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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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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.
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.
Movie Recommender System on Twitter Using Weighted Hybrid Filtering and GRU Valentino, Nico; Setiawan, Erwin Budi
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.v9i2.1941

Abstract

The development of the industry in the film sector has experienced rapid growth, marked by the emergence of film streaming platforms such as Netflix and Disney+. With the abundance of available films, users face difficulty in choosing films that suit their preferences. Recommender systems can be a solution to this problem for users. Recommender systems rely on user reviews, making Twitter a platform that can be used to collect user reviews of a film. This study will develop a recommender system that has the potential to provide item recommendations to users using the weighted hybrid filtering and GRU methods. The weighted hybrid filtering used is a combination of collaborative filtering and content-based filtering methods. The dataset used in this study was obtained by crawling tweets relevant to the feedback of specific accounts regarding a film. The dataset resulting from the data crawling consists of a total of 854 films, 45 users and 34,086 tweets consisting of film reviews from Twitter users. The GRU model classification is performed on the results of weighted hybrid filtering with model optimization involving testing various test size scenarios and optimizer methods. The test sizes used are 40%, 30%, and 20%. The optimizer methods used include Adam, Nadam, Adamax, Adadelta, Adagrad, and SGD. The research results show that the optimal outcome is obtained using the Nadam optimization method. The performance evaluation yielded 85.74% precision, 88.63% recall, 88.63% accuracy, and 86.30% F1-score.
Estimating Forest Carbon Stocks Using CNN and Vegetation Texture Features Extracted from UAV and Satellite Data in Telkom University Aydin, Raditya; 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.v10i4.30546

Abstract

Forests play a crucial role in mitigating climate change by acting as carbon sinks, yet traditional methods of carbon stock estimation, reliant on manual tree measurements, are costly, time-consuming, and geographically limited. Recent advancements in remote sensing technologies, such as the combination of Unmanned Aerial Vehicles (UAVs) and Google Earth Engine (GEE), offer a promising alternative by integrating high-resolution local observations with global-scale data. Using the power of Convolutional Neural Networks (CNNs), this study suggests an integrated method for classifying carbon stocks by fusing textural parameters like homogeneity and entropy with spectral indices like Green Chromatic Coordinates (GCC) and Excess Green Index (ExG). CNNs are used to capture the spectral richness and structural complexity of vegetation because of their propensity to extract hierarchical spatial characteristics. The research compares the performance of various feature combinations—color-based, texture-based, and mixed features—using a hybrid framework of UAV and GEE data. It is anticipated that the results will demonstrate how spectral and textural features work together to increase classification accuracy. In addition to tackling major issues in carbon stock estimation, this scalable and integrated framework is made to adapt to a variety of forest ecosystems and aid in the creation of conservation policies and the mitigation of climate change.
Social Media (Twitter) Based Movie Recommendation System On Disney+ With Hybrid Filtering Using Neighboor's K-Nearest Method Fazira Ansshory, Azrina; Budi Setiawan, Erwin
JINAV: Journal of Information and Visualization Vol. 4 No. 2 (2023)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1954

Abstract

The research aims on the development of a film recommendation system that combines the Hybrid Filtering and K-Nearest Neighbors method. (KNN). Hybrid Filtering combines a variety of recommendation techniques, including collaborative filtering and content-based filtering, while KNN is a simple method for classifying data based on similarities with close neighbors. The data set used is Kaggle’s Rotten Tomatoes, which includes information such as critics’ names, genres, movie titles, and review content. The aim of the study was to build an accurate system of recommendations based on user ratings on Disney+ Hotstar and measure its performance using MAE (Mean Absolute Error) and Confusion Matrix assessments.The results showed that the combination of Hybrid Filtering and KNN methods resulted in better accuracy values in giving film recommendations compared to using only the Collaborative Filtering method. Graphics and performance analysis show that the developed models are able to provide film recommendations with increasing accuracy over time. In conclusion, the combination of Hybrid Filtering and K-Nearest Neighbors methods is effective in improving the accuracy of the movie recommendation system, helping users choose films that match their preferences on the Disney+ Hotstar platform. This research contributes to the development of better and more accurate recommendation systems in the film industry.
Sentiment Analysis Accuracy for 2024 Indonesian Election Tweets Using CNN-LSTM With Genetic Algorithm Optimization Abdullah, Athallah Zacky; Setiawan, Erwin Budi
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 1 (2025): February 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i1.22999

Abstract

Background: The 2024 Indonesian Presidential Election is ideal for analyzing public sentiment on Twitter. Data collection began with crawling from the data source to create a dataset, which included 62,955 entries from Twitter, 126,673 entries from IndoNews, and a combined Tweet+IndoNews dataset totaling 189,628 entries. Objective: This study aims to explore sentiment using a hybrid model integrating Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) methods, with feature expansion via Word2Vec optimized by a Genetic Algorithm (GA). Methods: The research evaluates the effectiveness of the hybrid CNN-LSTM model in analyzing sentiment from 2024 Indonesian Presidential Election tweets, aiming for higher accuracy and deeper insights compared to traditional methods. Results: The hybrid CNN-LSTM model, optimized with a Genetic Algorithm, significantly enhances accuracy, achieving the highest accuracy of 84.78% for the news data, marking a 3.59% increase. Conclusion: This study illustrates the innovative application of a hybrid CNN-LSTM model with Word2Vec feature expansion and Genetic Algorithm optimization for sentiment analysis in a national election context, demonstrating how advanced techniques can improve accuracy and efficiency in sentiment analysis. 
Ekspansi Fitur dengan FastText untuk Analisis Sentimen di Media Sosial X Menggunakan Recurrent Neural Network dan Covlutional Neural Networ Naufal Razzak , Robith; Budi Setiawan, Erwin
eProceedings of Engineering Vol. 12 No. 1 (2025): Februari 2025
Publisher : eProceedings of Engineering

