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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 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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Social Media Based Film Recommender System (Twitter) on Disney+ with Hybrid Filtering Using Support Vector Machine Ramadhan, Helmi Sunjaya; Budi Setiawan, Erwin
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.v8i4.12876

Abstract

In the current era, the culture of watching TV shows and movies has been made easier by the presence of the internet. Now, watching movies on platforms can be done from anywhere, one of which is Disney+. At times, people find it challenging to decide which film to watch given the multitude of genres and film titles available on these platforms. One solution to this issue is a recommendation system that can suggest films based on ratings. The recommendation system to be utilized involves Collaborative Filtering, Content-Based Filtering, and Hybrid Filtering. This is because Collaborative Filtering and Content-Based Filtering encounter issues like cold start, sparsity, and overspecialization. Thus, the objective of this study is to develop a recommendation system using Hybrid Filtering combined with Support Vector Machine (SVM). In this research, classification will be carried out using poly, linear, and RBF kernels with varying parameters. Techniques such as TF-IDF, RMSE, tuning, and data balancing with SMOTEN will be implemented to enhance accuracy during the classification process. The evaluation employed in this study utilizes the confusion matrix. Support Vector Machine, when tuned and combined with SMOTEN, achieves noteworthy results, particularly with the RBF kernel which attains a Precision score of 0.94. Recall produces a value of 0.93 with the Poly kernel, while the highest Accuracy, at 0.93, is achieved with the RBF kernel. Furthermore, the RBF kernel also demonstrates the highest F1-Score of 0.93. These findings illustrate elevated precision, recall, accuracy, and F1-Score within the context of hybrid filtering, achieved through the application of Support Vector Machine for classification and the implementation of the SMOTEN technique.
Empowering hate speech detection: leveraging linguistic richness and deep learning Gde Bagus Janardana Abasan, I; Setiawan, Erwin Budi
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.6938

Abstract

Social media has become a vital part of most modern human personal life. Twitter is one of the social media that was formed from the development of communication technology. A lot of social media gives users the freedom to express themselves. This facility is misused by users, so hate speech is spread. Designing a system to detect hate speech intelligently is needed. This study uses the hybrid deep learning (HDL) and solo deep learning (SDL) approach with the convolutional neural networks (CNN) and bidirectional gated recurrent unit (Bi-GRU) algorithm. There are 4 models built, namely CNN, Bi-GRU, CNN+Bi-GRU, and Bi-GRU+CNN. Term frequency-inverse document frequency (TF-IDF) is used for feature extraction, which is to get linguistic features to be analyzed and studied. FastText is used to perform feature expansion to minimize mismatched vocabulary. Four scenarios are run. CNN with an accuracy of 87.63%, Bi-GRU produces an accuracy of 87.46%, CNN+Bi-GRU provides an accuracy of 87.47% and Bi-GRU+CNN provides an accuracy of 87.34%. The ability of this approach to understand the context is qualified. HDL outperforms SDL in terms of n-gram type, where HDL can understand sentences broken down by hybrid n-gram types, namely Unigram-Bigram-Trigram which is a complex n-gram hybrid.
Prediksi Kepribadian Big Five Pengguna Twitter Menggunakan Metode Decision Tree dengan Pendekatan Semantik BERT Widyanto, Jammie Reyhan; Setiawan, Erwin Budi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7311

Abstract

Individual personality can be seen easily in this day. There are several approaches in classifying personality, one of which is the big five personality. The big five personality consists of 5 dimensions, namely Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. One way of knowing an individual's personality can be seen from their social media, because today almost all individuals have social media. One of the social media that is still widely used is Twitter. Twitter is a social media that contains tweets from each individual with a maximum of 280 characters per tweet. There have been several studies related to the big five personalities of Twitter users. Based on previous big five personality research problems, this study carried out predictions of the big five personalities of Twitter users using the Decision Tree Classification And Regression Tree (CART), Term Frequency Inverse Document Frequency (TF-IDF), Synthetic Minority Oversampling Technique (SMOTE), Linguistic Inquiry Word Count (LIWC), and Bidirectional Encoder Representations from Transformers (BERT) methods. The study aims to determine the application of the methods used in this study to the prediction of big five personalities and to get better accuracy results than previous studies. Data obtained from 315 twitter users and 672,866 tweets obtained from surveys and have been labeled with big five personalities, resulting in an accuracy of 97.62% from the baseline with an increase of 23.1%, by applying the CART+TF-IDF+SMOTE+LIWC+BERT method.
Hybrid Deep Learning with GloVe and Genetic Algorithm for Sentiment Analysis on X: 2024 Election Fitria, Mahrunissa Azmima Fitria; Setiawan, Erwin Budi
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.8467

