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All Journal JINAV: Journal of Information and Visualization Quantitative Economics and Management Studies Daengku: Journal of Humanities and Social Sciences Innovation
Rahmat Hidayat
Department of Information Technology, Politeknik Negeri Padang, Limau Manis, Padang, 25164, Indonesia
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Other

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Forecasting the Value of Oil and Gas Exports in Indonesia using ARIMA Box-Jenkins Ansari Saleh Ahmar; Miguel Botto-Tobar; Abdul Rahman; Rahmat Hidayat
JINAV: Journal of Information and Visualization Vol. 3 No. 1 (2022)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav260

Abstract

The objective of the study was to forecast the value of oil and gas exports in Indonesia using the ARIMA Box-Jenkins. With this prediction, it is hoped that it can be a study for future policy making. This oil and gas export data is obtained from the Indonesian Central Bureau of Statistics (BPS) website, in raw data from January 2010 to March 2022. This data is predicted using the ARIMA method with the help of R software. The stages of data analysis with ARIMA include: data stationary test, build the model indication, parameter estimation and significance test, and residual diagnostic test of the model. The results of data analysis conducted in this study show that there are 3 indications of models that were generated, namely ARIMA(1,1,0); ARIMA(0,1,1); and ARIMA(1,1,0). From these 3 model indications, the best model was ARIMA(0,1,1) with AIC value of 2047.65.
Cross-Validation and Validation Set Methods for Choosing K in KNN Algorithm for Healthcare Case Study Robbi Rahim; Ansari Saleh Ahmar; Rahmat Hidayat
JINAV: Journal of Information and Visualization Vol. 3 No. 1 (2022)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1557

Abstract

KNN categorization is simple and successful in healthcare. In this research's example case study, the KNN algorithm classified the new record as "Abnormal." The classification method began with choosing K, then calculating the Euclidean distance between the new record and the training set, finding the K nearest neighbors, then classifying the new record based on those K neighbors. The findings show that the KNN algorithm is effective in healthcare and highlight several shortcomings that should be addressed in future study. Weighting variables, choosing the best K value, and handling non-uniform data are these restrictions. The findings show the KNN algorithm's medical potential.
The Comparison of Single and Double Exponential Smoothing Models in Predicting Passenger Car Registrations in Canada Ansari Saleh Ahmar; Sitti Masyitah Meliyana; Miguel Botto-Tobar; Rahmat Hidayat
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 4 No. 2 (2024)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku2639

Abstract

This study aims to compare the two main variants of exponential smoothing methods in the context of business forecasting: Single Exponential Smoothing (SES) and Double Exponential Smoothing (DES). In this study, we applied these three methods to the data on Monthly Passenger Car Registrations in Canada from 2019 to 2022. The performance of each method was evaluated using Root Mean Square Error (RMSE) as the primary metric. The analysis results showed that Single Exponential Smoothing (SES) produced the best performance with the lowest RMSE of 13.07859 for an alpha of 0.6, compared to DES, which yielded higher RMSE values. These findings indicate that although DES have the capability to handle trends and seasonality, in some cases, especially when the data has single fluctuations without significant seasonal patterns or trends, SES can provide more accurate forecasting results. This study provides valuable insights for practitioners in selecting the most appropriate forecasting method based on the characteristics of the data at hand.
The Implementation of Holt-Winters Method to Forecast the Loan Interest Rate of Indonesia Ansari Saleh Ahmar; Abdul Rahman; Mohd. Rizal Mohd. Isa; Rahmat Hidayat
Quantitative Economics and Management Studies Vol. 5 No. 3 (2024)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.qems2718

Abstract

This study aimed to anticipate the rupiah loan interest rates at commercial banks in Indonesia by employing the Holt-winters method. This study employs data on rupiah loan interest rates from commercial banks in Indonesia. The data comprises a time series element, with monthly intervals spanning from January 2013 to November 2015, which was obtained from the official website of BPS Indonesia. The study demonstrates that the Holt-winters technique yields the most accurate forecasts, as indicated by a Root Mean Square Error (RMSE) of 0.19720630. The parameters alpha, beta, and gamma, set at 0.6, 0.6, and 0.6 respectively, constitute the optimal configuration for this method. These results indicate that the Holt-winters method is an effective tool for capturing seasonality, trends, and patterns in credit interest rate data, making it a reliable choice for future loan interest rate forecasting. The findings of this study are expected to significantly contribute to strategic decision-making in the banking sector, particularly in risk management and loan interest rate strategy determination.
Co-Authors Ahmar, Ansari Saleh Miguel Botto-Tobar Mohd. Rizal Mohd. Isa Rahman, Abdul Robbi Rahim Sitti Masyitah Meliyana R.