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All Journal Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI)
Muhammad Al Adib
Universitas Potensi Utama
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 Muhammad Al Adib; Andri Armaginda Siregar; Bill Raj; Rahmat Humala Putra Hasibuan; Jalaluddin Nasution; Andreas Jorghy Parapat; Rika Rosnelly
Jurnal Komputer Teknologi Informasi Sistem Komputer (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.
Perbandingan Kinerja MetodeTF-IDF dan BM25 pada Pencarian Artikel Berita Bahasa Indonesia Muhammad Al Adib; Yiska Dayanti Zagoto; Bualazatulo Laia; Roslina
Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Vol. 8 No. 2 (2026): Edisi Oktober
Publisher : Universitas Harapan Medan

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Abstract

The growth of online media has increased the number of Indonesian-language news articles and created a need for effective and efficient retrieval systems. This study compares Term Frequency–Inverse Document Frequency (TF-IDF) and Best Matching 25 (BM25) using the Indonesia News Dataset (2025). Of 80,472 articles, 80,463 documents were retained after removing nine articles with empty content. The evaluation used 10 multi-category queries. Ground truth was constructed by pooling the top 20 results from each method, producing 304 query–document pairs manually assessed by one evaluator using relevance labels from 0 to 2. Retrieval effectiveness was measured using Precision@k, Recall@k, Mean Average Precision (MAP), Mean Reciprocal Rank (MRR), and normalized Discounted Cumulative Gain (nDCG). Retrieval time was measured over 30 repetitions after three warm-up runs. At Top-10, BM25 achieved a Precision@10 of 0.79, Recall@10 of 0.351630, MAP@10 of 0.728635, and nDCG@10 of 0.630976, which were higher than the corresponding TF-IDF scores. TF-IDF obtained a slightly higher MRR of 0.90 than BM25 at 0.85. In the evaluated implementation, BM25 required 0.010191 seconds on average, whereas TF-IDF required 0.066014 seconds. Descriptively, BM25 performed better on most Top-10 metrics and retrieval time, but the results depended on query characteristics and experimental configuration.
Co-Authors Andreas Jorghy Parapat Andri Armaginda Siregar Bill Raj Bualazatulo Laia Jalaluddin Nasution Rahmat Humala Putra Hasibuan Rika Rosnelly Roslina, Roslina Yiska Dayanti Zagoto