Abstract

Purpose: This research analyzes sentiment on the 2024 Indonesian Presidential Election using  data from X, employing a hybrid CNN-GRU model optimized with a Genetic Algorithm (GA) to improve accuracy and efficiency. It also explores GloVe feature expansion for enhanced sentiment classification, aiming for deeper insights into public opinion through advanced deep learning and optimization techniques. Methods: This research employs a deep learning approach that integrates Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) models, Term Frequency-Inverse Document Frequency (TF-IDF), Global Vectors (GloVe), and GA. The dataset comprises  62,955 Indonesian tweets focusing on the 2024 General Election using various keywords. Result: The results indicated that the Genetic Algorithm significantly improved model accuracy. The CNN-GRU + GA model achieved 84.72% accuracy for the Top 10 ranking, a 1.94% increase from the base model. In comparison, the GRU-CNN + GA model achieved 84.69% accuracy for the Top 5 ranking, a 2.76% increase from the base model, demonstrating enhanced performance with GA across configurations. Novelty: This research uses a hybrid CNN-GRU model to introduce a novel sentiment analysis approach for the 2024 Indonesian Presidential Election. The model enhances accuracy by combining CNN's spatial feature extraction with GRU's temporal context capture and GloVe's word semantics. Genetic Algorithm optimization further refines performance. Comprehensive pre-processing ensures high-quality data, and focusing on election-specific keywords adds relevance. This study advances sentiment analysis through its innovative hybrid model, feature expansion, and optimization techniques.
Genetic Algorithm Optimization of Hybrid LSTM-AutoEncoder in Tourism Recommendation System Bayu Surya Dharma Sanjaya; Erwin Budi Setiawan
JURNAL TEKNIK INFORMATIKA Vol 17, No 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v17i2.39760

Abstract

The tourism industry has rapid growth and has become one of the world's leading economic industries in recent years due to advances in information technology, such as the internet and social media. However, the overwhelming amount of information often makes it difficult for travelers to decide on their preferred travel destination. To address these issues, this research proposes a tourism recommendation system that combines Content-Based Filtering and Hybrid LSTM-AE, which is optimized using Genetic Algorithm (GA). There is no research that has developed a recommendation system using a combination of these methods and optimized using GA. So that this research can contribute to providing personalized recommendations and higher accuracy. The dataset consists of 9,504 ratings collected from the Ministry of Tourism and Creative Economy, Twitter, and web sources. The system was able to achieve a rating prediction accuracy of 96.82% by applying SMOTE to handle data imbalance and implementing a GA approach to the Hybrid LSTM-AE model. Accuracy has increased by 18.7% from the baseline model without using SMOTE and optimization. These results underscore that a strong integration between natural language processing and genetically optimized deep learning provides more accurate recommendations.
Optimizing the Learning Rate Hyperparameter for Hybrid BiLSTM-FFNN Model in a Tourism Recommendation System Aufa Ab'dil Mustofa; Erwin Budi Setiawan
JURNAL TEKNIK INFORMATIKA Vol 17, No 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v17i2.40250

