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All Journal Jurnal PkM MIFTEK Journal of Intelligent Systems Technology and Informatics
Yogi Sugiman
Institut Teknologi Garut
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Pembangunan Tugu Sebagai Simbol Identitas Untuk Meningkatkan Citra Desa Sirnajaya Muhammad Badru Zaman; Cep Imat Ginanjar; Sheilla Fitria; Muhammad Agus Sholehhudin; Dhea Muhamad Faisal Angriawan; Mochamad Risyad Fauzan; Muhamad Rijki Nurjakiah; Suci Nursania; Tsani Hisni Amala; Moh Ramdani; Alya Rahmawati; Yogi Sugiman; Rafi Miftahul Fauzi; Wida Rahayu; Rakhan Sulaeman DJ; Sendy Kresna Gumilar; Andri Hendriyanto; Muhamad Nizar Fadilah; Hari Muhamad Gifari; Siva Aulia Salsabila; Neng Refi Prayoga; Muhammad Firmasnyah Sofyan; Reza Rizkiansyah
Jurnal PkM MIFTEK Vol 7 No 1 (2026): Jurnal PkM Miftek
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/miftek/v.7-1.2894

Abstract

The village of Sirnajaya in Cisurupan Subdistrict, Garut Regency, has strong environmental and cultural potential, but lacks a physical symbol that represents the village's identity. The absence of such a marker results in low visual appeal, a lack of orientation points, and a decline in community pride. This community service program aims to solve these problems by constructing a monument as a symbol of the village's identity and a landmark that strengthens the image of the region. The method used is a limited participatory approach involving students and village officials in every stage, from planning, design, budgeting, implementation, to handover of results. The results of the activity show that the monument was successfully built in a strategic location with a design that combines aesthetic and local cultural values. In addition to producing a physical product, this program also encouraged collaboration between students, village officials, craftsmen, and the community, thereby strengthening social solidarity. Obstacles in the form of time constraints and weather conditions were overcome through good coordination and schedule adjustments. Overall, the construction of the monument not only resolved the issue of village identity but also increased collective pride and fostered a spirit of sustainable development.
Skin Disease Classification on the Body Area Using a Combination of Convolutional Neural Network and Vision Transformer Yogi Sugiman; Muhammad Daffa Adzdzikra Daniswara
Journal of Intelligent Systems Technology and Informatics Vol 2 No 2 (2026): JISTICS, July 2026
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v2i2.186

Abstract

Skin diseases affecting the body area represent a significant dermatological challenge due to the high visual similarity between conditions, which complicates accurate diagnosis. Prior studies have predominantly relied on pure CNN architectures, which are inherently limited in capturing long-range contextual relationships between distant lesion regions, and no existing work has integrated CNN with Vision Transformer specifically for body area skin disease classification. This study addresses this gap by applying the SEMMA (Sample, Explore, Modify, Model, Assess) methodology to develop a novel hybrid classification model integrating Convolutional Neural Network (CNN) and Vision Transformer (ViT) for classifying four types of skin diseases on the body area, namely Acne and Rosacea, Eczema, Psoriasis, and Tinea Ringworm and Fungal Infections, using the DermNet dataset. Two CNN backbones were evaluated: DenseNet201 and EfficientNetB4, each combined with four Pre-LayerNorm Transformer Blocks that feature learnable positional encoding to capture long-range spatial dependencies among feature tokens. A two-phase training strategy was implemented, consisting of feature extraction followed by fine-tuning, with balanced class weight applied to address class imbalance detected during exploratory data analysis. The original training data was split into training and validation sets at a 90:10 ratio, while the test data was sourced from the built-in test folder of the DermNet repository, yielding 4,301 training images, 479 validation images, and 1,298 test images. Evaluation on fully isolated test data showed that DenseNet201+ViT achieved 78% accuracy and EfficientNetB4+ViT achieved 77% accuracy. The application of the probability-averaging ensemble strategy further improved performance to 82% accuracy and a Macro F1-Score of 82%, surpassing prior CNN-based studies in the same domain. Grad-CAM visualization confirmed that the model focused attention on clinically meaningful lesion areas, indicating that its predictions are grounded in relevant morphological features rather than image artifacts. However, this study is limited by the absence of clinical validation on real patient data, and the current accuracy remains below the threshold required for standalone diagnostic use. The findings demonstrate that hybrid CNN+ViT architectures, combined with ensemble strategies, offer a promising and interpretable approach for automated skin disease classification, warranting further clinical validation before deployment as a diagnostic support tool.
Co-Authors Alya Rahmawati Andri Hendriyanto Cep Imat Ginanjar Dhea Muhamad Faisal Angriawan Hari Muhamad Gifari Mochamad Risyad Fauzan Moh Ramdani Muhamad Nizar Fadilah Muhamad Rijki Nurjakiah Muhammad Agus Sholehhudin Muhammad Badru Zaman Muhammad Daffa Adzdzikra Daniswara Muhammad Firmasnyah Sofyan Neng Refi Prayoga Rafi Miftahul Fauzi Rakhan Sulaeman DJ Reza Rizkiansyah Sendy Kresna Gumilar Sheilla Fitria Siva Aulia Salsabila Suci Nursania Tsani Hisni Amala Wida Rahayu