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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Pengabdian dan Pemberdayaan Nusantara (JPPNu)
Sari, Hetty Elvina
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Humanities Computer Science & IT Economics, Econometrics & Finance Education Public Health Social Sciences

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Peningkatan Poduktivitas Peternak Jangkrik (Gryllus bimaculatus) Melalui Inovasi Mesin Tetas Berbasis IoT Di Kabupaten Blitar Sari, Hetty Elvina; Palupi, Indah; Khusnita, Alfi; Amin, Iza Dzul Fikar; Zami, M. Najib Zam
Jurnal Abdimas UNU Blitar Vol 6 No 1 (2024): Vol 6 No 1 (2024): Volume 6 Nomor 1 : Juli 2024
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/jppnu.v6i1.245

Abstract

Anang farm merupakan peternak mandiri jangkrik (Gryllus bimmaculatus) yang berdiri sejak Maret 2015. Berdasarkan hasil diskusi dengan mitra diketahui mitra mempunyai 50 box kandang dengan kapasitas produksi per box mencapai 50-70 kg jangkrik yang berumur 35 hari. Omset yang didapat mitra kisaran 1-2 juta setiap 1 kali panen dengan masa panen 1-2 hari. Permasalahan mitra yang telah didiskusikan yaitu penetasan telur jangkrik dengan metode konvensional menggunakan media koran yang bersifat mudah rusak dan kesulitan dalam monitoring hasil tetas telur sebelum didistribusikan ke kandang pemeliharaan. Oleh karena hal ini tim PKM-PI membuat inovasi berupa mesin tetas berbasis IoT (Internet of Thing). Untuk mencapai tujuan inovasi dilakukan beberapa tahap kegiatan antara lain penyuluhan dan sosialisasi terkait dengan mesin tetas berbasis IoT, dilanjutkan dengan pembuatan alat, diseminasi alat kepada mitra, dan dilakukan pelatihan serta pendampingan penggunaan mesin tetas.
Coral Reef Image Classification Using Multilayer Perceptron Fauzan, Abd. Charis; Sari, Hetty Elvina
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 17, No 2 (2025): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v17i2.32134

Abstract

Coral reefs are one of the marine organisms that play many crucial roles for other organisms within them. Coral reefs are often referred to as tropical rainforests because they serve as shelters for small fish and produce food for other marine organisms. Over time, various threats have emerged that disrupt the stability of the marine ecosystem, one of which is coral reef degradation, such as bleaching or physical damage caused by multiple factors. These factors include climate change, chemicals resulting from fishing with explosives, and pollution. As a result, coral reefs become damaged and can no longer serve as a refuge for small species.  Therefore, this study aims to mitigate the impact of coral reef damage by developing a coral reef classification model using one of the deep learning algorithms and artificial neural networks, namely the Multilayer Perceptron (MLP), which employs multiple hidden layers in its modeling process. The classification results using this algorithm achieved an accuracy of 73%, indicating that the model performs well in classifying coral reefs in image form. Thus, it is hoped that deep learning innovations for coral reef classification can contribute significantly to coral reef conservation and marine resource management.
Co-Authors Abd. Charis Fauzan Amin, Iza Dzul Fikar Indah Palupi Khusnita, Alfi Zami, M. Najib Zam