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INDONESIA
JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI
Published by Universitas Hasanuddin
ISSN : 18581382     EISSN : 26148811     DOI : -
Core Subject : Education,
Mathematics
Jurnal ini mempublikasikan paper-paper original hasil-hasil penelitian dibidang Matematika, Statistika dan Komputasi Matematika.
Arjuna Subject : -
Articles 496 Documents
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Best proximity point theorems for $ \alpha^{+} F, (\theta-\phi )$-proximal contraction Mohamed Rossafi; Abdelkarim Kari
Jurnal Matematika, Statistika dan Komputasi Vol. 18 No. 2 (2022): JANUARY 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v18i2.17994

Abstract

In this paper, inspired by the idea of Suzuki type $ \alpha^{+} F$-proximal contraction in metric spaces, we prove a new existence of best proximity point for Suzuki type $ \alpha^{+} F$-proximal contraction and $ \alpha^{+} (\theta-\phi )$-proximal contraction defined on a closed subset of a complete metric space. Our theorems extend, generalize, and improve many existing results.
The Penerapan Metode Peramalan GARCH dalam Memprediksi Jumlah Penumpang Kereta Api (Ribu Orang) di Wilayah Jabodetabek Farin Cyntiya Garini; Warosatul Anbiya
Jurnal Matematika, Statistika dan Komputasi Vol. 18 No. 2 (2022): JANUARY 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v18i2.18382

Abstract

PT. Kereta Api Indonesia and PT. KAI Commuter Jabodetabek records time series data in the form of the number of train passengers (thousand people) in Jabodetabek Region in 2011-2020. One of the time series methods that can be used to predict the number of train passengers (thousand people) in Jabodetabek area is ARIMA method. ARIMA or also known as Box-Jenkins time series analysis method is used for short-term forecasting and does not accommodate seasonal factors. If the assumption of residual homoscedasticity is violated, the ARCH / GARCH method can be used, which explicitly models changes in residual variety over time. This study aims to model and forecast the number of train passengers (thousand people) in Jabodetabek area in 2021. Based on data analysis and processing using ARIMA method, the best model is ARIMA (1,1,1) with an AIC value of 2,159.87 and with ARCH / GARCH method, the best model is GARCH (1,1) with an AIC value of 18.314. Forecasting results obtained based on the best model can be used as a reference for related parties in managing and providing public transportation facilities, especially trains.
Penerapan Metode Autoregressive Integrated Moving Average Exogenous (ARIMAX) with Calendar Variation Effect untuk Peramalan Data Cokelat di Indonesia dan Amerika Serikat Andy Rezky Pratama Syam
Jurnal Matematika, Statistika dan Komputasi Vol. 18 No. 2 (2022): JANUARY 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v18i2.18460

Abstract

Forecasting chocolate consumption is required by producers in preparing the amount of production each month. The tradition of Valentine, Christmas and Eid al-Fitr which are closely related to chocolate makes it impossible to predict chocolate by using the Classical Time Series method. Especially for Eid al-Fitr, the determination follows the Hijri calendar and each year advances 10 days on the Masehi calendar, so that every three years Eid al-Fitr will occur in a different month. Based on this, the chocolate forecasting will show a variation calendar effect. The method used in modeling and forecasting chocolate in Indonesia and the United States is the ARIMAX (Autoregressive Integrated Moving Average Exogenous) method with Calendar Variation effect. As a comparison, modeling and forecasting are also carried out using the Naïve Trend Linear, Naïve Trend Exponential, Double Exponential Smoothing, Time Series Regression, and ARIMA methods. The ARIMAX method with Calendar Variation Effect produces a very precise MAPE value in predicting chocolate data in Indonesia and the United States. The resulting MAPE value is below 10 percent, so it can be concluded that this method has a very good ability in forecasting.
Peramalan Eksistensi Cokelat dengan Efek Calender Variation dan Seasonal Menggunakan Pendekatan Time Series Klasik I Gusti Bagus Ngurah Diksa
Jurnal Matematika, Statistika dan Komputasi Vol. 18 No. 2 (2022): JANUARY 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v18i2.18542

