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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 LOGIC: Jurnal Penelitian Informatika Proceeding of Community Service and Engagement
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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Enhancing Cyberbullying Detection on Platform 'X' Using IndoBERT and Hybrid CNN-LSTM Model Hafiza, Annisaa Alya; Setiawan, Erwin Budi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4321

Abstract

Cyberbullying on social media platforms has become widespread in society. Cyberbullying can take many forms, including hate speech, trolling, adult content, racism, harassment, or rants. One social media platform that has many cyberbullies is Twitter, which has been renamed 'X'. The anonymous nature of this 'X' platform allows users from all over the world to commit cyberbullying as they can freely share their thoughts and expressions without having to account for their identity. This research aims to explore the influence of IndoBERT’s semantic features on hybrid deep learning models for cyberbullying detection while integrating TF-IDF feature extraction and FastText feature expansion to enhance text classification performance. Specifically, this study examines how IndoBERT’s semantic capabilities affect the hybrid deep learning model in detecting cyberbullying on platform 'X'. This study has 30,084 tweets with a hybrid deep learning approach that combines CNN and LSTM. In the IndoBERT scenario, IndoBERT features were first combined with TF-IDF, then expanded using FastText before being applied to the hybrid deep learning model. The test results produced the highest accuracy rate by: CNN (80.69%), LSTM (80.67%), CNN- LSTM (81.18%), CNN-LSTM-IndoBERT (82.05%). This research contributes to informatics by integrating hybrid deep learning (CNN-LSTM) with IndoBERT and TF-IDF, demonstrating its effectiveness in improving cyberbullying detection in Indonesian text. Future research can explore the use of other transformer-based models such as RoBERTa or ALBERT to enhance contextual understanding in cyberbullying classification.
Sentiment Analysis on Social Media Using Word2Vec and Gated Recurrent Unit (GRU) with Genetic Algorithm Optimization Syafa Fahreza; Setiawan, Erwin Budi
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; Setiawan, Erwin Budi
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.
Content Based Filtering on Culinary Tourism Recommendation System Based on Social Media X Using Bi-LSTM Khamil, Muhammad Khamil; Erwin Budi Setiawan
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i2.967

Abstract

Advancing technology, especially on social media platforms like X, created a vibrant space for users to share culinary experiences and recommendations through opinions and reviews. X became critical in presenting reviews and recommending places to eat with an excessively high number of active users. Facing the challenge of information overload on X, this research proposed a culinary tourism recommendation system using the Content-Based Filtering (CBF) method with Word to Vector (Word2Vec) and Bidirectional Long Short-Term Memory (Bi-LSTM) for classification. Utilizing culinary tourism data from Tripadvisor and user threads on Twitter, the dataset used included 2,645 tweets and five web crawling results, resulting in a matrix with a total of 200 culinary places and 44 users. Data pre-processing, such as the calculation of sentiment polarity scores using TextBlob and the application of SMOTE technique to balance the data, contributed to the improved accuracy of this research. In addition, optimization of the Bi-GRU model with various optimization methods, such as Adam, and hyperparameter tuning using Learning Rate Finder, resulted in a maximum accuracy of 94.99%, an increase of 29.4% from the baseline. The results of this research contributed significantly to the development of a more accurate and personalized culinary tourism recommendation system.
Feature Expansion with GloVe and Particle Swarm Optimization for Detecting the Credibility of Information on Social Media X with Long Short-Term Memory (LSTM) Raffly, Famardi Putra Muhammad Raffly; Setiawan, Erwin Budi Setiawan
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.v11i3.7839

Abstract

Purpose: This research aims to develop a system for detecting the credibility of information on social media X by classifying tweets as credible or non-credible. Additionally, it seeks to improve the accuracy of classification and prediction of information credibility using feature extraction methods, semantic features, feature expansion, and optimization. Methods: The system is built using a deep learning approach with Long Short-Term Memory (LSTM), Term Frequency-Inverse Document Frequency (TF-IDF), Robustly optimized BERT Approach (RoBERTa), Global Vector (GloVe), and Particle Swarm Optimization (PSO). The dataset consists of 54,766 Indonesian tweets from social media X, focusing on the 2024 General Election and using several keywords such as ‘Pemilu 2024’, ‘Pilpres 2024’, ‘anies baswedan’, ‘Prabowo’, ‘#GanjarPranowo’, and ‘#debatCapres’. Result: The results of this study show that the highest accuracy achieved is 89.09% using LSTM with an 80:20 data split, baseline unigram, RoBERTa, Top1 corpus IndoNews, and PSO of the LSTM model’s hyperparameters, resulting in a highly significant statistical improvement of 0.96% over the baseline model. Novelty: This research contributes to information credibility classification research using RoBERTa to add semantic features and GloVe to expand features by utilizing a built corpus and finding similar words to connect with these expanded features. Additionally, PSO is applied to find the optimal hyperparameters, thereby improving the performance and accuracy of the LSTM classification model.
Genetic Algorithm Optimization of Hybrid LSTM-AutoEncoder in Tourism Recommendation System Sanjaya, Bayu Surya Dharma; Setiawan, Erwin Budi
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 Mustofa, Aufa Ab'dil; Setiawan, Erwin Budi
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.
Analysis of the Alignment of Bauran System Features Based on Outcome-Based Education Rules Using Feature-Oriented Domain Analysis Wicaksono, Galih Wasis; Saleh, Abd; Wahyuni, Evi Dwi; Setiawan, Erwin Budi
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.2895

