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All Journal G-Tech : Jurnal Teknologi Terapan Indonesian Community Journal
Bagus Gilang Pratama
Institut Teknologi Nasional Yogyakarta, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Environmental Science Public Health Social Sciences Other

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Uji Pasar Produk Eco enzyme Berbasis IoT sebagai Inovasi Pengelolaan Sampah Organik di Rumah Sampah Ringas Trengginas, Bantul, Yogyakarta Bagus Gilang Pratama; Sely Novita Sari; Oni Yuliani; Nanda Ramadhani; Ilham Nawawi; Silfia Dwi Putri
I-Com: Indonesian Community Journal Vol 5 No 3 (2025): I-Com: Indonesian Community Journal (September 2025)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/i-com.v5i3.7687

Abstract

Produk eco enzyme merupakan hasil fermentasi limbah organik yang ramah lingkungan dan multifungsi, seperti untuk pembersih alami, pupuk cair, dan pengusir serangga. Kegiatan pengabdian ini bertujuan untuk mendukung penguatan kapasitas ekonomi masyarakat melalui uji pasar produk eco enzyme berbasis IoT yang dikembangkan oleh Rumah Sampah Ringas Trengginas di Bantul, Yogyakarta. Produk ini dinilai potensial, namun belum memiliki strategi pemasaran berbasis data. Metode pelaksanaan meliputi penyebaran kuesioner, distribusi sampel, wawancara, dan simulasi penjualan terbatas kepada 150 responden dari lima segmen pasar. Hasil menunjukkan respons positif dari konsumen, dengan tingkat ketertarikan tinggi dan potensi pembelian ulang yang signifikan. Temuan ini menjadi dasar penyusunan strategi pemasaran awal yang dapat diterapkan mitra secara mandiri. Hasil pengabdian ini penting sebagai langkah awal dalam memperluas pasar produk berbasis lingkungan dan teknologi, serta mendukung pemberdayaan ekonomi lokal yang berkelanjutan.
Application of Genetic Algorithm Neural Network in Identifying Buildings in Landslide-Prone Areas Bagus Gilang Pratama; Sely Novita Sari; Joko Prasojo
G-Tech: Jurnal Teknologi Terapan Vol 9 No 3 (2025): G-Tech, Vol. 9 No. 3 July 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i3.7168

Abstract

Indonesia is a disaster-prone country, one of which is landslides, which often occur in hilly areas with high rainfall. The impact damages the environment and infrastructure, especially buildings. For effective mitigation, a risk identification system based on artificial intelligence technology is needed. This study applies Genetic Algorithm Neural Network (GANN) in identifying buildings in landslide-prone areas. GANN was chosen for its ability to optimize network weights globally through selection, crossover, and mutation mechanisms, thus avoiding suboptimal local solutions. The dataset consists of 169 data with 12 structural features of the building. The model was configured with genetic parameters such as the number of generations 500, population size of 50, mutation rate of 10%, and the Stochastic Universal Sampling selection method. To Evaluate the performance of model created from dataset, we employed accuracy, precision, recall, and F1-score. The results showed an accuracy of 81% and an average F1-score of 0.82, with the best performance in the "Unsafe" class (recall 0.84). Although it still needs improvement, GANN has proven to have the potential as a decision support tool in data-driven landslide risk mitigation.
Co-Authors Ilham Nawawi Joko Prasojo Nanda Ramadhani Oni Yuliani Sely Novita Sari Silfia Dwi Putri