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All Journal Sistemasi: Jurnal Sistem Informasi Journal Research and Multidisciplinary (JRM) JISKa (Jurnal Informatika Sunan Kalijaga)
Murdifin, Murdifin
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Computer Science & IT Education Electrical & Electronics Engineering Library & Information Science Social Sciences Other

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Klasifikasi Hewan Anjing, Kucing, dan Harimau Menggunakan Metode Convolutional Neural Network (CNN) Murdifin, Murdifin; Uyun, Shofwatul
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 3 (2025): September 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.3.331-340

Abstract

Animal classification is a complex challenge due to variations in shape, color, and patterns across species. Traditional methods, which rely on manual feature extraction, are often ineffective in handling such complexities. Therefore, this study employs Convolutional Neural Networks (CNNs) as a more accurate approach for automatic feature extraction and image classification. This research aims to develop an animal image classification model, specifically for dogs, cats, and tigers, utilizing CNNs. The dataset consists of 4,800 images obtained from Kaggle, which were divided into training, testing, and validation sets. The CNN model was built using TensorFlow/Keras, trained for 50 epochs, and evaluated using accuracy, precision, recall, F1-score, and a confusion matrix. The experimental results show that the model achieved an overall accuracy of 88%, with the highest performance in tiger classification (99% accuracy). However, distinguishing between dogs and cats remains a challenge, with an accuracy of 81% for both classes. The findings indicate that CNNs are effective in automatically classifying animal images, although challenges persist in differentiating visually similar species. This study lays the groundwork for further enhancements, such as refining the model architecture or utilizing data augmentation techniques to boost classification accuracy.
High Precision Deep Learning Model for Road Damage Classification using Transfer Learning Ghofur, Muhammad Abdul; Murdifin, Murdifin; Hardandrito, Awan Gumilang; 'Uyun, Shofwatul
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i6.5707

Abstract

Roads are critical infrastructure that frequently experience damage, directly impacting transportation safety and efficiency. Manual road damage inspection is time-consuming and resource-intensive, highlighting the need for automated, image-based approaches. This study compares two Convolutional Neural Network (CNN) architectures—MobileNetV2 with transfer learning and a custom-built CNN—for classifying road surface damage severity. The dataset consists of 1,800 road surface images evenly distributed across three categories: good, minor damage, and severe damage. All images were normalized, augmented, and resized, followed by evaluation using 5-Fold Cross-Validation to ensure robust performance. Experimental results show that MobileNetV2 achieved an accuracy of 98%, outperforming the custom CNN, which achieved 89%. These findings demonstrate the effectiveness of transfer learning in improving classification accuracy with limited data and highlight the potential of MobileNetV2 for efficient, real-time road damage detection systems that can be integrated into intelligent infrastructure monitoring solutions.
Semiotic Analysis of Cultural Da’wah: Decoding Religious Symbols and Public Reception in the Pepe’-pepeka ri Makka Dance of Makassar Murdifin, Murdifin; Masry, Abd. Rasyid; Anshar, Muhammad; AB, Syamsuddin; Mirwan, Mirwan; Riswandi, Riswandi
Journal of Research and Multidisciplinary Vol 8 No 2 (2025): Journal of Research and Multidisciplinary
Publisher : Lembaga Sembilan Tiga Community

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/jrm.v8i2.126

Abstract

This research investigates the construction of religious messages and the efficacy of cultural preaching (da’wah) within the Pepe’-pepeka ri Makka (Fire in Mecca) dance tradition in Paropo Village, Makassar. While traditionally viewed as a cultural spectacle, this dance functions as a sophisticated medium for Islamic proselytization. Employing a qualitative descriptive method, this study utilizes Charles Sanders Peirce’s semiotic framework—comprising signs, objects, and interpretants—to analyze the ritual’s poetry and choreography. Data were collected through participatory observation, documentation, and in-depth interviews with key informants, including traditional maestros and religious figures. The findings reveal that the dance is a complex symbolic system representing core Islamic tenets, specifically Tawhid (monotheism), angelic hierarchies, and Thaharah (ritual purification). The fire serves as a potent index of divine light (Nur) and a symbol of spiritual resilience, echoing the miracle of Prophet Abraham. However, the analysis through the Stimulus-Organism-Response (SOR) model indicates a significant gap in public reception. While the performance elicits strong affective responses, such as awe and emotional engagement, the cognitive and behavioral impacts remain limited. This disconnect is attributed to linguistic barriers in the classical Makassarese poetry and the lack of explicit narrative interpretation. The study concludes that for cultural da’wah to achieve its transformative potential, "intersemiotic translation" is required to bridge the gap between traditional symbolism and contemporary public understanding.
Co-Authors Hardandrito, Awan Gumilang Masry, Abd. Rasyid Mirwan, Mirwan MUHAMMAD ABDUL GHOFUR Muhammad Anshar Riswandi Riswandi Shofwatul ‘Uyun Syamsuddin AB, Syamsuddin