Abstract

Chocolate is the raw material for making cakes, so consumption of chocolate also increases on Eid al-Fitr. However, this is different in the United States where the tradition of sharing chocolate cake is carried out on Christmas. To monitor the existence of this chocolate can be through the movement of data on Google Trends. This study aims to predict the existence of chocolate from the Google trend where the use of chocolate by the community fluctuates according to the calendar variance and seasonal rhythm. The method used is classic time series, namely nave, double exponential smoothing, multiplicative decomposition, addictive decomposition, holt winter multiplicative, holt winter addictive, time series regression, hybrid time series, ARIMA, and ARIMAX. Based on MAPE in sample, the best time series model to model the existence of chocolate in Indonesia is ARIMAX (1,0,0) while for the United States it is Hybrid Time Series Regression-ARIMA(2,1,[10]). For forecasting the existence of chocolate in Indonesia, the best models in forecasting are ARIMA (([11],[12]),1,1) and Naïve Seasonal. In contrast to the best forecasting model for the existence of chocolate in the United States, namely Hybrid Naïve Seasonal-SARIMA (2,1,0)(0,0,1)12 Hybrid Time Series Regression- ARIMA(2,1,[10]), Time Series Regression, Winter Multiplicative, ARIMAX([3],0,0).  
Perbandingan Estimasi M, Estimasi S, dengan Estimasi MM untuk Mendapatkan Estimasi Robust Regression Terbaik dalam Perkara Pidana di Indonesia: Perbandingan Estimasi M, Estimasi S, dengan Estimasi MM Malecita Nur Atala Singgih; Achmad Fauzan
Jurnal Matematika, Statistika dan Komputasi Vol. 18 No. 2 (2022): JANUARY 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v18i2.18630

Abstract

Crime incidents that occurred in Indonesia in 2019 based on Survey Based Data on criminal data sourced from the National Socio-Economic Survey and Village Potential Data Collection produced by the Central Statistics Agency recorded 269,324 cases. The high crime rate is caused by several factors, including poverty and population density. Determination of the most influential factors in criminal acts in Indonesia can be done with Regression Analysis. One method of Regression Analysis that is very commonly used is the Least Square Method. However, Regression Analysis can be used if the assumption test is met. If outliers are found, then the assumption test is not completed. The outlier problem can be overcome by using a robust estimation method. This study aims to determine the best estimation method between Maximum Likelihood Type (M) estimation, Scale (S) estimation, and Method of Moment (MM) estimation on Robust Regression. The best estimate of Robust Regression is the smallest Residual Standard Error (RSE) value and the largest Adjusted R-square. The analysis of case studies of criminal acts in Indonesia in 2019 showed that the best estimate was the S estimate with an RSE value of 4226 and an Adjusted R-square of 0.98  
Optimalisasi Pengendalian Persediaan Bahan Baku CV. Dirga Eggtray Pinrang Menggunakan Model Probabilistik pada Kondisi Pemesanan Ulang dan Kehilangan Penjualan Aprizal Resky; Aidawayati Rangkuti; Georgina M. Tinungki
Jurnal Matematika, Statistika dan Komputasi Vol. 18 No. 2 (2022): JANUARY 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v18i2.18659

Abstract

This research discusses about the comparison of raw material inventory control CV. Dirga Eggtray Pinrang. It starts with forecasting inventory for the next 12 periods using variations of the time series forecasting method, where the linear regression method provides accurate forecasting results with a Mean Absolute Percentage Error (MAPE) value of 1,9371%. The probabilistic models of inventory control used are the simple probabilistic model, Continuous Review System (CRS) model, and Periodic Review System (PRS) model. The CRS model with backorder condition is a model that provides the minimum cost of Rp. 969.273.706,20 per year compared to another probabilistic model with the largest difference of Rp. 1.291.814,95 per year, with the optimum number of order kg, reorder level kg, and safety stock kg.
Determinan Capaian Pembangunan Kabupaten/Kota Jawa Tengah Tahun 2019 Cesaria Dewi; Ekaria Ekaria
Jurnal Matematika, Statistika dan Komputasi Vol. 18 No. 2 (2022): JANUARY 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v18i2.18811

