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Prediksi Penjualan Bisnis Rumah Properti Dengan Menggunakan Metode Autoregressive Integrated Moving Average (ARIMA)

*Jefri Junifer Pangaribuan orcid scopus  -  Program Studi Sistem Informasi, Fakultas Ilmu Komputer, Universitas Pelita Harapan, Medan, Indonesia 20112, Indonesia
Fanny Fanny  -  Program Studi Sistem Informasi, Fakultas Ilmu Komputer, Universitas Pelita Harapan, Medan, Indonesia 20112, Indonesia
Okky Putra Barus orcid scopus  -  Program Studi Sistem Informasi, Fakultas Ilmu Komputer, Universitas Pelita Harapan, Medan, Indonesia 20112, Indonesia
Romindo Romindo orcid scopus  -  Program Studi Sistem Informasi, Fakultas Ilmu Komputer, Universitas Pelita Harapan, Medan, Indonesia 20112, Indonesia
Open Access Copyright (c) 2023 JSINBIS (Jurnal Sistem Informasi Bisnis)

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Abstract

Abstract - Sales forecasting plays an important role in determining the company's strategy in the future because it allows control of planning and availability of home production according to consumer needs. Forecasting accuracy provides significant advantages for companies, including production cost savings and avoidance of unnecessary costs. Without accurate forecasting, a company will face difficulties in determining the quantity of house production, which can have a negative impact on the company's financial balance if the houses do not sell. This research implements the Autoregressive Integrated Moving Average (ARIMA) model to forecast property business house sales with a high level of accuracy to support future business decisions. The results of the research on the application of the Autoregressive Integrated Moving Average algorithm show that the ARIMA model (9,1,10) provides good forecasting results measured by the lowest AIC and BIC values compared to the other 4 models, namely ARIMA (10,1,9); ARIMA(8,1,9); ARIMA(10,1,10); and ARIMA (12,1,12) accompanied by an evaluation of measuring the accuracy of the model using RMSE, MSE, and MAPE with each value of 0.281409; 0.079191 and MAPE of 3.4% so that it can be said that sales forecasting provides a good level of accuracy.

Abstrak - Prediksi penjualan memegang peran penting dalam menentukan strategi perusahaan di masa depan karena memungkinkan pengendalian perencanaan dan ketersediaan produksi rumah sesuai dengan kebutuhan konsumen. Keakuratan prediksi memberikan keuntungan signifikan bagi perusahaan, termasuk penghematan biaya produksi dan menghindari biaya yang tidak perlu. Kesulitan dalam menentukan jumlah produksi rumah tanpa prediksi yang tepat dapat berdampak negatif pada keseimbangan keuangan perusahaan jika rumah tidak terjual. Penelitian ini mengimplementasikan model Autoregressive Integrated Moving Average untuk melakukan prediksi penjualan bisnis rumah properti dengan tingkat akurasi yang baik untuk dapat mendukung keputusan bisnis kedepannya. Hasil penelitian pada pengaplikasian algoritma Autoregressive Integrated Moving Average menunjukkan bahwa model ARIMA (9,1,10) memberikan hasil nilai prediksi yang baik diukur dari nilai AIC dan BIC yang paling rendah dibandingkan 4 model lainnya yaitu ARIMA (10,1,9); ARIMA (8,1,9); ARIMA (10,1,10); dan ARIMA (12,1,12) disertai evaluasi pengukuran keakuratan model dengan menggunakan RMSE, MSE, dan MAPE dengan masing-masing nilai yaitu 0.281409; 0.079191 dan MAPE sebesar 3.4% sehingga dapat dikatakan prediksi penjualan memberikan tingkat akurasi yang baik.

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Keywords: Data Mining; Autoregressive Integrated Moving Average; Forecasting; Sales

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