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All Journal Journal of Applied Data Sciences Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat NU-JST
Faris Abdi El Hakim, Faris Abdi
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Perbandingan Algoritma YOLOv4 dan YOLOv4-tiny dalam Deteksi Korban Bencana Alam: Comparison of YOLOv4 and YOLOv4-tiny Algorithms in the Detection of Victims of Natural Disasters El hakim, Faris Abdi; Islam Mashuri, M Adamu
Nusantara Journal of Science and Technology Vol 1 No 2 (2024): Published in November of 2024
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) Universitas Nahdlatul Ulama Kalimantan Selatan

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Abstract

Currently, artificial intelligence technology is widely discussed by researchers and this technology can help us in our daily lives. So that there are many applications in various fields, one of which is the topic in our paper namely the detection of victims of natural disasters. This is really needed by the rescue team in speeding up the search for victims of natural disasters because the tools currently used are only heavy equipment, so it takes a long time to search for victims of natural disasters. In this paper we will compare the speed of detection and accuracy in detecting victims of natural disasters using the You Only Look Once (YOLO) version 4 and YOLOv4-tiny algorithms. We train with the same parameters and dataset but with a different architecture. From the results, we get the YOLOv4-tiny algorithm is faster in detecting disaster victims but has an accuracy of 75% whereas the YOLOv4 algorithm takes longer to detect victims of natural disasters but has an accuracy of 54%.
Pembuatan dan Pelatihan Sistem Informasi Pondok Pesantren Putri KH. Ahmad Basthomi Sebagai Media Promosi dan Informasi El Hakim, Faris Abdi; Pratama, Moch Deny; Asmunin, Asmunin; Fitria, Fitria; Rosita, Rosita
Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 5 No. 6 (2025): November 2025 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/altifani.v5i6.940

Abstract

Kegiatan pengabdian masyarakat ini bertujuan untuk mengembangkan Sistem Informasi Pondok Pesantren Putri KH. Ahmad Basthomi berbasis web sebagai media promosi dan informasi resmi lembaga. Pengembangan dilakukan karena sebelumnya pondok pesantren belum memiliki sarana digital untuk menyebarkan informasi secara luas dan efisien. Metode pelaksanaan meliputi analisis kebutuhan, pengembangan sistem menggunakan framework Laravel, implementasi, sosialisasi, serta pelatihan pengelolaan konten. Evaluasi dilakukan melalui survei kepuasan pengguna menggunakan skala Likert terhadap aspek tampilan, navigasi, kecepatan akses, akurasi informasi, dan kepuasan umum. Hasil menunjukkan nilai rata-rata kepuasan sebesar 4,51 yang menandakan bahwa sistem mudah digunakan, informatif, dan responsif. Penerapan sistem informasi ini terbukti meningkatkan efektivitas penyebaran informasi dan citra pesantren di masyarakat serta mendorong pesantren beradaptasi dengan perkembangan teknologi digital.
Hybrid Transformer-XGBOOST Model Optimized with Ant Colony Algorithm for Early Heart Disease Detection: A Risk Factor-Driven and Interpretable Method Pratama, Moch Deny; El Hakim, Faris Abdi; Aditia Syahputra, Dimas Novian; Dermawan, Dodik Arwin; Asmunin, Asmunin; Nudin, Salamun Rohman; Nurhidayat, Andi Iwan
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.969

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of death worldwide, with significant socioeconomic consequences due to premature death and chronic disability. Although clinical screening techniques have evolved, early and accurate prediction of heart disease is still partial due to the limited capacity of conventional machine learning algorithms to model the complex nonlinear interactions among various contributing risk factors e.g., hypertension, diabetes, hyperlipidemia, and genetic predisposition. To address these challenges, this research introduces a hybrid framework that combines the Transformer architecture known for its robust self-attention mechanism and high representational capabilities with Ant Colony Optimization (ACO), a nature-inspired metaheuristic algorithm modeled on the foraging behavior of ants, to enable adaptive and efficient hyperparameter optimization. The proposed model processes structured clinical data by encoding categorical variables into embeddings and normalizing numerical features, resulting in a unified tabular representation suitable for transformer-based analysis. ACO improves model efficiency by optimizing key parameters e.g., embedding configuration, learning rate, and depth, reducing manual intervention and computational overhead. The proposed Hybrid Transformer-ACO model focuses on interpretable clinical features to provide actionable risk stratification. Model evaluation was performed using classification metrics e.g., accuracy, precision, recall, F1 score, and time complexity to measure predictive performance and computational efficiency during the training and inference phases. These evaluation criteria provide evidence of the model's diagnostic reliability, generalizability, and practical feasibility for clinical application.. The model achieved 100% accuracy, sensitivity, specificity, and F1-score, outperforming several models. Time complexity analysis demonstrated efficient training and testing, while the model interpretability supports transparency and trust.
Co-Authors Aditia Syahputra, Dimas Novian Andi Iwan Nurhidayat Asmunin Asmunin Dodik Arwin Dermawan Fitria Fitria Islam Mashuri, M Adamu Pratama, Moch Deny Rosita Rosita SALAMUN ROHMAN NUDIN