Journal of Applied Computer Science and Technology https://journal.isas.or.id/index.php/JACOST <p align="justify">The <strong>Journal of Applied Computer Science and Technology (JACOST)</strong> is a blind, peer-reviewed journal dedicated to the publication of quality research results in the field of computer science and technology. The scope of the journal is not implicitly limited; all publications in JACOST are open access, allowing articles to be freely available online without any subscription.</p> en-US <h1 class="page_title">Pernyataan Hak Cipta dan Lisensi</h1> <div class="page"> <div class="page"> <p><span class="" lang="id"><span title="International Journal on Informatics Visualization (JOIV) are published under the terms of the Creative Commons Attribution-ShareAlike."><span class="VIiyi" lang="id"><span class="JLqJ4b" data-language-for-alternatives="id" data-language-to-translate-into="en" data-phrase-index="0">Dengan mengirimkan manuskrip ke <strong>Journal of Applied Computer Science and Technology (JACOST)</strong>, penulis setuju dengan kebijakan ini. Tidak diperlukan persetujuan dokumen khusus.</span></span></span></span></p> <ol> <li class="show">Hak cipta pada setiap artikel adalah milik penulis.</li> <li class="show"><span class="VIiyi" lang="id"><span class="JLqJ4b" data-language-for-alternatives="id" data-language-to-translate-into="en" data-phrase-index="0">Penulis mempertahankan semua hak mereka atas karya yang diterbitkan, tak terbatas pada hak-hak yang diatur dalam laman ini.</span></span></li> <li class="show">Penulis mengakui bahwa <strong>Journal of Applied Computer Science and Technology (JACOST)</strong> sebagai yang pertama kali mempublikasikan dengan lisensi <em><span id="result_box" class="" lang="id"><span title="International Journal on Informatics Visualization (JOIV) are published under the terms of the Creative Commons Attribution-ShareAlike."><a href="https://creativecommons.org/licenses/by-sa/4.0" target="_blank" rel="license noopener">Creative Commons Atribusi 4.0 Internasional (CC BY-SA)</a>.</span></span></em></li> <li class="show">Penulis dapat memasukan tulisan secara terpisah, mengatur distribusi non-ekskulif &nbsp;dari naskah yang telah terbit di jurnal ini kedalam versi yang lain (misal: dikirim ke respository institusi penulis, publikasi kedalam buku, dll), dengan mengakui bahwa naskah telah terbit pertama kali pada&nbsp;<strong>Journal of Applied Computer Science and Technology (JACOST)</strong>;</li> <li class="show"><span class="VIiyi" lang="id"><span class="JLqJ4b" data-language-for-alternatives="id" data-language-to-translate-into="en" data-phrase-index="0">Penulis menjamin bahwa artikel asli, ditulis oleh penulis yang disebutkan, belum pernah dipublikasikan sebelumnya, tidak mengandung pernyataan yang melanggar hukum, tidak melanggar hak orang lain, tunduk pada hak cipta yang secara eksklusif dipegang oleh penulis</span></span>.</li> <li class="show"><span class="VIiyi" lang="id"><span class="JLqJ4b" data-language-for-alternatives="id" data-language-to-translate-into="en" data-phrase-index="0">Jika artikel dipersiapkan bersama oleh lebih dari satu penulis, setiap penulis yang mengirimkan naskah menjamin bahwa dia telah diberi wewenang oleh semua penulis bersama untuk menyetujui hak cipta dan pemberitahuan lisensi (perjanjian) atas nama mereka, dan setuju untuk memberi tahu rekan penulis persyaratan kebijakan ini.&nbsp;</span></span>&nbsp;<strong>Journal of Applied Computer Science and Technology (JACOST)</strong><span class="VIiyi" lang="id"><span class="JLqJ4b" data-language-for-alternatives="id" data-language-to-translate-into="en" data-phrase-index="0"> tidak akan dimintai pertanggungjawaban atas apa pun yang mungkin timbul karena perselisihan internal penulis. </span></span></li> </ol> <p><strong><span class="" lang="id"><span title="International Journal on Informatics Visualization (JOIV) are published under the terms of the Creative Commons Attribution-ShareAlike.">Lisensi :</span></span></strong></p> <p><span id="result_box" class="" lang="id"><span title="International Journal on Informatics Visualization (JOIV) are published under the terms of the Creative Commons Attribution-ShareAlike."