scholarly journals PERAMALAN PRODUKSI PADI DI KABUPATEN BADUNG, GIANYAR, DAN TABANAN DENGAN METODE VECTOR AUTOREGRESSION (VAR)

2021 ◽  
Vol 10 (1) ◽  
pp. 32
Author(s):  
I GUSTI AYU MEIGAYONI LESTARI ◽  
I WAYAN SUMARJAYA ◽  
I NYOMAN WIDANA
Keyword(s):  
Time Series ◽  
Vector Autoregression ◽  
Rice Production ◽  
Percentage Error ◽  
Causality Analysis ◽  
Third Order ◽  
Public Consumption ◽  
Time Series Method ◽  
The Mean ◽  
The Relationship

Rice is one of the staple foodstuffs whose availability is very important for public consumption in Indonesia, especially Bali Province. The three regencies that produce the most rice in Bali they are Badung, Gianyar and Tabanan. This study aims to model, predict, and analyze the relationship between rice production in Badung, Gianyar, and Tabanan Regency from January 2018 to December 2019 using vector autoregression (VAR) method. VAR method is a time series method that can be used to model and predict time series with more than one variable simultaneously. The results of this study, namely the VAR model obtained to predict the amount of rice production in Badung, Gianyar, and Tabanan Regencies is third order VAR (VAR (3)). Based on the forecasting criteria for the mean absolute percentage error (MAPE) in this model, a reasonable forecast is obtained for the rice production variables in Badung and Gianyar regencies, and good forecasting for the rice production variables in Tabanan Regency is obtained. Then, based on the granger causality analysis, it is found that the amount of rice production in Gianyar Regency affects the amount of rice production in Badung and Tabanan Regencies, and the amount of rice production in Badung Regency affects the amount of rice production in Gianyar Regency.

scholarly journals PREDICTION MODEL FOR THE NUMBER OF ARI CASES IN CHILDREN IN SURABAYA USING ARIMA METHOD

2020 ◽  
Vol 9 (1) ◽  
pp. 18
Author(s):  
Alvin Zulhazmi Priambodo ◽  
Mahmudah Mahmudah
Keyword(s):  
Time Series ◽  
Respiratory Infections ◽  
Health Information System ◽  
Percentage Error ◽  
Acute Respiratory Infections ◽  
Future Events ◽  
Planning Decision ◽  
Time Series Method ◽  
The Subject ◽  
The Mean

Forecasting is an important element in planning decision-making related to estimating future events. Forecasting techniques that are often developed and used today are the Time Series. Time series is a measurement of events through the stages of time in hours, days, months, and years format. This research uses the ARIMA time series method. The ARIMA method is used to model acute respiratory infections (ARI) in children. The best model is determined using the smallest error through the Mean Absolute Percentage Error (MAPE). The study aims to predict the number of ARI cases in children in Surabaya. This research is an unobtrusive/nonreactive research. The researcher conditioned the subjects to not being aware that the subject is being studied and therefore, left the subject uninterrupted. The data used was the number of ARI cases in children from January 2014 to December 2018. The data was obtained from the monthly report of the Health Information System Unit (HIS) of the Surabaya Health Office. The conclusion from this study showed that the ARIMA method obtained the best model results, namely ARIMA (2,1,2) with a MAPE value of 15.024. Forecasting results fluctuated and a downward trend in the case of ARI children in Surabaya. In certain months, the number of acute respiratory infections has increased significantly, including in February and March. 

scholarly journals Distance variable improvement of time-series big data stream evaluation

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ari Wibisono ◽  
Petrus Mursanto ◽  
Jihan Adibah ◽  
Wendy D. W. T. Bayu ◽  
May Iffah Rizki ◽  
...  
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Standard Method ◽  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Traffic Demand ◽  
Incremental Model ◽  
Chernoff Bound ◽  
The Mean

Abstract Real-time information mining of a big dataset consisting of time series data is a very challenging task. For this purpose, we propose using the mean distance and the standard deviation to enhance the accuracy of the existing fast incremental model tree with the drift detection (FIMT-DD) algorithm. The standard FIMT-DD algorithm uses the Hoeffding bound as its splitting criterion. We propose the further use of the mean distance and standard deviation, which are used to split a tree more accurately than the standard method. We verify our proposed method using the large Traffic Demand Dataset, which consists of 4,000,000 instances; Tennet’s big wind power plant dataset, which consists of 435,268 instances; and a road weather dataset, which consists of 30,000,000 instances. The results show that our proposed FIMT-DD algorithm improves the accuracy compared to the standard method and Chernoff bound approach. The measured errors demonstrate that our approach results in a lower Mean Absolute Percentage Error (MAPE) in every stage of learning by approximately 2.49% compared with the Chernoff Bound method and 19.65% compared with the standard method.

