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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Telematika : Jurnal Informatika dan Teknologi Informasi International Journal of Artificial Intelligence Research Jurnal Pilar Nusa Mandiri Jusikom: Jurnal Sistem Informasi Ilmu Komputer INTI Nusa Mandiri Journal of Intelligent Decision Support System (IDSS) Jurnal Sistem Informasi Journal of Applied Data Sciences JUSTIN (Jurnal Sistem dan Teknologi Informasi) Journal Medical Informatics Technology Journal of Hypermedia & Technology-Enhanced Learning
Nita Merlina, Nita
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Evaluation of Pap Smear Nucleus Cell Image Segmentation: The Impact of Enhancement Processes on Segmentation Result Nainggolan, Esron; Merlina, Nita; Setiadi, Farisya
Journal Medical Informatics Technology Volume 4 No. 1, March 2026
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v4i1.93

Abstract

Accurate nucleus segmentation is vital for automated cervical cancer diagnosis, yet it remains challenging due to overlapping cells and uneven lighting. This study evaluates Polynomial Contrast Enhancement (PCE) using a second-degree polynomial function to improve segmentation on 100 images from the RepomedUNM dataset. The pipeline integrates grayscale conversion, Gaussian blur, and PCE prior to Canny edge detection. Results demonstrate near-perfect Precision (0.9999–1.0000) across all categories (Normal, H-SIL, L-SIL, and Koilocyt), effectively eliminating false positives. However, Recall and Accuracy remained low (max 0.0634 in H-SIL), a technical consequence of Canny’s limitation in capturing thin boundaries versus solid nuclear areas. The study’s novelty lies in the application of second-degree PCE to stabilize intensity variations across multiple diagnostic categories. While PCE ensures exceptional localization precision, future systems should integrate deep learning to enhance recall in complex overlapping structures.
Butterfly species identification using glcm features and edge detection using KNN (K-Nearest Neighbor) and decision tree algorithm (C.45) Hasan, Muhamad; Riana, Dwiza; Merlina, Nita
Journal of Intelligent Decision Support System (IDSS) Vol 9 No 1 (2026): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v9i1.341

Abstract

Butterflies are insects come from the kingdom Animalia, which are the Insecta class, the Lepidoptera order, and the sub-order of Rhopalocera. Butterflies can classified according to the patterns found on the butterfly's wings. Butterfly species have different patterns based on pigment, scale structure, and sunlight fall structure. The weakness of the human eye in specific the patterns in butterflies is the foundation in basis butterfly identification based on pattern recognition. This study used 3 butterfly species: Adonis, Black Hairstreak, and Gray Hairstreak. The butterfly dataset used was 150 which were obtained online. The pre-processing stage used segmentation and edge detection methods. The feature extraction stage used the Gray-level Co-occurrence Matrix (GLCM) method which extracted 8 shape and texture features including area, perimeter, metric, eccentricity, contrast, correlation, energy, and homogeneity. Classification phase used K-Nearest Neighbor (KNN) method with the values of k = 3, 5, 7, 9, 11, 13, 15, 17, and 19 as well as the Decision Tree method (C.45). The results of the identification of butterflies with the highest accuracy were obtained by the KNN Algorithm on the testing with a value of k = 3 of 93.33%, and the accuracy results using the Decision Tree method (C.45) is 84.44% while the results of identification using an application made using the GUI Matlab2017 with the KNN algorithm obtained an accuracy of 93.33% with a value of k= 3.
Co-Authors Ade Chandra Agus Subekti Aziz, Faruq Bayhaqy, Achmad Dwiza Riana Ernest Dwi Saptorini, Ernest Dwi Fahrurrozi4, Nabil Ali Farisya Setiadi Filda Angellia Fitri Rahmawati Frieyadie Gata, Windu Guntur Guntur Herlawati Herlawati iboy, rahmat satria buana Ida Zuniarti, Ida Jessika Aryani Juniarti Karnita Afnisari, Karnita Kurniawan, Setiadi Mayangky, Nissa Almira Mega Maharani, Mega Muhamad Hasan Mulyana, Kaka Nainggolan, Esron Nurmalasari Nurmalasari Nurul Aisyah Oktaviani, Nia Oktaviani, Nia Panjaitan, Yogi Yosua Prasetio, Arfhan Pratama, Kasahana Indra Putra, Ananta Kusuma Putra, Septian Ade Rahmadya Trias Handayanto Rahmat Hidayat Sarifah Sarifah Siti Fauziah Sulaeman, Okky Robiana Sulistyowati, Daning Nur Utami, Cheilla Cahya Utami Wardiani, Ditta Sri