Hanugraheni, Jupita Sari Ika (2021) Pendekatan Nowcasting untuk Menganalisis Faktor-Faktor yang Mempengaruhi Penjualan Online Selama Pandemi COVID-19 Beberapa Kota/Kabupaten di Jawa Timur. Masters thesis, Institut Teknologi Sepuluh Nopember.
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09211850096008-Master_Thesis.pdf - Accepted Version Restricted to Repository staff only until 1 October 2023. Download (3MB) | Request a copy |
Abstract
Sampai dengan 21 Juni 2021, Jawa Timur adalah provinsi dengan tingkat kematian COVID-19 tertinggi nasional, yakni mencapai 7,8 persen dari total keseluruhan kasus. Kebijakan physical distancing dan social distancing dilakukan untuk mencegah penyebaran COVID-19. Akibatnya masyarakat lebih banyak terhubung melalui jaringan internet, yang pada akhirnya mempengaruhi kebiasaan masyarakat dalam berbelanja online. Selama pandemi COVID-19, terdapat perubahan kebutuhan terhadap beberapa produk kesehatan yang semula tidak terlalu diperlukan menjadi kebutuhan sekunder bahkan kebutuhan primer. Lonjakan permintaan yang tidak dapat dipenuhi, menyebabkan kelangkaan dan kenaikan harga barang. Selain itu, kegagalan dalam memprediksi peningkatan penjualan online, menyebabkan meningkatnya estimasi sampainya barang kepada pembeli yang melakukan transaksi online tersebut. Oleh karena, itu diperlukan analisis mengenai faktor-faktor yang mempengaruhi penjualan online di beberapa kota/kabupaten di Jawa Timur. Analisis ini memerlukan data yang akurat dan tepat waktu (timely), sehingga tidak ada kesalahan data dan permasalahan data yang tertunda. Pendekatan nowcasting dengan melibatkan data pencarian internet yang bersifat real time dianggap mampu untuk predict the present atau memprediksi kondisi saat ini. Adapun metode pemodelan yang dapat digunakan adalah regresi data panel yang melibatkan data cross section dan data time series. Hasil penelitian menunjukkan metode estimai terbaik untuk tingkat penjualan online adalah Random Effect Model. Keseluruhan variabel prediktor yang digunakan, yakni indeks GT “jualan online”, indeks GT “masker”, dan positivity rate berpengaruh secara signifikan. Nilai koefisien determinasi yang diperoleh dari model adalah sebesar 80,69 persen.
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As of June 21, 2021, East Java Province has the highest national COVID-19 death rate, reaching 7.8 percent of the total cases in Indonesia. The government implements various policies, such as physical distancing and social distancing to prevent the spread of COVID-19. As a result, more people are connected through the internet network, which in turn affects people's habits on shopping online. During the COVID-19 pandemic, the needs for several health/hygiene/medical products shifted, originally classified as tertiary necessity, become secondary needs and even primary needs. The surge in demand that cannot be fulfilled, causes scarcity and increases the price of goods. In addition, the failure to predict the increase in online sales has led to an increase in estimates of the arrival of goods to buyers doing who make online transactions. Therefore, it is necessary to analyze the factors that influence online sales in several cities/regency in East Java. This analysis requires accurate and timely data, so that there are not data errors and pending data problems. The nowcasting approach involving real-time internet search data is considered capable of predicting the present. The modeling method that can be used is panel data regression involving cross section data and time series data. The results showed that the best estimation method for the online sales was the Random Effect Model. The predictor variable GT index of “jualan online", GT index of “makser”, and the positivity rate are statistically significant. The coefficient of determination obtained from the model is 80.69 percent.
Item Type: | Thesis (Masters) |
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Uncontrolled Keywords: | COVID-19, Google Trends, Nowcasting, Penjualan Online, Regresi Data Panel |
Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression |
Divisions: | Faculty of Creative Design and Digital Business (CREABIZ) > Technology Management > 61101-(S2) Master Thesis |
Depositing User: | JUPITA SARI IKA HANUGRAHENI |
Date Deposited: | 23 Aug 2021 09:10 |
Last Modified: | 23 Aug 2021 09:10 |
URI: | http://repository.its.ac.id/id/eprint/88199 |
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