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All Journal Jurnal Teknologi Terpadu ILKOMNIKA: Journal of Computer Science and Applied Informatics
Nanda Martyan Anggadimas
Universitas Merdeka Pasuruan
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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Sistem Rekomendasi Penerima Bantuan Sosial APBD Menggunakan Metode Support Vector Machine Moch Yazid; Dian Ahkam Sani; Nanda Martyan Anggadimas
ILKOMNIKA: Journal of Computer Science and Applied Informatics Vol 6 No 1 (2024): Volume 6, Nomor 1, April 2024
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v6i1.619

Abstract

Pemberian bantuan sosial APBD (Anggaran Pendapatan dan Belanja Daerah) merupakan aspek krusial dalam upaya pemerintah untuk mendukung masyarakat yang membutuhkan. Untuk meningkatkan efektivitas dan efisiensi dalam penentuan penerima bantuan, perancangan sistem rekomendasi menjadi suatu kebutuhan. Penelitian ini bertujuan merancang Sistem Rekomendasi Penerima Bantuan Sosial APBD dengan metode metode Support Vector Machine (SVM) dan dilakukan pada Dinas Sosial Kota Pasuruan. Dengan penerapan system diharapkan dapat meningkatkan transparansi, kecepatan, dan ketepatan dalam penentuan penerima bantuan, sehingga sumber daya dapat dialokasikan secara lebih efisien untuk mendukung kesejahteraan masyarakat. Hasil evaluasi model menunjukkan tingkat akurasi sebesar 95%, presisi mencapai 100%, dan recall sekitar 93%. F1-Score model mencapai 96.5.
Deteksi Penyakit Kulit dengan Metode Convolutional Neural Network Menggunakan Arsitektur VGG19 Ainunnisa Indah Rizqya; Nanda Martyan Anggadimas; Muhammad Misdram
Jurnal Teknologi Terpadu Vol 11 No 2 (2025): Desember, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v11i2.1900

Abstract

Early detection of skin diseases remains a major challenge, particularly in regions with limited access to dermatological services. This issue is further exacerbated by the shortage of medical specialists and the widespread presence of inaccurate health information online. This study aims to develop an automated image-based classification system capable of identifying five types of skin diseases: Eczema, Melanocytic Nevus, Melanoma, Benign Keratosis, and Basal Cell Carcinoma. The proposed method utilizes a Convolutional Neural Network (CNN) with the VGG19 architecture, enhanced through transfer learning and partial fine-tuning at the block4_conv1 layer. A dataset of 10,000 JPG images was used, with preprocessing steps including normalization, data augmentation, edge detection, and class balancing. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix. Experimental results show that the model achieved an accuracy of up to 84% in the best scenario, with balanced performance across other metrics, indicating strong multiclass classification capabilities. These findings demonstrate the effectiveness of VGG19 in detecting skin diseases from images. The results also suggest the potential development of mobile-based early detection systems to support communities in underserved areas.
Co-Authors Ainunnisa Indah Rizqya Dian Ahkam Sani Moch Yazid Muhammad Misdram