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All Journal JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) JiTEKH (Jurnal Ilmiah Teknologi Harapan) Prosiding Snastikom Journal of Practical Computer Science (JPCS) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Prioritas : Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmu Komputer dan Sistem Informasi Jurnal Kecerdasan Buatan dan Teknologi Informasi
Ilham Faisal
Universitas Harapan Medan
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering

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IMPLEMENTATION OF THE ARIMA ALGORITHM FOR ENHANCING AND FORECASTING COMPANY REVENUE Nanang Prayugo; Ilham Faisal
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 1 (2026): January 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i1.392

Abstract

In an increasingly competitive business environment, accurate revenue forecasting is crucial for strategic decision-making. This study implements the ARIMA (AutoRegressive Integrated Moving Average) model to predict the monthly revenue of CV. Yusindo Mega Persada using historical data from January to December 2024. The ARIMA(1,1,1) model was selected based on stationarity tests and analysis of Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots. Diagnostic tests confirmed that the model met key assumptions, including normality of residuals and absence of significant autocorrelation, ensuring reliable predictions. The forecasting results for January to March 2025 indicated a relatively stable revenue trend, with values ranging from IDR 483 million to IDR 489 million. Model accuracy was evaluated using the Mean Absolute Percentage Error (MAPE), which resulted in 9.41%, suggesting reasonable predictive performance. The findings demonstrate that ARIMA is capable of capturing trends and fluctuations in dynamic revenue data, providing actionable insights for management in financial planning, resource allocation, and risk mitigation. Despite the model’s effectiveness, external factors such as market fluctuations and seasonal events may influence actual revenue, indicating the need to combine quantitative forecasts with expert judgment. Overall, this study confirms that the ARIMA(1,1,1) model is a practical and reliable tool for revenue forecasting in a dynamic business environment.
Co-Authors Ahmad fauzi Akbar Adam Pratama Almayadi Pratama Arief Budiman Divi Handoko Dodi Siregar Eko Somba Harahap Halimansyah Putra Mhd. Tri Arief Mufida Khairani Muhammad Agri Febriansyah Nanang Prayugo Nurharianna Siregar Tengku Mohd Diansyah, Tengku Mohd Tomi Hidayat