Puspitasari, Reny Dyah (2023) Analisis Penggunaan Google Trends Dalam Peramalan Penjualan Mobil Menggunakan Kombinasi Metode Time Series Regression Dan Support Vector Regression. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Industri otomotif merupakan sektor andalan yang memberikan kontribusi signifikan terhadap perekonomian nasional. Selama pandemi COVID-19 terjadi penurunan penjualan mobil tahunan hingga 40%, bahkan terjadi penurunan penjualan mobil bulanan pada Mei 2020 hingga 81,8%. Di sisi lain, penjualan mobil mungkin berhubungan dengan perilaku pencarian pengguna internet sebelum membeli mobil. Tingginya pertumbuhan pengguna internet di Indonesia akhir-akhir ini, yang mencapai 210 juta pengguna, sangat berperan dalam perilaku masyarakat dalam mencari informasi secara online sebelum membeli produk. Google telah meringkas lalu lintas pencarian untuk istilah tertentu ke dalam Indeks Google Trends. Penelitian ini menggunakan indeks google trends dari kata kunci yang dipilih sebagai prediktor tambahan untuk meningkatkan kinerja peramalan mobil. Penelitian ini melakukan pemodelan peramalan penjualan dengan menggabungkan model linier dan model nonlinier melalui dua tahap pemodelan. Langkah pertama menggunakan time series regression (TSR) dengan penggunaan dummy variabel untuk mengakomodir pola trend, musiman, dan pola variasi kalender. Pada langkah berikutnya, model nonlinier support vector regression (SVR) digunakan untuk memodelkan residual dari TSR. Penggunaan indeks google trends sebagai prediktor tambahan pada pemodelan TSR dapat meningkatkan akurasi model, dimana hasil evaluasi model menunjukkan model TSR dengan indeks google trends, memberikan hasil MAPE dan RMSE lebih baik daripada model hybrid (TSR – SVR) ataupun model TSR tanpa indeks google trends.
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The automotive industry is a leading sector that significantly contributes to the national economy. During the COVID-19 pandemic, there was a decrease in annual car sales by up to 40%, even a decrease in monthly car sales in May 2020 by up to 81,8%. On the other hand, car sales may relate with the searching behavior of internet users before the buy the car. The high growth of internet users in Indonesia recently, which has reached 210 million users, plays a significant role in people’s behaviour for seeking information online, before buying products. Google has summarized this search traffic for particular terms into the so-called Google Trends Index. This research utilizes the Google Trends indices of chosen terms as additional predictors to improve the forecasting performance of car sales. This research performs sales forecasting modeling by combining linear models and non-linear models through two modeling stages. The first step employs Time Series regression (TSR) with dummy variables to accommodate the trends pattterns, seasonality, and calendar variations pattern. In the second step, non-linear model Support Vector regression (SVR) is employed to model the residual of TSR. Google Trends indices which are used as additional predictors during the modeling TSR can improve model accuracy, as the model evaluation results show TSR with Google Trends indices, gives better MAPE and RMSE results than hybrid TSR-SVR model or TSR model without Google Trends indices.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | google trends, peramalan penjualan mobil, model hybrid, support vector regression, time series regression, car sales forecasting, hybrid model |
| Subjects: | Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression |
| Divisions: | Interdisciplinary School of Management and Technology (SIMT) > 61101-Master of Technology Management (MMT) |
| Depositing User: | Reny Dyah Puspitasari |
| Date Deposited: | 03 Feb 2023 08:18 |
| Last Modified: | 03 Feb 2023 08:18 |
| URI: | http://repository.its.ac.id/id/eprint/96140 |
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