Peramalan Data Inflow dan Outflow Uang Kartal Bank Indonesia Provinsi DKI Jakarta Menggunakan Model ARIMAX dan SARIMAX

<p><strong>A</strong><strong>bstract</strong><strong>.</strong> DKI Jakarta Province plays a crucial role as the center of government and economy in Indonesia. The description of currency inflows and outflows is highly required before Bank Indonesia formulates the appropriate policies to control the circulation of money. The monthly data of currency inflow and outflow of Bank Indonesia of DKI Jakarta show a significant increase in each year particularly before, during, and after Eid al-Fitr. The determination of Eid al-Fitr does not follow the Gregorian calendar but based on the Islamic calendar. The difference in the use of the Gregorian and Islamic calendars in a time series causes a calendar variation. Thus, the determination of Eid al-Fitr in the Gregorian calendar changes as it goes forward eleven days each year or one month every three years. This study aims to obtain the best model and forecast currency inflows and outflows of Bank Indonesia DKI Jakarta using the ARIMAX and SARIMAX models. The study used in-sample data from January 2009 to December 2018 and out-sample data from January to October 2019. The best model was selected based on the smallest out-sample MAPE value. The result showed that the best forecasting model of inflow was ARIMAX (1,0,1). Meanwhile, the best forecasting model for outflow was SARIMAX (2,0,1)(0,0,1)¹².</p><p><strong>Keywords: </strong>ARIMAX, calendar variation, forecasting, SARIMAX</p>

Penerapan Metode Limited-Fluctuation Credibility dalam Menentukan Premi Murni pada Asuransi Kendaraan Bermotor di PT XYZ

<p><strong>Abstract. </strong>This paper discusses the method of limited-fluctuation credibility, also known as classic credibility. Credibility theory is a technique for predicting future premium rates based on past experience data. Limited fluctuation credibility consists of two credibility, namely full credibility if Z = 1 and partial credibility if Z &lt;1. Full credibility is achieved if the amount of recent data is sufficient for prediction, whereas if the latest data is insufficient then the partial credibility approach is used. Calculations for full and partial credibility standards are used for loss measures such as frequency of claims, size of claims, aggregate losses and net premiums. The data used in this paper is secondary data recorded by the company PT. XYZ in 2014. This data contains data on the frequency of claims and the size of the policyholder's partial loss claims for motor vehicle insurance products category 4 areas 1. Based on the results of the application, the prediction of pure premiums for 2015 cannot be fully based on insurance data for 2014 because the credibility factor value is less than 1. So based on the limited-fluctuation credibility method, the prediction of pure premiums for 2015 must be based on manual values for pure premiums as well as insurance data for 2014. If manual values for pure premium is 2,000,000 rupiah, then the prediction of pure premium for 2015 is 1,849,342 rupiah.</p><p><strong>Keywords</strong><strong>: </strong>limited fluctuation credibility, full credibility, partial credibility and partial loss</p>

Early Detection of South Korean Financial Crisis using MS-GARCH Based on Term of Trade Indicator

<p><strong>A</strong><strong>bstract</strong><strong>.</strong> The 1997 Asian financial crisis, which occurred until 1998, had a significant impact on the economies of Asian countries, including South Korea. The crisis brought down the South Korean currency quickly and sent the economy into sudden decline. Because the impact of the financial crisis was severe and sudden, South Korean requires a system which able to sight crisis signals, therefore that, the crisis will be fended off. One in all the indicators that can detect the financial crisis signals is that the term of trade indicator which has high fluctuation and change in the exchange rate regime. The mixture of Markov Switching and volatility models, Generalized Autoregressive Conditional Heteroscedasticity (GARCH), or MS-GARCH could explain the crisis. The MS-GARCH model was built using data from the South Korean term of trade indicator during January 1990 until March 2020. The findings obtained in this research can be inferred that the best model of the term of trade is MS-GARCH (2,1,1). Term of trade indicator on that model could explain the Asian monetary crisis in 1997 and also the global monetary crisis in 2008. The smoothed probability of term of trade indicators predicts in April till December 2020 period, there will be no signs of the monetary crisis in South Korea.</p><p><strong>Keywords</strong><strong>: </strong>financial crisis, MS-GARCH, South Korea, term of trade indicator</p>

