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Prediction Analysis of Tobacco Production in Temanggung District

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Prediction Analysis of Tobacco Production in Temanggung District Journal of Agribusiness Management and Development, Vol 1 No 1, September 2020, Page 126-136 ISSN 2775-0248 (Print) ISSN 2775-0256 (Online) Available at https://journal.ugm.ac.id/v3/JAMADEV/ PREDICTION ANALYSIS OF TOBACCO PRODUCTION IN TEMANGGUNG DISTRICT Ruddy Chrisdiawan Poetra1 & Masyhuri2 1,2Department of Agricultural Socio-Economics, Faculty of Agriculture, Universitas Gadjah Mada Corresponding author: [email protected] ABSTRACT Tobacco is a raw material for making cigarettes and one of the most considerable income contributions of Indonesia. This research aims to determine tobacco productivity and predict tobacco production from 2017–2021 in Temanggung, Central Java, based on the previous tobacco production of 1987–2016. Trend analysis was used to estimate tobacco productivity in Temanggung. ARIMA method predicts tobacco production for five years, namely 2017–2021, but does not require a specific data pattern. ARIMA method also works effectively for short period time data. The research results show that tobacco productivity in Temanggung increased by 0.0053 ton/ha per year. ARIMA model (0,1,1) showed that tobacco is predicted to increase during the 2017-2021 period in Temanggung. Keywords: Box Jenkins–ARIMA, prediction, production, tobacco INTRODUCTION Tobacco can be used in several functions. It Indonesia is one of the world’s largest producers of can be used as a pesticide. In the form of nicotine tobacco plants. This plant is called “green gold” tartrate, it can be used as medicine. Generally, based on information from Tobacco Farmers. tobacco is made into cigarettes, chewing tobacco, Indonesian local tobacco is also known as the highest and other products. Tobacco has long been used as tobacco quality. Each tobacco producer’s region has an entheogen in America. The arrival of Europeans its distinctive taste. The type of Indonesian tobacco to North America popularized the tobacco trade, is most sought after in the international tobacco especially as a sedative. This popularity led to the market (Padmo and Djatmiko, 1991). Tobacco plants economic growth of the southern United States. first entered Indonesia around 1630 (Dutch Tobacco After the United States civil war, changes in demand Growers, 1951), then expanded to various regions in and labor led to the cigarette industry’s development. Indonesia, one of which is on the eastern and This new product quickly developed into tobacco northern slopes of Mount Sumbing and Mount companies until scientific controversy broke out in Sindoro, Temanggung Regency, Central Java. the mid-20th century. Finally, through a lengthy adaptation process, the Plantation tobacco plants are Indonesia’s tobacco population in Temanggung Regency was foreign exchange earner. It means that tobacco is formed, which has distinctive morphological and cultivated on an extensive production scale up to physiological characteristics. Temanggung tobacco thousands of hectares. Thus, considerable capital is a kind of Voor Oogst (Nicotiana tabacum). It is investment is needed, and it is hoped that a large planted and harvested during the dry season. income or profit will be obtained. Tobacco in Indonesia comes from smallholder plantations, 126 JAMADEV Vol 1/No 1, September 2020 private plantations, and government plantations. as necessary information for planning and making Tobacco plantations are scattered in several regions decisions in the future. Forecasting is an essential in Indonesia, one of which is tobacco plantations in tool in planning effectively and efficiently. the Temanggung Regency. Temanggung Regency i Forecasting is an activity of the business s the best producer of Voor-Oogst tobacco in function that estimates the sales and use of products Indonesia, unique properties, namely distinctive to be made in the right quantity. A forecast estimates aroma, and high nicotine compounds. Based on data future demand based on several forecasting from the Directorate General of Plantations at the variables, often based on historical time-series data. Ministry of Agriculture, national tobacco production Forecasting uses forecasting techniques that are both in 2016 reached 196,154 tons, up 1.22 percent from formal and informal (Gaspersz, 1998). the previous year, which weighed 193,790 tons. Indonesia’s tobacco production once reached a METHOD record high in 2012, weighing 260,818 tons. This research’s primary method is descriptive However, after that, it tends to decline. Last year’s quantitative analytic based on solving actual tobacco production consisted of 195,559 tons or problems that exist today. Data is shown, compiled, about 97.7 percent of which resulted from explained, and analyzed to provide an overview of Smallholders Plantation (PR). the phenomena that occur, explain relationships, test Meanwhile, 462 tonnes (0.24 percent) are hypotheses, make predictions, and get the meaning State Large Plantations (PBN). The rest, as much as