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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 117 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.
Design and Implementation of an Offline-First Mobile Grocery Arisan Information System Using Flutter and Firebase: Desain dan Implementasi Sistem Informasi Toko Kelontong Seluler Arisan yang Mengutamakan Mode Offline Menggunakan Flutter dan Firebase Devina; Putri Eli Sandra Hasibuan; Dzikra Azzahra; Adetia Anggriani; Gunawan
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.v20i2.582

Abstract

Conventional grocery savings schemes (arisan sembako) managed manually often encounter challenges such as inefficient contribution recording, limited transparency, and difficulties in managing participant data. This study aims to design a mobile-based grocery arisan information system to support the digitalization of community-scale grocery savings management. A descriptive research approach was employed, with data collected through observation and interviews conducted at Mila Sembako.The proposed system was designed using Flutter and Firebase and includes system architecture, use case modeling, Firestore database design, and interface mockup, with an offline-first synchronization mechanism to ensure data accessibility under limited or intermittent internet connectivity. The system supports participant management, grocery package management, contribution recording, winner selection, notification services, and cloud-based data synchronization through Firebase. By centralizing data management in a cloud environment, the system enables more efficient record keeping and improved information accessibility for administrators and participants. The resulting system design provides a structured framework for improving the efficiency, transparency, and reliability of grocery arisan management. The study demonstrates the feasibility of utilizing Flutter and Firebase technologies to support the digital transformation of community-based grocery savings schemes and serves as a reference for future system development and implementation
TinyML-Based Stress Detection Using Time-Domain HRV Features and a Lightweight DNN on ESP32: Deteksi Stres Berbasis TinyML Menggunakan Fitur HRV Domain Waktu dan DNN Ringan pada ESP32 Sarmayanta Sembiring; Kemahyanto Exaudi; Abdurahman -; Jorena; Hadir Kaban; M. Buffon Prima; Rahmat Fadli Isnanto
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.v20i2.624

Abstract

Stress is a psychophysiological condition that requires continuous and objective monitoring. However, existing wearable stress detection systems often rely on cloud-based processing or computationally intensive algorithms, limiting their applicability for real-time inference on resource-constrained embedded devices. This study presents a TinyML-based framework for real-time stress detection using the MAX30102 sensor and an ESP32 microcontroller. The proposed framework integrates four time-domain Heart Rate Variability (HRV) features (BPM, SDNN, RMSSD, and pNN50), a lightweight Deep Neural Network (DNN), full INT8 TensorFlow Lite quantization, and on-device inference to enable efficient edge-based stress classification. The DNN model was trained and evaluated using the WESAD dataset. Experimental results showed that a decision threshold of 0.70 yielded the best classification performance, achieving an accuracy of 82% and an F1-score of 0.63 for the stress class. The quantized TensorFlow Lite INT8 model preserved 100% prediction compatibility between the Python and ESP32 implementations. Furthermore, the MAX30102 sensor achieved a BPM measurement accuracy of 98.74%, while the HRV feature extraction implemented on the ESP32 produced results consistent with the reference calculations. These findings demonstrate that the proposed end-to-end TinyML framework enables accurate and computationally efficient HRV-based stress detection on resource-constrained microcontrollers, providing a practical foundation for real-time wearable edge-health monitoring
Web-Based IT Helpdesk System with Naive Bayes Automatic Ticket Classification Using Laravel: Sistem Helpdesk TI Berbasis Web dengan Klasifikasi Tiket Otomatis Naive Bayes Menggunakan Laravel Gallen Cakra Adhi wibowo; Dita Madonna Simanjuntak; Henoch Juli christanto
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.v20i2.626

Abstract

Managing IT support requests in a university environment remains largely manual, leading to untracked ticket backlogs, slow response times, and misrouted requests caused by incorrect manual categorization. This study presents the design and implementation of a web-based IT Helpdesk Ticketing System integrated with an automatic ticket category classification module using Multinomial Naive Bayes with TF-IDF feature extraction, developed for Universitas Kristen Indonesia (UKI) using the Waterfall SDLC on the Laravel 10 framework. The novelty of this work lies in the real-time integration of a Python-based Naive Bayes microservice directly into the Laravel ticket submission workflow. The classifier was trained on 450 domain-labeled IT support tickets across five categories and evaluated using 5-fold cross-validation (mean accuracy 88.7% +/- 0.9%) and a held-out test set (accuracy 88.9%, Macro-F1 87.6%), outperforming SVM (85.6%) and KNN (81.5%). Black-Box testing yielded 100% pass rate. ISO 9241-11 usability evaluation (n=25) produced effectiveness 95%, efficiency 92%, SUS 76.25 (Grade B)
Development and Evaluation of a Web-Based Geographic Information System (GIS) for Agricultural Land Management Using Agile Extreme Programming: Pengembangan dan Evaluasi Sistem Informasi Geografis (SIG) Berbasis Web untuk Pengelolaan Lahan Pertanian Menggunakan Agile Extreme Programming Akbar Aulia Rachman; Darsanto; Mukhsin
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.v20i2.635

Abstract

Precision agriculture is a modern approach that applies technology to improve efficiency and productivity in the agricultural sector. This study develops a Web Geographic Information System (WebGIS)-based agricultural data management system that integrates spatial and non-spatial data into a single platform. The system supports land mapping, crop age monitoring, planting distribution visualization, and harvest status tracking, and was implemented in the administrative area of Indramayu Regency, Indonesia, as the study site. The system was developed using the Agile Extreme Programming (XP) methodology, which provides flexibility in accommodating user requirements throughout the development process. Functional testing was conducted using the Black Box Testing method to verify that all system features performed as intended. System performance was evaluated using the PIECES framework (Performance, Information, Economy, Control, Efficiency, and Service), resulting in scores of 6.70, 5.97, 5.93, 5.83, 5.93, and 5.97, respectively. The evaluation showed that the system met user expectations, particularly those of farmers, with overall performance rated from good to very good. The proposed system provides an effective solution for improving agricultural land management through integrated and efficient monitoring
Evaluating a Geopolitical-Regime-Aware Hybrid Ensemble for IHSG Forecasting Imam Prasodjo
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.v20i2.654

Abstract

This study evaluates a geopolitical-regime-aware hybrid ensemble (GRAHE) for one-day-ahead forecasting of the Indonesia Composite Index (IHSG) using observations from 2015 to 2025. The framework combines long short-term memory, extreme gradient boosting, and random forest forecasts through a softmax gate conditioned on one-month-lagged geopolitical risk and rolling market volatility. Hyperparameters were selected with Bayesian optimization on the 2022 validation period, and the locked test used monthly expanding-window retraining from 2 January 2023 to 5 December 2025 (697 forecasts). GRAHE obtained a return RMSE of 0.009334, directional accuracy of 50.50%, and Theil’s U of 1.001. It did not outperform the strongest benchmark: Ridge regression achieved an RMSE of 0.009316, while XGBoost produced the highest directional accuracy of 54.23%. The RMSE difference between Ridge and GRAHE was not statistically significant according to the Diebold–Mariano test (p = 0.744). In high-GPR observations, GRAHE recorded an RMSE of 0.009626, 0.30% above the best baseline. Removing GPR features increased ensemble RMSE slightly to 0.009350, but market-only XGBoost remained competitive at 0.009326. These findings do not support a strong claim that geopolitical conditioning improves daily IHSG point forecasts, although the framework provides a transparent basis for regime-dependent risk analysis and further robustness testing.

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