><strong>Journal of Applied Computer Science and Technology (JACOST)</strong> diterbitkan berdasarkan ketentuan <a href="https://creativecommons.org/licenses/by-sa/4.0" target="_blank" rel="license noopener">Lisensi Creative Commons Atribusi 4.0 Internasional (CC BY-SA)</a>. </span></span><span class="" lang="id"><span title="Authors retain the copyright to their work.">Lisensi ini mengizinkan setiap orang untuk :</span></span><span class="" lang="id"><span title="Authors retain the copyright to their work.">.</span></span></p> <ul> <li class="license share"><strong>Berbagi</strong> — menyalin dan menyebarluaskan kembali materi ini dalam bentuk atau format apapun;</li> <li class="license remix"><strong>Adaptasi</strong> — menggubah, mengubah, dan membuat turunan dari materi ini untuk kepentingan apapun.</li> </ul> <p><strong>Lisensi :</strong></p> <ul class="license-properties col-md-offset-2 col-md-8" dir="ltr" style="text-align: left;"> <li class="license by"> <p><strong>Atribusi</strong> — Anda harus mencantumkan <a id="appropriate_credit_popup" class="helpLink" tabindex="0" title="" href="https://creativecommons.org/licenses/by-sa/4.0/deed.id#" data-original-title="">nama yang sesuai</a>, mencantumkan tautan terhadap lisensi, dan <a id="indicate_changes_popup" class="helpLink" tabindex="0" title="" href="https://creativecommons.org/licenses/by-sa/4.0/deed.id#" data-original-title="">menyatakan bahwa telah ada perubahan yang dilakukan</a>. Anda dapat melakukan hal ini dengan cara yang sesuai, namun tidak mengisyaratkan bahwa pemberi lisensi mendukung Anda atau penggunaan Anda. <span id="by-more-container"></span></p> </li> <li class="license sa"> <p><strong>BerbagiSerupa</strong> — Apabila Anda menggubah, mengubah, atau membuat turunan dari materi ini, Anda harus menyebarluaskan kontribusi Anda di bawah <a id="same_license_popup" class="helpLink" tabindex="0" title="" href="https://creativecommons.org/licenses/by-sa/4.0/deed.id#" data-original-title="">lisensi yang sama</a> dengan materi asli.</p> </li> </ul> </div> </div> [email protected] (Dr. Ir. Yuhefizar, S.Kom., M.Kom., IPM) [email protected] (Dodi Nofriyoliadi, M.Kom) Sun, 22 Jun 2025 00:00:00 +0100 OJS 3.1.2.1 http://blogs.law.harvard.edu/tech/rss 60 Pengembangan Aplikasi Augmented Reality untuk Edukasi Keselamatan Kebakaran: Metode Prototyping dan Usability https://journal.isas.or.id/index.php/JACOST/article/view/1028 <p><em>This study aims to develop an Augmented Reality (AR) application to introduce fire safety equipment as part of Occupational Health and Safety (OHS) education for the general public. The research employed a prototyping method, which involved iterative stages from requirements analysis to user evaluation, as well as functional testing using Black Box Testing and usability testing with 20 respondents. The results showed that all application features functioned according to specifications, and the usability testing yielded a user satisfaction score of 80.1%. These findings indicate that AR is effective as an interactive educational medium to enhance public understanding of fire protection equipment. The implication of this study is the potential for wider use of AR technology in public education to support fire risk mitigation efforts.</em></p> Ridwan Setiawan, Sri Rahayu, Iis Komalasari Copyright (c) 2025 Ridwan Setiawan, Sri Rahayu, Iis Komalasari https://creativecommons.org/licenses/by-sa/4.0 https://journal.isas.or.id/index.php/JACOST/article/view/1028 Sun, 22 Jun 2025 14:27:06 +0100 Konsistensi Model Regresi Empat Variabel Pada Populasi dan Sampel untuk Prediksi Temperatur https://journal.isas.or.id/index.php/JACOST/article/view/971 <p><em>The ability to predict future events or trends has become very important today. One method that can be used to predict the future is to use linear regression. Accurate regression modeling requires sampling representative data, especially when working with large datasets. This research takes a relatively large volume as a data set by looking at the accuracy and consistency of the coefficients of a multi-variable linear regression model for temperature prediction which is built based on all the data, and looks at the differences in the regression model built from the sample data. The number of sample data (n) is determined based on the Slovin formula which depends on the number of population data (N) and the level of </em><em>confidence</em><em> (ơ), so