PERBANDINGAN FUZZY TIME SERIES DENGAN METODE CHEN DAN METODE S. R. SINGH (Studi Kasus : Nilai Impor di Jawa Tengah Periode Januari 2014 – Desember 2019)

2020 ◽  
Vol 9 (3) ◽  
pp. 306-315
Author(s):  
Febyani Rachim ◽  
Tarno Tarno ◽  
Sugito Sugito
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Percentage Error ◽  
Series Method ◽  
Fuzzy Variables ◽  
Central Java ◽  
Time Series Method ◽  
Fuzzy Logical

Import is one of the efforts of an area to meet the needs of its population in order to stabilize prices and maintain stock availability. The value of imports in Central Java throughout 2016 amounted to 8811.05 Million US Dollars. The value of imports in Central Java is the top 10 in all provinces in Indonesia with a percentage of 6.50%. Import data in Central Java is included in the time series data category. To maintain the stability of imports in Central Java, it is deemed necessary to make a plan based on a statistical model. One of the time series models that can be applied is the fuzzy time series model with the Chen method approach and the S. R. Singh method because the method is suitable for cyclical patterned data with monthly time periods such as Import data in Central Java. Important concepts in the preparation of the model are fuzzy sets, membership functions, set basic operators, fuzzy variables, universe sets and domains. The fuzzy time series modeling procedure is carried out through several stages, namely the determination of universe discourse which is divided into several intervals, then defines the fuzzy set so that it can be performed fuzzification. After that the fuzzy logical relations and fuzzy logical group relations are determined. The accuracy calculation in both methods uses symmetric Mean Absolute Percentage Error (sMAPE). In this study the sMAPE value obtained in the Fuzzy Time Series Chen method of 10.95% means that it shows good forecasting ability. While the sMAPE value on the Fuzzy Time Series method of S. R. Singh method by 5.50% shows very good forecasting ability. It can be concluded that the sMAPE value in the S. R. Singh fuzzy time series method is better than the Chen method.Keywords: Import value, fuzzy time series , Chen, S. R. Singh, sMAPE

scholarly journals HUBUNGAN TINGKAT PENDAPATAN DENGAN TINGKAT KONSUMSI MASYARAKAT DI KECAMATAN BONTONOMPO KABUPATEN GOWA

2013 ◽  
Vol 9 (1) ◽  
Author(s):  
Sanusi Am Sanusi Am ◽  
Ansar Ansar
Keyword(s):  
Time Series ◽  
Significant Relationship ◽  
Time Series Data ◽  
Pearson Correlation ◽  
Income Level ◽  
Analytical Tool ◽  
Central Bureau ◽  
Series Data ◽  
Public Consumption ◽  
The Relationship

The purpose of this study is to explain the relationship between the level of income with the level of public consumption in District Bontonompo Gowa District. This research uses time series data obtained from Central Bureau of Statistics (BPS). The analytical tool used is Pearson correlation formula with the help of SPSS For Windows Release 16. The results concluded that the income level has a significant relationship to the level of public income in District Bontonompo Gowa Regency. It is expected that the Gowa Regency government can pursue programs that can encourage the creation of more and more diverse employment so that the communities of each bias can earn a decent income and meet their consumption needs

scholarly journals Effect of the Rise in Online Motorcycle Taxi Services on the Number of Motorcycles using the Interrupted Time Series Method

2021 ◽  
Vol 23 (2) ◽  
pp. 123-130
Author(s):  
Yovita Fabriska Laras Anindityas ◽  
M. Rizki ◽  
T.B. Joewono
Keyword(s):  
Time Series ◽  
Government Policy ◽  
Interrupted Time Series ◽  
Series Method ◽  
Estimation Model ◽  
Massive Growth ◽  
Time Series Method ◽  
Motorcycle Industry ◽  
Motorcycle Taxi ◽  
The Relationship

The substantial growth of motorcycle users in Indonesia is hypothesized to be influenced by a government policy on motorcycle purchase waivers and the massive growth of online motorcycle taxis. This study aims to analyse the relationship between the emergence of online motorcycle taxis and government policy changes towards the number of motorcycles and compare the estimation model seen from the consumer and sales sides. The data were collected from the Indonesian Bureau of Statistics, Motorcycle Industry Association, and World Bank. Several estimation models were built using the interrupted time series method. The results showed that changes in government policy and income per capita significantly increased the number of motorcycles. However, the emergence of online motorcycle taxis negatively affected the increasing number of motorcycles. The results also showed that models with data representing motorcycle usage behavior provided better results than the model with motorcycle sales.

