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All Journal NUCLEUS: Jurnal Sains dan Teknologi Perwira Journal of Science and Engineering (PJSE)
Nayla Desviona
Institut Teknologi dan Bisnis Muhammadiyah Purbalingga
Chemistry Computer Science & IT Electrical & Electronics Engineering Library & Information Science Materials Science & Nanotechnology Physics

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Implementasi Metode Filter Kalman dalam Memprediksi Curah Hujan yang Diperoleh Melalui Model Arima di Kota Jambi Mellyani Aprilia; Nayla Desviona
NUCLEUS Vol 2 No 2 (2021): NUCLEUS
Publisher : Neolectura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37010/nuc.v2i2.607

Abstract

In the last three years the climatic conditions in Jambi City have experienced erratic weather conditions. One way to predict rainfall is using the Kalman Filter approach. However, in this case, the Kalman Filter method is implemented on the forecasting results from ARIMA (Autoregressive Integrated Moving Average) because there has been rainfall measurement data from 2008 to 2017 at the BMKG Muaro Jambi Climatology Station which is also a function of time and the existing pattern will be described with using Time Series Analysis. Time series data is data that has a time series of more than one year on one object or data collected from time to time on one object. ARIMA model will be used to predict the next data. Kalman filter is a model part of state space that can be applied in forecasting models. The Kalman filter consists of a prediction stage and a correction stage. This method uses a recursive technique to integrate the latest observational data into the model to correct previous predictions and make further predictions. This study aims to determine the implementation of the kalman filter method in predicting rainfall obtained through the ARIMA model in Jambi City. The results of the 2018 Jambi City rainfall prediction research show that the best ARIMA model formed is the ARIMA model (1,0,1). In the Kalman Filter model, a MAPE value of 24.92% is obtained, which indicates that the Kalman Filter has a fairly good predictive ability.
Estimasi Tingkat Pengangguran Terbuka di Provinsi Jambi dengan Metode Double Exponential Smoothing Anisa Rahmawati; Nayla Desviona; Tiara Puapita Sari
NUCLEUS Vol 3 No 1 (2022): NUCLEUS
Publisher : Neolectura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37010/nuc.v3i1.675

Abstract

Unemployment is an employment problem that is often faced in every region, especially in Jambi Province.  The indicator used to determine the number of unemployed is the open unemployment rate (TPT).  High unemployment can be a major source of poverty that can lead to crime and hinder the process of economic development.  As an initial precaution for the government before the increase in unemployment, which is to estimate the future TPT in Jambi Province.  One of the methods used in estimating TPT in Jambi Province is the Double Exponential Smoothing Method from Holt.  The measure of the forecast error uses the Mean Absolute Percent Error (MAPE).  The aim is to obtain estimated TPT results in Jambi Province in the coming year, using Holt's Double Exponential Smoothing Method.  The estimation results obtained, for TPT data there is a MAPE value of 18.96% with parameters  = 0.9 and  = 0.2.  The estimation model obtained is = .  The estimation results show that the TPT rate will continue to increase next year, it can be taken into consideration for the government in determining future plans to reduce TPT in order to accelerate economic development in Jambi Province. 
PERAMALAN JUMLAH KLAIM DENGAN MEMBANDINGKAN METODE DOUBLE EXPONENTIAL SMOOTHING DARI BROWN DAN DOUBLE EXPONENTIAL SMOOTHING DARI HOLT Nayla Desviona; Trima Asriyati; Ditayatul Rahayu; Bangkit Prima Yudha
Perwira Journal of Science & Engineering Vol 1 No 2 (2021)
Publisher : Universitas Perwira Purbalingga

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (356.449 KB) | DOI: 10.54199/pjse.v1i2.58

Abstract

Time series model is that model uses to prediction the future with the past data, one of example of time series is exponential smoothing. Exponential smoothing is repair procedure where’s it done continuely at forecasting by new data. In this research Exponential Smoothing metode applied to forecasting amount of claim in Health BPJS at jambi with use the data since january 2018 until Juli 2021,the steps use to get output of research there are 4 steps,these are 1) Data Identification, 2) Modeling, 3) Forecasting, 4) Evaluation of Forecasting Result with MAE(Mean Absolute Error) and MSE (Mean Square Error). Brown’s model for PNS class 1: PNS class 2 : NON PNS class 1 : , NON PNS class 2 : . Holt’s model for PNS class 1: ,.PNS class 2 : NON PNS class 1 : and NON PNS class 2 : . Both of two metodes have similar good performance. but Holt’s metode has error value (MAE) more little then Brown’s metode. this thing is so important for looked considering its importance a forecasting for he best increase quality and contribution by Health BPJS at Jambi staff for future. Time series model is that model uses to prediction the future with the past data, one of example of time series is exponential smoothing. Exponential smoothing is repair procedure where’s it done continuely at forecasting by new data. In this research Exponential Smoothing metode applied to forecasting amount of claim in Health BPJS at jambi with use the data since january 2018 until Juli 2021,the steps use to get output of research there are 4 steps,these are 1) Data Identification, 2) Modeling, 3) Forecasting, 4) Evaluation of Forecasting Result with MAE(Mean Absolute Error) and MSE (Mean Square Error). Brown’s model for PNS class 1: PNS class 2 : NON PNS class 1 : , NON PNS class 2 : . Holt’s model for PNS class 1: ,.PNS class 2 : NON PNS class 1 : and NON PNS class 2 : . Both of two metodes have similar good performance. but Holt’s metode has error value (MAE) more little then Brown’s metode. this thing is so important for looked considering its importance a forecasting for he best increase quality and contribution by Health BPJS at Jambi staff for future.
Co-Authors Anisa Rahmawati Bangkit Prima Yudha Ditayatul Rahayu Fatmah Fatmah Mellyani Aprilia Tiara Puapita Sari Trima Asriyati