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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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Analisis Sentimen pada X Terhadap Pilkada 2024 Menggunakan Ekspansi Fitur FastText dan CNN dengan Optimasi Bat Algorithm Firdaus, Dzaki Afin; Setiawan, Erwin Budi
eProceedings of Engineering Vol. 12 No. 3 (2025): Juni 2025
Publisher : eProceedings of Engineering

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

Abstract

Pilkada 2024 merupakan momentum penting dalamdemokrasi Indonesia yang akan menentukan arahpembangunan daerah. Dalam konteks ini, analisis sentimendapat menjadi alat yang efektif untuk memahami opinipublik terhadap calon pemimpin dan isu-isu yang berkaitandengan Pilkada. Penelitian ini bertujuan untukmengimplementasikan sistem analisis sentimenmenggunakan Convolutional Neural Network (CNN) yangdioptimalkan dengan Bat Algorithm dan ekspansi fiturmenggunakan FastText. Metode ini diterapkan pada datatweet berbahasa Indonesia yang dikumpulkan selamaperiode Pilkada 2024. Hasil evaluasi menunjukkan bahwaakurasi tertinggi diperoleh dengan menggunakan maxfeature sebesar 15.000 (73,01%), konfigurasi Uni-Bigram(73,30%), dan ekspansi fitur menggunakan FastText dengankorpus Tweet + IndoNews pada Top 1 (73,82%). Optimasimenggunakan Bat Algorithm memberikan peningkatansebesar 0,05% (73,82% menjadi 73,87%), yangmenunjukkan bahwa FastText secara signifikanmeningkatkan akurasi model. Bat Algorithm terbukti efektifdalam mengoptimalkan parameter model dan memberikankontribusi positif dalam peningkatan kinerja. Penelitian inimenunjukkan bahwa penggunaan FastText dapatmemperbaiki akurasi model analisis sentimen, sementara BatAlgorithm juga memberikan kontribusi yang berhargadalam optimasi model. Kata kunci: analisis sentimen, CNN, bat algorithm, fasttext, pilkada 2024, optimasi
Depression Detection on Social Media X Using Hybrid Deep Learning CNN-BiGRU with Attention Mechanism and FastText Feature Expansion Widiarta, I Wayan Abi; Setiawan, Erwin Budi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i2.30687

Abstract

Depression is a global mental health disorder affecting over 280 million people, with significant challenges in identifying sufferers due to societal stigma. In Indonesia, the National Adolescent Mental Health Survey in 2022 revealed that 17.95 million adolescents experience mental health disorders, with a portion of them suffering from depression. Social media platform X offers an alternative for individuals to share their mental health status anonymously, bypassing societal stigma. This study proposes a hybrid deep learning model combining CNN and BiGRU with an attention mechanism, TF-IDF for feature extraction, and FastText for feature expansion to detect depression in Indonesian tweets. The dataset comprises 50,523 Indonesian tweets, supplemented by a similarity corpus of 151,117 data. To optimize model performance, five experimental scenarios were conducted, focusing on split ratios, n-gram configurations, maximum features, feature expansion, and attention mechanisms. The main contribution of this research is the novel integration of FastText for feature expansion and the attention mechanism within a CNN-BiGRU hybrid model for depression detection. The results demonstrate the effectiveness of this combination, with the BiGRU-ATT-CNN-ATT model achieving an accuracy of 84.40%. However, challenges such as handling noisy, ambiguous social media data and addressing out-of-vocabulary words remain. Future research should explore additional feature expansion techniques, optimization algorithms, and approaches to handle noisy data, improving model robustness for real-world applications in mental health detection.
Depression Detection using Convolutional Neural Networks and Bidirectional Long Short-Term Memory with BERT variations and FastText Methods Widjayanto, Leonardus Adi; Setiawan, Erwin Budi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4874

Abstract

Depression has become a significant public health concern in Indonesia, with many individuals expressing mental distress through social media platforms like Twitter. As mental health issues like depression are increasingly prevalent in the digital age, social media provides a valuable avenue for automated detection via text, though obstacles such as informal language, vagueness, and contextual complexity in social media complicate precise identification. This study aims to develop an effective depression detection model using Indonesian tweets by combining Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM). The dataset consisted of 58,115 tweets, labeled into depressed and non-depressed categories. The data were preprocessed, followed by feature extraction using BERT and feature expansion using FastText. The FastText model was trained on three corpora: Tweet, IndoNews, and combined Tweet+IndoNews corpus; the total corpus will be 169,564 entries. The best result was achieved by BiLSTM model with 84.67% accuracy, a 1.94% increase from the baseline, and the second best was the BiLSTM-CNN hybrid model achieved 84.61 with an accuracy increase of 1.7% from the baseline. These result indicate that combining semantic feature expansion with deep learning architecture effectively improves the accuracy of depression detection on social media platforms. These insights highlight the importance of integrating semantic enrichment and contextual modeling to advance automated mental health diagnostics in Indonesian digital ecosystems.
Genetic Algorithm and GloVe for Information Credibility Detection Using Recurrent Neural Networks on Social Media Twitter (X) Ramadhani, Andi Nailul Izzah; Setiawan, Erwin Budi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.29185

