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All Journal Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Pengabdian Kepada Masyarakat Abdi Nusa
Junmulyana, Satria
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Public Health Other

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SOSIALISASI FAKTOR-FAKTOR YANG MEMPENGARUHI STUNTING DAN CARA PENCEGAHAN STUNTING PADA ANAK DAN BALITA DI DESA KARANGPAPAK Junmulyana, Satria; Maulana, Taufik Bima; Amelia, Salsa; Suwandana, Erik; Wulandari, Siska
Jurnal Abdi Nusa Vol. 5 No. 1 (2025): Februari 2025
Publisher : LPPM Universitas Nusa Putra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/abdinusa.v5i1.177

Abstract

Kegiatan Kuliah Kerja Nyata (KKN) di Desa Karangpapak bertujuan untuk mengatasi prevalensistunting pada anak dan balita. Melalui pemberian makanan tambahan, penimbangan posyandu, dan kegiatan sosialisasi, program ini bertujuan meningkatkan pengetahuan dan kesadaran masyarakat mengenai faktor-faktor yang mempengaruhi stunting dan upaya pencegahannya. Dengan pendekatan holistik, kegiatan pemberian makanan tambahan terbukti efektif dalam meningkatkan status gizi anak. Penimbangan posyandu memberikan pemantauan teratur terhadap pertumbuhan anak, memungkinkan deteksi dini stunting. Sosialisasi membangun pengetahuan dan kesadaran masyarakat, menciptakan lingkungan yang mendukung pertumbuhan optimal anak- anak. Meskipun menghadapi beberapa tantangan, kolaborasi erat antara tim KKN, pihak desa, dan kader kesehatan menjadi kunci keberhasilan program. Hasil ini menjadi landasan untuk upaya pencegahan stunting berkelanjutan di Desa Karangpapak dan dapat diadopsi sebagai model untuk desa-desa sekitarnya.
Implementasi Convolutional Neural Network untuk Deteksi Penyakit pada Daun Cengkeh Berbasis Mobile: Bahasa Indonesia Junmulyana, Satria; Fergina, Anggun; Insany, Gina Purnama
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.9895

Abstract

Clove (Syzygium aromaticum) is a spice crop that has high economic value, but faces serious threats from various diseases that can reduce yields. Early detection of disease in clove plants is very important to prevent greater losses. This research aims to develop a disease detection system for clove plants using Convolutional Neural Network (CNN) implemented in a mobile application. This method is expected to provide a faster and more accurate solution compared to traditional detection methods that are often inefficient. This research was conducted by collecting datasets of infected and healthy clove leaf images, which were then used to train the CNN model. The results show that the developed CNN model is able to achieve high disease detection accuracy, and can be integrated with mobile technology to facilitate farmers in identifying diseases in real-time. Thus, this research not only contributes to increasing agricultural productivity, but also supports the application of digital technology in the agricultural sector. The results of this research are expected to benefit farmers, researchers, and the agricultural industry as a whole.
Co-Authors adang badru jaman,anggun fergina, adang badru jaman,anggun fergina Amelia, Salsa Insany, Gina Purnama Maulana, Taufik Bima Siska Wulandari, Siska Suwandana, Erik