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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Teknologi International Journal of Advances in Intelligent Informatics Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Artificial Intelligence and Data Mining JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Building of Informatics, Technology and Science Journal of Applied Engineering and Technological Science (JAETS) Journal of Applied Data Sciences Charity : Jurnal Pengabdian Masyarakat Indonesian Community Journal Jurnal Pengabdian Masyarakat Bhinneka eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT)
Mahmud Dwi Sulistiyo
Universitas Telkom
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Public Health Social Sciences Other

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Journal : Building of Informatics, Technology and Science

 1 
Polyp Identification from a Colonoscopy Image Using Semantic Segmentation Approach Wahyu Hauzan Rafi; Mahmud Dwi Sulistiyo; Sugondo Hadiyoso; Untari Novia Wisesty
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4083

Abstract

Colorectal Cancer (CRC) is a major contributor to cancer-related mortality worldwide, necessitating early detection and treatment of polyps to prevent cancer progression. A colonoscopy is a critical diagnostic procedure for identifying colon abnormalities and removing premalignant polyps. However, accurately segmenting polyps in colonoscopy images poses challenges due to their diverse appearance and indistinct boundaries. In this study, we investigate augmentation techniques to enhance polyp semantic segmentation using the U-Net model. Our analysis reveals that the most effective technique is found in sub-scenario 2.6.c with an input size of 320×320, striking a favorable balance between accuracy and efficiency. Additionally, we explore the benefits of larger input sizes, taking into account resource considerations. Moreover, we conduct further testing of the best augmentation technique identified in previous experiments with the SegNet model. The results show a 3.5% improvement in the dice coefficient and slightly better qualitative outcomes. However, it is important to note that this enhancement comes with a nearly fivefold increase in training time. Moving forward, our objective is to develop a unified model for segmenting diverse medical images, pushing the boundaries of polyp detection and medical imaging. This research provides valuable insights and lays the foundation for more advanced applications in polyp detection and medical image analysis.
Mask Detection on Motorcyclists Using YOLOv4 Firdauz, Salma Salsabila; Rachmawati, Ema; Sulistiyo, Mahmud Dwi
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.2980

Abstract

The use of mask is a mandatory for everyone in the pandemic regulation to prevent the spread of COVID-19 infection. This becomes a pandemic regulation for everyone, especially in public places like in traffic situation, such as pedestrian and motorcyclists. However, many motorcyclists ignore this rule or do not use the mask properly, let alone they have high risk in being infected by the virus; Thus, a computer vision-based solution is required to help monitoring it. This study aims to built a system to automatically detect the use of mask on motorcyclists. Here, we propose a YOLOv4 model, one of YOLO variants, which is popular in the object detection task and featured with a considerably high speed in real-time situation. This study also implements domain adaptation to discuss the object detection performances. Based on the experimental results in various scenarios, our model obtained average accuracy of 78.3% and IoU of 64.8% for class with_mask, average accuracy of 78.4% and IoU of 56.3% for class without_mask, and average accuracy of 87% and IoU of 55.5% for class incorrect_mask
Co-Authors Ade Romadhony Aditya Firman Ihsan Adiwijaya Agung Toto Wibowo Aji Gautama Putrada Akbar, M Raehan Alfian Akbar Gozali Andre Sitompul Azzam, Muhammad Abdullah Bayu Prabawa Bedy Purnama Bimmo Satryo Wicaksono Dawami, Hasbi Deny Haryadi Dewi Nasien Ema Rachmawati Ema Rachmawati Fachri Pane, Syafrial Faturahman, Farhan Febryanti Sthevanie Firdauz, Salma Salsabila Gamma Kosala Ganjar Gingin Tahyudin Gia Septiana Wulandari Hamdy Nur Saidy Hifzi Priabdi Jondri Jondri Marliani Harahap Muhammad Arzaki Muhammad Helmy Faishal Muhammad Rakha Muhammad Ridha Prasti Eko Yunanto Pratama, Alif Adwitiya Rasyid Kurniawan Rasyid Kurniawan Richasdy, Donny Rifqi Luthfil Hadi Rita Rismala Sabrina Adinda Sari Sailellah, Hassan Rizky Putra Sugondo Hadiyoso Syafrial Fachri Pane, Syafrial Fachri Tahyudin, Ganjar Gingin Tjokorda Agung Budi Wirayudha Tri Kusnandi Fazarudin Tri Kusnandi Fazarudin Tri Kusnandi Fazarudin, Tri Kusnandi Ubaidillah, Rifky Fahrizal Untari Novia Wisesty Wahyu Hauzan Rafi Wahyu Rismawan