https://ejournal.unitomo.ac.id/index.php/ijair <hr> <p>Accredited by the Minister of Education, Culture, Higher Education, and Research, Republic of Indonesia, Number <strong><a href="https://drive.google.com/file/d/1N-OfAfF_yizw64Xar6hxY1ljQD9Q3IRv/view?usp=share_link">1429/E5.3/HM.01.01/2022</a></strong> as <strong>Ranking 4 (SINTA 4)</strong>.</p> <p><strong>International Journal of Artificial Intelligence &amp; Robotics (IJAIR)</strong> is one of the journals published by the Informatics Engineering Department of Dr. Soetomo University, which was established in November 2019. <strong>IJAIR </strong>is a journal with a Double-Blind Peer Review process dedicated to the publication of quality research results in the fields of Computer Science and Technology, Artificial Intelligence &amp; Robotics, but not implicitly limited to them. All publications in the IJAIR journal are open access, which allows articles to be freely available online without any subscription and free of charge. The journal publishes twice a year in November and May, containing five (5) articles per issue. Crossref provides a unique DOI for each article in this journal.</p> <p><a href="https://portal.issn.org/resource/ISSN/2686-6269"><strong>ISSN (online) : 2686-6269</strong></a></p> <p><strong>Focus and Scope</strong>:</p> <p>Machine Learning &amp; Soft Computing, Data Mining &amp; Big Data, Computer Vision &amp; Pattern Recognition, and Robotics.</p> Informatics Department-Universitas Dr. Soetomo en-US International Journal of Artificial Intelligence & Robotics (IJAIR) 2686-6269 <p dir="ltr">Authors who publish with&nbsp;<strong>International Journal of Artificial Intelligence &amp; Robotics (IJAIR)</strong> agree to the following terms:</p> <ol> <li class="show" dir="ltr"> <p dir="ltr">Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a <a href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution License (CC BY-SA 4.0)</a> that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.&nbsp;</p> </li> <li class="show" dir="ltr"> <p dir="ltr">Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.</p> </li> <li class="show" dir="ltr"> <p dir="ltr">Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.</p> </li> </ol> https://ejournal.unitomo.ac.id/index.php/ijair/article/view/9351 <p>One of the parts of a railroad track is a bolt. The role of bolts is significant in railway tracks, namely as a fastener between rails. Considering the importance of bolts on railway tracks, every morning, an officer would be assigned to go to the railway tracks to check the bolts. This inspection is done manually on foot or by driving along the railway tracks. Inspection performed manually has the possibility of errors in recognizing the condition of the bolt. In addition, if performed manually, there will be no record of the condition of the bolt. This data will be used to consider whether the condition of the bolt is still suitable for use or needs to be replaced. The formulation of the problem of the research conducted is how to detect bolts on railroad tracks using deep learning, with the purpose of this study to use a model to recognize and be able to detect bolts on railroad tracks. This study uses deep learning with the deep learning method used SSD Resnet 50 V1. The first step that must be taken in the study is to identify the object of the bolt located on the railway tracks. Further research can be carried out. This research has successfully detected bolts on railway tracks. This study used a dataset of 200 datasets in the first experiment and 300 datasets in the second experiment. The model used in the study is the Resnet 50 V1 SSD model, where using 2,000 steps, the precision value is 92.64%, and the Recall value is 64.87%.</p> Helfy Susilawati Ginaldi Ari Firman Firman Copyright (c) 2025 Helfy Susilawati, Ginaldi Ari, Firman Firman http://creativecommons.org/licenses/by-sa/4.0 2025-05-31 2025-05-31 7 1 1 9 10.25139/ijair.v7i1.9351 https://ejournal.unitomo.ac.id/index.php/ijair/article/view/9953 <p>This study aims to analyze public sentiment regarding Indonesia's declining marriage rates and identify the most accurate algorithm for sentiment analysis. Data were collected from the social media platform X using crawling techniques, resulting in 1,082 tweets that were processed and classified into positive, negative, and neutral sentiments. The findings reveal that most sentiments are positive at 41.31%, negative at 30.59%, and neutral at 28.10%. The classification model evaluation shows that SVM outperforms Naïve Bayes, achieving an accuracy of 74% compared to 53%. This study is limited to data collected from a single social media platform X. Future research is encouraged to expand the scope by collecting opinions from various social media platforms and exploring other machine learning or deep learning algorithms. The findings of this study are expected to contribute to policy-making efforts to improve marriage stability and well-being in Indonesia. This study also serves as a reference for academics and practitioners in understanding public opinion patterns on emerging social issues and providing a foundation for future studies on similar topics.</p> Novi Kristanti Wildan Mahmud Galuh Wilujeng Saraswati Erba Lutfina Copyright (c) 2025 Novi Kristanti, Wildan Mahmud, Galuh Wilujeng Saraswati, Erba Lutfina http://creativecommons.org/licenses/by-sa/4.0 2025-06-09 2025-06-09 7 1 10 17 10.25139/ijair.v7i1.9953