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Nuansa Informatika
Published by Universitas Kuningan
ISSN : 18583911     EISSN : 26145405     DOI : https://doi.org/10.25134/nuansa
Core Subject : Science,
Computer Science & IT
NUANSA INFORMATIKA adalah jurnal peer-review tentang Informasi dan Teknologi yang mencakup semua cabang IT dan sub-disiplin termasuk Algoritma, desain sistem, jaringan, game, IoT, rekayasa Perangkat Lunak, aplikasi Seluler, dan lainnya
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 112 Documents
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Comparative Evaluation of Machine Learning Algorithms for Breast Cancer Classification on a Curated Public Dataset: Evaluasi Komparatif Algoritma Machine Learning untuk Klasifikasi Kanker Payudara pada Curated Public Dataset Resad Setyadi; Aedah Abd Rahman
NUANSA INFORMATIKA Vol. 20 No. 2 (2026): Nuansa Informatika 20.2 July 2026
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v20i2.621

Abstract

Breast cancer classification using machine learning has been widely studied, particularly with the Breast Cancer Wisconsin Diagnostic dataset. Therefore, the main issue is not merely to report high accuracy, but to present a reproducible and clinically cautious comparative evaluation that prioritizes malignant-case performance, prevents data leakage, reports model configurations, and provides consistent interpretation. This study compares Logistic Regression, Support Vector Machine with radial basis function kernel, Gradient Boosting, and Random Forest using 569 instances and 30 numerical features extracted from digitized fine-needle aspiration cell nuclei. Standardization for scale-sensitive algorithms was placed inside the cross-validation pipeline. The malignant class was treated as the clinically critical positive class, and the models were evaluated using accuracy, malignant precision, malignant recall, malignant F1-score, specificity, balanced accuracy, Matthews Correlation Coefficient, and AUC. SVM RBF achieved the strongest overall test performance with accuracy of 0.9825, malignant recall of 0.9762, malignant F1-score of 0.9762, balanced accuracy of 0.9812, MCC of 0.9623, and AUC of 0.9977. A Wilcoxon signed-rank comparison across ten folds showed no significant difference between SVM RBF and Logistic Regression, while SVM RBF was significantly better than Gradient Boosting for malignant F1-score. Permutation importance applied to the selected SVM RBF model indicated that worst smoothness, worst texture, worst area, radius error, and worst radius contributed strongly to the predictions. The findings are limited to one curated public dataset and do not establish clinical validity
Smart CCTV Face Detection System Based on the Internet of Things and Artificial Intelligence: Sistem Deteksi Wajah CCTV Cerdas Berbasis Internet of Things dan Kecerdasan Buatan Alfian Pabet; Mamay Syani
NUANSA INFORMATIKA Vol. 20 No. 2 (2026): Nuansa Informatika 20.2 July 2026
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v20i2.628

Abstract

The high rate of motor vehicle theft in Bandung City indicates that conventional security surveillance systems still have limitations in providing effective and responsive monitoring. Therefore, this study aims to design and implement a smart Closed Circuit Television (CCTV) face detection system based on the Internet of Things (IoT) and Artificial Intelligence (AI) capable of performing real-time monitoring and face detection. The research employed the User Centered Design (UCD) method, which consists of user needs identification, system design, implementation, and evaluation. The system was developed using a Raspberry Pi as the IoT edge device, a camera for image acquisition, the InsightFace algorithm for face recognition, and Firebase and the Telegram Bot API for data storage and notification delivery. The novelty of this study lies in the development of a smart CCTV system that integrates the User Centered Design (UCD) method, Internet of Things (IoT) technology, the InsightFace algorithm based on Artificial Intelligence (AI), an automatic pan-tilt module, and Telegram notifications into a single real-time security monitoring platform. The results show that the system achieved a face recognition accuracy of 92%, delivered Telegram notifications with an average response time of 2.8 seconds, and performed object tracking using the pan-tilt module with a response time of less than 0.5 seconds. In addition, the system successfully detected unknown faces, synchronized data to Firebase instantly, managed video storage automatically through a rolling buffer mechanism, and provided a responsive web-based dashboard. Based on the overall testing results, the developed system effectively improved security monitoring, facilitated remote surveillance, and met user requirements in accordance with the User Centered Design (UCD) approach.

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