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

Abstract

Abstrak - Dalam era digital, media X telah menjadi platform penting bagi masyarakat untuk ekspresi dan pertukaran informasi, terutama selama pemilihan umum, memungkinkan analisis sentimen untuk memahami opini publik secara real-time. Namun, tantangan utama adalah pengolahan teks yang tidak terstruktur dan kompleks, yang sering menghasilkan analisis yang kurang akurat. Untuk mengatasi ini, penelitian ini mengembangkan model hybrid deep learning yang mengintegrasikan Recurrent Neural Networks (RNN) dan Convolutional Neural Networks (CNN). Selain itu, penggunaan ekspansi fitur FastText memungkinkan model untuk mengatasi inkonsistensi kosakata dan meningkatkan pemahaman kontekstual, dengan mengidentifikasi dan menggantikan kata-kata yang kurang representatif dengan alternatif yang memiliki kemiripan semantik. Dua dataset digunakan untuk membangun similarity corpus dengan dataset media X sebesar 62.955 dan datset IndoNews sebesar 126.673. Sistem menunjukkan tingkat akurasi maksimum pada model hybrid deep learning RNN-CNN yaitu sebesar 73,00% dengan peningkatan 2,50% terhadap model RNN dan 3,00% terhadap model CNN. Hasil ini menunjukkan bahwa integrasi RNN dan CNN, bersama dengan teknik ekspansi fitur FastText, meningkatkan kemampuan analisis sentimen, memberikan metode yang lebih efisien dan akurat dalam mengklasifikasikan sentimen publik. Kata kunci - hybrid, RNN, CNN, fasttext, analisis sentimen
Sistem Rekomendasi Destinasi Wisata di Kota Bandung dengan Collaborative Filtering Menggunakan K-Nearest Neighbors Nuril Adlan , Muhammad; Budi Setiawan, Erwin
eProceedings of Engineering Vol. 12 No. 1 (2025): Februari 2025
Publisher : eProceedings of Engineering

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

Abstract

Abstrak - Kota Bandung adalah salah satu destinasi wisata populer di Indonesia. Banyaknya jumlah destinasi wisata di Kota Bandung, ditambah dengan kurangnya informasi tentang pariwisata, menimbulkan hambatan bagi kebutuhan masyarakat dalam memilih destinasi wisata. Oleh karena itu, diperlukan sebuah sistem rekomendasi untuk membantu wisatawan dalam menentukan destinasi mereka. Penelitian ini mengembangkan sistem rekomendasi destinasi wisata di Kota Bandung dengan menerapkan algoritma user-based collaborative filtering dan K-Nearest Neighbors untuk membantu wisatawan memutuskan destinasi mereka berdasarkan tempat-tempat yang sebelumnya telah mereka kunjungi. Dua metode kesamaan yang digunakan adalah cosine similarity dan pearson correlation. Mean Absolute Error (MAE) dan hasil rekomendasi digunakan untuk mengevaluasi kinerja sistem. Hasil penelitian menunjukkan bahwa sistem rekomendasi yang dibangun cukup memberikan rekomendasi kepada user, dengan nilai MAE sebesar 2.59 untuk metode cosine similarity dan nilai MAE sebesar 2.67 untuk metode Pearson correlation. Selain itu, hasil rekomendasi wisata yang diberikan dianggap memadai karena sesuai dengan profil wisatawan. Kata kunci - Sistem Rekomendasi, Collaborative Filtering, User-Based, Cosine Similarity, Pearson Correlation, K-Nearest Neighbors
THE IMPACT OF WORD EMBEDDING ON CYBERBULLYING DETECTION USING HYBIRD DEEP LEARNING CNN-BILSTM Moh. Hilman Fariz; Erwin Budi Setiawan
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.6270

Abstract

Cyberbullying can be perpetrated by anyone, whether children or adults, with the primary aim of belittling or attacking specific individuals. Social media platforms like X (formerly Twitter) often serve as the primary medium for cyberbullying, where interactions frequently escalate into retaliatory attacks, intimidation, and insults. In detecting these actions, short tweets are often difficult to understand without context, making specialized approaches like word embedding important. This research uses GloVe feature expansion, utilizing a corpus generated from the IndoNews dataset containing 127,580 entries to enhance vocabulary understanding in tweets that include the use of Indonesian language in both formal and informal forms. This data was then classified using the Hybrid Deep Learning method, which combines Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) with used 30,084 tweets taken from platform X as the dataset. The analysis results show that the application of expansion features using GloVe can improve the performance of the BiLSTM-CNN hybrid model, with the highest accuracy reaching 83.88%, an increase of +3.65% compared to the hybrid model without GloVe. This research successfully detected cyberbullying on platform X, making a significant contribution to efforts to create a safer and more positive social media environment for users.
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