Pemodelan Determinan Pernikahan Dini di Daerah Pedesaan dengan Pendekatan Regresi Logistik Biner

<p><strong>A</strong><strong>bstract</strong><strong>.</strong> The behavior of early marriage in Indonesia is still high and most prevalent in rural areas. In addition to violating the law, a marriage performed before reaching 19 years also has many negative effects. One of them is the death of the mother and the baby. Using data from the Demographic and Health Survey 2017, this study aims to analyze the determinants of early marriages in rural areas in Indonesia. The response variable used is binary categorical data, namely the status of early marriage and not early marriage, so we use a binary logistic regression. The steps performed on this model include estimates of parameters, parameter testing either simultaneously or partially, and a test of the goodness of fit. The results show that the variables of education level, internet access, and wealth index significantly affected early marriage status in rural areas in Indonesia in 2017. Based on the goodness of fit result, this model is proper for modeling early marriage behavior in Indonesia. The study results can be used as a reference for the government in formulating policies to overcome the problem of early marriage in rural areas in Indonesia.</p><p> <strong>Keywords</strong><strong>: </strong>early marriage, rural area, categorical response variable, binary logistic regression</p>

Model Variasi Kalender pada Regresi Runtun Waktu untuk Peramalan Jumlah Pengunjung Grojogan Sewu

<p><strong>Abstract.</strong> Grojogan Sewu visitors experience a significant increase during school holidays, year-end holidays, and also Eid al-Fitr holidays. The determination of Eid Al-Fitr uses the Hijriyah calendar so that the occurrence of Eid al-Fitr will progress 10 days when viewed from the Gregorian calendar, this causes calendar variations. The objective of this paper is to apply a calendar variation model based on time series regression and SARIMA models for forecasting the number of visitors in Grojogan Sewu. The data are Grojogan Sewu visitors from January 2009 until December 2019. The results show that time series regression with calendar variation yields a better forecast compared to the SARIMA model. It can be seen from the value of root mean square error (<em>RMSE</em>) out-sample of time series regression with calendar variation is less than of SARIMA model.</p><p><strong>Keywords: </strong>Calendar variation, time series regression, SARIMA, Grojogan Sewu</p>

Application of Analytic Hierarchy Process and Weighted Product Methods in Determining the Best Employees

<p><strong>A</strong><strong>bstract</strong><strong>.</strong> Employees are one of the company's assets that must be managed properly. Therefore the selection of the best employees is now needed. The problem faced in determining the best and qualified employees is that there are still no standards in assessing only one person subjectively in determining the best employee, which consequently lacks appropriate or objective results. To provide rewards for the best employees, we need a system to support the decisions of the best employees who deserve to receive rewards to be on target. The purpose of this research is to design and build a decision support system application in determining the best employees using the analytic hierarchy process and weighted product methods. Stages of software development of the Software Development Life Cycle (SDLC) uses a waterfall, that is data analysis, system design, construction, coding, testing and implementation. The results of this process are in the form of calculation applications that have been obtained from the analytic hierarchy process and weighted product methods in determining the best employee. The result gives an accuracy rate of 82.3%.</p><p><strong>Keywords</strong><strong>: </strong>analytic hierarchy process, weighted product, decision support system, employees</p>

Comparison of Random Forest, Logistic Regression, and MultilayerPerceptron Methods on Classification of Bank Customer Account Closure

<p>The Bank is a business entity that is dealing with money, accepting deposits from customers, providing funds for each withdrawal, billing checks on the customer's orders, giving credit and or embedding the excess deposits until required for repayment. The purpose of this research is to determine the influence of age, gender, country, customer credit score, number of bank products used by the customer, and the activation of the bank members in the decision to choose to continue using the bank account that he has retained or closed the bank account. The data in this research used 10,000 respondents originating from France, Spain, and Germany. The method used is data mining with early stage preprocessing to clean data from outlier and missing value and feature selection to select important attributes. Then perform the classification using three methods, which are Random Forest, Logistic Regression, and Multilayer Perceptron. The results of this research showed that the model with Multilayer Perceptron method with 10 folds Cross Validation is the best model with 85.5373% accuracy.</p><strong>Keywords:</strong> bank customer, random forest, logistic regression, multilayer perceptron

