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All Journal E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis JETT (Jurnal Elektro dan Telekomunikasi Terapan) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Informatika Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Infomedia Aksiologi : Jurnal Pendidikan dan Ilmu Sosial Jurnal Sistem Informasi
Dwiza Riana
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Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

 1 
Comparison of Segmentation Analysis in Nucleus Detection with GLCM Features using Otsu and Polynomial Methods Dwiza Riana; Jufriadif Na'am; Saputri, Daniati Uki Eka Saputri; Sri Hadianti; Faruq Aziz; Suryadi Putra Liawatimena; Alya Shafra Hewiz; Dika Putri Metalica; Teguh Herwanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5420

Abstract

Pap smear is a digital image generated from the recording of cervical cancer cell preparation. Images generated are susceptible to errors due to the relatively small cell sizes and overlapping cell nuclei. Therefore, accurate Pap smear image analysis is essential to obtain the right information. This research compares nucleus segmentation and detection using Grey Level Co-occurrence Matrix (GLCM) features in two methods: Otsu and Polynomial. The tested data consisted of 400 images sourced from RepoMedUNM, a publicly accessible repository containing 2,346 images. Both methods were compared and evaluated to obtain the most accurate features. The research results showed that the average distance of the Otsu method was 6.6457, which was superior to the Polynomial method with a value of 6.6215. Distance refers to the distance between the nucleus detected by the Otsu and the Polynomial method. Distance is an important measure to assess how closely the detection results align with the actual nucleus positions. It indicates that the Polynomial method produces nucleus detections that are on average closer to the actual nucleus positions compared to the Otsu method. Consequently, this research can serve as a reference for further studies in developing new methods to enhance the accuracy of identification.
Automated Indonesian Plate Recognition: YOLOv8 Detection and TensorFlow-CNN Character Classification Windu Gata; Dwiza Riana; Muhammad Haris; Maria Irmina Prasetiyowati; Dika Putri Metalica
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6310

Abstract

The precise identification and reading of Indonesian vehicle number plates are important in many areas, including the enforcement of law, collection of charges, management of parking areas, and safety measures. This study integrates the implementation of the YOLOv8 object detection algorithm with three OCR methods: EasyOCR, TesseractOCR, and TensorFlow. YOLOv8 is capable of identifying license plates from images and videos at a high speed and reliability under different conditions and therefore is used in this study to perform plate detection in images and videos. After licenses are detected, OCR techniques are performed to segment and read the letters. Both EasyOCR and TesseractOCR performed moderately well on static images achieving accuracy rates of 70% and 68% respectively, but both suffered significantly lower performance in video scenarios. Of the 100 video frames, EasyOCR was able to correctly identify characters in 61 frames and TesseractOCR in 58 frames, while the TensorFlow-based model outperformed the other two with 75 correct recognitions. Furthermore, easy OCR and static images as input while the TensorFlow-based models completed them with 100% accuracy. This observation can be explained by its design, which utilizes a CNN with ReLU activation and Softmax outputs, trained on 10,261 annotated characters and was enhanced with five different data augmentation techniques. The model shows strong performance in its ability to handle dynamic conditions such as motion blur, changing light conditions, and rotation of the plate angle. The results underscore the drawbacks of one-size-fits-all OCR applications in real-world use cases and stress the need for bespoke model training, as well as hierarchical contouring, in the context of automatic license plate recognition (ALPR). This study provides additional insights into ALPR systems by delivering a robust, scalable, and real-time tool for plate and character recognition, which is essential for intelligent transportation systems.
Co-Authors Achmad Fatkharrofiqi Ade Mubarok Alya Shafra Hewiz Asep Herman Bambang Kelana Simpony Cahyo, Karno Nur Dika Putri Metalica Eni Heni Hermaliana Faruq Aziz Fitriyani Fitriyani Gata, Windu Gita Indah Hapsari Haris Dermawan Iedam Fardian Anshori, Iedam Fardian Jamal Maulana Hudin Jufriadif Na`am, Jufriadif Maria Irmina Prasetiyowati Mayya Nurbayanti Shobary Mohamad Noor Muhammad Haris Rangga Sanjaya Renny Oktapiani Saputri, Daniati Uki Eka Saputri Satia Suhada, Satia Satria Budi Sri Hadianti SRI RAHAYU Suryadi Putra Liawatimena Teguh Herwanto Toni Arifin Yudi Ramdhani