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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TEKNIK INFORMATIKA Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIN (Jurnal Online Informatika) Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Jurnal Teknik Informatika (JUTIF) JINAV: Journal of Information and Visualization eProceedings of Engineering Eduvest - Journal of Universal Studies Scientific Journal of Informatics Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika
Erwin Budi Setiawan
Telkom University
Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Mathematics Neuroscience Social Sciences

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Topic Detection on Twitter using GloVe with Convolutional Neural Network and Gated Recurrent Unit Moh Adi Ikfini M; Erwin Budi Setiawan
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4057

Abstract

Twitter is a social media platform that allows users to share thoughts or information with others for all to see. However, twitters often use abbreviations, slang, and incorrect grammar because tweets are limited to 280 characters. Topic detection often has problems with low accuracy, one method that can be used to overcome this problem is feature expansion. Feature expansion on Twitter is a semantic addition to the process of expanding the original text syllables to make it look like a large Document. That way, feature expansion is used to reduce word mismatches. This study uses the expansion of the GloVe feature with the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) classification methods. The results show that the topic detection system with the GloVe feature extension and CNN-GRU hybrid classification has an accuracy of 94.41%
Weight-Based Hybrid Filtering in a Movie Recommendation System Based on Twitter with LSTM Classification Muhammad Nur Ilyas; Erwin Budi Setiawan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6668

Abstract

With the era of digitalization, movie-watching has gained immense popularity, with platforms like Disney+ offering easy access to a variety of films. After watching, users frequently share their opinions on social media platforms such as Twitter, because of it is freedom of expression. With numerous movies available, users frequently encounter challenges in deciding what to watch. To address this, a recommendation system is proposed to streamline the decision-making process for users. Collaborative Filtering (CF), Content-Based Filtering (CBF), and Hybrid Filtering are common techniques used in recommendation systems. However, CF and CBF techniques face issues like cold start, sparse data, and overspecialization. To overcome these, this research constructs a Hybrid Filtering recommendation system, with a weight-based of CF-CBF coupled with Long Short-Term Memory (LSTM) classification. The classification uses various optimizers, including Adam, SGD, Nadam, RMSprop, and Adamax. Dataset is sourced from Kaggle website, which includes movie-related tweets linked to the Disney+ platform. The results indicate that Weight-Based Hybrid Filtering utilizing Adamax optimizer in LSTM classification yields superior performance metrics, by having 78% Precision, 79% Recall, 79% Accuracy, and 77% F1-Score value.
2024 Presidential Election Sentiment Analysis in News Media Using Support Vector Machine Bayu Muhammad Iqbal; Kemas Muslim Lhaksmana; Erwin Budi Setiawan
Journal of Computer System and Informatics (JoSYC) Vol 4 No 2 (2023): Februari 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i2.3051

Abstract

The 2024 presidential election is an event for all Indonesian people to determine their best leader. The presidential and vice presidential candidates are also competing to give their best efforts so that they can be elected as President and Vice President. The news media also provide news related to the 2024 presidential election with various titles that can interest their readers. Not infrequently the titles raised contain words that have sentiments, both positive and negative. In order to facilitate the analysis of the sentiments of these news titles, it is necessary to build a system that can detect the sentiments of these titles. In this study, we built a sentiment analysis system using the Support Vector Machine (SVM) method on news headline data obtained from online news media to detect whether news headlines contain positive or negative sentiments. For feature exctraction we compare the effect of FastText word embedding with TF-IDF for feature extraction. In the SVM method, several experiments were carried out on the attributes of C, kernel, gamma, and the ratio of the test data. The results obtained are a FastText can outperform TF-IDF for feature extraction. Also, combination of Kernel, C, and gamma values that give the best accuracy score of rbf, 1, and auto respectively at a test data ratio of 90:10, with an accuracy score of 99%.
SENTIMENT ANALYSIS USING CONVOLUTIONAL NEURAL NETWORK (CNN) AND PARTICLE SWARM OPTIMIZATION ON TWITTER Regina Anatasya Rudiyanto; Erwin Budi Setiawan
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.5201

