PREDIKSI KEDATANGAN WISATAWAN MANCANEGARA DI BALI MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE DAN REGRESI LINEAR

WIBISONO, INDRA (2024) PREDIKSI KEDATANGAN WISATAWAN MANCANEGARA DI BALI MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE DAN REGRESI LINEAR. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

The island of Bali, a famous tourist destination in Indonesia, has experienced an increase and decline in its tourism sector. To anticipate the arrival of international tourists, a thorough analysis and prediction approach is needed. The main objective of this study is to analyze and predict the arrival of foreign tourists in the coming year. The use of machine learning algorithms can be important in analyzing data and forecasting the development of international tourist arrivals. This research details the use of several machine learning algorithms, namely Support Vector Machine (SVM), and Linear Regression. The test results show that the SVM program evaluation provides accurate predictions with a low error rate in the test data. Meanwhile, the error rate in the evaluation of the Linear Regression program is slightly higher. However, the model was still able to make good predictions on the test data. Keywords: Prediction, Tourism, Bali, Support Vector Machine (SVM), Linear Regression, Mercu Buana University Pulau Bali, sebuah destinasi wisata terkenal di Indonesia, telah mengalami peningkatan dan penurunan dalam sektor pariwisatanya. Untuk mengantisipasi datangnya wisatawan internasional, pendekatan analisis dan prediksi yang menyeluruh sangat diperlukan. Tujuan utama dari penelitian ini adalah untuk menganalisis dan memprediksi kedatangan wisatawan asing dalam tahun mendatang. Penggunaan algoritma machine learning dapat menjadi hal yang penting dalam analisis data dan meramalkan perkembangan kedatangan wisatawan internasional. Penelitian ini merinci penggunaan beberapa algoritma machine learning, yaitu Support Vector Machine (SVM), dan Regresi Linear. Hasil pengujian menunjukkan bahwa evaluasi program SVM memberikan prediksi yang akurat dengan tingkat kesalahan yang rendah pada data uji. Sedangkan, tingkat kesalahan pada evaluasi program Linear Regression sedikit lebih tinggi. Akan tetapi, model tersebut masih mampu membuat prediksi yang baik pada data uji. Kata kunci: Prediksi, Pariwisata, Bali, Support Vector Machine (SVM), Linear Regression, Universitas Mercu Buana

Item Type:	Thesis (S1)
Call Number CD:	FIK/SI. 24 085
NIM/NIDN Creators:	41820010066
Uncontrolled Keywords:	Prediksi, Pariwisata, Bali, Support Vector Machine (SVM), Linear Regression, Universitas Mercu Buana
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 > 003.7 Kinds of Systems/Macam-macam Sistem > 003.74 Linear Systems/Sistem Linear 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma 900 Geography and History/Sejarah, Geografi dan Disiplin Ilmu yang Berkaitan > 910 Geography and Travel/Geografi dan Perjalanan > 910.1-910.9 Standard Subdivisions of Geography and Travel/Subdivisi Standar dari Geografi dan Perjalanan > 910.9 Historical, Geographic, Persons Treatment/Perawatan, Historis, Geografi > 910.92 Travelers/Turis, Wisatawan, Pelancong
Divisions:	Fakultas Ilmu Komputer > Sistem Informasi
Depositing User:	khalimah
Date Deposited:	23 Jul 2024 03:55
Last Modified:	23 Jul 2024 03:55
URI:	http://repository.mercubuana.ac.id/id/eprint/89760

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