Darma, Maria Goretti Kalinda (2024) Analisis Proyeksi Net Income dengan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA). Project Report. [s.n.], [s.l.]. (Unpublished)
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Abstract
Model SARIMA(1,1,0)(0,1,1)₄ terpilih sebagai model terbaik dalam peramalan net income kuartalan berdasarkan data periode Desember 2015 hingga September 2023. Model ini memenuhi seluruh kriteria evaluasi, termasuk signifikansi parameter, asumsi residual white noise dan normalitas, serta memiliki nilai Akaike Information Criterion (AIC) terkecil sebesar 459,927. Hasil peramalan untuk lima periode berikutnya menunjukkan pola fluktuatif dengan nilai estimasi berkisar antara Rp5,3 miliar hingga Rp9,4 miliar. Temuan ini menunjukkan bahwa metode SARIMA efektif dalam menangkap pola musiman dan dapat digunakan sebagai dasar dalam penyusunan proyeksi keuangan jangka pendek.
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The SARIMA(1,1,0)(0,1,1)₄ model was identified as the best-fitting model for forecasting quarterly net income based on data from December 2015 to September 2023. This model satisfied all evaluation criteria, including parameter significance, white noise residual assumptions, and normality tests, while also yielding the lowest Akaike Information Criterion (AIC) value of 459.927. The forecast for the subsequent five periods exhibits a fluctuating pattern, with estimated values ranging between IDR 5.3 billion and IDR 9.4 billion. These findings indicate that the SARIMA method is effective in capturing seasonal patterns and can be reliably used for short-term financial forecasting.
Item Type: | Monograph (Project Report) |
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Uncontrolled Keywords: | Forecasting, Net Income, SARIMA, Seasonal Model, Model Musiman, Net Income, Peramalan, SARIMA |
Subjects: | Q Science > QA Mathematics > QA276 Mathematical statistics. Time-series analysis. Failure time data analysis. Survival analysis (Biometry) |
Divisions: | Faculty of Science and Data Analytics (SCIENTICS) > Actuaria > 94203-(S1) Undergraduate Thesis |
Depositing User: | Maria Goretti Kalinda Darma |
Date Deposited: | 10 Jul 2025 07:10 |
Last Modified: | 10 Jul 2025 07:10 |
URI: | http://repository.its.ac.id/id/eprint/119511 |
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