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All Journal JTAM (Jurnal Teori dan Aplikasi Matematika) Al-Aqlu : Jurnal Matematika, Teknik dan Sains
Yulianty, Sherly
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Engineering Mathematics

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Comparing the Accuracy of Markov Switching – AR and Prophet Models in Predicting the Blue Bird Stock Prices Yulianty, Sherly; Mangku, I Wayan; Budiarti, Retno
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 2 (2025): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i2.30096

Abstract

One form of investment asset that is in high demand for profit is stocks. However, stock prices fluctuate, so a mathematical model is needed to model the movement and calculate stock price predictions. Stock price movements often form several groups (states) of change, so the Markov Switching Autoregressive (MS-AR) model can be used to model and calculate stock price predictions. In addition, stock price movements often contain trend and seasonal patterns, so the Prophet model can be used to model movements and calculate stock price predictions. In this study, the Prophet model is modified by generating random numbers that spread normally with parameter values obtained from the error value of the Prophet base model. This study aims to compare the performance of the MS-AR model with the Prophet model in predicting BIRD stock prices. This research is a quantitative study with secondary data in the form of BIRD stock closing price data for the period 11 February 2023 to 11 February 2024. In this study, two models, MS-AR and Prophet, were built separately. In the MS-AR model, it is necessary to pay attention to the assumptions of the data used, namely normal distribution and stationary. In the Prophet model, there are no special assumptions like those of the MS-AR model, but the Prophet model is good for data containing trends and seasonal patterns. The results of this study show that among the MS-AR models, the MS(2)-AR(3) model is the best model. In addition, the results show that the modified Prophet model performs better than the basic Prophet model. The goodness of model performance is measured by the Mean Absolute Percentage Error (MAPE) metric, with MAPE values for each model being 5.54% for MS(2)-AR(3), 3.38% for the Prophet base model, and 2.88% for Prophet modification. Based on the MAPE value, the Prophet (modified) model is able to predict the closing price of shares better than the MS(2)-AR(3) and Prophet (basic) models. The results of this study can be used by investors as a measuring tool in reading and determining stock price predictions.
PERBANDINGAN KINERJA METODE K-NEAREST NEIGHBOR (KNN), RANDOM FOREST, DAN DECISION TREE DALAM MEMPREDIKSI DIABETES: Comparing the Accuracy of K-Nearest Neighbour (KNN), Random Forest, and Decision Tree Methods in Predicting Diabetes Yulianty, Sherly; Najib, Mohamad Khoirun
Al-Aqlu: Jurnal Matematika, Teknik dan Sains Vol. 3 No. 2 (2025): Juli 2025
Publisher : Yayasan Al-Amin Qalbu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59896/aqlu.v3i2.299

Abstract

Diabetes is a disease with a growing number of sufferers and is the cause of death of 1.5 million people in the world in 2019. A treatment for diabetes is needed, one of which is by predicting diabetics. The K-Nearest Neighbour (KNN), Random Forest, and Decision Tree methods are some methods that can be used to predict diabetes classification. This research aims to compare the performance of KNN, Random Forest, and Decision Tree methods based on accuracy and computation time. The data used in this study are Pregnancies, Glucose, Insulin, Body Mass Index (BMI), and Age as independent variables and Outcome as a dependent variable. The results of research on data that has not been normalised with Min-Max show that the KNN method has a faster computation time than the other two methods, while based on the accuracy value the Decision Tree method has a higher value than the other two methods. Furthermore, the Min-Max normalised data shows a decrease in the accuracy value of the Decision Tree and Random Forest methods, while the accuracy value of the KNN method has increased. Therefore, the Min-Max normalisation treatment is better used for the KNN method.
Co-Authors I Wayan Mangku Najib, Mohamad Khoirun Retno Budiarti