SANTOSO, HARPAN BUDI (2023) ANALISIS PREDIKSI PENJUALAN FTTH MENGGUNAKAN METODE EXPONENTIAL SMOOTHING DAN RANDOM FOREST (STUDI KASUS: PT. XYZ). S1 thesis, Universitas Mercu Buana Bekasi.
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Abstract
Keunggulan sistem AIS (Automatic Identification System) satelit dalam hal jangkauannya yang lebih luas jika dibandingkan dengan AIS konvensional, memiliki konsekuensi potensi munculnya masalah lain dimana posisi kapal tidak dapat dipantau pada periode waktu tertentu sehingga lintasan kapal akan memiliki kekosongan pada periode waktu tersebut. Pada jurnal ini kami mencoba merekonstruksi lintasan tersebut dengan cara melakukan prediksi terhadap lintasan kapal menggunakan algoritma deep learning LSTM (Long Short Term Memory). Adapun langkah yang kami lakukan adalah dengan 1) data cleansing; 2) data preprocessing dengan melakukan rekayasa fitur; dan 3) prediksi lintasan kapal. Hasil dari penelitian ini diharapkan dapat digunakan oleh pemilik kepentingan atau sebagai acuan untuk penelitian selanjutnya. Kata kunci: satelit AIS, LSTM, prediksi lintasan kapal. The advantages of satellite AIS (Automatic Identification System) systems in terms of their more broader range when compared to conventional AIS have the consequence of the potential emergence of other problems where the position of the ship cannot be monitored at a certain period so that the ship's trajectory will have a void in that period. In this journal, we tried to reconstruct the trajectory by predicting the ship's trajectory using the LSTM (Long Short Term Memory) deep learning algorithm. We take steps 1) data cleansing, 2) data pre-processing by performing feature engineering, and 3) ship trajectory predictions. The research results are expected to be used by the interest owner or as a reference for further research. Keywords: AIS satellite, LSTM, ship trajectory prediction
| Item Type: | Thesis (S1) |
|---|---|
| Call Number CD: | FIK/INFO 23 013 |
| NIM/NIDN Creators: | 41519310029 |
| Uncontrolled Keywords: | satelit AIS, LSTM, prediksi lintasan kapal. |
| 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 > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | siti maisyaroh |
| Date Deposited: | 18 Dec 2023 06:52 |
| Last Modified: | 18 Dec 2023 06:52 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/84754 |
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