and implications of a solved (Assauri,1984). The 133 tons (0.07 percent) of large private plantations data obtained in this study were analyzed using (PBS). Thus the plan to increase cigarette excise will Eviews 4.0 and Microsoft Excel 2007 software. have an impact on small tobacco farmers. If cigarette This study uses secondary data. Secondary production decreases, it will reduce smallholder data is data that is recorded systematically in the tobacco demand. form of time-series data. The data used are Based on data from the Central Bureau of production data, area size, and tobacco productivity Statistics for 2014–2016, Central Java is one of the in the Temanggung Regency for the last 30 years. provinces that produce the most massive tobacco Secondary data is a literature study or literature production in Indonesia. In 2014, tobacco production sourced from the Directorate General of Plantation’s in Central Java produced 34,309 tons, then in 2015, internal reports, the Central Bureau of Statistics it produced 34,302 tons, and in 2016 Central Java (2017), data from related agencies, and other relevant province produced 34,309 tons of tobacco. From literature. The data presented are: these results, in 2014–2016, tobacco production in 1. The total area of tobacco plantations in Central Java could be stable, or there was no Temanggung District, 1987-2016. significant increase or decrease in production. 2. Tobacco Plantation Production in One way to determine the Temanggung Temanggung Regency according to its Regency’s ability to produce tobacco is to make exploitation in 1987–2016. forecasts for the next few years. Forecasts are needed 127 JAMADEV Vol 1/No 1, September 2020 3. The productivity of tobacco plantations in Trend analysis tests several variables, one of which the Temanggung Regency according to is testing using the coefficient of determination (R2). their exploitation from 1987 to 2016. The coefficient of determination is expressed in Each variable and its measurements need to percent form, which shows the magnitude of the be explained to obtain a common understanding of independent variable’s influence on the dependent the concepts in this study, namely: variable (Widarjono, 2007). The coefficient of 1. Production is the number of tobacco determination is used to measure the correctness of products produced from tobacco leaves in the relationship from the model used, namely a the Temanggung district with a scale number that shows the amount of (ton/land). variance/distribution ability of the independent 2. The area is the amount of land used for variable, which explains the dependent variable. tobacco cultivation in Temanggung The general Box-Jenkins model is denoted as district (hectare/land) follows: 3. Productivity is the ability of existing ARIMA (p,d,q) resources to produce tobacco products = (1-B)d (1 – θ B –θ B2 -…. -θ Bp) Y (ton/hectare/year) 1 2 p t = δ+(1– θ B – θ B2 -….- θ Bq) ε .........................(2) One of the statistical tools used to estimate a 1 2 q t in which: business’s state in the future based on past data is a p = autoregressive degree (AR) trend. The trend predicts a variable with time- d = differencing degree (difference) independent variables or the occasional series q = moving average degree (MA) movement over several years and tends to go in a ε = forecast error period t direction, where the direction can be up, flat, or down t δ = constants (Soejoeti, 1987). Forecasting is a continuation of the B2Y = Y trend line from the last observation to the time for the t t-2 BpY = Y forecast to be made. Trend analysis was conducted to t t-p Bqε = ε see the trend of tobacco area development, t t-q productivity, and production in the Temanggung The steps in the ARIMA method are as Regency. The trend equation with the regression follows (Mulyono, 1999): model is as follows: 1. Data Stationing Stage The model identification stage determines Y = a + bx + e ......................................................(1) whether the time series data to be used is stationary in which: or not. If the time series data is not stationary, it can Y = predicted variable (tobacco productivity) usually be converted into stationary time series data a = constanta using the differencing method. If the time series data b = trendline coefficient is stationary, the analyst must identify the model’s x = time (year) form to be used. This stage is carried out by e = residual 128 JAMADEV Vol 1/No 1, September 2020 comparing the autocorrelation coefficient and the one or two. Third, a correlogram with an partial autocorrelation coefficient of the data with the autocorrelation coefficient that does not cut off but distribution for various ARIMA models accordingly. decreases to near zero in a fast pattern is referred to Data stationarity checks can be done in the following as a rapidly dying down pattern. ACF shows several ways: dying patterns, namely a decreasing exponential a. Graph, that is, with a simple data plot, if the data pattern, a sine wave pattern, and a combination of the is close to the average and there is no tendency to two patterns.
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