https://ojs.uajy.ac.id/index.php/jbi/issue/feed Jurnal Buana Informatika 2025-05-05T11:21:43+07:00 Herlina, S.Kom., M.Eng. jbi@uajy.ac.id Open Journal Systems <p><strong><a href="https://fti.uajy.ac.id/informatika/">Universitas Atma Jaya Yogyakarta - Prodi Informatika</a></strong></p> <table class="data" width="100%" bgcolor="#f0f0f0"> <tbody> <tr valign="top"> <td width="20%">Journal title</td> <td width="40%"><strong>Jurnal Buana Informatika</strong></td> <!--td rowspan="8" width="40%"> <img style="width: 80%; height: Auto; max-width: 300px; display: block; margin-left: auto; margin-right: auto;" src="/public/journals/1/homepageImage_en_US.png" alt="" /></td--></tr> <tr valign="top"> <td width="20%">Abbreviation</td> <td width="40%"><strong>JBI</strong></td> </tr> <tr valign="top"> <td width="20%">Frequency</td> <td width="40%"><strong>Two issues per year (April and October)<br /></strong></td> </tr> <tr valign="top"> <td width="20%">DOI</td> <td width="40%"><strong>prefix 10.24002 </strong><strong><br /></strong></td> </tr> <tr valign="top"> <td width="20%">Print ISSN</td> <td width="40%"><strong><a href="https://issn.brin.go.id/terbit/detail/1271083534">2087-2534</a></strong></td> </tr> <tr valign="top"> <td width="20%">Online ISSN</td> <td width="40%"><strong><a href="https://issn.brin.go.id/terbit/detail/1326768860">2089-7642</a> </strong></td> </tr> <tr valign="top"> <td width="20%">Editor-in-chief</td> <td width="40%"><strong><a href="https://scholar.google.com/citations?user=S054sdEAAAAJ&amp;hl=en">Yonathan Dri Handarkho, S.T., M.T., Ph.D.</a></strong></td> </tr> <tr valign="top"> <td width="20%">Publisher</td> <td width="40%"><strong><a href="http://www.uajy.ac.id/">Universitas Atma Jaya Yogyakarta</a></strong></td> </tr> </tbody> </table> <hr /> <p><strong>Journal of Buana Informatika </strong>is published by Faculty of Industrial Technology University of Atma Jaya Yogyakarta as a medium to channel understanding about technological aspects of information technology in the form of result of field research or laboratory or literature study. This journal is published twice a year in April and October. The editor receives manuscript contributions from lecturers, researchers, students and practitioners discussing the scopes of computational science, graphics and visualization, human-computer interaction, information management, information assurance and security, platform-based development, parallel and distributed computing, and software engineering. Please kindly follow the checklist of writing guideline and the manuscript template that can be downloaded in this site.</p> https://ojs.uajy.ac.id/index.php/jbi/article/view/11384 Implementasi Algoritma Apriori sebagai Association Rule Learning untuk Mengidentifikasi Pola Item Dataset Penjualan 2025-04-15T18:58:30+07:00 I Wayan Supriana wayan.supriana@unud.ac.id Luh Arida Ayu Rahning Putri rahningputri@unud.ac.id <p><em>Retail store competition is becoming more intense, so marketing and product arrangement are crucial for shopping efficiency, maintaining comfort, and increasing profits. This study analyzes consumer shopping habits for goods in each transaction through market basket analysis. The Apriori algorithm is a common technique for finding frequent item search techniques in building association rules, namely the relationships between item combinations in a dataset. The aim is to implement the Apriori algorithm as an association rule learning method to identify patterns within sales data. The Apriori association rule is compared to the frequent pattern growth algorithm, which finds the most frequently occurring patterns in a dataset. Based on the tests, the average lift ratio for the Apriori algorithm is 1.58, while for the frequent pattern growth algorithm, it is 1.28. This indicates that the Apriori algorithm performs better than the frequent pattern growth algorithm.</em></p> 2025-04-01T00:00:00+07:00 Copyright (c) 2025 https://ojs.uajy.ac.id/index.php/jbi/article/view/11357 Sistem Pakan Cerdas Berbasis IoT Untuk Optimalisasi Peternakan Kambing Umbaran di Era Digital Farm 2025-04-12T14:12:53+07:00 Rizal Zahrowani rizzahro@students.amikom.ac.id Jeki Kuswanto jeki@amikom.ac.id Eko Pramono eko.p@amikom.ac.id <p><em><span class="NormalTextRun SCXW181151911 BCX4" data-ccp-parastyle="Title">This research aims to </span><span class="NormalTextRun SCXW181151911 BCX4" data-ccp-parastyle="Title">create</span><span class="NormalTextRun SCXW181151911 BCX4" data-ccp-parastyle="Title"> a smart feeding system based on the Internet of Things (IoT) to enhance the efficiency of feed delivery in goat farming. The system automatically regulates the feed dispenser according to a predetermined schedule, making it easier for farmers to manage </span><span class="NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW181151911 BCX4" data-ccp-parastyle="Title">feed</span><span class="NormalTextRun SCXW181151911 BCX4" data-ccp-parastyle="Title">. System testing </span><span class="NormalTextRun SCXW181151911 BCX4" data-ccp-parastyle="Title">demonstrates</span><span class="NormalTextRun SCXW181151911 BCX4" data-ccp-parastyle="Title"> its effectiveness in reducing feed delivery time and minimizing waste.