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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Pseudocode Jurnal Penelitian Ekonomi dan Bisnis International Journal of Advances in Intelligent Informatics JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Teknik Informatika (JUTIF) Advances: Jurnal Ekonomi & Bisnis
Edi Noersasongko
Faculty Of Economics And Business, Dian Nuswantoro University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering

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Journal : joiv international journal on informatics visualization

 1 
Optimization of the Preprocessing Method for Edge Detection on Overlapping Cells at PAP Smear Images Nita Merlina; Edi Noersasongko; Pulung Nurtantio Andono; M Arief Soeleman; Dwiza Riana
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1329

Abstract

The complexity of the cell structure and high overlap cause poor image contrast. Complex imaging factors can make automatic visual interpretation more difficult. Segmentation separates a digital image into different parts with homogeneous attributes so that different areas have different features. The challenges faced in performing nucleus segmentation on Pap Smear (PS) images are poor contrast, the presence of neutrophils, and uneven staining of overlapping cells. This research was conducted to improve image quality in identifying the nucleus accurately. The method used is the Polynomial Contrast Enhancement (PCE) model as an approach to preprocessing. This method functions to change the contrast of the Pap smear image against the overlapping cells so that it becomes a significant contrast in detecting the edge of the nucleus object. The detection process uses the Robert and Prewitt edge detection method to test the identification of the nucleus object on 797 PS Repository images of the University of Nusa Mandiri (RepomedUNM). The accuracy result obtained is 86.8%. Comparing Robert's edge detection and Prewitt's edge detection shows that the PCE approach as a filter method can overcome color contrast problems and detect more accurately. The difficulty in detecting the nucleus from the PS image against the overlapping cells can be solved. This method can distinguish overlapping cells from their core during testing, thus becoming a reference in identifying cells with improved accuracy and testing larger data sets.
Enhancing Low-Resolution Images of Mustard Leaves Affected by Pests with Thermal Sensor using Super-Resolution Convolutional Neural Network Optimization Susanto, Fredy; Nurtantio, Pulung; Soeleman, Arief; Pujiono, Pujiono; Noersasongko, Edi; Dedi, Dedi
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2841

Abstract

With urban areas facing limited agricultural land, hydroponic systems offer a solution to increase food storage and variety. Hydroponics, a farming technique that relies on water as a growing medium rather than soil, provides essential nutrients and oxygen for plants. This paper explores the use of thermal sensors to capture images of mustard leaves in a hydroponic system. In addition, it also explores thermal sensor images. These images are analyzed to detect pest attacks, with red leaves indicating the presence of pests and green/blue leaves unaffected by pests. These pests emit hot air; consequently, they turn red. The method of increasing resolution is to compare the Long Short-Term Memory (LSTM) algorithm with the Super-Resolution Convolutional Neural Network (SR-CNN) to improve the quality of images obtained from low-resolution sensors (AMG8833/Grid-EYE). The results show that the SR-CNN method is better than the LSTM (Long Short-Term Memory) method, although limitations remain due to the sensor resolution. After conducting the research, it could be observed that using LSTM resulted in a Mean Square Error (MSE) value of 0.001551685, while SR-CNN indicated an MSE value of 8.873. Furthermore, LSTM produces a Peak Signal-to-Noise Ratio (PSNR) value of 37.10797726, whereas SR-CNN achieves a PSNR of 39.199. The accuracy rates (SSIM) for LSTM and SR-CNN are 0.991538522961364 and 0.997747, respectively. These findings show that using the SR-CNN algorithm can effectively improve the quality of images produced by thermal sensors, even though the sensor pixel capacity is limited.
Co-Authors Affandy Alif Wahyu Oktaputra Dedi Dedi Dwi Jayanti Amanda Sari Dwiza Riana Evanita Evanita, Evanita Fandi Yulian Pamuji Fredy Susanto Happy Tatemba Harke Revo Leonard Polii Hendro Subagyo Jutono Gondohanindijo M Arief Soeleman Moch Arief Soeleman Mochammad Arief Soeleman Mohamad Jamhari Muhammad Misdram Muhammad Munsarif Mula Agung Barata Muljono, - Nita Merlina Nurtantio, Pulung Pujiono Pujiono Pulung Nurtantio Andono Purwanto Purwanto Ricardus Anggi Pramunendar Rofiqi, Harri Kurniawan Sandra J. R Kainde Setiyani, Safira Hasna Soeleman, Arief Soeleman, M. Arief Sri Winarno Sudiarti, Ni Made Watimena Syamsiar, Syamsiar Wijaya, Danang Bagus Yuventius Tyas Catur Pramudi