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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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Film Recommendation System Using Content-Based Filtering and the Convolutional Neural Network (CNN) Classification Methods Arliyanna Nilla; Erwin Budi Setiawan
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.28113

Abstract

Managing large amounts of data is a challenge faced by users, so a recommendation system is needed as an information filter to provide relevant item suggestions. Twitter is often used to find information about movie reviews that can be used a basis for developing recommendation systems. This research contributes to applying content-based filtering in the context of Convolutional Neural Network (CNN). To the best of the researcher's knowledge, there has been no research addressing this combination of method and classification. The main focus is to evaluate the development of a recommendation system by integrating and comparing similarity identification methods using the RoBERTa and TF-IDF approaches. In this research, Roberta and TF-IDF as vectorizer and classification methods are applied to form a model that can recognize patterns in data and produce accurate predictions based on its features. The total data used is 854 movies and 34086 film reviews from 44 Twitter accounts. The SMOTE method was applied as a technique to overcome data imbalance. The research was conducted three times with increasing accuracy results. The first experiment TF-IDF as baseline, SMOTE on CNN classification. The second experiment, applying baseline, SMOTE, embedding on CNN classification. The third experiment applied baseline, SMOTE, embedding, and optimizer to CNN classification. The experimental results show that TF-IDF as baseline, SMOTE, embedding and SGD optimizer with the best learning rate on CNN classification can provide optimal results with an accuracy rate of 86.41%. Thus, the system can provide relevant movie recommendations with good prediction accuracy and performance.
Movie Recommender System Using Cascade Hybrid Filtering with Convolutional Neural Network Ihsani Hawa Arsytania; Erwin Budi Setiawan; Isman Kurniawan
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.v10i2.28146

Abstract

The current technological advancements have made it easier to watch movies, especially through online streaming platforms such as Netflix. Social media platforms like Twitter are used to discuss, share information, and recommend movies to other users through tweets. The user tweets from Twitter are utilized as a film review dataset. Film ratings can be used to build a recommendation system, incorporating Collaborative Filtering (CF) and Content-based Filtering (CBF). However, both methods have their limitations. Therefore, a hybrid filtering approach is required to overcome this problem. The filtering approach involves CF and CBF processes to improve the accuracy of film recommendations. No current research employs the Cascade Hybrid Filtering method, particularly within the context of movie recommendation systems. This study addresses this gap by implementing the Cascade Hybrid Filtering method, utilizing the Convolutional Neural Network (CNN) as the evaluative instrument. This research presents a significant contribution by implementing the Cascade Hybrid Filtering method based on CNN. This research uses several scenarios to compare methods to produce the most accurate model. This study's findings demonstrate that the application of Cascade Hybrid Filtering, incorporating CNN and optimized with RMSProp, yields a movie recommendation system with notable performance metrics, including an MAE of 0.8643, RMSE of 0.6325, and the highest accuracy rate recorded at 86.95%. The RMSprop optimizer, facilitating a learning rate of 6.250551925273976e-06, enhances accuracy to 88.40%, showcasing a remarkable improvement of 6.00% from the baseline. These outcomes underscore the significant contribution of the paper in enhancing the precision and effectiveness of movie recommendation systems.
Content-Based Filtering in Recommendation Systems Culinary Tourism Based on Twitter (X) Using Bidirectional Gated Recurrent Unit (Bi-GRU) Adhyasta Naufal Faadhilah; Erwin Budi Setiawan
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.v10i2.29010

Abstract

To address the challenge of information overload in the rapidly expanding culinary sector, a recommendation system using Content-Based Filtering (CBF) and the Bidirectional Gated Recurrent Unit (Bi-GRU) algorithm was developed. This system can help users to suggest culinary options based on user profiles and preferences. Twitter (X) is frequently used to gather culinary reviews in Bandung, forming the foundation for developing recommendation systems. This research contributes to integrating CBF and Bi-GRU to enhance the relevance of culinary recommendations. The system uses Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction and Cosine Similarity for item matching. Research adapting CBF and Bi-GRU methods specifically for culinary recommendations, especially in Bandung, remains limited. This study focuses on evaluating the performance of a culinary recommendation system. Data collected from Twitter (X) and PergiKuliner includes 2,645 reviews from 44 Twitter (X) accounts and on 200 culinary places. The culinary recommendation model, using CBF with TF-IDF and Cosine Similarity, achieved a Mean Absolute Error (MAE) of 0.254 and Root Mean Square Error (RMSE) of 0.425, indicating high accuracy in rating predictions compared to previous studies. From the experiments conducted, the third experiment using Bi-GRU, SMOTE, and the Nadam algorithm showed the best improvement with a learning rate of 0.014563484775012459, achieving an accuracy of 86.8%, precision of 86.3%, recall of 85.2%, and an F1-Score of 85.5%, with a 16.2% increase in accuracy from the baseline. Thus, this system effectively helps users with culinary recommendations in Bandung, providing good performance based on user preferences.
Sistem Pendukung Keputusan Pemilihan Saham Untuk Portofolio Dengan Menggunakan Metode Support Vector Regression (SVR) Dan Analisis Fundamental (FA) Rangga Lesmana; Deni Saepudin; Erwin Budi Setiawan
LOGIC: Jurnal Penelitian Informatika Vol. 4 No. 1 (2026): June 2026
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/logic.v4i1.11853

