ANALISIS FORECASTING MENGGUNAKAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DAN VISUALISASI DASHBOARD TABLEAU PADA DATA PENJUALAN PAKAIAN THRIFT CLOTH.NYTA

Dona Monicha Margarettha, - (2023) ANALISIS FORECASTING MENGGUNAKAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DAN VISUALISASI DASHBOARD TABLEAU PADA DATA PENJUALAN PAKAIAN THRIFT CLOTH.NYTA. S1 thesis, Universitas Pendidikan Indonesia.

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Abstract

Penelitian ini fokus pada forecasting dan visualisasi data penjualan pada Cloth.Nyta. Metode Autoregressive Integrated Moving Average (ARIMA) digunakan untuk meramalkan penjualan pada beberapa jenis pakaian selama rentang waktu bulan April 2023 hingga Maret 2024. Dalam penelitian ini, model ARIMA dapat digunakan untuk melakukan peramalan data penjualan Cloth.Nyta dengan hasil yang terbaik, hasil peramalan penjualan menunjukkan jumlah unit yang diantisipasi terjual pada setiap jenis pakaian selama rentang waktu bulan April 2023 hingga Maret 2024. Penelitian ini menggunakan metode penelitian terapan dengan pendekatan kuantitif. Dalam pengolahan data, penelitian ini menggunakan bahasa pemrograman Pyhton dan visulisasi menggunakan Tableau. Implementasi model ini memungkinkan perusahaan merencanakan pengadaan stok yang lebih akurat, menghindari kelebihan atau kekurangan persediaan. Selain itu, visualisasi dashboard menggunakan platform Tableau menyajikan informasi berharga seperti total pendapatan, total quantity, dan hasil peramalan. Hal ini membantu pengambilan keputusan dalam menganalisis performa penjualan dan merumuskan strategi pemasaran yang tepat. Implikasi dari penelitian ini adalah penerapan metode ARIMA selama 1 tahun dapat membantu perusahaan mengoptimalkan perencanaan dan pengambilan keputusan. Rekomendasi untuk pemilik Cloth.Nyta termasuk pemanfaatan hasil peramalan dalam pengelolaan persediaan, serta penggunaan informasi peramalan untuk merencanakan strategi pemasaran yang lebih tepat. Peneliti selanjutnya disarankan untuk mengeksplorasi metode peramalan lainnya atau mempertimbangkan faktor eksternal untuk meningkatkan akurasi prediksi di masa mendatang. This study aims to focus on the forecasting and visualization of sales data at Cloth.Nyta. The Autoregressive Integrated Moving Average (ARIMA) method is employed to forecast sales for various types of clothing during the period from April 2023 to March 2024. In this study, the ARIMA model can be utilized to perform sales forecasting for Cloth.Nyta with the best possible outcomes. The results of sales forecasting indicate the anticipated number of units to be sold for each type of clothing during the mentioned period. This research employs an applied research method with a quantitative approach. In data processing, this study utilizes the Python programming language and visualization is carried out using Tableau. The implementation of this model allows companies to plan for more accurate inventory procurement, thereby avoiding inventory excess or shortage. Additionally, the visualization dashboard through the Tableau platform presents valuable information such as total revenue, total quantity, and forecasting results. This aids in decision-making by analyzing sales performance and formulating appropriate marketing strategies. The implication of this study is that the effective implementation of the ARIMA method in 1 year can assist companies in optimizing planning and decision-making processes. Recommendations for Cloth.Nyta owners include the utilization of forecasting results in inventory management, as well as leveraging forecast information for precise marketing strategy planning. Subsequent researchers are advised to explore alternative forecasting methods or consider external factors to enhance predictive accuracy in the future.

Item Type: Thesis (S1)
Additional Information: ID SINTA Dosen Pembimbing: Rangga Gelar Guntara: 6738149 Oding Herdiana: 0011078607
Uncontrolled Keywords: Forecasting, ARIMA, Visualisasi Dashboard, Pengelolaan Persediaan, Dashboard Visualization, Inventory Management.
Subjects: L Education > L Education (General)
Divisions: UPI Kampus Tasikmalaya > S1 Bisnis Digital
Depositing User: Dona Monicha Margarettha
Date Deposited: 08 Nov 2023 06:32
Last Modified: 08 Nov 2023 06:32
URI: http://repository.upi.edu/id/eprint/112206

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