Abstract

In 2019, Badan Perencanaan Pembangunan Nasional (Bappenas) awarded Central Java as the province with the best Perencanaan dan Pembangunan Daerah (PPD). However, if it is reviewed at the district/city level, it shows that there are still many areas that have low development achievements. In accordance with the United Nations Development Programme (UNDP) proposal, the Human Development Index (HDI) is used as an indicator of the achievement of district/city development whose calculations are good enough to describe development from both a social and economic perspective. The large difference in HDI between districts/cities in Central Java and the distribution of development achievements are still centered around the provincial capital, namely Semarang City, this indicates the occurrence of inequality in development achievements at the district/city level in Central Java. Because the observations in this study are districts/cities in Central Java, the linkage between district/city causes spatial autocorrelation. Therefore, spatial regression model is used to determine the model that has spatial autocorrelation. This study aims to determine the achievements of development and its determinants in the districts/cities of Central Java in 2019 using the spatial regression analysis method. From the results of the study, it is known that there is a dependence on development achievements between districts/cities in Central Java which is influenced by the regional capacity factor is characterized by PAD and economic growth; operational resource factors characterized by DAU, DAK and technology; and the level of poverty.
Forecasting Stock Price PT. Telkom Using Hybrid Time Series Regression Linear– Autoregressive Integrated Moving Average Model Kartika Ramadani; Sri Wahyuningsih; Memi Nor Hayati
Jurnal Matematika, Statistika dan Komputasi Vol. 18 No. 2 (2022): JANUARY 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v18i2.18837

Abstract

The hybrid method is a method of combining two forecasting models. Hybrid method is used to improve forecasting accuracy. In this study, the Time Series Regression (TSR) linear model will be combined with the Autoregressive Integrated Moving Average (ARIMA) model. The TSR linear model is used to obtain the model and residual value, then the residual value of the TSR linear model will be modeled by the ARIMA model. This combination method will produce a hybrid TSR linear-ARIMA model. The case study in this research is stock closing price (daily) of PT. Telkom Indonesia Tbk. The stock closing price (daily) of PT. Telkom Indonesia Tbk in 2020 showed an decreasing and increasing trend pattern. The results of this study obtained the best model of hybrid TSR linear-ARIMA (2,1,1) with the proportion of data training and testing is 70:30. In the best model, the MAD value is 56.595, the MAPE value is 1.880%, and the RMSE value is 78.663. It is also found that the hybrid TSR linear-ARIMA model has a smaller error value than the TSR linear model. The results of forecasting the stock price of PT. Telkom Indonesia Tbk for the period 02 January 2021 to 29 January 2021 formed a decreasing trend pattern.
Solusi Primitif Persamaan Diophantine x^2+pqy^2=z^2 untuk bilangan-bilangan prima p dan q Aswad Hariri Mangalaeng
Jurnal Matematika, Statistika dan Komputasi Vol. 18 No. 2 (2022): JANUARY 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v18i2.19018

Abstract

In this paper, we determine the primitive solutions of diophantine equations x^2+pqy^2=z^2, for positive integers x, y, z, and primes p,q. This work is based on the development of the previous results, namely using the solutions of the Diophantine equation x^2+y^2=z^2, and looking at characteristics of the solutions of the Diophantine equation x^2+3y^2=z^2 and x^2+9y^2=z^2.
Aplikasi Double Exponential Smoothing Holt dan Triple Exponential Smoothing Holt-Winter dengan Optimasi Golden Section untuk Meramalkan Nilai Ekspor Provinsi Kalimantan Timur Novita Andriani; Sri Wahyuningsih; Meiliyani Siringoringo
Jurnal Matematika, Statistika dan Komputasi Vol. 18 No. 3 (2022): MAY, 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v18i3.17492

Abstract

Exponential smoothing is one of the short term forecasting methods. The selection of the forecasting method can be done by considering the type of data pattern, such as the Double Exponential Smoothing (DES) Holt method which can be used on trend patterned data and the Triple Exponential Smoothing (TES) Holt-Winter method which can be used on trend and seasonal patterned data. The main problem in using the Holt DES and Holt-Winter TES methods is the parameter selection which is usually done by trial and error, but this method takes a long time so that in this research a more efficient method is used to obtain optimal parameters, namely the golden section method. The purpose of this research was to forecast and obtain the best method for forecasting the export value of East Borneo Province. The results showed that the forecasted of export values used the Holt DES, the additive Holt-Winter TES, and the multiplicative Holt-Winter TES with golden section optimization method had a MAPE of less than 10%, which means that the forecast used these methods were very good. The best method to predict the export value of East Borneo Province was the additive Holt-Winter TES with golden section optimization method.

Page 34 of 50 | Total Record : 496

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