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All Journal Jurnal Teknologi dan Manajemen Informatika SMATIKA Journal of Information Technology and Computer Science (JOINTECS) Jurnal SOLMA Jurnal Masyarakat Merdeka (JMM) Community Empowerment JAPI: Jurnal Akses Pengabdian Indonesia Journal Innovations Computer Science Smatika Jurnal : STIKI Informatika Jurnal Journix: Journal of Informatics and Computing
Kukuh Yudhistiro, Kukuh
Faculty Of Technology Information, University Of Merdeka Malang
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Nursing Public Health Social Sciences Other

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Implementasi Blackbox Testing Pada Aplikasi Real-Time Thermal Video Detection (Studi Kasus Deteksi Demam/Covid-19) Kukuh Yudhistiro; Aditya Galih Sulaksono; Aditya Hidayat Pratama
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 11 No 01 (2021): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v11i01.561

Abstract

During the emergence of the Covid-19 pandemic whose vaccines have not been spread evenly, all countries in the world, especially Indonesia have taken several preventive steps to prevent the spread of the virus. One of the initial actions is to detect every person entering and leaving the country through airports or land transportation. This early action was carried out by detecting the body temperature of residents passing in and out of locations such as airports and train stations. The fever detection is generally carried out using a thermal gun in the form of an infrared gun aimed at individuals who pass the inspection. This research discusses a series of tools consisting of a camera with a thermal sensor where the captured data will be processed through software that displays a histogram of the temperature from the chest to the person's head in real time. Each capture result is used as a dataset that can be used for tracing the needs of visitors to public places. In this research, we will discuss functional testing (blackbox) of the application of thermal video detection in case studies of fever detection.
FCM-Guided CNN with Fuzzy Membership Maps for Robust Brain MRI Tumor Classification Firnanda Al-Islama Achyunda Putra; Kukuh Yudhistiro; Sutriawan; Zumhur Alamin
Journix: Journal of Informatics and Computing Vol. 1 No. 3 (2025): December
Publisher : Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/journix.v1i3.9

Abstract

Accurate brain MRI classification is critical for early tumor diagnosis and computer-aided clinical decision support. Conventional convolutional neural networks (CNNs) are effective in learning deep hierarchical features but often struggle with intensity heterogeneity and partial volume effects inherent to MRI data. To address these limitations, this study proposes a hybrid Fuzzy C-Means–CNN (FCM–CNN) framework that integrates unsupervised soft clustering with deep feature learning. The fuzzy segmentation stage preserves boundary uncertainty by generating multi-channel membership maps, which are then fed into a CNN for robust classification. Evaluations conducted on the Kaggle brain MRI dataset (3,264 slices across four diagnostic categories) under Stratified 5-Fold Cross-Validation show consistent improvements over baseline models. The proposed FCM–CNN achieves a mean accuracy of 96.26% and Macro-F1 of 0.9622, surpassing both CNN-only and K-Means+CNN by +4.84% and +2.74% respectively. Ablation analysis confirms that soft memberships enhance discrimination between visually similar tumors, while statistical testing verifies that the gains are systematic and reproducible. Furthermore, the fuzzy membership maps provide interpretable visual cues, aligning with recent trends in explainable AI (XAI) for medical imaging. Overall, the FCM–CNN framework demonstrates that combining fuzzy logic with deep learning yields a balanced trade-off between performance, interpretability, and computational efficiency, making it promising for clinical-grade brain MRI analysis.
Co-Authors Aditya Galih Sulaksono Aditya Galih Sulaksono Aditya Hidayat Pratama Aditya Hidayat Pratama Bambang Nurdewanto Devita Maulina Putri, Devita Maulina Dwi Arman Prasetya Elta Sonalitha Elta Sonalitha Sonalitha Gina Oktavia Heris Pamuntjar Mirza Raziq Akbar, Muhammad Muhammad Ifan Ali Mustofa Mustofa, Muhammad Ifan Ali Nanda, Hanjar Ikrima Pramaditya, Himawan Pratama, Aditya Hidayat Putra, Firnanda Al Islama Achyunda Rofiqul Muslikh, Ahmad Roikhah, Elfiatur Santi, Fitriana Sheila Febriani Putri Silalahi, Elfrida BR. Soelaksono, Aditya Galih Sumartono Sumartono Sumartono Sutriawan Sutriawan Tyas, Eva Wahyuning Zumhur Alamin