MACADAFI, FAHAD HAKIM (2023) PERAMALAN HARGA PANGAN CABAI MERAH BESAR DI PROVINSI PULAU JAWA PADA MASA KENAIKAN BBM PADA TAHUN 2022 MENGGUNAKAN ALGORITMA LONG SHORT TERM MEMORY. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
Big Red Chili is one type of food whose demand in Indonesia continues to increase every year along with population growth. The dynamics of chili prices is a problem for the government. Farmers often plant and harvest chilies at the same time. The government must regulate the stability of chili supply. According to research by Mujiono Sadik et al. (2022) entitled "Forecasting Raw Material Demand Using Comparison of LSTM and ARIMA Algorithms" and research by Handrie Noprisson et al. (2020) "Deep Learning Predictor (LSTM) Human Mobility Prediction Algorithm" both use the LSTM algorithm. which gives good results in predicting data with a small error. The price of large red chili peppers can change every day, so forecasting is the art and science of estimating future events. This can be done by looking at historical data. The LSTM method is used because the LSTM architecture can adapt to non-linear learning and complex time series data. Kata Kunci : Cabai, Pangan, LSTM, Harga Cabai Merah Besar adalah salah satu jenis pangan yang kebutuhan di Indonesia terus meningkat setiap tahunnya seiring dengan pertumbuhan jumlah penduduk. Dinamika harga cabai menjadi masalah tersendiri bagi pemerintah. Petani sering kali menanam dan memanen cabai dalam waktu yang bersamaan. Pemerintah harus mengatur stabilitas pasokan cabai. Menurut penelitian Mujiono Sadik dkk. (2022) yang berjudul "Peramalan Permintaan Bahan Baku Menggunakan Perbandingan Algoritma LSTM dan ARIMA" dan penelitian Handrie Noprisson dkk. (2020) "Deep Learning Predictor (LSTM) Human Mobility Prediction Algorithm" sama-sama menggunakan algoritma LSTM. yang memberikan hasil yang baik dalam memprediksi data dengan error yang kecil. Harga cabai merah besar dapat berubah setiap harinya, sehingga peramalan adalah seni dan ilmu untuk memperkirakan kejadian di masa depan. Hal ini dapat dilakukan dengan mencari data historis. Metode LSTM digunakan karena arsitektur LSTM dapat beradaptasi dengan pembelajaran non-linier dan data time series yang kompleks. Kata Kunci : Cabai, Pangan, LSTM, Harga
| Item Type: | Thesis (S1) |
|---|---|
| Call Number CD: | FIK/INFO. 23 164 |
| Call Number: | SIK/15/23/051 |
| NIM/NIDN Creators: | 41519010061 |
| Uncontrolled Keywords: | Cabai, Pangan, LSTM, Harga |
| 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 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 > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | Annas Tsabatulloh |
| Date Deposited: | 21 Oct 2023 06:33 |
| Last Modified: | 21 Oct 2023 06:33 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/80795 |
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