Abstract

Social media, especially X, has become a key source of information for many individuals, but the level of trust in the information spread on these platforms is a critical issue. To overcome this problem, this research proposed an information credibility detection system using a Recurrent Neural Network (RNN) with the utilization of TF-IDF feature extraction, GloVe feature expansion, BERT word embedding, and Genetic Algorithm (GA) optimization. This research contributes to assessing the credibility of tweets related to the 2024 Indonesian election by integrating TF-IDF to identify important words, GloVe to enhance word context, BERT for deeper understanding, and GA is present to optimize RNN performance. The main focus is to provide maximum accuracy by integrating these methods. In this research, the dataset used consists of 54,766 tweets relating to the 2024 Indonesia election and includes relatively equal numbers of credible and non-credible labels. The corpus construction utilized source X with a total of 40,466 data, IndoNews with a total of 131,580, and a combination of both with a total of 150,943. This research conducted six experimental scenarios, namely optimal data split, max features, N-grams, Top-N rank similarity corpus, BERT and GA application. Through these scenarios, the model achieved a significant accuracy improvement of 1.81% over the baseline, reaching an accuracy of 90.60%. This result demonstrates the effectiveness of the proposed system by presenting a higher quality of accuracy compared to the baseline model. Moreover, this research underscores the significant contribution of increasing the accuracy of information credibility detection.
Impact of Feature Selection on XGBoost Model with VGG16 Feature Extraction for Carbon Stock Estimation Using GEE and Drone Imagery Adyatma, I Made Darma Cahya; Setiawan, Erwin Budi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i4.30484

Abstract

Carbon stocks are critical to climate change mitigation by capturing atmospheric carbon and storing it in biomass. However, carbon stock estimation faces challenges due to data complexity and the need for efficient analytical methods. This study introduces a carbon stock estimation method that integrates the XGBoost algorithm with VGG16 feature extraction and feature selection techniques to analyze GEE and Drone image datasets. The model is evaluated through four scenarios: without feature selection, using Information Gain, using Feature Importance, and using Recursive Feature Elimination. These scenarios aim to compare feature selection methods to identify the best one for processing complex environmental data. The experimental results show that RFE significantly outperforms other methods, achieving an average RMSE of 6651.62, MAE of 2297.57, and R² of 0.7673. These findings underscore the importance of feature selection in optimizing model performance, particularly for high-dimensional environmental datasets. RFE shows superior accuracy and efficiency by retaining the most relevant features but requires more computational resources. For applications that prioritize time and resource efficiency, Information Gain or Feature Importance can serve as a practical alternative with slightly reduced accuracy. This research highlights the value of integrating feature selection techniques into machine learning models for environmental data analysis. Future research could explore alternative feature extraction methods, combine RFE with other approaches, or apply advanced techniques such as Boruta or genetic algorithms. These efforts will further refine carbon stock estimation models, paving the way for broader applications in environmental data analysis.
Implementation of Ant Colony Optimization – Artificial Neural Network in Predicting the Activity of Indenopyrazole Derivative as Anti-Cancer Agent Kurniawan, Isman; Kamil, Nabilla; Aditsania, Annisa; Setiawan, Erwin Budi
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1055

Abstract

Cancer is a disease induced by the abnormal growth of cells in body tissues. This disease is commonly treated by chemotherapy. However, at first, cancer cells can respond to the activity of chemotherapy over time, but over time, resistance to cancer cells appears. Therefore, it is required to develop new anti-cancer drugs. Indenopyrazole and its derivative have been investigated to be a potential drug to treat cancer. This study aims to predict indenopyrazole derivative compounds as anti-cancer drugs by using Ant Colony Optimization (ACO) and Artificial Neural Network (ANN) methods. We used 93 compounds of indenopyrazole derivative with a total of 1876 descriptors. Then, the descriptors were reduced by using the Pearson Correlation Coefficient (PCC) and followed by the ACO algorithm to get the most relevant features. We found that the best number of descriptors obtained from ACO is ten descriptors. The ANN prediction model was developed with three architectures, which are different in hidden layer number, i.e., 1, 2, and 3 hidden layers. Based on the results, we found that the model with three hidden layers gives the best performance, with the value of the R2 test, R2 train, and Q2 train being 0.8822, 0.8495, and 0.8472, respectively.
Implementation Information Gain Feature Selection for Hoax News Detection on Twitter using Convolutional Neural Network (CNN) Farid, Husnul Khotimah; Setiawan, Erwin Budi; Kurniawan, Isman
Indonesian Journal on Computing (Indo-JC) Vol. 5 No. 3 (2020): December, 2020
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2020.5.3.506