Abstract

Pemilihan saham yang tepat sangat penting dalam membangun portofolio investasi yang optimal karena dapat memaksimalkan keuntungan dan mengurangi risiko, yang merupakan fokus utama investor dalam pengambilan keputusan. Penelitian ini bertujuan mengembangkan sistem pendukung keputusan menggunakan Support Vector Regression (SVR) dan analisis fundamental (FA) untuk memprediksi return saham serta memilih saham terbaik untuk portofolio. SVR diterapkan dengan kernel Radial Basis Function (RBF) untuk memprediksi return saham berdasarkan data historis, sementara FA menganalisis indikator keuangan seperti Return On Asset (ROA), Return On Equity (ROE), Debt to Equity Ratio (DER), dan Price to Book Value (PBV). Portofolio kemudian dibentuk menggunakan model Equal-Weighted (EW). Hasil penelitian menunjukkan bahwa kombinasi SVR dan FA menghasilkan rata-rata return portofolio sebesar 0,0099 dengan standar deviasi 0,0726, lebih tinggi dibandingkan dengan SVR saja yang memiliki rata-rata return sebesar 0,0047 dan standar deviasi 0,0668. Temuan ini menegaskan bahwa pendekatan kombinasi SVR dan FA mampu meningkatkan kinerja portofolio dan mengurangi risiko secara signifikan, sehingga menawarkan strategi investasi yang lebih optimal. Penelitian ini juga merekomendasikan eksplorasi lebih lanjut dengan memperluas data yang digunakan serta mempertajam penerapan SVR dan analisis fundamental untuk meningkatkan akurasi prediksi dan optimasi portofolio.
Sentiment Analysis on Social Media Using Long Short-Term Memory and Word2Vec Feature Expansion Methods with Adam Optimization Sanabila Khoirunnisa; Erwin Budi Setiawan
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 11 No. 1 (2025): April 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v11i1.3957

Abstract

Twitter is one of Indonesia's most popular social media, so it has many users. The intensity of Twitter use can be used to carry out sentiment analysis related to topics being widely discussed, especially regarding the 2024 Indonesian presidential election. To understand public views, public opinion is divided from text data into positive and negative polarities to measure public sentiment. The classification model uses Long Short-Term Memory (LSTM) for feature extraction, utilizing TF-IDF. In addition, this model also combines Word2Vec based on the Indonews corpus, which contains 142,545 articles for feature expansion. This model is further optimized using the Adam optimization technique to improve accuracy. By using a dataset of 37,391 data, the results of this research obtained an accuracy score of 83.04% and an f1 score of 82.62%. This is an increase in accuracy of 11.22%; for the f1 score, it was a 10.92% increase from the baseline. This indicates that the classification model using Long Short-Term Memory (LSTM) with the application of TF-IDF as feature extraction, Word2Vec as feature expansion, and Adam optimization successfully produced optimal sentiment predictions regarding the 2024 Indonesian Presidential Election.
Twitter Social Media-Based Sentiment Analysis Using Bi-LSTM Method With Genetic Algorithms Optimization Girindra Syukran Prahasto; Erwin Budi Setiawan
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 11 No. 1 (2025): April 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v11i1.3959

Abstract

Advances in information technology, particularly social media platforms such as Twitter, can be used to explore public sentiment around the much-discussed 2024 Indonesian Presidential Election. Using sentiment analysis as part of text mining, we focus on distinguishing positive and negative polarity using Natural Language Processing (NLP) techniques with to detect the accuracy of tweet polarity regarding the 2024 Indonesian Presidential Election. Specifically, we implement the Bidirectional Long Short-term Memory (Bi-LSTM) method, an enhanced version of LSTM, for sentiment analysis. The text is preprocessed, TF-IDF is used for word importance weighting, and Word2Vec is used for efficient learning of high-quality words. To optimize the accuracy of the model, we used Genetic Algorithm (GA), a heuristic approach rooted in the principles of genetics and natural selection. GA operates on a chromosome-based population, aligned with Darwinian evolutionary concepts. This research aims to compare the accuracy of the Bi-LSTM model with various feature extraction methods, including TF-IDF and Word2Vec, in measuring the polarity of election-related tweets. This research highlights the comparison and improvement of the accuracy of each scenario in the built model. The accuracy score results in this research was 83%, where the accuracy score increases from the baseline by 7.98%.
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