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All Journal Instal : Jurnal Komputer Jurnal Pengabdian Masyarakat IPTEK
Ramlan Marbun
Universitas Pembangunan Panca Budi
Computer Science & IT Decision Sciences, Operations Research & Management

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Pemberdayaan Digital Untuk Mendukung UMKM Pertanian dalam Pemasaran Hasil Panen di Pedesaan Menggunakan Apliksi “Jangkrik iConnect” Muhammad Irfan Sarif; Ramlan Marbun; Maulisa Syahputri; Jelly Rolleys Sitompul
Jurnal Pengabdian Masyarakat IPTEK Vol. 6 No. 1 (2026): Edisi Januari 2026
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/abdi.v6i1.12459

Abstract

Usaha Mikro, Kecil, dan Menengah (UMKM) sektor pertanian memiliki peran strategis dalam mendukung ketahanan pangan dan perekonomian nasional, khususnya di wilayah perdesaan. Namun demikian, masih banyak UMKM pertanian yang menghadapi kendala serius dalam pemanfaatan teknologi digital akibat keterbatasan infrastruktur. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk memberikan pendekatan digital sebagai solusi pendukung pengembangan UMKM pertanian di daerah yang terbatas dari segi penjualan, biaya promosi yang tinggi, dan dukungan teknis yang minim. Hambatan ini secara langsung mempengaruhi produktivitas dan potensi pertumbuhan bisnis. Pengabdian ini bertujuan untuk merancang sebuah solusi inovatif bernama "Jangkrik i-connect," yaitu sebuah platform digital yang menyediakan promosi, penjualan, monitoring, serta model bisnis yang fleksibel. Metode ini digunakan dapat menjadi sala satu solusi bagi UMKM untuk bertumbuh dan meningkatkan efisiensi operasional melalui adopsi teknologi digital.
Application of Convolutional Neural Network (CNN) in Facial Expression Detection for Classifying the Level of Learning Concentration of Senior High School Students (SMA) Ramlan Marbun; Muhammad Irfan Sarif; Muhammad Syahputra Novelan
Bahasa Indonesia Vol 18 No 06 (2026): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v18i06.542

Abstract

Student learning concentration is one of the key factors influencing academic achievement and the effectiveness of the learning process. However, monitoring students' concentration levels manually is often subjective and challenging, especially in classrooms with a large number of students. This study aims to implement a Convolutional Neural Network (CNN) model for facial expression detection to classify the learning concentration levels of Senior High School (SMA) students. The research employs a quantitative experimental approach using facial image datasets collected during classroom learning activities. The dataset undergoes several preprocessing stages, including face detection, cropping, image resizing, and pixel normalization before being used for model training. The CNN architecture is designed to automatically extract facial features and classify students' concentration levels into three categories: high, medium, and low concentration. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results indicate that the CNN model is capable of recognizing facial expression patterns related to learning concentration effectively and achieving high classification performance. Furthermore, the developed system provides a more objective and efficient approach for monitoring student concentration compared to conventional observation methods. Therefore, the implementation of CNN-based facial expression recognition has significant potential to support intelligent educational systems and improve learning evaluation processes in school environments.  
Co-Authors Jelly Rolleys Sitompul Maulisa Syahputri Muhammad Irfan Sarif Muhammad Syahputra Novelan