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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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Journal : Building of Informatics, Technology and Science

 1 
Recommender System Movie Netflix using Collaborative Filtering with Weighted Slope One Algorithm in Twitter Rakhmat Rifaldy; Erwin Budi Setiawan
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1959

Abstract

Movies are entertainment that many people enjoy filling their spare time. After watching a movie, people usually write reviews about the movie on social media such as Twitter. As the number of movies grows, a recommendation system is created, which is useful for finding movies they might like based on the movies they have seen. This study developed a movie recommendation system using Collaborative Filtering (CF) with the Weighted Slope One (WSO) algorithm. The dataset used is taken from tweet data on Twitter. Then the tweet dataset is converted into a rating value which will later be used in the recommendation system. This study uses Mean Absolute Error (MAE) to measure accuracy. In Collaborative Filtering, the system gets the best MAE of 0.924. Then for Weighted Slope One, the system gets the best MAE of 0.568.
Fashion Recommendation System using Collaborative Filtering Muhammad Khiyarus Syiam; Agung Toto Wibowo; Erwin Budi Setiawan
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.3690

Abstract

Collaborative Filtering is an method used to build a recommendation system with the concept that conclusions from different clients are used to anticipate things that may be of interest to users. This research uses data from Rent the Runway and the method used is Item-based Collaborative filtering, where the system will look for similarities in products that have been purchased by customers and then look for predictive values. Fashion requires recommendations because it plays a crucial role in helping individuals express their identity, personal style, and personality through clothing choices, accessories, and dressing styles.The recommendation system uses the item method based on analyzing the number of purchases or sales and grouping according to each product category so that it can help consumers in choosing fashion products. It was found that the use of Adjusted Cosine Similarity produces better recommendations with an average MAE value of 0.2750, while Cosine Similarity with an average MAE difference of 0.3989. This proves that the use of adjusted cosine similarity can produce better recommendations because the adjustment algorithm not only considers user behavior, but also produces lower performance errors.
Topic Detection on Twitter using GloVe with Convolutional Neural Network and Gated Recurrent Unit Moh Adi Ikfini M; Erwin Budi Setiawan
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4057

Abstract

Twitter is a social media platform that allows users to share thoughts or information with others for all to see. However, twitters often use abbreviations, slang, and incorrect grammar because tweets are limited to 280 characters. Topic detection often has problems with low accuracy, one method that can be used to overcome this problem is feature expansion. Feature expansion on Twitter is a semantic addition to the process of expanding the original text syllables to make it look like a large Document. That way, feature expansion is used to reduce word mismatches. This study uses the expansion of the GloVe feature with the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) classification methods. The results show that the topic detection system with the GloVe feature extension and CNN-GRU hybrid classification has an accuracy of 94.41%
Hoax Detection Using Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) on Social Media Putra, Dion Pratama; Setiawan, Erwin Budi
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3084

Abstract

There are negative effects of the ease of obtaining information in today's society, one of which is the rise of hoaxes on the internet. As much as 92.40% of social media platforms such as Twitter are used to spread hoaxes. Launched on July 13, 2006, Twitter is a microblogging service where users can spread information at no cost to themselves or others. In this study, the authors will conduct hoax news detection on Twitter social media using the Long Short - Term Memory (LSTM) method and Gate Recurent Unit (GRU) and gloVe feature expansion. with a dataset of 25,234 data which produces accuracy results in TF-IDF on each model, namely 97.33% in LSTM and 96.75% in GRU, and an increase in accuracy of 0.22% in the tweet corpus on LSTM and an increase in accuracy of 0.15 in the BeritaTweet corpus on GRU.
Aspect-based Sentiment Analysis on Social Media Using Convolutional Neural Network (CNN) Method Ramadhan, Ananta Ihza; Setiawan, Erwin Budi
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3103

Abstract

Social media are a platform for people to express their opinions on various topics, one of which is Twitter. Movie reviews are a frequently found topic on Twitter that contains a person's opinion of a movie that has been watched. But since opinions are subjective, it is difficult to determine an accurate assessment of a movie. In addition, the diverse aspects of a movie make it difficult to judge whether a review is positive or negative. Referring to that problem, a method is needed to perform sentiment analysis of the problem. In this study, sentiment analysis of movie reviews was carried out based on aspects of plot, acting, and director. This research also performs classification using a CNN model and combines several techniques, namely TF-IDF feature extraction, FastText feature expansion, and SMOTE to calculate the accuracy value and F1-Score. The final results obtained in this study are in the aspect of the plot getting an accuracy of 73.81% (+12,22%) and F1-score 73.72% (+15,93%), the acting aspect obtaining an accuracy value of 89.30% (+0,54%) and F1-score 89.26% (+50,80%), and in the aspect of the director having an accuracy of 87.37% (+0,28%) and F1-score 87.35% (+84,39%). Based on these results, each application of techniques such as TF-IDF, FastText, and SMOTE can increase the accuracy value and F1-Score of the model built.
Aspect-Level Sentiment Analysis on Social Media Using Gated Recurrent Unit (GRU) Kamil, Ghani; Setiawan, Erwin Budi
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3105

Abstract

Twitter is one of the popular social media for sharing opinions, one of which is about movie reviews. There are many opinions related to movie reviews on Twitter social media so the assessment of a movie can vary. Therefore, aspect-level sentiment analysis is needed to classify movie reviews to provide optimal results. This research was conducted by building a system using the Gated Recurrent Unit (GRU) method to perform sentiment analysis at the aspect level on movie reviews taken from Twitter. The aspects used in this research are plot, acting, and director. This research also conducted experiments by combining three techniques, which are feature extraction using TF-IDF, feature expansion with GloVe, and the application of SMOTE to improve model accuracy. The results show that each test scenario can improve the accuracy and F1-Score values of each aspect. The final value of each aspect is the accuracy value for the plot aspect is 70.40%(+7.62%) and F1-Score is 70.35%(+9.70%), the accuracy value is 93.75%(+6.28%) and F1-Score is 93.70%(+65.19%) for the acting aspect, and the accuracy value is 90.44%(+4.60%) and F1-Score is 90.17%(+122.80%) for the director aspect.
Detecting Hoax Content on Social Media Using Bi-LSTM and RNN Aji, Hilman Bayu; Setiawan, Erwin Budi
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3585

Abstract

Online media, such as websites and applications, have become a communication tool available on the internet. Social media is a part of online media that can be used to spread news, opinions, or even hoaxes, such as through Twitter. Although hoaxes are difficult to eliminate, several systems have been built using deep learning approaches that can process text and images to detect the truthfulness of news. In this study, four systems were built using four deep learning methods, namely Bi-directional Long Short-Term Memory (Bi-LSTM), Recurrent Neural Network (RNN), hybrid RNN-Bi-LSTM, and hybrid Bi-LSTM-RNN. Feature extraction was performed using Term Frequency - Inverse Document Frequency (TF-IDF) and feature expansion was performed using Global Vectors (GloVe). The data used has been adjusted according to the keyword of fake news on mainstream news portals. This study attempted several scenarios to compare the various methods that have been built, with the aim of finding the best method that provides the highest accuracy. The results showed that the Bi-LSTM method had the highest accuracy of 96.48%, while the hybrid Bi-LSTM-RNN method ranked second with an accuracy of 96.36%, followed by the RNN method with an accuracy of 95.49%, and the hybrid RNN-Bi-LSTM method with an accuracy of 95.34%.
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