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All Journal AL-HAYAT: Journal of Biology and Applied Biology Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI)
Parapat, Andreas Jorghy
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering

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Kajian Ukuran Rajungan (Portunus pelagicus) Menurut Jenis Kelamin, Tingkat Kematangan Gonad dan Faktor Kondisi di Perairan Pulau Baai Bengkulu Maylandia, Chantika Rachma; Matondang, Dina Ratnasari; Ilhami, Sitti Alya; Parapat, Andreas Jorghy; Bakhtiar, Deddy
Al-Hayat: Journal of Biology and Applied Biology Vol. 4 No. 2 (2021)
Publisher : Fakultas Sains dan Teknologi, UIN Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/ah.v4i2.7874

Abstract

The resources of the blue swim crab are currently under pressure on survival due to the increasing effort to catch in nature. Management of blue swim crab resources requires information on the biological conditions of the crab to determine the size, sex, and number that can be caught. This study aims to analyze the structure of carapace width concerning differences in sex, gonad maturity level, and crab condition factors. The method used is the method of observation by measuring the length, weight, and maturity level of the gonads and then analyzed descriptively. The results showed that the size of the crabs was included in the category of juvenile to adult crabs for both male and female crabs. The growth pattern of male crabs with a coefficient of b value of 2.47 and female crabs of 2.78 shows that the growth patterns of crabs in Pulau Baai waters are negative allometric. Most of the female crabs are in the immature stage of the gonads, so the condition factor for the female crabs tends to be lower than the male crabs, this is because most of the female crabs have just passed the spawning phase.
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.
Co-Authors Al Adib, Muhammad Deddy Bakhtiar, Deddy Hasibuan, Rahmat Humala Putra Ilhami, Sitti Alya Matondang, Dina Ratnasari Maylandia, Chantika Rachma Nasution, Jalaluddin Raj, Bill Rika Rosnelly, Rika Siregar, Andri Armaginda