scholarly journals Comparison of Fuzzy Time Series and ARIMA to Forecast Tourist Arrivals to Homestay in Pahang

2021 ◽  
Vol 6 (4) ◽  
pp. 80-89
Author(s):  
Maizatul Akhmar Jafridin ◽  
Nur Fatihah Fauzi ◽  
Rohana Alias ◽  
Huda Zuhrah Ab Halim ◽  
Nurizatul Syarfinas Ahmad Bakhtiar ◽  
...  
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Demand Forecasting ◽  
Secondary Data ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Strategic Decisions ◽  
Forecasting Performance ◽  
Future Events ◽  
Time Series Method

Predictions of future events must be incorporated into the decision-making process. For tourism demand, forecasting is very important to help directors and investors to make decisions in operational, tactical, and strategic decisions. This study focuses on forecasting performance between Fuzzy Time Series and ARIMA to forecast the tourist arrivals in homestays in Pahang. The main objective of this study is to compare and identify the best method between Fuzzy Time Series and Autoregressive Integrated Moving Average (ARIMA) in forecasting the arrival of tourists based on the secondary data of tourist arrivals to homestay in Pahang from January 2015 to December 2018. ARIMA models are flexible and widely used in time-series analysis and Fuzzy Time Series which do not need large samples and long past time series. These two methods have been compared by using the mean square error (MSE) and mean absolute percentage error (MAPE) as the forecast measures of accuracy. The results show that Fuzzy Time Series outperforms the ARIMA. The lowest value of MSE and MAPE was obtained from using the Fuzzy Time Series method at values 2192305.89 and 11.92256, respectively.

scholarly journals A Higher Order Fuzzy Logic Model with Genetic Algorithm Used to Predict the Rice Production in India

2019 ◽  
Vol 8 (12) ◽  
pp. 771-775
Keyword(s):  
Time Series ◽  
Production Management ◽  
Time Series Data ◽  
Critical Time ◽  
Higher Order ◽  
Rice Production ◽  
Series Data ◽  
Percentage Error ◽  
Data Set ◽  
Computing Technique

Forecasting paddy production is considered as a difficult problem in the real world due to in deterministic behavior of the nature. Specifically, rice production is forecasted for a leading year for overall planning of the crop, utilization of the agricultural resources and the rice production management. Likewise, the key challenge of the forecasting rice production is to create a realistic model that can able to handle the critical time series data and forecast with minor error. Prognostication of the Future data is highly correlated with the time series data set. If the accuracy of your prediction is more appropriate, then the value of the forecast will improve as well. This paper represents a new technique depends on Higher Order Fuzzy Logical Relationship. Here, Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) are used to estimate the errors of predicted data. historical data relating to the rice production of 1981 to 2003 is used as secondary data and the error of the predicted data is further reduced using different soft computing technique.

scholarly journals Peramalan Nilai Tukar Petani Subsektor Peternakan Menggunakan Fuzzy Time Series Lee

2021 ◽  
Vol 3 (1) ◽  
pp. 1-15
Author(s):  
Mahadi Muhammad ◽  
Sri Wahyuningsih ◽  
Meiliyani Siringoringo
Keyword(s):  
Time Series ◽  
Fuzzy Sets ◽  
Historical Data ◽  
Mean Absolute Percentage Error ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Logical Relationship ◽  
Absolute Percentage Error ◽  
The Mean ◽  
Fuzzy Logical

