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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan DIKEMAS (Jurnal Pengabdian Kepada Masyarakat)
Lusiana, Evelin Dewi
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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IMPLEMENTASI SISTEM BUDIDAYA SEMI INTENSIF UDANG VANAMEI (LITOPENAEUS VANNAMEI) DI DESA TEMAJI KECAMATAN JENU KABUPATEN TUBAN Buwono, Nanik Retno; Mahmudi, Muhammad; Musa, Muhammad; Arsyad, Sulastri; Lusiana, Evelin Dewi
DIKEMAS (Jurnal Pengabdian Kepada Masyarakat) Vol 4, No 1 (2020)
Publisher : Politeknik Negeri Madiun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32486/jd.v4i1.396

Abstract

Kabupaten Tuban merupakan salah satu wilayah percontohan industrialisasi tambak udang khususnya udang vanamei. Desa Temaji merupakan salah satu desa di Tuban yang memiliki potensi budidaya udang vanamei untuk dikembangkan. Pembudidaya udang vanamei bernaung pada Kelompok tani Riswada. Tujuan pelaksanaan pengabdian di Desa Temaji adalah berbagi pengetahuan, peningkatan pemahaman dan keterampilan mitra dalam peningkatan teknologi semi intensif di tambak budidaya udang vanamei, pemantauan kualitas air melalui pelatihan pengukuran kualitas air, dan proses budidaya udang vanamei melalui pemanfaatan teknologi tepat guna dengan introduksi alat automatic feeder (autofeeder) secara mandiri. Pengabdian masyarakat ini terlaksana berdasarkan permasalahan yang terjadi pada mitra terkait kegiatan budidaya udang vanami seperti proses budidaya yang dilakukan oleh mitra masih secara tradisional/ekstensif dan efesiensi pakan yang masih rendah karena pemberian pakan masih secara konvensional.Kata Kunci : udang vanamei, automatic feeder, budidaya semi-intensif, Desa Temaji
BAYESIAN NEURAL NETWORK RAINFALL MODELLING: A CASE STUDY IN EAST JAVA Astutik, Suci; Rahmi, Nur Silviyah; Irsandy, Diego; Saniyawati, Fang You Dwi Ayu Shalu; Mashfia, Fidia Raaihatul; Lusiana, Evelin Dewi; Risda, Intan Fadhila; Susanto, Mohammad Hilmi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1105-1116

Abstract

Rainfall is an important parameter in meteorology and hydrology, and it measures the amount of rain that falls from the atmosphere to the ground surface in liquid form. However, in the process of measuring rainfall, changes in the rainfall cycle sometimes occur due to climate change, global warming, and other factors. Therefore, this research aims to model daily rainfall using the Bayesian Neural Network (BNN) approach, combining the Bayesian Method and Artificial Neural Network (ANN). ANN is suitable for rainfall models that have intermittent characteristics. Meanwhile, the Bayesian method provides advantages in producing model parameter inferences that provide uncertainty measurements in predictions. BNN is expected to deliver better daily rainfall predictions than ANN. This research used daily rainfall data in East Jawa, and the results show that the Bayesian Neural Network produces better rainfall predictions when describing rainfall in East Java. These predictions will be very useful for the government and the people of East Java province to prevent flooding. Also, with rainfall predictions, people will know more about what crops should be planted during the rains.
Co-Authors Arsyad, Sulastri Irsandy, Diego Mashfia, Fidia Raaihatul Muhammad Mahmudi, Muhammad Muhammad Musa Nanik Retno Buwono Rahmi, Nur Silviyah Risda, Intan Fadhila Saniyawati, Fang You Dwi Ayu Shalu Suci Astutik Susanto, Mohammad Hilmi