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All Journal Jurnal Gizi dan Pangan Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) BAREKENG: Jurnal Ilmu Matematika dan Terapan EIGEN MATHEMATICS JOURNAL Majalah Ilmiah Matematika dan Statistika (MIMS) Journal of Mathematics: Theory and Applications STATISTIKA Pangulu Abdi: Jurnal Pengabdian Kepada Masyarakat Journal of Mathematics, Computation and Statistics (JMATHCOS)
Nurfadilah, Khalilah
Universitas Islam Negeri Alauddin Makassar
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Other

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Journal : EIGEN MATHEMATICS JOURNAL

 1 
Comparison of Fuzzy Time Series Methods and Autoregressive Integrated Moving Average (ARIMA) for Inflation Data Asyifah Qalbi; Khalilah Nurfadilah; Wahidah Alwi
Eigen Mathematics Journal Vol. 4 No. 2 Desember 2021
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v4i2.122

Abstract

This study compares the Fuzzy Time Series (FTS) method with the Autoregressive Integrated Moving Average (ARIMA) method on time series data. These two methods are often used in predicting future data. Forecasting or time-series data analysis is used to analyze data in the form of time series. In this study, Indonesian inflation data was used to be analyzed in comparing the FTS and ARIMA methods. Inflation is one of the economic indicators used to measure the success of a country's economy. If the inflation rate is low and stable, it will stimulate economic growth. This inflation value is calculated every month where the value can decrease and increase from one period to another. Comparison of the FTS and ARIMA methods is seen in the error value generated by the two methods. A method can be better than other methods if the value of the resulting forecast error is smaller. In this study, Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) were used to see the magnitude of the error value of the two methods on the fives data testing used. The results obtained in this study are the results of Indonesia's inflation forecast for the period January to May 2021 using the FTS method, respectively, at 0.57%, 0.375%, 0.2%, 0.2%, and 0.1125%, while the forecast results using the ARIMA method, respectively. Of 0.3715848%, 0.2362817%, 0.1508295%, 0.1731906%, and 0.2432851% and the results of calculating the size of error using MSE and MAPE indicate that the ARIMA method with the model ARIMA(3,0,0) is better at predicting inflation data in Indonesia with a value of MSE of 0.009 and MAPE of 64.987% compared to the FTS method resulted in MSE values of 0.047 and MAPE of 132.548%. 
APPLICATION OF EXPONENTIAL SMOOTHING METHOD TO FORECASE THE AMOUNT OF RICE PRODUCTION IN TANATE RIAJA DISTRICT, BARRU REGENCY Khalilah Nurfadilah; Adnan Sauddin; Winda Saputri
Eigen Mathematics Journal Vol. 5 No. 1 Juni 2022
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v5i1.127

Abstract

Exponential smoothing is a forecasting method with data that tends to fluctuate. Rice production is one of the data with these properties. This study discusses the agricultural production, the variable used to predict the level of rice production in Tanete Rilau District, Barru Regency . This study aims to predict the total production of rice plants from 2021 to 2025. The analysis results show that the forecast values for the entire production of rice plants from 2021 to 2025 are 24016.6, 24613.14, 25018.36, 25342.54, and 25601.88, respectively. It can be seen that rice production forecasting using the exponential smoothing method fluctuates yearly.
Negative Binomial and Generalized Poisson Regression Model for Death Due to Dengue Hemorrhagic Fever Data Risnawati Ibnas; Satriani Satriani; Khalilah Nurfadilah
Eigen Mathematics Journal Vol. 6 No. 1 Juni 2023
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v6i1.153

Abstract

Data on the number of deaths due to Dengue Fever in statistics is count data often approximated by a Poisson distribution. However, if overdispersion occurs, Poisson regression is no longer sufficient, so the Negative Binomial and Generalized Poisson Regression approaches are used. From the two models, the best model was chosen based on the smallest AIC value, 66.50, namely the Negative Binomial Regression model. From this model, factors that have a significant effect are determined based on the p-value, and the factor ratio of health facilities per 100,000 population  is obtained.
Co-Authors Adiatma Adnan Sauddin Ahmad Zikir Aldi Setiawan, Aldi Ale Miftahulhaer, Ale Miftahulhaer Ambarwati B. Sarabi , Valina Andi Mariani Anugrawati, Sri Dewi Asfar Asfar Asfar, Asfar Asmia Astrid Putri Shafira Asyifah Qalbi Azlam Nas Bagus Sartono Basri, Nurzarina Budi Susetyo C, Fery Ramadhan DEWI SARTIKA Efriwati Efriwati Ermawati Fadjryani, Fadjryani Fitrah Ernawati FITRI RAHMAWATI Hasmira Irwan Irwan Kasse Iyut Wahyu Saputri Jannah , Miftahul Khairil Anwar Notodiputro La Ode Abdul Rahman Lestari, Nila Lukmar T Muafikah Tul Azizah Muh. Algifari Alayubi Muhammad Ihsan Salim Muhammad Nur Aidi Munawwarah Munira Munira Anwar Musdalifah Nawawi, M. Ichsan Nur Aidi, Muhammad Nur Ismi Tahir Nur Pratiwi Baso Nurfadilah Nurhikma Nurma, Try Azisah Nurul Qalbi Putri Saras Rayyan, Ismi Risnawati Ibnas Satriani Satriani Suryanty, Santy Tiara Anastasya Halik Ula, Rahmatul Wahidah Alwi Wahidah Alwi Winda Saputri Yuli Hastuti Yulia, Yulia Novita Sari Yusran, Andi Muhammad Fauzan Shadiq Zulfikar Alifahmi