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All Journal Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI)
Hasibuan, Rahmat Humala Putra
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering

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Pendekatan Explainable Deep Learning pada Klasifikasi Citra Sampah Menggunakan MobileNetV2 dan Teknik Grad-CAM serta SHAP Al Adib, Muhammad; Siregar, Andri Armaginda; Raj, Bill; Hasibuan, Rahmat Humala Putra; Nasution, Jalaluddin; Parapat, Andreas Jorghy; Rosnelly, Rika
Jurnal Komputer Teknologi Informasi Sistem Informasi (JUKTISI) Vol. 4 No. 3 (2026): Februari 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i3.739

Abstract

The increasing volume of waste resulting from urbanization and population growth poses significant challenges to waste management systems, particularly in the sorting stage. Deep learning approaches, especially Convolutional Neural Networks (CNNs), have been widely employed for waste image classification due to their ability to automatically extract complex visual features. However, a major limitation of these approaches lies in their limited interpretability, which may hinder user trust and real-world adoption. This study proposes an Explainable Deep Learning Framework for organic and inorganic waste image classification by integrating the MobileNetV2 architecture with Explainable Artificial Intelligence (XAI) methods, namely Gradient-weighted Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP). MobileNetV2 is utilized as a feature extractor due to its computational efficiency and suitability for deployment on resource-constrained devices. The dataset used in this study consists of a combination of a public benchmark dataset and field-acquired waste images, processed using a transfer learning approach. Model performance is evaluated using accuracy, precision, recall, and f1-score metrics. Experimental results demonstrate that the proposed model achieves a validation accuracy of 90.25% with balanced performance across both classes. Furthermore, interpretability analysis using Grad-CAM and SHAP reveals that the model focuses on semantically relevant visual features and provides explainable feature contributions. These findings confirm that integrating lightweight CNN architectures with XAI techniques can produce waste classification systems that are accurate, transparent, and accountable.
A Analisis Perbandingan CNN, SVM, dan Hybrid CNN-SVM untuk Deteksi Anomali Trafik Jaringan Khosasih, Susiana; Antoni, Romi; Irnanda, Ricky; Iswanto; Hasibuan, Rahmat Humala Putra
Jurnal Komputer Teknologi Informasi Sistem Informasi (JUKTISI) Vol. 4 No. 3 (2026): Februari 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i3.748

Abstract

The rapid growth of information technology has significantly increased the volume and complexity of network traffic, leading to cyber security threats that are increasingly dynamic and difficult to detect using traditional security systems. The limitations of signature-based detection systems in identifying new attacks, including zero-day attacks, necessitate the adoption of more adaptive anomaly detection approaches through the utilization of machine learning and deep learning within Network Intrusion Detection Systems (NIDS). This study aims to analyze and compare the performance of Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and a hybrid CNN–SVM model in detecting network traffic anomalies. This research employs a quantitative approach using an experimental method to evaluate the performance of the three models based on the CIC-IDS2017 dataset. The experimental process includes data preprocessing, model development, and performance evaluation using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results indicate that the CNN and SVM baseline models achieve high accuracy levels of 98.85% and 98.66%, respectively, but still exhibit limitations in detecting minority attack classes. The hybrid CNN–SVM model achieves the best performance with an accuracy of 99.41% and a more balanced macro-average recall, indicating improved generalization across classes. The integration of CNN as a feature extractor and SVM as a classifier is proven to be effective in leveraging the complexity of network traffic features while enhancing classification stability. Therefore, the hybrid CNN–SVM approach can be recommended as a more effective and reliable network traffic anomaly detection method compared to single-model approaches in supporting modern network security systems.
Co-Authors Al Adib, Muhammad Antoni, Romi Irnanda, Ricky iswanto Khosasih, Susiana Nasution, Jalaluddin Parapat, Andreas Jorghy Raj, Bill Rika Rosnelly, Rika Siregar, Andri Armaginda