Abstract

Over time, social media has always changed quickly. People can voice their ideas on various topics and communicate with each other through social media. One social media platform that allows users to express their ideas through tweets is Twitter. Sentiment is the route via which each person can express their ideas on a variety of subjects. The sentiment can be positive or negative. Sentiment analysis can be used to determine how Twitter users feel about particular subjects. Sentiment analysis on popular subjects in 2023, specifically the 2024 presidential contenders, will be done in this research. The dataset used in this research consists of 37,391 entries with 5 keywords. The research aims to understand how Twitter users respond and express their sentiments towards the presidential candidate through the use of deep learning classification techniques with Convolutional Neural Network (CNN), feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) method, and feature expansion with Word2Vec. Furthermore, this study employs Particle Swarm Optimization as an optimization technique to enhance the sentiment analysis model's performance. The test's results demonstrate a high degree of accuracy, offering a comprehensive picture of Twitter users' sentiments and perspectives toward the 2024 presidential contenders. This research helps to understand the dynamics of public opinion in the political context. Based on the evaluation results of the research, it yielded an accuracy of 78.2%, showcasing an improvement of 10.07% compared to the baseline.
Sentiment Analysis on Social Media Using Word2Vec and Gated Recurrent Unit (GRU) with Genetic Algorithm Optimization Syafa Fahreza; Erwin Budi Setiawan
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i1.903

Abstract

The evolution of information technology has changed the function of social media from a mere information repository to a platform for expressing opinions and aspirations. One of the most used social media is Twitter. Twitter users can express opinions according to their conscience. Therefore, a sentiment analysis process is needed to classify the opinion as positive or negative. Sentiment analysis on social media is important to understand user opinions, monitor public perception, measure campaign performance, identify trends and opportunities, and improve customer service. This research builds a model to perform sentiment analysis on the topic the president election with a total dataset of 39,791 with GRU method, TF-IDF feature extraction, Word2Vec feature expansion with 142,545 corpus from IndoNews, and Genetic Algorithm optimization. The test results show that the highest accuracy achieved is 83.39%, which shows an improvement of 1.42% compared to the baseline. This performance was achieved when combining of TF-IDF with a 5,000 maximum features, applying Word2Vec at top 1 similarity, and applying Genetic Algorithm for feature optimization. This study proves the relationship between the use of Word2Vec feature expansion and Genetic Algorithms as optimization in improving the accuracy of the model created.
Sentiment Analysis on Social Media Using Fasttext Feature Expansion and Recurrent Neural Network (RNN) with Genetic Algorithm Optimization Inggit Restu Illahi; Erwin Budi Setiawan
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i1.905

Abstract

Social media is a place to express opinions or feelings, both positive and negative. One of them is to express opinions or feelings about a topic that is currently being discussed. The number of opinions or sentiments related to a topic can be challenging to assess if it leans towards positivity or negativity. Therefore, Sentiment analysis is essential for examining the viewpoints or sentiments on the topic. In this study, 37,391 Twitter user comments on the 2024 Indonesian presidential election were tested. This research employs the RNN methodology, TF-IDF feature extraction, and FastText feature expansion utilizing an IndoNews corpus of as much as 142,545 data and using Genetic Algorithm optimization. The outcomes of this study yielded the highest accuracy when combining TF-IDF feature extraction with max 7000 features, FastText feature expansion on top 5 features, and implementing Genetic Algorithm optimization with a value of 82.72%, accuracy increased by 3.4% from the baseline.
Performance of Deep Feed-Forward Neural Network Algorithm Based on Content-Based Filtering Approach Maulana, Fikri; Setiawan, Erwin Budi
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 8 No 2 (2024): August 2024
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v8i2.22904

Abstract

Background: Selecting a restaurant in a diverse city like Bandung can be challenging. This study leverages Twitter data and local restaurant information to develop an advanced recommendation system to improve decision-making. Objective: The system integrates content-based filtering (CBF) with deep feedforward neural network (DFF) classification to enhance the accuracy and relevance of restaurant recommendations. Methods: Data was sourced from Twitter and PergiKuliner, with restaurant-related tweets converted into rating values. The CBF combined Bag of Words (BoW) and cosine similarity, followed by DFF classification. SMOTE was applied during training to address data imbalance. Results: The initial evaluation of CBF showed a Mean Absolute Error (MAE) of 0.0614 and a Root Mean Square Error (RMSE) of 0.0934. The optimal DFF configuration in the first phase used two layers with 32/16 nodes, a dropout rate of 0.3, and a 20% test size. This setup achieved an accuracy of 81.08%, precision of 82.89%, recall of 76.93%, and f1-scores of 79.23%. In the second phase, the RMSprop optimizer improved accuracy to 81.30%, and tuning the learning rate to 0.0596 further increased accuracy to 89%, marking a 9.77% improvement. Conclusion: The research successfully developed a robust recommendation system, significantly improving restaurant recommendation accuracy in Bandung. The 9.77% accuracy increase highlights the importance of hyperparameter tuning. SMOTE also proved crucial in balancing the dataset, contributing to a well-rounded learning model. Future studies could explore additional contextual factors and experiment with recurrent or convolutional neural networks to enhance performance further.
Aspect-Based Analysis of Telkomsel User Sentiment on Twitter Using the Random Forest Classification Method and Glove Feature Expansion Zakaria, Aditya Mahendra; Setiawan, Erwin Budi
Jurnal Teknologi dan Sistem Komputer Volume 10, Issue 2, Year 2022 (April 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2022.14558

