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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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Aspect-Based Sentiment Analysis on iPhone Users on Twitter Using the SVM Method and Optimization of Hyperparameter Tuning I Gusti Ayu Putu Sintha Deviya Yuliani; Yuliant Sibaroni; Erwin Budi Setiawan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5430

Abstract

One form of information and communication technology development is a smartphone. Today's popular smartphone products are the iPhone and the social media used to share opinions is Twitter. One of the topics that is often discussed on Twitter is related to iPhone reviews which can refer to different aspects. Therefore, aspect-based sentiment analysis can be applied to iPhone reviews to get more detailed results. This study applies TF-IDF feature extraction as a weighting vocabulary and the Support Vector Machine classification method. This study also uses hyperparameter tuning to optimize parameters to get the best performance. The results of this study obtained the highest accuracy performance results by using the Support Vector Machine classification on the linear kernel and TF-IDF feature extraction on the camera aspect with accuracy 98.07%, battery aspect with accuracy 97.52%, design aspect with accuracy 96.82%, price aspect with accuracy 98.62%, and specification aspect with accuracy 97.07%. As well as getting an increase in the results of the highest accuracy performance by using hyperparameter tuning on the linear kernel for the camera aspect with accuracy 98.07%, battery aspect with accuracy 97.52%, design aspect with accuracy 97.02%, price aspect with accuracy 98.82%, and specification aspect with accuracy 97.22%.
Comparison of IndoBERTweet and Support Vector Machine on Sentiment Analysis of Racing Circuit Construction in Indonesia Hanvito Michael Lee; Yuliant Sibaroni
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5380

Abstract

The construction of the circuit is one of the policies made by the Indonesian government to advance the tourism sector and improve the national economy. This policy triggers various opinions given by the public, primarily through social media Twitter, both in the form of positive and negative opinions. This study compares machine learning and deep learning algorithms, Support Vector Machine and IndoBERTweet, that will be used as a model to predict the sentiment of racing circuit construction tweets. These models are built with K-Fold cross-validation to obtain the overall model’s performance for the entire dataset. Based on the experiments that have been carried out, it shows that IndoBERTweet performs better than the Support Vector Machine, with an overall accuracy score of 86%, a precision score of 88.2%, a recall score of 88.6%, and an f1-score of 88.4% for the entire dataset. Meanwhile, the Support Vector Machine model only achieves 82% for the accuracy score, 87.3% for the precision score, 84.3% for the recall score, and 85.8% for the f1-score. In addition, the best accuracy value from each iteration for IndoBERTweet is 94%, and the Support Vector Machine is 93%.
Big Five Personality Detection Based on Social Media Using Pre-Trained IndoBERT Model and Gaussian Naive Bayes Ni Made Dwipadini Puspitarini; Yuliant Sibaroni; Sri Suryani Prasetiyowati
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5439

Abstract

A person's personality offers a thorough understanding of them and has a significant role in how well they perform at work in the future. No wonder it attracted the interest of the researcher to develop a personality detection system. Although much research about personality detection through social media was conducted, this task has been challenging to implement, especially using conventional machine learning. The issue is conventional machine learning still insufficient to make the personality detection system perform better. The purpose of this research is to detect Big Five personalities based on Indonesian tweets and increase its performance by combining machine learning with deep learning, which is Gaussian Naive Bayes and IndoBERT model. The proposed combined model in this research is summing the log probability vector on each model. Gathered 3.342 tweets from 111 Twitter accounts that were used as a dataset. This research also implemented min-max normalization to rescale the data. The result showed that for the entire dataset, the combined model has more accuracy score than Gaussian Naive Bayes by 5.42% and IndoBERT by almost 2%, which indicates the combined model is better than the Gaussian Naive Bayes and IndoBERT models.
Covid-19 Fake News Detection on Twitter Based on Author Credibility Using Information Gain and KNN Methods Nanda Ihwani Saputri; Yuliant Sibaroni; Sri Suryani Prasetiyowati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4871