Abstract

Implementing information systems in higher education curriculum design is a crucial tool for academics, enabling them to design, develop, and evaluate the curriculum more dynamically, responsively, and structurally. However, it is not just about having a tool. It is about ensuring that the tool aligns with curriculum design standards. This study, therefore, measures and analyses the conformity of the Bauran system as a curriculum management information system with the established stages and standards of curriculum design. The analysis is based on the Indonesia National Standards for Higher Education (Standar Nasional Pendidikan Tinggi (SN DIKTI)) by referring to the Guidebook for Higher Education Curriculum Development in Indonesia and best practices in the implementation of Outcome-Based Education (OBE) curriculum design. The method used in this research is feature-oriented domain analysis (FODA), which includes context analysis, domain modeling, and architecture modeling. Experts in the field of OBE curriculum then validate the results of feature measurement and mapping. The study compares 27 Bauran features to 10 stages in the curriculum design guidebook and nine stages in the OBE curriculum design flow. The analysis results show that the Bauran system has implemented 10 out of 10 stages (100%) of curriculum design according to the curriculum design guidebook. However, Bauran has only implemented 8 out of 9 stages (89%) in the OBE curriculum flow. These findings not only provide feature recommendations for future Bauran development and other higher education curriculum management systems but also highlight the potential of the Bauran system for future development.
Ekpansi Fitur dengan Word2vec dalam klasifikasi Hoax di Twitter Cahyudi, Ridho Maulana; Setiawan, Erwin Budi
eProceedings of Engineering Vol. 10 No. 2 (2023): April 2023
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Abstrak-Media sosial sekarang sudah banyak digunakan untuk berbagi informasi, dan juga tempat untuk berkomunikasi. Dalam berbagi informasi banyak peluang untuk menyebarkan hoax, contohnya seperti diaplikasi Twitter. Terkadang ada ketidaksesuaian kosa kata dalam setiap tweet. Oleh karena itu pada penelitian ini dilakukan penerapan metode fitur ekpansi menggunakan Word2vec untuk meminimalisir ketidaksesuaian kosakata tersebut. metode klasifikasi yang digunakan adalah Naive bayes, ANN, Decision Tree. Hasil dari penelitian ini, nilai tertinggi sebesar 82,44% yang menggunakan ekspansi fitur Word2vec pada metode klasifikasi ANN yang meningkat sebesar 1,17%.Kata kunci - hoax, fitur ekspansi, twitter.
Teknik Recommender System Menu Makanan dengan Pendekatan Contextual Model dan Multi-Criteria Decision Making pada Orang Dewasa Kacaribu, Isabella Vichita; Setiawan, Erwin Budi; Lhaksmana, Kemas Muslim
eProceedings of Engineering Vol. 10 No. 4 (2023): Agustus 2023
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Wisata Kuliner adalah kegiatan yang popular pada saat ini. Banyak tempat makan yang menawarkan makanan-makanan dengan tampilan yang menarik, murah, atau enak. Beberapa masyarakat mendapatkan informasi mengenai wisata kuliner atau daftar makanan melalui media sosial, berita maupun melalui media cetak. Sehingga banyak dari mereka menentukan menu makanan yang mereka santap melalui media sosial. Banyak kriteria yang digunakan dalam memilih makanan, seperti ada yang melihat kandungan kalorinya, harganya, lokasinya, atau yang lainnya. Seiring berkembangnya teknologi informasi, sistem rekomendasi telah semakin dibutuhkan oleh masyarakat untuk membantu pengguna dalam mendapatkan informasi menu makanan yang relavan. Ada metode untuk merekomendasikan makanan berdasarkan contextual model dan multi-criteria decision yang dapat membantu pengguna memilih makanan yang cocok. Berdasarkan pada metode Weighted Sum Model, penelitian ini ingin membuat suatu teknik yang lebih baik dengan menggunakan terapan Contextual Model. Contextual Model membuat pengguna menjadi lebih mengerti dalam penggunaan sistem dan mudah dimengerti.Kata kunci— wisata kuliner, recommender system, contextual model, multi-criteria decision, weighted sum model.
Co-Authors Abdullah, Athallah Zacky Adhyasta Naufal Faadhilah 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 Arliyanna Nilla 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 Danang Triantoro Murdiansyah Daniar Dwi Pratiwi Daniar Dwi Pratiwi Dea Alfatihah Nindya Erlani Dede Tarwidi Dedy Handriyadi Deni Saepudin Dery Anjas Ramadhan Dhinta Darmantoro Diaz Tiyasya Putra Dion Pratama Putra, Dion Pratama Diyas Puspandari Evi Dwi Wahyuni Faidh Ilzam Nur Haq Farid, Husnul Khotimah Fathin Thariq Wiyono 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 Girindra Syukran Prahasto 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 Gusti Bagus Bagaskara Kerta Yasa I Kadek Candradinata Ibnu Sina, Muhammad Noer Ihsani Hawa Arsytania 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 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 Arif Dwi Putra 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 Nadim Rafli Hamzah Naufal Adi Nugroho Naufal Razzak , Robith Nisa Maulia Azahra Nur Ihsan Putra Munggaran Nuril Adlan , Muhammad Putri, Karina Khairunnisa Raffly, Famardi Putra Muhammad Raffly Rafi Anandita Wicaksono Raisa Sianipar Rakhmat Rifaldy Ramadhan, Ananta Ihza Ramadhan, Helmi Sunjaya Ramadhani, Andi Nailul Izzah Rangga Lesmana Rayhan Rahmanda Refka Muhammad Furqon Regina Anatasya Rudiyanto Rendo Zenico Riaji, Dwi Hariyansyah Rizki Annas Sholehat Roji Ellandi Saleh, Abd Salsabil, Adinda Arwa Sanabila Khoirunnisa 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 Prasetyowati 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 Windy Ramadhanti Yoan Maria Vianny Yuliant Sibaroni Zahwa Dewi Artika Zakaria, Aditya Mahendra ZK Abdurahman Baizal