a hate speech detection system using Convolutional Neural Network model and FastText word embedding. The performance of Convolutional Neural Network classification model and FastText as word embedding provide excellent performance results in detecting hate speech, by involving the K-Fold Cross Validation process to the appropriate dropout value is able to achieve an accuracy value of 80%. The resulting accuracy value can be a benchmark that the model that has been built is able to avoid the spread of hate speech on social media.
Sentiment Classification of Fuel Price Increase With Gated Recurrent Unit (GRU) and FastText Aditya Andar Rahim; Yuliant Sibaroni; Sri Suryani Prasetiyowati
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6391

Abstract

The government usually implements a policy of increasing fuel prices and reducing subsidized fuel every year. Rising fuel prices have had a mixed impact on society. The rapid development of information technology has led to easy access and an increase in the number of internet users. Social media platforms, such as Twitter, are widely used by people to express themselves in everyday life. Through this social media, the public can submit reviews regarding public policies implemented by the government regarding fuel prices. The reviews submitted varied, ranging from positive, neutral to negative. Sentiment analysis can analyze the types of reviews submitted by people, including positive, negative, or neutral. This research uses Gated Recurrent Unit and FastText feature expansion to classify sentiments related to rising fuel prices on Twitter. This system was developed through several stages, namely data crawling, data labeling, data initial processing, feature expansion, classification, and evaluation. This study aims to determine the classification performance using Gated Recurrent Unit and FastText. The data used was 8,635, and the highest accuracy reached 90.15% with an F1 score of 90.06%. The research results may help the government in determining how individuals feel about fuel price increases. By understanding public sentiment, the government can reevaluate its policies or even establish new ones that serve the public interest.
Detection of Fraudulent Financial Statement based on Ratio Analysis in Indonesia Banking using Support Vector Machine Yuliant Sibaroni; Muhammad Novario Ekaputra; Sri Suryani Prasetiyowati
JOIN (Jurnal Online Informatika) Vol. 5 No 2 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i2.646

Abstract

This study proposes the use of ratio analysis-based features combined with the SVM classifier to identify fraudulent financial statements. The detection method used in this study applies a data mining classification approach. This method is expected to replace the expert in forensic accounting in identifying fraudulent financial statements that are usually done manually. The experimental results show that the proposed classifier model and ratio analysis-based features provide more than 90% accuracy results where the optimal number of features based on ratio analysis is 5 features, namely Capital Adequacy Ratio (CAR), (ANPB) to total earning assets and non-earning assets (ANP), Impairment provision on earning assets (CKPN) to earning assets, Return on Asset (ROA), and Return on Equity (ROE). The contribution of the study is to complement the research of fraudulent financial statements detection where the classifier method used here is different compare to other research. The selection of banking cases in Indonesia is also unique in this research which distinguishes it from other research because the financial reporting standards in each country can be different. 
Detection of Indonesian Hate Speech in the Comments Column of Indone-sian Artists' Instagram Using the RoBERTa Method Adhe Akram Azhari; Yuliant Sibaroni; Sri Suryani Prasetiyowati
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 8, No 3 (2023)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v8i3.3898

Abstract

This study detects hate speech comments from Instagram post comments where the method used is RoBERTa. Roberta's model was chosen based on the consideration that this model has a high level of accuracy in classifying text in English compared to other models, and possibly has good potential in detecting Indonesian as used in this research. There are two test scenarios namely full-preprocessing and non full-preprocessing where the experimental results show that non full-preprocessing has an average value of accuracy higher than full-preprocessing, and the average value of non full-preprocessing accuracy is 85.09%. Full-preprocessing includes several preprocessing stages, namely cleansing, case folding, normalization, tokenization, and stemming. While non full-preprocessing includes all processes in preprocessing except the stemming process. This shows that RoBERTa predicts comments well when not using full-preprocessing.
Multi-aspect sentiment analysis on netflix application using latent dirichlet allocation and support vector machine methods Attala Rafid Abelard; Yuliant Sibaroni
JURNAL INFOTEL Vol 13 No 3 (2021): August 2021
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v13i3.670