</span><span class="NormalTextRun SCXW181151911 BCX4" data-ccp-parastyle="Title"> T</span><span class="NormalTextRun SCXW181151911 BCX4" data-ccp-parastyle="Title">he system features an LCD screen that displays the dispenser status, providing real-time information to farmers. This technology also allows for remote monitoring, enabling farmers to manage feed more effectively. The implementation of this system is expected to improve productivity and animal welfare while promoting modernization in farming practices in Indonesia. This innovation is </span><span class="NormalTextRun SCXW181151911 BCX4" data-ccp-parastyle="Title">anticipated</span><span class="NormalTextRun SCXW181151911 BCX4" data-ccp-parastyle="Title"> to offer a sustainable solution to challenges in feed management, providing long-term benefits for farmers and the livestock industry.</span></em></p> 2025-04-01T00:00:00+07:00 Copyright (c) 2025 https://ojs.uajy.ac.id/index.php/jbi/article/view/11354 Importance of Feature Selection for Multiple Disease Classification 2025-04-11T19:42:52+07:00 Rio Arya Andika 672021166@student.uksw.edu Christine Dewi christine.dewi@uksw.edu <p><em><span class="NormalTextRun SCXW143621542 BCX4" data-ccp-parastyle="Title">The performance of machine learning in disease classification heavily depends on effective feature selection. This study explores feature selection methods—Boruta and Recursive Feature Elimination (RFE)—with ensemble models like Random Forest, Decision Tree, Gradient Boosting, </span><span class="NormalTextRun SpellingErrorV2Themed SCXW143621542 BCX4" data-ccp-parastyle="Title">LightGBM</span><span class="NormalTextRun SCXW143621542 BCX4" data-ccp-parastyle="Title">, and </span><span class="NormalTextRun SpellingErrorV2Themed SCXW143621542 BCX4" data-ccp-parastyle="Title">XGBoost</span><span class="NormalTextRun SCXW143621542 BCX4" data-ccp-parastyle="Title"> using Electronic Health Records (EHR) data. Results show that combining Boruta with </span><span class="NormalTextRun SpellingErrorV2Themed SCXW143621542 BCX4" data-ccp-parastyle="Title">LightGBM</span><span class="NormalTextRun SCXW143621542 BCX4" data-ccp-parastyle="Title"> achieves the highest accuracy of 99%. Feature selection enhances precision by focusing on relevant variables and removing unnecessary ones. Further analysis reveals that features such as Red Blood Cells, Insulin, Heart Rate, and Cholesterol significantly influence the classification of specific diseases. These findings highlight the importance of feature selection in multi-disease classification and medical data analysis, improving the efficiency of machine learning systems. Future research should develop more flexible feature selection methods and test models on diverse disease datasets.</span></em></p> 2025-04-01T00:00:00+07:00 Copyright (c) 2025 https://ojs.uajy.ac.id/index.php/jbi/article/view/11269 Ekstraksi Pengetahuan dari Ulasan Aplikasi CapCut Menggunakan Metode Aspect-Based Sentiment Analysis dan Klasifikasi 2025-05-05T11:21:43+07:00 Ishlah Putri Ariyani ishlahariyani@gmail.com Ken Ditha Tania kenya.tania@gmail.com Ari Wedhasmara a_wedhasmara@unsri.ac.id Allsela Meiriza allsela@unsri.ac.id <p><em>Indonesia is experiencing rapid technological development, especially in the use of the internet and editing platforms like CapCut. These platforms enable video editing on various devices; however, user satisfaction is not always guaranteed due to individual differences in experience. This research aims to identify user sentiment towards the CapCut application based on aspects, using an Aspect-Based Sentiment Analysis (ABSA) approach supported by Machine Learning algorithms for the aspect-based sentiment classification task. The algorithm used in the classification process is Support Vector Machine. The data used are reviews of the CapCut application from the Google Play Store, with a total of 22,668 data points. The results show that the Support Vector Machine (SVM) algorithm performs well in each aspect, with accuracy values of 0.88 for the feature aspect and 0.87 for the user experience aspect.</em> <em>The results of knowledge extraction are obtained in the form of XML, which contains user sentiment information on two main aspects: features and user experience. </em></p> 2025-04-01T00:00:00+07:00 Copyright (c) 2025 https://ojs.uajy.ac.id/index.php/jbi/article/view/10836 Peningkatan Akurasi Rekomendasi Dokter pada Kondisi Data Sparsity Menggunakan Algoritma Content-Based Filtering 2025-01-31T20:37:17+07:00 Alwan Prasetya alwanprasetya9@gmail.com Ahsanun Naseh Khudori Ahsanunnaseh@itsk-soepraoen.ac.id Risqy Siwi Pradini risqypradini@itsk-soepraoen.ac.id <p><em>The growth of healthcare applications such as Halodoc, Alodokter, and Klikdokter has enabled easier access to doctor recommendations. However, generating relevant recommendations remains challenging. One key issue is data sparsity, where limited doctor attributes reduce the system’s accuracy. This study develops a doctor recommendation system using a Content-Based Filtering (CBF) approach based on five main attributes: specialization, rating, consultation fee, years of practice, and gender. Data imputation and attribute weighting techniques are applied to enhance accuracy. Results show that the proposed method reduces the Mean Absolute Error (MAE) from 0.142 to 0.102 and the Root Mean Squared Error (RMSE) from 0.205 to 0.150. These findings indicate that the implemented techniques improve the recommendation system under sparse data conditions.</em></p> 2025-04-01T00:00:00+07:00 Copyright (c) 2025