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Journal : JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI

Perbandingan Metode Peramalan Jumlah Produksi Palm Kernel Oil (PKO) Menggunakan Metode Double Moving Average, Double Exponential Smothing dan Box Jenkins IKA MEIZA MAHARANI; ACHMAD FAUZAN
Jurnal Matematika, Statistika dan Komputasi Vol. 16 No. 2 (2020): JMSK, JANUARY, 2020
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (619.121 KB) | DOI: 10.20956/jmsk.v16i2.7795

Abstract

One of Indonesia's significant results is oil palm. The reality of this plantation is not only owned by the government (BUMN) but also the private sector. Every period, the company does forecasting in terms of production, especially for the next period. Among them is to set production targets, company operations, and financial planning. Based on this, a study was conducted with the aim to predict the amount of palm kernel oil (PKO) production at PT. Mitra Mendawai Sejati for the next six (6) months. The method used is Double Moving Average, Double Exponential Smoothing and Box Jenkins. While the data used is historical data from the amount of palm kernel oil production for five (5) years obtained from the company. Based on the results of the study, received the forecast value of the Suayap output in 2019 with the best method, namely the Double Exponential Smoothing method. Based on the forecast we got in January at 949181.5 Kg, February at 963505.8 Kg, March at 977830.1 Kg, April at 992154.4 Kg, May at 1006478.6 Kg and June 1020802.9 Kg with MSE value of 47031163817, and RMSE of 216866.7 and parameter values (optimum weighting) for α = 0.616667 and β = 0.1548939
COMPARING NAIVE BAYES, K-NEAREST NEIGHBOR, AND NEURAL NETWORK CLASSIFICATION METHODS OF SEAT LOAD FACTOR IN LOMBOK OUTBOUND FLIGHTS Mega Luna Suliztia; Achmad Fauzan
Jurnal Matematika, Statistika dan Komputasi Vol. 16 No. 2 (2020): JMSK, JANUARY, 2020
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (372.512 KB) | DOI: 10.20956/jmsk.v16i2.7864

Abstract

Classification is the process of grouping data based on observed variables to predict new data whose class is unknown. There are some classification methods, such as Naïve Bayes, K-Nearest Neighbor and Neural Network. Naïve Bayes classifies based on the probability value of the existing properties. K-Nearest Neighbor classifies based on the character of its nearest neighbor, where the number of neighbors=k, while Neural Network classifies based on human neural networks. This study will compare three classification methods for Seat Load Factor, which is the percentage of aircraft load, and also a measure in determining the profit of airline.. Affecting factors are the number of passengers, ticket prices, flight routes, and flight times. Based on the analysis with 47 data, it is known that the system of Naïve Bayes method has misclassifies in 14 data, so the accuracy rate is 70%. The system of K-Nearest Neighbor method with k=5 has misclassifies in 5 data, so the accuracy rate is 89%, and the Neural Network system has misclassifies in 10 data with accuracy rate 78%. The method with highest accuracy rate is the best method that will be used, which in this case is K-Nearest Neighbor method with success of classification system is 42 data, including 14 low, 10 medium, and 18 high value. Based on the best method, predictions can be made using new data, for example the new data consists of Bali flight routes (2), flight times in afternoon (2), estimate of passenger numbers is 140 people, and ticket prices is Rp.700,000. By using the K-Nearest Neighbor method, Seat Load Factor prediction is high or at intervals of 80% -100%.
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  
The Forecasting Result Study of the Poverty Line and Number of Poor Population in DIY using DES and ARIMA Shazia Ayesha Azzahra; Wiranti Nugrah Andini; Achmad Fauzan; Irwan Sutisna
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 2 (2025): JANUARY 2025
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i2.36734

Abstract

The poverty rate in DIY, based on BPS data, stands at 11.04%, which remains above the national average of 9.36%. This study aims to predict poverty patterns in the Special Region of Yogyakarta (DIY) using DES and ARIMA methods. The data utilized in this research is sourced from BPS, focusing on poverty line data and the number of impoverished individuals. The DES model is employed to estimate the increase in the poverty line, demonstrating good accuracy with a MAPE value of 2.968%. Meanwhile, the ARIMA(0,2,1) model is applied to forecast a reduction in the number of impoverished individuals, yielding a MAPE of 3.543% through 2028. The findings of this study indicate that government policies have had a positive impact on reducing poverty, although challenges remain. The results of this analysis are expected to guide policymakers in crafting more effective and targeted poverty alleviation strategies in the DIY region. These findings suggest that government policies have had a positive impact on reducing poverty, despite ongoing challenges.