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SISTEM INFORMASI DINAS KELAUTAN DAN PERIKANAN KABUPATEN DOMPU PROVINSI NUSA TENGGARA BARAT BERBASIS WEBSITE MENGGUNAKAN FRAMEWORK BOOTSTRAP DAN CODEIGNITER: Information Systems for Marine Affairs and Fisheries of Dompu District, West Nusa Tenggara Province Based on a Website Using Bootstrap and Codeigniter Framework Munirah, Zahrahtun; Murpratiwi, Santi Ika; Ramlah, Ramlah
Jurnal Begawe Teknologi Informasi (JBegaTI) Vol. 5 No. 2 (2024): JBegaTI
Publisher : Program Studi Teknik Informatika, Fakultas Teknik Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jbegati.v5i2.1294

Abstract

Dinas Kelautan dan Perikanan Kabupaten Dompu Provinsi Nusa Tenggara Barat adalah lembaga pemerintah yang bertanggung jawab atas pengelolaan, pengembangan, dan perlindungan sumber daya kelautan dan perikanan. Salah satu peran pentingnya yaitu mengkoordinasikan kegiatan pengelolaan sumber daya kelautan dan perikanan, termasuk penangkapan ikan yang berkelanjutan, pengembangan budidaya perikanan serta pelestarian lingkungan laut. Saai ini Kelautan dan Perikanan sudah memiliki website company profile untuk menyajikan informasi terkait kantor tersebut, akan tetapi website tersebut memiliki fitur-fitur yang kurang lengkap seperti tidak adanya foto-foto dari unit kerja dari seluruh pegawai yang bekerja serta tidak adanya sistem validasi oleh Kapala Bidang pada saat admin menginputkan berita. Dengan dibuatnya sistem informasi ini, dapat memberikan aksesibilitas informasi kepada masyarakat terkait dengan layanan, program, kebijakan, dan kegiatan yang diselenggarakan oleh Dinas Kelautan dan Perikanan Kabupaten Dompu Provinsi Nusa Tenggara Barat.
Prediksi Sebaran Hama Tikus Pada Tanaman Padi Menggunakan Metode Backpropagation Neural Network Arimawarni, Rafika; Sugiartawan, Putu; Murpratiwi, Santi Ika
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 5 No 1 (2022): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.175

Abstract

OPT (Plant Pest Organisms) is any activity or activities that damage and kill plants, one of which is caused by pests, diseases, viruses, etc. In Bali, especially in Tabanan Regency, OPT cases are still very high. OPT in rice plants caused by rats is a problem faced by farmers and in the future, it must be prevented by knowing the spread of rats. Therefore, the purpose of this research is to help farmers prevent pest attacks so that rice productivity can be increased. In this study, the backpropagation neural network method was used to predict the distribution of rat pests on rice plants. This method uses previous data, namely from 2012-2021 when the data is processed and calculated until the smallest error value is obtained. In this study, data were obtained from calculating the distribution of pests in hectares which showed a percentage difference in accuracy error of 16.2%, which means that the prediction of this calculation is good enough to be used as a reference for further research
Prediksi Sebaran Hama Padi Dengan Metode LSTM Pada Pertanian Padi Di Buleleng Negara, I Gede Sunia; Sugiartawan, Putu; Murpratiwi, Santi Ika
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 5 No 1 (2022): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.176

Abstract

Prediction is a systematic process of estimating future values based on patterns contained in data that has been converted into numerical form. In this study, the aim was to predict the distribution of rice borer in Buleleng district which could endanger the productivity of the rice agricultural sector. One of the methods used in this research is Long Short Term Memory (LSTM), a form of development of Recurrent Neural Network (RNN) which is suitable for processing and predicting time series data. The data used in this study is rice borer attack data for the last ten years, from 2012 to 2021. The results show that the LSTM model has an MAE data testing of 16.8149 and MAPE data testing of 2.356%, and MAE data training of 16.8149 and MAPE data training of 2,356%. These values measure the prediction error with the MAE and MAPE techniques. With these results, the agricultural service can recognize the pattern of distribution of rice borer attacks in the region and take appropriate action to overcome them.