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All Journal Tematik : Jurnal Teknologi Informasi Komunikasi TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi AMMA : Jurnal Pengabdian Masyarakat
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STMIK IKMI CIREBON
Humanities Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing

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Peningkatan Kompetensi Guru Ciayumajakuning Melalui Bimbingan Teknis Teknologi AI Gemini Dadang Sudrajat; Denni Pratama; Fatkhan Mubarok; Muhammad Zamil Farhan
AMMA : Jurnal Pengabdian Masyarakat Vol. 3 No. 2 : Maret (2024): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

The advancement of Artificial Intelligence (AI) technology has created significant transformations across various sectors, including education. However, the adoption of AI in the educational sector—particularly in the Ciayumajakuning region (Cirebon, Indramayu, Majalengka, and Kuningan)—still faces several challenges, such as low digital literacy among teachers, limited access to technological training, and a lack of understanding regarding the practical application of AI in learning and school administration contexts. The Gemini AI Technical Training Program is part of a Community Service initiative aimed at enhancing teachers' capacity to use AI technology in a practical and ethical manner within educational environments.
Peningkatan Kompetensi Akademik Dosen Melalui Pelatihan Systematic Literature Review (SLR) Dadang Sudrajat; Denni Pratama; Luthfi Adianto; Luthfiyyah Iffah Adella
AMMA : Jurnal Pengabdian Masyarakat Vol. 2 No. 2 (2023): AMMA : Jurnal Pengabdian Masyarakat (INPRESS)
Publisher : CV. Multi Kreasi Media

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Abstract

The quality of academic research is significantly influenced by the ability of lecturers to conduct systematic and comprehensive literature reviews. This Community Partnership Program aims to enhance the competence of Kopertip Indonesia lecturers in conducting Systematic Literature Reviews (SLR) as an essential part of the academic research process. This training is designed to provide an in-depth understanding of SLR methodology, including formulating appropriate research questions, effective literature search strategies, study inclusion and exclusion criteria, synthesis and analysis of literature data, and the preparation of high-quality SLR reports. It is expected that, through this training, Kopertip Indonesia lecturers can improve their ability to produce more relevant, comprehensive research that significantly contributes to the advancement of knowledge.
Pengembangan Sistem Informasi Learning Analytics untuk Monitoring dan Evaluasi Kompetensi Digital Pelaku UMKM pada Platform SkillUP Denni Pratama; Dian Ade Kurnia; Saeful Anwar
TEMATIK Vol. 13 No. 1 (2026): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2026
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v13i1.2994

Abstract

Transformasi digital telah mendorong kebutuhan peningkatan kompetensi digital bagi pelaku Usaha Mikro, Kecil, dan Menengah (UMKM) di Indonesia. Platform microlearning SkillUP telah berhasil dikembangkan dan diterima pengguna berdasarkan Technology Acceptance Model (TAM), namun masih belum dilengkapi mekanisme monitoring dan evaluasi kompetensi digital secara komprehensif. Penelitian ini bertujuan mengembangkan Sistem Informasi Learning Analytics (SILA) untuk monitoring dan evaluasi kompetensi digital pelaku UMKM pada platform SkillUP menggunakan metode Design Science Research (DSR). Sistem yang dikembangkan mengintegrasikan data aktivitas pembelajaran ke dalam tiga lapisan pengumpulan data, mesin analitik, dan dashboard. Novelty penelitian mencakup Digital Competency Monitoring Framework dan Digital Competency Progress Index (DCPI) yang dihitung dari Learning Engagement Score (LES), Learning Completion Rate (LCR), Quiz Achievement Score (QAS), dan Competency Achievement Score (CAS). Evaluasi sistem menggunakan standar ISO/IEC 25010 dengan fokus pada Functional Suitability, Usability, dan Performance Efficiency. Hasil evaluasi menunjukkan nilai Functional Suitability sebesar 1,00 (sangat baik), Usability sebesar 87,5 (excellent berdasarkan SUS), dan rata-rata response time 1,87 detik. Sistem ini berkontribusi dalam menyediakan data berbasis bukti untuk pengambilan keputusan strategis terkait pengembangan kompetensi digital UMKM di Indonesia.
Optimalisasi Convolutional Neural Network Kontra VGG16 Klasifikasi Citra Daun Sawi Rio Febriyan; Ade irma Purnamasari; Denni Pratama; Puji Pramudya Marta; Yudhistira Arie Wijaya
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 6 No 1 (2026): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol6No1.pp30-34

Abstract

Manual detection of pests on mustard greens (caisim) is a major constraint in reducing harvest productivity, as manual methods are inefficient, time-consuming, and require specialized expertise. Furthermore, deep learning models often suffer from overfitting when applied to limited agricultural datasets. This study aimed to develop and compare the effectiveness of a Convolutional Neural Network (CNN) from scratch model versus the VGG16 transfer learning architecture for automatic classification of healthy and pest-affected mustard leaf images. A dataset of 1,000 images was used for training and testing across four experimental scenarios (A to D), with Percobaan C being the optimized CNN from scratch model (using data augmentation) and Percobaan D using VGG16. The results showed that the VGG16 transfer learning model achieved the highest test accuracy of 95.0% (F1-score: 0.95), while the optimized CNN from scratch model achieved 92.0% (F1-score: 0.92). Therefore, transfer learning with VGG16 is the most effective and optimal approach, demonstrating superior performance and efficiency by achieving high accuracy without complex data augmentation.
Co-Authors Ade Irma Purnamasari Dadang Sudrajat Dian Ade Kurnia Fatkhan Mubarok Luthfi Adianto Luthfiyyah Iffah Adella Muhammad Zamil Farhan Puji Pramudya Marta Rio Febriyan Saeful Anwar Yudhistira Arie Wijaya