NURLITASARI, SHEBA (2026) IMPLEMENTASI DAN EVALUASI ALGORITMA ARIMA DAN MEMBANDINGKAN DENGAN ALGORITMA PROPHE UNTUK FORECASTING PENJUALAN BERBASIS BUSINESS INTELLIGENCE. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
The development of Business Intelligence has encouraged organizations to utilize historical data as a basis for more effective decision-making. However, CV Magix Metro Media has not yet implemented a forecasting feature capable of automatically predicting future sales, causing inventory planning and product distribution to rely primarily on experience. This study aims to implement the AutoRegressive Integrated Moving Average (ARIMA) algorithm in the AROMETRIC Business Intelligence application and compare its forecasting accuracy with the Prophet algorithm using Root Mean Square Error (RMSE). The research methodology consists of data collection, time series preparation, stationarity testing, differencing, model selection based on the Akaike Information Criterion (AIC), development of the ARIMA(0,1,1) model, implementation in a web-based application, and model evaluation using RMSE. The dataset consists of 65 monthly sales observations collected from January 2021 to May 2026. The experimental results show that the ARIMA model achieved an RMSE of 4,103.963, outperforming the Prophet model, which obtained an RMSE of 10,889.179. These findings indicate that ARIMA provides better forecasting accuracy for the sales data used in this study. Furthermore, the implementation of the ARIMA algorithm in the AROMETRIC application enables automatic sales forecasting and supports more effective data-driven decision-making through Business Intelligence.Keywords: ARIMA, Prophet, Forecasting, RMSE, Business Intelligence. Keywords: ARIMA, Prophet, Forecasting, RMSE, Business Intelligence. Perkembangan Business Intelligence mendorong perusahaan memanfaatkan data historis sebagai dasar pengambilan keputusan yang lebih efektif. Namun, CV Magix Metro Media belum memiliki fitur forecasting yang mampu memprediksi penjualan secara otomatis sehingga perencanaan persediaan dan distribusi masih dilakukan berdasarkan pengalaman. Penelitian ini bertujuan mengimplementasikan algoritma AutoRegressive Integrated Moving Average (ARIMA) pada aplikasi Business Intelligence AROMETRIC serta membandingkan tingkat akurasinya dengan algoritma Prophet menggunakan Root Mean Square Error (RMSE). Metode penelitian meliputi pengumpulan data, pembentukan data time series, pengujian stasioneritas, proses differencing, pemilihan model terbaik berdasarkan nilai Akaike Information Criterion (AIC), pembentukan model ARIMA(0,1,1), implementasi pada aplikasi berbasis web, serta evaluasi hasil prediksi menggunakan RMSE. Dataset yang digunakan berupa 65 data penjualan bulanan periode Januari 2021 hingga Mei 2026. Hasil penelitian menunjukkan bahwa algoritma ARIMA memperoleh nilai RMSE sebesar 4.103,963, lebih rendah dibandingkan algoritma Prophet dengan nilai RMSE sebesar 10.889,179, sehingga ARIMA memberikan tingkat akurasi yang lebih baik pada data penelitian. Implementasi algoritma ARIMA pada aplikasi AROMETRIC mampu menghasilkan prediksi penjualan secara otomatis dan mendukung proses pengambilan keputusan yang lebih efektif melalui penyajian informasi prediktif berbasis Business Intelligence. Kata Kunci: ARIMA, Prophet, Forecasting, RMSE, Business Intelligence.
| Item Type: | Thesis (S1) |
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| NIM/NIDN Creators: | 41822110036 |
| Uncontrolled Keywords: | ARIMA, Prophet, Forecasting, RMSE, Business Intelligence. |
| Subjects: | 300 Social Science/Ilmu-ilmu Sosial > 320 Political dan Government Science/Ilmu Politik dan Ilmu Pemerintahan > 322 Relation of The State of Organized Groups/Hubungan Negara dengan Kelompok Sosial yang Terorganisir > 322.3 Business and Industry/Bisnis dan Industri 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma |
| Divisions: | Fakultas Ilmu Komputer > Sistem Informasi |
| Depositing User: | khalimah |
| Date Deposited: | 01 Sep 2026 03:31 |
| Last Modified: | 01 Sep 2026 03:31 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/103530 |
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