Abstract

The development of information and communication technology is currently increased, especially related to social media. Nowadays, many people get information through social media, especially Twitter, because of its easy access and it doesn't cost much. However, it has a negative impact in the form of spreading fake news or hoaxes that are difficult to detect. In this research, the authors developed a hoax news detection model using the Convolutional Neural Network and the TF-IDF weighting method. Feature selection is performed using Information Gain with various features, such as unigram, bigram, trigram and a combination of the three. Testing is done with 3 scenarios, classification, classification by weighting, classification by weighting and feature selection. The parameter used in the information gain feature selection is the threshold 0.8. The results showed that the classification by weighting and feature selection produced the highest accuracy that is equal to 95.56% on the unigram + bigram features with a comparison of training data and test data 50:50.
TOPIC CLASSIFICATION ON TWITTER USING CNN WITH WORD2VEC FEATURE EXPANSION Bintang Ramadhan, Rifaldy; Budi Setiawan, Erwin
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1342

Abstract

Twitter is a social networking site that enables users to communicate with their followers by sending them short messages known as "tweets." Each tweet has a character limit of 280 characters. The minimum limit of tweets resulted in writing short tweets and increased use of word variations. This makes tweets difficult to understand without the help of the topic, thus tweets should be classified. This study aims to classify topics of Twitter using word2vec feature expansion to decrease vocabulary ambiguities in topic classification. This type of research is system design research. Feature expansion is a machine learning technique used to extract new features (or variables) from the dataset's existing features. A model's complexity and expressive power are intended to be increased through feature expansion in order to improve performance and generalization. Data were processed using Convolutional Neural Network (CNN). The results indicate that there is an important contribution in increasing understanding of topic classification in Twitter data with Word2Vec, and the CNN application is able to assist some obstacles in analyzing short text with high word variations.
FILM RECOMMENDATION USING CONTENT-BASED USING ARTIFICIAL NEURAL NETWORK METHOD AND ADAM OPTIMIZATION Riaji, Dwi Hariyansyah; Setiawan, Erwin Budi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1729

Abstract

This research aims to develop a more accurate and relevant content-based film recommendation system from the Netflix and Disney+ streaming platforms using the ANN method. Movie recommendation systems are a popular solution to help users find movies that match their preferences. The ANN method develops a model to learn complex patterns from film features. Additionally, Adam optimization is used to improve the speed and accuracy of the model training process. The advantage of using an ANN is its ability to learn complex patterns and improve the performance of the recommendation system over time. Adam Optimization helps improve the speed, accuracy and quality of ANN models. From this research, researchers, based on the evaluation results using the confusion matrix, obtained an accuracy value of 88.30%, using a split ratio of 80:20 and a learning rate of 0.04469992592930794. This means that most classifications can detect correctly according to sufficient data. Combining these two methods allows the film recommendation system to provide better recommendations as more data becomes available.
Depression Detection in Indonesian X Social Media Text using Convolutional Neural Networks and Long Short-Term Memory with TF- IDF and FastText Methods Putri, Karina Khairunnisa; 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.2025.6.2.4206

Abstract

Depression is a growing mental health issue in the modern era, with social media offering a unique opportunity for automated detection through text analysis. However, challenges such as unstructured language, ambiguity, and contextual complexity in social media text hinder accurate detection. This research aims to develop and evaluate a hybrid deep learning model to detect depression in Indonesian social media text. A data set of 50523 entries was obtained and cleaned and TF-IDF was used for feature extraction while FastText was used for feature expansion. The classification was done by using Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a combination of both CNN and LSTM models and the performance of the models was measured using the accuracy, precision and recall scores. The experimental results showed that the LSTM model gave the best result in terms of accuracy which is 83.58%, the second best was the LSTM-CNN hybrid model with an accuracy of 83.20%. The current study thus provides a new approach for identifying depression in Indonesian language data and can be said to significantly advance the fields of informatics and computer science. It also shows how AI can be utilized in improving mental health practices and in designing better social media environments. The findings of this study contribute to the growing body of research on cross-cultural mental health detection and highlight the importance of developing language-specific machine learning models.
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