Abstract

Indonesia, with its abundant natural resources, is rich in captivating tourist attractions. Tourism, a vital economic sector, can be significantly influenced by digitalization through social media. However, the overwhelming amount of information available can confuse tourists when selecting suitable destinations. This research aims to develop a tourism recommendation system employing content-based filtering (CBF) and hybrid Bidirectional Long Short-Term Memory Feed-Forward Neural Network (BiLSTM-FFNN) model to assist tourists in making informed choices. The dataset comprises 9,504 rating matrices obtained from tweet data and reputable web sources. In various experiments, the hybrid BiLSTM-FFNN model demonstrated superior performance, achieving an accuracy of 93.36% following optimization with the Stochastic Gradient Descent (SGD) algorithm at a learning rate of about 0.193. The accuracy, after applying Synthetic Minority Over-sampling Technique (SMOTE) and fine-tuning the learning rate hyperparameter, showed a 14.3% improvement over the baseline model. This research contributes by developing a recommendation system method that integrates CBF and hybrid deep learning with high accuracy and provides a detailed analysis of optimization techniques and hyperparameter tuning.
CONTENT-BASED FILTERING CULINARY RECOMMENDATION SYSTEM USING DEEP CONVOLUTIONAL NEURAL NETWORK ON TWITTER (X) Zahwa Dewi Artika; Erwin Budi Setiawan
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5640

Abstract

Along with the development of technology, social media has become integral to everyday life, especially for sharing content like culinary reviews. Social media platform X (formerly Twitter) is often used for sharing culinary recommendations, but the abundance of information makes it difficult for users to find relevant suggestions. In order to improve rating prediction performance, this study suggests a recommendation system model that is more thoroughly created utilizing Content-Based Filtering (CBF) combined with Deep Convolutional Neural Network (CNN) and optimised with Particle Swarm Optimization (PSO). Data was collected from PergiKuliner and Twitter, totaling 2644 reviews and 200 cuisines. The preprocessing involved text processing, translation, and polarity assessment. Post-labeling, 7438 data were labeled with 0 and 1562 with 1. Label 0 means not recommended while label 1 means recommended. The imbalance is handled by applying the SMOTE method after observing that the fraction of data labeled 0 and 1 is 65.2%. CBF employed TF-IDF feature extraction and FastText word embedding, while Deep CNN handled classification. PSO optimisation was applied to enhance the accuracy of culinary rating predictions. The results showed an initial accuracy of 76.32% with the baseline Deep CNN model, which increased to 86.06% after Nadam optimisation with the best learning rate, and further reached 86.18% after PSO optimisation on dense units. The 9.86% accuracy improvement from the baseline model demonstrates the effectiveness of the combined methods.
The HYBRID CONTENT-BASED FILTERING AND CLASSIFICATION RNN WITH PARTICLE SWARM OPTIMIZATION FOR TOURISM RECOMMENDATION SYSTEM Syahdan Naufal Nur Ihsan; Erwin Budi Setiawan
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5674

Abstract

Economic recovery in the tourism sector after the COVID-19 pandemic is one of the main focuses of the Indonesian government at the moment, especially in Bandung City. This research aims to develop a personalized tourist spot recommendation system, by addressing the gaps in the existing literature through the integration of Content-Based Filtering (CBF) and Simple Recurrent Neural Network (RNN) methods that aim to improve recommendation accuracy. This study uses a hybrid approach that combines Term Frequency - Inverse Document Frequency (TF-IDF) and word embedding with the Robustly Optimized BERT (RoBERTa) model to identify similarities between tourist destinations based on their content characteristics. Simple RNN is used to analyze user preference patterns over time, which is then further optimized using Particle Swarm Optimization (PSO). As a result, the Simple RNN model that has been optimized with PSO shows an increased accuracy of up to 94.37%, outperforming other optimizations such as Adam and SGD. This research makes a novel contribution by applying advanced machine learning techniques to improve personalization in travel recommendation systems.
Hate Comment Detection On Twitter Using Long Short Term Memory (LSTM) With Genetic Algorithm (GA) Dea Alfatihah Nindya Erlani; Erwin Budi Setiawan
Eduvest - Journal of Universal Studies Vol. 4 No. 11 (2024): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v4i11.1758

