FAJRI, AULIA KHOIRUNNISA (2020) IMPLEMENTASI ALGORITMA SELF ORGANIZING MAP REGRESSOR UNTUK PREDIKSI KEKERINGAN LAHAN DI PERKEBUNAN TEBU (STUDI KASUS: DESA DJENGKOL, KEDIRI, JAWA TIMUR). S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
Sugarcane is one of plantation commodities in Indonesia which has a big potential. Sugarcane growth consists of 4 phases that happens in a year. In the Grand Growth phase, sugarcane needs an appropriate condition so it can grow well and enter the next phase. The factors that affect sugarcane’s Grand Growth phase are water, temperature, and sunlight. Rainfall is one of sugarcane water source that needed, but the amount of the rainfall is different and the distribution of the rainfall is uneven every year. The uneven rainfall will cause water stress in sugarcane plantation. That’s why it’s necessary to identify the water content in sugarcane plantation to maintain the quality of sugarcane. This study will predict the water content of sugarcane plantation so the areas indicated with water stress can be anticipated. With Self-Organizing Map Regressor algorithm, Landsat-8 satellite imagery is processed to predict the water content of sugarcane plantation. The prediction result is water index value that can be used as reference to overcome water stress. Key words: Self-organizing map, sugarcane, water stress, algorithm Tebu merupakan salah satu komoditas perkebunan di Indonesia yang memiliki potensi besar. Pertumbuhan tanaman tebu terdiri dari 4 fase yang berlangsung selama kurang lebih 1 tahun. Pada fase pemanjangan batang, tebu membutuhkan kondisi yang memadai agar tumbuh dengan baik untuk masuk ke fase berikutnya. Faktor yang mempengaruhi fase pemanjangan batang yaitu air, suhu udara, dan sinar matahari. Curah hujan menjadi salah satu sumber untuk memenuhi kebutuhan air yang dibutuhkan tebu, namun jumlah dan penyebaran curah hujan tidak merata setiap tahunnya. Curah hujan yang tidak memadai akan menyebabkan kekeringan lahan pada perkebunan tebu. Penelitian ini akan melakukan prediksi kadar air pada perkebunan tebu sehingga dapat dilakukan antisipasi pada area yang terindikasi mengalami kekeringan. Dengan algoritma Self-Organizing Map Regressor, citra satelit Landsat-8 diolah untuk melakukan prediksi kadar air pada perkebunan tebu. Hasil prediksi berupa nilai indeks kadar air yang dapat dijadikan acuan untuk mengatasi kekeringan lahan. Kata kunci: Self-organizing map, tebu, kekeringan, algoritma
| Item Type: | Thesis (S1) |
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| NIM/NIDN Creators: | 41817120017 |
| Uncontrolled Keywords: | Self-organizing map, tebu, kekeringan, algoritma |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 003 Systems/Sistem-sistem 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 003 Systems/Sistem-sistem > 003.5 Computer Modeling and Simulation/Model dan Simulasi Komputer > 003.54 Information Theory/Teori Informasi |
| Divisions: | Fakultas Ilmu Komputer > Sistem Informasi |
| Depositing User: | Dede Muksin Lubis |
| Date Deposited: | 17 Oct 2023 03:33 |
| Last Modified: | 17 Oct 2023 03:33 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/82642 |
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