Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Seminar Nasional Teknologi dan Multidisiplin Ilmu
Cindi Adam
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Prediksi Harga Saham Menggunakan ARIMA Outlier sebagai Pendekatan Awal Menuju Analisis AI Keuangan Cindi Adam; Mohammad Idhom; Trimono Trimono
Seminar Nasional Teknologi dan Multidisiplin Ilmu (SEMNASTEKMU) Vol. 5 No. 1 (2025): SEMNASTEKMU
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/zwvk1v20

Abstract

The development of artificial intelligence (AI) has driven innovation in financial analysis, including the prediction of volatile stock prices. This study aims to predict the stock price of PT Garudafood Putra Putri Jaya Tbk using an ARIMA model with Outlier handling as an initial approach towards a more adaptive prediction system. Daily closing price data from Yahoo Finance was analyzed through stationarity testing, ARIMA model identification, log-return-based Outlier detection, and performance evaluation using RMSE, MAE, and MAPE. The results show that ARIMA Outlier performs better than the basic ARIMA. The standard ARIMA produces a MAPE of 1.32% and an AIC of –899.46, while ARIMA with three dummy Outliers achieves a MAPE of 1.16% and an AIC of –900.37. The 14-day forecast shows a stable pattern in the range of Rp 370–371. In the test data, the basic ARIMA provided the best accuracy in mid-August, while ARIMA Outlier achieved the highest accuracy at the end of August with a prediction of Rp 370.2, which was very close to the actual price of Rp 370.4. These results show that handling Outliers improves the accuracy of the model, so that ARIMA Outlier can be used as a starting point for the development of an AI-based financial prediction system.
Co-Authors Mohammad Idhom Trimono Trimono