Abstract

In this modern era, people certainly very easy to access social media, one of which is Twitter. Twitter is usually used by the public in expressing opinions regarding current issues, product reviews, and many other things positive, negative, or neutral opinions, or can be interpreted as sentiment. This study aims to analyze the aspect-based sentiment of Telkomsel users on Twitter using random forest classification and the extension of the Glove feature. This study uses signal aspects and service aspects with a total dataset of 16988 data. A Random forest can be classified as relevant and accurate for sentiment analysis with the greatest accuracy of 80.37% in the signal aspect and 80.12% in the service aspect, and the expansion feature is proven to be able to increase the performance value of this study by 13.15% in the signal aspect. and 5.37% in the service aspect.
Hoax Detection Using Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) on Social Media Putra, Dion Pratama; Setiawan, Erwin Budi
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3084

Abstract

There are negative effects of the ease of obtaining information in today's society, one of which is the rise of hoaxes on the internet. As much as 92.40% of social media platforms such as Twitter are used to spread hoaxes. Launched on July 13, 2006, Twitter is a microblogging service where users can spread information at no cost to themselves or others. In this study, the authors will conduct hoax news detection on Twitter social media using the Long Short - Term Memory (LSTM) method and Gate Recurent Unit (GRU) and gloVe feature expansion. with a dataset of 25,234 data which produces accuracy results in TF-IDF on each model, namely 97.33% in LSTM and 96.75% in GRU, and an increase in accuracy of 0.22% in the tweet corpus on LSTM and an increase in accuracy of 0.15 in the BeritaTweet corpus on GRU.
Aspect-based Sentiment Analysis on Social Media Using Convolutional Neural Network (CNN) Method Ramadhan, Ananta Ihza; Setiawan, Erwin Budi
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3103