Abstract

Twitter is one of the social media that is used as a tool to share various kinds of information about various kinds of things that are of concern to social media users. One of the information shared is information about COVID-19, which is known that the COVID-19 pandemic is currently spreading throughout the world at a very alarming rate. COVID-19 is an infectious disease caused by SARS-COV-2. The World Health Organization (WHO) claims that the spread of COVID-19 is supported by the spread of false/fake news. So to find out the truth of the news, a COVID-19 fake news detector is needed so that users don't fall for the hoaxes circulating. This study aims to classify COVID-19 news on Twitter based on author credibility. Credibility in question is a person's perception of the validity of information and is a multidimensional concept that is used as a means of receiving information to assess the source of communication. The method used in this research is Information Gain and KNN. KNN (K-Nearest Neighbor) is a supervised learning algorithm that works by classifying a set of data based on classified training data. Information Gain is used to ranking the most influential attributes, and KNN is used to classify data based on learning data taken from the nearest neighbors. The research consists of 6 main stages, namely data collection (crawling data), data preprocessing, feature extraction, feature selection, data split into training data and testing data, KNN stage, and data evaluation stage. The research carried out succeeded in obtaining an accuracy value of 91%, a correlation value between credibility and hoax of 0.115, and a p-value <0.005.
Xiaomi Smartphone Sentiment Analysis on Twitter Social Media Using IndoBERT Priyan Fadhil Supriyadi; Yuliant Sibaroni
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5540

Abstract

The extraordinary evolution of technology has resulted in smartphones becoming important devices in people's daily lives. As a result, today's smartphones impact many people's lives, with more and more people owning smartphones. One of the most popular smartphone products today is Xiaomi. This popularity cannot be separated from various opinions on Twitter. Twitter is a social media that makes it easy for people to express their opinions regarding Xiaomi products called sentiment. Sentiment analysis is needed to classify various opinions on Twitter into positive, neutral, and negative classes. This study aims to analyze the sentiment of public opinion on Xiaomi smartphone products on Twitter social media. The models used in this study were BERT and IndoBERT because they produced a good performance in previous studies. This study's stages of work consisted of collecting, preprocessing, separating training and test data, building models with BERT and IndoBERT to detect sentiment, and carrying out training and testing stages. Test results using IndoBERT get a very good accuracy value with an accuracy value above 90%. The sentiment classification results for Xiaomi smartphone products show that positive sentiment on batteries has a greater number, with a positive percentage of 78%. In comparison, neutral sentiment is 4%, and negative sentiment is 18%. Furthermore in the camera aspect, positive sentiment has a greater number, with a positive percentage of 68%, while neutral sentiment is 18% and negative sentiment is 14%. Moreover, on the screen, positive sentiment has more numbers, with a positive percentage of 67%, neutral sentiment is 10%, and negative sentiment is 23%. Last, in the ram aspect, positive sentiment has a greater number with a positive percentage of 76%, while neutral sentiment is 17% and negative sentiment is 7%. The highest number of positive sentiments is in the camera aspect, which has 1935 positive sentiments from 2830 data. The sentiment analysis results can be used as an evaluation along with insights for the Xiaomi company so that in the future, the company can maintain and even improve the quality of the aspects that smartphone users like about Xiaomi products, namely cameras.
Comparative Analysis of Naive Bayes Model Performance in Hate Speech Detection in Media Social Twitter Muhammad Hadyan Baqi; Yuliant Sibaroni; Sri Suryani Prasetiyowati
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5493

Abstract

Twitter is a popular social media in Indonesia, and for some people, it is a place to find and disseminate information. Hate speech is aggressive behavior against individuals or groups such on race, gender, religion, nationality, ethnicity, sexual orientation, gender identity, or disability. In this study, hate speech is modeled using Naive Bayesian models, which consist of Multinomial, Bernoulli, and Gaussian Naïve Bayes Models. These methods were chosen because Naïve Bayes is a simple method but has good performance in the case of sentiment analysis. This research aims to get the method with the highest accuracy value in analyzing hate speech. Thus, the Naïve Bayes model can provide the best solution for hate speech problems. The process carried out in this study is to process all data which obtained from Twitter social media and then classify it using the Multinomial Naïve Bayes, Gaussian Naïve Bayes, and Bernoulli Naive Bayes models based on the classification of HS and non-HS sentiment categories.  In this study, to get the best accuracy, two different scenarios were used. The result of the analysis of the accuracy is 82.13% of the Multinomial Naïve Bayes model which is the best accuracy rate value compared with other models.
Comparison of Word2Vec with GloVe in Multi-Aspect Sentiment Analysis Classification of Nvidia RTX Products with Naïve Bayes Classifier Wira Abner Sigalingging; Sri Suryani Prasetyowati; Yuliant Sibaroni
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5528