that as many as (N/n) new regression models can be built. Each group of sample data is divided into 75% for modeling and 25% testing data. The dataset used is weather information in the Szeged area which was measured in 2006 - 2016. So the regression model is in the form of Y (temperature value) which is influenced by Xi (weather factors), namely humidity, wind speed, wind direction and visibility. Using 96,453 data records and a 1% significance level in Slovin's formula, 10 samples were generated. Nine out of ten sample regression models agree with the population model, with positive coefficients for visibility and wind direction and negative values for humidity and wind speed. There is an abnormality in sample #4. While the other nine sample regression models are consistent with positive R<sup>2&nbsp; </sup>values, Sample #1 displays an oddity with negative values. The RMSE interval values for each regression model in this study fall between 4.334 and 9.582.</em></p> Nurjannah Syakrani, Naufal Athaya S. R Copyright (c) 2025 Nurjannah Syakrani, Naufal Athaya S. R https://creativecommons.org/licenses/by-sa/4.0 https://journal.isas.or.id/index.php/JACOST/article/view/971 Sun, 22 Jun 2025 00:00:00 +0100 Performance Comparison of Support Vector Machine (SVM) and k-Nearest Neighbors (kNN) in Verifying Material Orientation https://journal.isas.or.id/index.php/JACOST/article/view/1037 <p>In automated manufacturing, verifying material orientation is essential to ensure the product assembly proceeds without errors. For instance, in the beverage industry, incorrect orientation of materials, such as bottle caps, can lead to failures in the packaging process, resulting in improperly sealed bottles that may compromise product quality and safety. This study compares the performance of Support Vector Machine (SVM) and k-Nearest Neighbors algorithms for verifying material orientation verification through automated optical inspection. The images were processed using the Inception V3 Convolutional Neural Network (CNN) to extract relevant image features, which were then classified using SVM and kNN algorithms. As a result, SVM achieved high classification performance during testing, with classification accuracy, precision, recall, and F1 score of 1.0 compared to kNN, which achieved only 0.967. However, kNN demonstrated superior computational efficiency, with a training time of 1.126 seconds and a validation time of 0.713 seconds, compared to SVM's training time of 3.101 seconds and validation time of 1.479 seconds. These results indicate that while both methods are highly effective for material orientation verification, kNN offers significant advantages in terms of computational speed, making it more suitable for real-time applications. The implications of this study highlight the potential for integrating the proposed method in industrial applications, promoting enhanced efficiency and reducing error rates in automated assembly lines.</p> Eldio Utama, Eko Rudiawan Jamzuri Copyright (c) 2025 Eldio Utama, Eko Rudiawan Jamzuri https://creativecommons.org/licenses/by-sa/4.0 https://journal.isas.or.id/index.php/JACOST/article/view/1037 Sun, 22 Jun 2025 15:25:13 +0100 Analisis Penerapan Mutual Information pada Klasifikasi Status Studi Mahasiswa Menggunakan Naïve Bayes https://journal.isas.or.id/index.php/JACOST/article/view/1106 <p><em>Early identification of Student Study Status is essential for higher education institutions to implement proactive and strategic measures that facilitate timely completion of studies and mitigate dropout rates. This research intends to predict student study status with the Naïve Bayes method based on the features obtained from the implementation of Mutual Information. Feature selection through Mutual Information seeks to analyse the factors that most significantly impact the classification of student study status. The study status is categorized into three classes: dropout, enrolled, and graduate, based on 36 factors. The Mutual Information approach is employed to diminish data dimensions by discarding less relevant features while preserving critical information based on score values to achieve enhanced predictive accuracy. The selection of appropriate attributes enables the model to maintain simplicity while incorporating critical information