Abstract

Social media are a platform for people to express their opinions on various topics, one of which is Twitter. Movie reviews are a frequently found topic on Twitter that contains a person's opinion of a movie that has been watched. But since opinions are subjective, it is difficult to determine an accurate assessment of a movie. In addition, the diverse aspects of a movie make it difficult to judge whether a review is positive or negative. Referring to that problem, a method is needed to perform sentiment analysis of the problem. In this study, sentiment analysis of movie reviews was carried out based on aspects of plot, acting, and director. This research also performs classification using a CNN model and combines several techniques, namely TF-IDF feature extraction, FastText feature expansion, and SMOTE to calculate the accuracy value and F1-Score. The final results obtained in this study are in the aspect of the plot getting an accuracy of 73.81% (+12,22%) and F1-score 73.72% (+15,93%), the acting aspect obtaining an accuracy value of 89.30% (+0,54%) and F1-score 89.26% (+50,80%), and in the aspect of the director having an accuracy of 87.37% (+0,28%) and F1-score 87.35% (+84,39%). Based on these results, each application of techniques such as TF-IDF, FastText, and SMOTE can increase the accuracy value and F1-Score of the model built.
Co-Authors Abdullah, Athallah Zacky Adriana, Kaysa Azzahra Adyatma, I Made Darma Cahya Agung Toto Wibowo Ahmad Zahri Ruhban Adam Aji Reksanegara Aji, Hilman Bayu Alvi Rahmy Royyan Anang Furkon RIfai Anindika Riska Intan Fauzy Annisa Aditsania Annisa Cahya Anggraeni Annisa Cahya Anggraeni Annisa Rahmaniar Dwi Pratiwi Arie Ardiyanti Arki Rifazka Arsytania, Ihsani Hawa Athirah Rifdha Aryani Aufa Ab'dil Mustofa Aydin, Raditya Bagas Teguh Imani Bayu Muhammad Iqbal Bayu Surya Dharma Sanjaya Billy Anthony Christian Martani Bintang Ramadhan, Rifaldy Brenda Irena Brigita Tenggehi Cahyudi, Ridho Maulana Crisanadenta Wintang Kencana Damarsari Cahyo Wilogo Daniar Dwi Pratiwi Daniar Dwi Pratiwi Dede Tarwidi Dedy Handriyadi Dery Anjas Ramadhan Dhinta Darmantoro Diaz Tiyasya Putra Dion Pratama Putra, Dion Pratama Diyas Puspandari Evi Dwi Wahyuni Faadhilah, Adhyasta Naufal Faidh Ilzam Nur Haq Farid, Husnul Khotimah Fathurahman Alhikmah Fathurahman Alhikmah Fazira Ansshory, Azrina Febiana Anistya Feby Ali Dzuhri Fhina Nhita Fhina Nhita Fida Nurmala Nugraha Fikri Maulana, Fikri Firdaus, Dzaki Afin Fitria, Mahrunissa Azmima Fitria Gde Bagus Janardana Abasan, I Ghina Dwi Salsabila Gita Safitri Grace Yohana Grace Yohana Hafiza, Annisaa Alya Hanif Reangga Alhakiem Hildan Fawwaz Naufal Husnul Khotimah Farid I Gusti Ayu Putu Sintha Deviya Yuliani I Kadek Candradinata Ibnu Sina, Muhammad Noer Ilyana Fadhilah Inggit Restu Illahi Inggit Restu Illahi Irma Palupi Isep Mumu Mubaroq Isman Kurniawan Kacaribu, Isabella Vichita Kamil, Ghani Kamil, Nabilla Kartika Prameswari Kemas Muslim Lhaksmana Kevin Usmayadhy Wijaya Khamil, Muhammad Khamil Khoirunnisa, Sanabila Luthfi Firmansah M. Arif Bijaksana Mahmud Imrona Mansel Lorenzo Nugraha Marissa Aflah Syahran Marissa Aflah Syahran Maulina Gustiani Tambunan Mela Mai Anggraini Moh Adi Ikfini M Moh. Hilman Fariz Muhammad Afif Raihan Muhammad Faiq Ardyanto Putro Muhammad Khiyarus Syiam Muhammad Kiko Aulia Reiki Muhammad Nur Ilyas Muhammad Shiba Kabul Muhammad Tsaqif Muhadzdzib Ramadhan Mustofa, Aufa Ab'dil Nabilla Kamil Naufal Adi Nugroho Naufal Razzak , Robith Nilla, Arliyanna Nindya Erlani, Dea Alfatihah Nisa Maulia Azahra Nur Ihsan Putra Munggaran Nuril Adlan , Muhammad Prahasto, Girindra Syukran Putri, Karina Khairunnisa Rafi Anandita Wicaksono Raisa Sianipar Rakhmat Rifaldy Ramadhan, Ananta Ihza Ramadhan, Helmi Sunjaya Ramadhani, Andi Nailul Izzah Ramadhanti, Windy Rayhan Rahmanda Refka Muhammad Furqon Regina Anatasya Rudiyanto Rendo Zenico Riaji, Dwi Hariyansyah Rizki Annas Sholehat Roji Ellandi Saleh, Abd Salsabil, Adinda Arwa Sanjaya, Bayu Surya Dharma Sari Ernawati Saut Sihol Ritonga Septian Nugraha Kudrat Septian Nugraha Kudrat Setiawan, Rizki Tri Shakina Rizkia Siti Inayah Putri Sri Suryani Sri Suryani Sukmawati Dwi Lestari Syafa Fahreza Syafa Fahreza Syahdan Naufal Nur Ihsan Valentino, Nico Wicaksono, Galih Wasis Wida Sofiya Widiarta, I Wayan Abi Widjayanto, Leonardus Adi Widyanto, Jammie Reyhan Wijaya, Kevin Usmayadhy Windy Ramadhanti Yoan Maria Vianny Yuliant Sibaroni Zahwa Dewi Artika Zakaria, Aditya Mahendra ZK Abdurahman Baizal