Abstract

Among many film streaming platforms that have sprung up, Netflix is ​​the platform that has the most subscribers compared to the other platforms. However, not all reviews provided by the Netflix users are good reviews. These reviews will later be analyzed to determine what aspects are reviewed by the users based on reviews written on the Google Play Store, using the Latent Dirichlet Allocation (LDA) method. Then, the classification process using the Support Vector Machine (SVM) method will be carried out to determine whether each of these reviews is included in the positive or negative class (Sentiment Analysis). There are 2 scenarios that were carried out in this study. The first scenario resulted that the best number of LDA topics to be used is 40, and the second scenario resulted that the use of filtering process in the preprocessing stage reduces the score of the f1-score. Thus, this study resulted in the best performance score on LDA and SVM testing with 40 topics, and without running the filtering process with the score of 78.15%.
Word2Vec Optimization on Bi-LSTM in Electric Car Sentiment Classification Siti Uswah Hasanah; Yuliant Sibaroni; Sri Suryani Prasetyowati
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7200

Abstract

The Indonesian government is actively promoting electric vehicles. This policy has generated many sentiments from the public, both positive and negative. Public sentiment can have a significant impact on the success of government policies. Therefore, it is important to understand public sentiment towards these policies. This research develops a sentiment classification model to understand public sentiment towards electric vehicles in Indonesia. Sentiment classification is the process of identifying and measuring the positive or negative sentiment in a text. This research uses a Bi-LSTM model to perform classification on a dataset of tweets related to electric vehicles. To evaluate the performance, testing was conducted through two main scenarios. In Scenario I, the focus was on finding the optimal embedding size for two Word2Vec architectures, namely CBOW and Skip-gram. Model evaluation was performed using cross-validation to gain a deeper understanding of model performance. Scenario II focused on searching for the best dropout parameters for the Bi-LSTM model. This step aimed to find the optimal configuration for the model to generate more accurate and consistent predictions in classifying tweets related to electric vehicles. The results showed that in the context of sentiment classification on tweets about electric vehicles, the combination of CBOW with an embedding size of 200 and the Bi-LSTM model with a Dropout value of 0.2 is the best choice and achieves an accuracy of 96.31%, precision of 92.57%, Recall of 98.61%, and F1-Score of 95.49%.
Clustering Content Types and User Motivation Using DBSCAN on Twitter Made Mita Wikantari; 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.v4i4.3750

Abstract

We are currently in an era full of information and communication technology. One of the communication media used is Twitter. Twitter is a microblogging service that is used by its users to express their thoughts on a topic called a tweet. Tweets that are posted can be either positive tweets or negative tweets. One of the topics that is currently being discussed by Twitter users is Anies Baswedan as a 2024 Indonesian Presidential Candidate. Many people have tweeted this but it is not known how many users support or reject Anies Baswedan to run as a 2024 Indonesian presidential candidate. To assist the analysis, use the method clustering namely algorithm (Density-Based Spatial Clustering of Application with Noise). DBSCAN has the advantage of being able to detect data that is not included in a cluster and will be considered noise. This can improve the accuracy of the grouping because the data in the cluster will be cleaner. The TF-IDF Vectorizer is used to make it easier for programs to manage data because it can turn sentences into vectors that can be processed by the algorithm. To determine the evaluation of the program, the silhouette score method will be used. The results of calculating the silhouette score show a value of 0.29 with the formation of 3 clusters. Then an analysis is carried out based on the top words from each cluster and it can be identified that cluster 0 has a positive category supporting Anies Baswedan to run for the 2024 Presidential Candidate and cluster 1 has a negative category that does not support Anies Baswedan not advancing for the 2024 Presidential Candidate.
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