aspects that significantly impact performance. Experiments were performed on a dataset comprising student academic variables, with data partitioning ratios of 80:20, 70:30, and 50:50 for training and testing datasets. The classification outcomes utilizing Naïve Bayes, without the use of Mutual Information across the three testing ratios, exhibited the accuracy of 68.29% in the 70:30 data split. Simultaneously, the classification outcomes utilizing Mutual Information across three test ratios are as follows: 71.64% accuracy at an 80:20 ratio with 10 selected attributes, 72.06% at a 70:30 ratio with 10 selected attributes, and the highest accuracy of 72.65% at a 50:50 ratio using 15 attributes. The utilization of the Naïve Bayes method for classifying student study status demonstrates enhanced accuracy when integrated with Mutual Information for feature selection. The findings of this study demonstrate that Mutual Information can streamline data by considering the quantity of attribute selections according to the ranking of their score values.</em></p> Sulfayanti Situju, Nahya Nur, Nursan Halal Copyright (c) 2025 Sulfayanti Situju, Nahya Nur, Nursan Halal https://creativecommons.org/licenses/by-sa/4.0 https://journal.isas.or.id/index.php/JACOST/article/view/1106 Sun, 22 Jun 2025 15:32:23 +0100 Penentuan Faktor Pemicu Gejala Penyakit Mata Glaukoma, Astigmatis, Hipermetropi, dan Miopi https://journal.isas.or.id/index.php/JACOST/article/view/1113 <p><em>Expert systems support medical problem-solving, including the analysis of eye diseases. According to BPS RI (2022), over 8 million Indonesians suffer from visual impairments. In diagnosis, doctors often struggle to identify the primary causes of symptoms, impacting treatment effectiveness. This study proposes a system that combines Backward Chaining and Simple Additive Weighting (SAW) to systematically identify and prioritize causal factors of eye diseases. Backward Chaining is used to trace relationships between symptoms and the causes of glaucoma, astigmatism, hyperopia, and myopia. SAW is applied to assign weights to each causal factor and determine priority based on score ranking. Testing with 45 patient cases shows the system achieves 91% accuracy in identifying dominant causes. The 9% error rate stems from data limitations, subjective weighting in SAW, and inference rules in Backward Chaining. This system offers valuable support in early decision-making by helping doctors prioritize handling strategies based on the most significant underlying factors, thereby enhancing diagnostic efficiency and consistency.</em></p> I Kadek Arta Wiguna, Dewa Gede Hendra Divayana, Gede Indrawan Copyright (c) 2025 I Kadek Arta Wiguna, Dewa Gede Hendra Divayana, Gede Indrawan https://creativecommons.org/licenses/by-sa/4.0 https://journal.isas.or.id/index.php/JACOST/article/view/1113 Sun, 22 Jun 2025 16:00:14 +0100 News Classification using Natural Language Processing with TF-IDF and Multinomial Naïve Bayes https://journal.isas.or.id/index.php/JACOST/article/view/1099 <p><em>Online news contains valuable insights into public phenomena that can support statistical analysis by institutions like BPS Riau. However, current methods of classifying news are manual, time-consuming, and prone to human error. This study proposes an automated news classification system using Natural Language Processing (NLP) techniques with Term Frequency–Inverse Document Frequency (TF-IDF) for feature extraction and the Multinomial Naïve Bayes algorithm for classification. The dataset was collected via web scraping and manually labeled across five statistical categories: poverty, unemployment, democracy, inflation, and economic growth. The system achieved a validation accuracy of 83%, a test accuracy of 90%, with an average precision of 0.85, recall of 0.93, and f1-score of 0.87. These results demonstrate that the proposed system can significantly reduce the manual workload of news classification and be practically implemented by BPS Riau to support accurate and timely statistical reporting.</em></p> Nadira Alifia Ionendri, Feri Candra, Afdi Rizal Copyright (c) 2025 Nadira Alifia Ionendri, Feri Candra, Afdi Rizal https://creativecommons.org/licenses/by-sa/4.0 https://journal.isas.or.id/index.php/JACOST/article/view/1099 Tue, 24 Jun 2025 09:24:35 +0100