Abstract

The increasing number of gamers has increased the demand for Graphics Processing Unit (GPU) products, one example of which is the Nvidia RTX product. Many users submit their reviews on social media Twitter in the form of tweets. These Tweets can be analyzed to determine the quality of a product. But most of the tweets talking about the product as a whole ignoring the category aspects of the product, making it difficult for both users and companies to pinpoint which aspects need attention. In this research, a multi-aspect based sentiment analysis will be carried out on tweets on Nvidia RTX products based on aspects of the product. The classification method used is Naive Bayes Classifier which will then compare feature extraction using Word2Vec and GloVe. Performance parameters are measured using a confusion matrix to produce values for accuracy, precision, recall, and f1-score. The highest accuracy results obtained were 60.71% on the price aspect, GloVe feature extraction, and classification with Gaussian Naive Bayes.Keywords: naive bayes classifier; Word2Vec; GloVe; confusion matrix; multi-aspect sentiment analysis
Performance of ANN and RNN in Predicting the Classification of Covid-19 Diseases based on Time Series Data Ridho Isral Essa; Sri Suryani Prasetyowati; Yuliant Sibaroni
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5557

Abstract

Indonesia is one of the countries with the highest confirmed cases of COVID-19. The city of Bandung is an area in Indonesia where the number of confirmed cases have continued to increase from 2021 to 2023. Currently there are around 103,574 cases with a total of deaths of around 1485 people. This is bad news for the city of Bandung because of the increasing number of confirmed cases. Various precautions against factors that might affect the rapid spread of COVID-19 in the city of Bandung have been carried out. But the confirmation cases still can't be stopped. Therefore, in this study we made a classification of the spread of COVID-19 in the city of Bandung with 25 features which will later be expanded using feature expansion techniques. This aims to analyze what factors have a major influence on the spread of COVID-19 in the city of Bandung. The method used are ANN and RNN methods. Where in this study the two methods were compared to determine which model had the best performance. Modeling is done by building models 2, 3, 4, and 5 months then the best model accuracy results from the ANN method are 79% and 81% for the RNN method. The author's contribution in this research is to build 2, 3, 4, and 5 month models, compare the performance results of ANN and RNN models, analyze the results of the confusion matrix, and make conclusions about what features are often used in each modeling.
Fake News (Hoaxes) Detection on Twitter Social Media Content through Convolutional Neural Network (CNN) Method Fauzaan Rakan Tama; Yuliant Sibaroni
JINAV: Journal of Information and Visualization Vol. 4 No. 1 (2023)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1525

Abstract

The use of social media is very influential for the community. Users can easily post various activities in the form of text, photos, and videos in social media. Information on social media contains fake news and hoaxes that will have an impact on society. One of the most social media used is Twitter. This study aims to detect fake news found on the Tweets using the Convolutional Neural Network (CNN) method by comparing the weighting features used of the Term Frequency Inverse Document Frequency (TF-IDF) and the Term Frequency-Relevance Frequency (TF-RF). The highest accuracy was obtained in the Term Frequency-Relevance Frequency (TF-RF) weighting feature with an accuracy of 84.11%, while in the Term Frequency Inverse Document Frequency (TF-IDF) weighting feature with an accuracy of 80.29%.
PERFORMANCE ANALYSIS OF THE IMBALANCED DATA METHOD ON INCREASING THE CLASSIFICATION ACCURACY OF THE MACHINE LEARNING HYBRID METHOD Azmi Aulia Rahman; Sri Suryani Prasetiyowati; Yuliant Sibaroni
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 8, No 1 (2023)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v8i1.3286

Abstract

This study analyzes the performance of hybrid methods in improving accuracy on imbalanced data using Dengue Hemorrhagic Fever Case Data from 2017 to 2021 in Bandung City. The attributes used in this study consist of Total Population, Total Male, Elementary School Graduation, Junior High School Graduation, High School Graduation, College Graduation, Rainfall, Average Temperature, Humidity, Male Cases, Number of Cases, and Class. This research combines five Machine Learning methods, such as Decision Tree, Support Vector Machine, Artificial Neural Network, K-Nearest Neighbor, and Nave Bayes. Hybrid Methods used in this research are Voting and Stacking methods. The oversampling methods used to handle imbalanced data in this study are Random Oversampling and Adasyn. The results show that Voting and Stacking without Random Oversampling and Adasyn get the same accuracy of 88,88%. While using Random Oversampling, voting gets an accuracy of 95,37% and stacking gets an accuracy of 96,29%. While using Adasyn, voting gets an accuracy of 94,44% and stacking gets an accuracy of 97,22%. Based on the results obtained, it can be concluded that the Random Oversampling and Adasyn Method can improve the performance of the Machine Learning hybrid method on imbalanced data. The contribution of this research is to provide information on the study and analysis of the implementation of the Random Oversampling and Adasyn methods in improving the performance of the Voting and Stacking methods in hybrid classification.
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