Peramalan Arus Kas dengan Pendekatan Time Series Menggunakan Support Vector Machine

<p>Cash flow is a form of financial report that is used as a measure of the company success in the investment world. So that companies need to forecast the cash flow to manage their finances. Statistics can be applied for the forecasting of cash flow using the <em>Support Vector Machine </em>(SVM) method on the time series data. The aim of this research is to determine the optimal parameter pair model of the <em>Radial Basic Function</em> kernel and to obtain the forecasting results of cash flow using the SVM method on the time series data. The independent variable is needed the data on cash flow from operating income, expenditure and investment expenditure, sum of all cash flow. While the dependent variable is the financial condition based on the <em>Free Cash Flow</em>. The result of this research is a model with the best parameter pairs of the SVM tuning results with the greatest accuracy that is 75%, 82%, 88%, 64% and the forecasting financial condition of PT Cakrawala for the next 16 months.</p><p><strong>Keywords: </strong>cash flow, forecasting, time series, support vector machine.</p>

Peramalan Banyak Pengunjung Pantai Pandasimo Bantul Menggunakan Regresi Runtun Waktu dan Seasonal Autoregressive Integrated Moving Average Exogenous

<p>Forecasting is a ways to predict what will happen in the future based on the data in the past. Data on the number of visitors in Pandansimo beach are time series data. The pattern of the number of visitors in Pandansimo beach is influenced by holidays, so it looks like having a seasonal pattern. The majority of Indonesian citizens are Muslim who celebrate Eid Al-Fitr in every year. The determination of Eid Al-Fitr does not follow the Gregorian calendar, but based on the Lunar calendar. The variation of the calendar is about the determination of Eid Al-Fitr which usually changed in the Gregorian calendar, because in the Gregorian calendar, Eid Al-Fitr day will advance one month in every three years. Data that contain seasonal and calendar variations can be analyzed using time series regression and Seasonal Autoregressive Integrated Moving Average Exogenous (SARIMAX) models. The aims of this study are to obtain a better model between time series regression and SARIMAX and to forecast the number of Pandansimo beach visitors using a better model. The result of this study indicates that the time series regression model is a better model. The forecasting from January to December 2018 in succession are 13255, 6674, 8643, 7639, 13255, 8713, 22635, 13255, 13255, 9590, 8549, 13255 visitors.</p><strong>Keywords: </strong>time series regression, seasonal, calendar variations, SARIMAX, forecasting

Analisis Dampak Covid-19 Terhadap Indeks Harga Konsumen dengan K-Means dan Regresi Berganda

<p>The Indonesian economy during the global pandemic entered the brink of economic recession. This problem occurs because the state of public consumption has decreased due to the limited space for community movement and sluggish economic activities due to preventing the transmission of Covid-19. This affects the decline in public consumption in economic activities. In this case, it can be seen from the statistical news published by the official website of the Badan Pusat Statistik (BPS) which reports that the inflation rate in the previous months was around 0.10%, while in April 2020 it decreased by 0.08%. Based on these, a <em>K</em>-means grouping study was conducted by dividing the cluster into 3 parts and modeling using multiple regression methods. In this study, the variable used was the price index. The results of the <em>K</em>-means cluster analysis with the division of 3 clusters, namely cluster 3 (high CPI cluster) consisting of 66 cities, cluster 1 (moderate CPI cluster) consisting of 2 cities, and cluster 2 (low CPI cluster) consisting of 22 cities. Furthermore, the multiple regression results obtained 12 variables that have a significant effect on the Consumer Price Index (CPI). The results of regression modeling are the highest coefficient is food at 0.236 and the lowest coefficients are cigarettes and tobacco at 0.008. Therefore can be concluded that the grouping of the CPI indicator obtained 75% of cities with high index prices, especially in big cities such that economic activity, in general, was still consumptive during the pandemic and multiple regression modeling resulted from 37 indicator variables, only 12 indicator variables had a significant effect on the CPI.</p><strong>Keywords: </strong><em>k</em>-means, CPI, multiple regression, and price index