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All Journal Journal of Information System and Technology (JOINT) Automotive Experiences
Salim, Steven
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Mechanical Engineering

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Implementasi Big Data terhadap Pengecekan Medis dan Konsultasi Kesehatan di Indonesia Saudjhana, Audrey; Budiman, Arif; Fernando, Harley; Juliantio, Juliantio; Junianto, Kendy; Venessa, Kisusyenni; Salim, Steven; Tomy, Tomy
Journal of Information System and Technology (JOINT) Vol. 5 No. 1 (2024): Journal of Information System and Technology (JOINT)
Publisher : Program Sarjana Sistem Informasi, Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/joint.v5i1.4323

Abstract

The occurrence of the Covid-19 pandemic tests the resilience of the health service system in countries around the world, especially Indonesia, in responding and acting quickly and appropriately. This incident is a separate evaluation of the health service system in this country. As part of the global action plan to achieve Sustainable Development Goals (SDGs) point number 3, namely ensuring a healthy life and supporting welfare for all and efforts to improve public health as measured by the Public Health Development Index (IPKM), therefore, an increase in quality of health services is needed. The quality of health services includes issues of performance and affordability among society. With the development of innovations in database technology aspect, namely big data and artificial intelligence, it is hoped that this can be a breakthrough in the world of Indonesian health so that the goals of Indonesia in improving public health can be achieved.
Implementasi Big Data terhadap Pengecekan Medis dan Konsultasi Kesehatan di Indonesia Saudjhana, Audrey; Budiman, Arif; Fernando, Harley; Juliantio, Juliantio; Junianto, Kendy; Venessa, Kisusyenni; Salim, Steven; Tomy, Tomy
Journal of Information System and Technology (JOINT) Vol. 5 No. 1 (2024): Journal of Information System and Technology (JOINT)
Publisher : Program Sarjana Sistem Informasi, Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/joint.v5i1.4323

Abstract

The occurrence of the Covid-19 pandemic tests the resilience of the health service system in countries around the world, especially Indonesia, in responding and acting quickly and appropriately. This incident is a separate evaluation of the health service system in this country. As part of the global action plan to achieve Sustainable Development Goals (SDGs) point number 3, namely ensuring a healthy life and supporting welfare for all and efforts to improve public health as measured by the Public Health Development Index (IPKM), therefore, an increase in quality of health services is needed. The quality of health services includes issues of performance and affordability among society. With the development of innovations in database technology aspect, namely big data and artificial intelligence, it is hoped that this can be a breakthrough in the world of Indonesian health so that the goals of Indonesia in improving public health can be achieved.
Visual Inspection Improvement of Engine Components Using Deep Learning with Pre-processed Dataset Augmentation: Case Study Salim, Steven; Wiratama, Sandy; Sarifudin, Alfan; Yuliatin, Eka Prita
Automotive Experiences Vol 8 No 3 (2025)
Publisher : Automotive Laboratory of Universitas Muhammadiyah Magelang in collaboration with Association of Indonesian Vocational Educators (AIVE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.14207

Abstract

Lighting instability, sharp shadows, and visual disturbances caused by mechanical vibrations are significant challenges in the application of computer vision-based visual inspection systems in automotive industrial environments. This study aims to enhance the accuracy and robustness of the YOLOv8 object detection model for detecting machine component completeness by applying an adaptive pre-processing strategy. The techniques employed include grayscale conversion, brightness adjustment, and blurring to simulate common visual conditions encountered in real-world production processes. The model was trained using 1,281 instances from 52 component classes and evaluated based on the metrics of precision, recall, mAP@50, and mAP@50–95. The results show an average precision of 0.971, a recall of 0.990, and mAP@50 of 0.991, with spatial variation reflected in the standard deviation of mAP@50–95 of 0.149. The pre-processing technique improves the detection precision of shape-based components by up to 19% and colour-based components by up to 31%. Testing on ten appearance variations showed 100% detection accuracy with no misclassification, indicating the model’s generalizability to data in the training distribution. These findings confirm that visual modification of training data significantly improves the reliability and efficiency of the YOLOv8-based automated inspection system. Further implications include reduced human intervention, accelerated production flow, and optimization of operational energy consumption through faster and more accurate detection. Therefore, this system contributes to energy-efficient and sustainable innovative manufacturing practices.
Co-Authors Arif Budiman Fernando, Harley Juliantio, Juliantio Junianto, Kendy Sarifudin, Alfan Saudjhana, Audrey Tomy Tomy Venessa, Kisusyenni Wiratama, Sandy Yuliatin, Eka Prita