Bolt Detection on Railway Rails Using ResNet-50 V1 with SSD Framework

2025-06-26T06:38:53+07:00

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%.

Sentiment Sentiment Analysis of Social Media X Users on the Decline of Marriage Rates in Indonesia

2025-06-26T06:38:50+07:00

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.

International Journal of Artificial Intelligence & Robotics (IJAIR)

Bolt Detection on Railway Rails Using ResNet-50 V1 with SSD Framework

Sentiment Sentiment Analysis of Social Media X Users on the Decline of Marriage Rates in Indonesia