ABSTRAKFuzzy time series (FTS) Lee adalah suatu metode peramalan yang digunakan ketika jumlah data historis yang tersedia sedikit, serta tidak mensyaratkan asumsi-asumsi tertentu yang harus terpenuhi. Metode ini menggunakan data historis berupa himpunan fuzzy yang berasal dari bilangan real atas himpunan semesta pada data aktual. FTS Lee adalah perkembangan dari FTS Song dan Chissom, FTS Cheng, serta FTS Chen. Pada penelitian ini dibahas penerapan FTS Lee pada data Nilai Tukar Petani Subsektor Peternakan (NTPT) di Kalimantan Timur. Tujuan penelitian ini adalah memperoleh hasil peramalan NTPT di Kalimantan Timur pada bulan Januari 2020 dengan menggunakan FTS Lee. Langkah awal dalam penelitian ini yaitu menentukan himpunan semesta pembicaraan, langkah kedua menentukan banyaknya himpunan fuzzy, langkah ketiga mendefinisikan derajat keanggotaan himpunan fuzzy terhadap  dan melakukan fuzzyfikasi pada data aktual, langkah keempat membuat fuzzy logical relationship, langkah kelima membuat fuzzy logical relationship group, langkah keenam melakukan defuzzyfikasi sehingga diperoleh hasil peramalan, serta dilanjutkan dengan menghitung nilai mean absolute percentage error. Hasil penelitian menunjukkan bahwa peramalan menggunakan FTS Lee pada bulan Januari 2020 adalah 110,25. Nilai mean absolute percentage error pada  hasil peramalan dengan menggunakan FTS Lee adalah sangat baik.  ABSTRACTLee’s Fuzzy time series (FTS) is a forecasting method that is used when the number of historical data that available was small and does not require certain assumptions to be fulfilled. This method uses historical data in the form of fuzzy sets derived from real numbers over the set of universes in the actual data. FTS Lee is a development of FTS Song and Chissom, FTS Cheng, and FTS Chen. This research discusses the application of FTS Lee to the Exchange Rate of Farmers Subsectors Farm (ERFSF) in Kalimantan Timur. The purpose of this study was to obtain the results of ERFSF forecasting in Kalimantan Timur in January 2020 using FTS Lee. The first step during research is to determine the set of speech universes, the second step is to determine the number of fuzzy sets, the third step is to define the degree of fuzzy association membership and fuzzification on the actual data, the fourth step is to create a fuzzy logical relationship, the fifth step is to create a fuzzy logical relationship group, the sixth step is to perform defuzzification in order to obtain forecasting results, and continue by calculating the mean absolute percentage error value. The results showed that forecasting using FTS Lee in January 2020 was 110,25. The mean absolute percentage error value in forecasting results using FTS Lee is very good.

scholarly journals Model Heuristic Time Invariant Fuzzy Time Series dan Regresi Untuk Prediksi Laba dan Analisis Variabel yang Mempengaruhi

2016 ◽  
Vol 6 (2) ◽  
pp. 144
Author(s):  
Ica Admirani ◽  
Rachmat Gernowo ◽  
Suryono Suryono
Keyword(s):  
Time Series ◽  
Prediction Error ◽  
Fuzzy Time Series ◽  
Actual Data ◽  
Percentage Error ◽  
Time Series Method ◽  
Past Data ◽  
Administrative Expenses ◽  
Time Invariant ◽  
Fuzzy Logical

Model of prediction with fuzzy time series method has ability to capture the pattern of past data to predict the fu ture of data does not need a complicated system, making it easier to use. The research aims to built prediction system using model of  heuristic time invariant fuzzy time series and multiple linear regression to predict profit and analysis of variables that affect profit. Profit forecasting aims to determine the company's prospects in the future in order to remain exist in doing its business. The variables that use in the modelling are profit as the dependent variable, and sales, cost of goods sold, general and administrative expenses, selling and marketing expenses and interest income as the indepent variables. Profit forecasting modelling begins by defining universe of discourse and interval actual data of profit, then determine fuzzy set and actual data fuzzified. Furthermore, fuzzy logical relationship and fuzzy logical relationships group to fuzzified data. The prediction process consist of two prediction phase there are training phase aimed to determine trend predictor and testing phase to determine prediction results. By using 24 profit data samples resulted prediction error by using Mean Absolute Percentage Error is 11,64% and added 13 data for testing obtained prediction error is 22,27%.  In analysis of variables that affect profit is known that sales variable most effect on profit than other variables with a regression coefficient 0.976.

Forecasting Technique for the Production Plan of the Local Earthenware Industries of Thailand

2014 ◽  
Vol 577 ◽  
pp. 1279-1282
Author(s):  
Weerapol Namboonruang ◽  
N Amdee
Keyword(s):  
Time Series ◽  
Classical Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Time Series Models ◽  
Production Plan ◽  
Absolute Percentage Error ◽  
The Mean

The purpose of this work is to compare the forecasting of time series models between two different models. One is the classical model and another is the Box-Jenkins model. The data are calculated using the circulation of Angbuaand Ahongwhich are the local earthenware products from Ratchaburi province, Thailand. Results show that the mean absolute percentage error (MAPE) of Angbua and Ahong are 17.80, 36.12 and 16.38,17.21 respectively. Also,prediction using the Box-Jenkins Model by ARIMA form of both products are (1, 0, 0) and (1, 1, 1). From this work the Box-Jenkins Model shows more appropriate method than the classical model considered by the less error.

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