Abstract

In the era of social media like today, one social media that is currently quite popular is Twitter. This study explores the use of the Long Short Term Memory (LSTM) method optimized with the Genetic Algorithm (GA) to detect hate speech in Twitter data in Indonesia. We use TF-IDF and GloVe feature extraction techniques to produce effective word vector representations in natural language processing. This study also introduces feature expansion and similarity corpus construction to improve the performance of the LSTM classification model. Evaluation is carried out through a confusion matrix to measure accuracy, precision, recall, and F1 score. The results show that the LSTM model with TF-IDF and GloVe feature extraction achieves the best performance with an accuracy of up to 92.91%. We also found that the combination of Unigram + Bigram + Trigram, max feature 10000, and Glove corpus with Top 20 similarity gave optimal results. In addition, parameter optimization using genetic algorithms has been shown to improve accuracy and F1-Score. The resulting LSTM model is able to classify test data with high accuracy, which has the potential to help in the detection and handling of hate speech on social media, as well as improving the model's ability to identify and understand text content in the Indonesian language context.
Content-based Filtering Movie Recommender System Using Semantic Approach with Recurrent Neural Network Classification and SGD Salsabil, Adinda Arwa; Setiawan, Erwin Budi; Kurniawan, Isman
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 2, May 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i2.1940

Abstract

The application of recommendation systems has been applied in various types of platforms, especially applications for watching movies such as Netflix and Disney+. The recommendation system is purposed to make it easier for users, especially in choosing a movie because currently the number of movie productions is increasing every day. This research proposed a CBF movie recommendation system by comparing the performance of several semantic methods to be able to get the best rating prediction results. In order to improve the performance quality to get the best rating prediction results, this research  utilized semantic feature methods by comparing the performance of the evaluation results produced by the TF-IDF method and word embedding applications, such as BERT, GPT-2, RoBERTa, and implemented RNN model to classify the results of rating prediction. The data were used to generate the recommendation system by involving 854 data movie and 39 accounts with a total of 34,056 movie reviews on Twitter. This research has succeeded in getting a method that produced rating predictions, namely RoBERTa. In the classification process with the RNN model and SGD optimization, the measurement results with confusion matrix by classifying the RoBERTA rating prediction obtained an evaluation value of 0.6514 loss, 95.59% accuracy, and 0.6514 precision.
Co-Authors Abdullah, Athallah Zacky Adriana, Kaysa Azzahra 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 Brenda Irena Brigita Tenggehi Crisanadenta Wintang Kencana Damarsari Cahyo Wilogo Daniar Dwi Pratiwi Daniar Dwi Pratiwi Dea Alfatihah Nindya Erlani Dede Tarwidi Dedy Handriyadi Dery Anjas Ramadhan Dhinta Darmantoro Diaz Tiyasya Putra Dion Pratama Putra, Dion Pratama Diyas Puspandari Dwi Hariyansyah Riaji Faidh Ilzam Nur Haq Famardi Putra Muhammad Raffly Raffly 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 Hanif Reangga Alhakiem Hildan Fawwaz Naufal Husnul Khotimah Farid I Gusti Ayu Putu Sintha Deviya Yuliani I Kadek Candradinata I Made Darma Cahya Adyatma Ibnu Sina, Muhammad Noer Ilyana Fadhilah Inggit Restu Illahi Irma Palupi Isabella Vichita Kacaribu Isep Mumu Mubaroq Isman Kurniawan Kamil, Ghani Kartika Prameswari Kemas Muslim Lhaksmana Kevin Usmayadhy Wijaya 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 Nabilla Kamil Naufal Adi Nugroho Naufal Razzak , Robith Nilla, Arliyanna Nisa Maulia Azahra Nur Ihsan Putra Munggaran Nuril Adlan , Muhammad Prahasto, Girindra Syukran Rafi Anandita Wicaksono Raisa Sianipar Rakhmat Rifaldy Ramadhan, Ananta Ihza Ramadhan, Helmi Sunjaya Rayhan Rahmanda Refka Muhammad Furqon Regina Anatasya Rudiyanto Rendo Zenico Ridho Maulana Cahyudi Rifaldy Bintang Ramadhan Rizki Annas Sholehat Roji Ellandi Salsabil, Adinda Arwa 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 Syahdan Naufal Nur Ihsan Valentino, Nico Wida Sofiya Widiarta, I Wayan Abi Widjayanto, Leonardus Adi Widyanto, Jammie Reyhan Windy Ramadhanti Yoan Maria Vianny Yuliant Sibaroni Zahwa Dewi Artika Zakaria, Aditya Mahendra ZK Abdurahman Baizal