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Sugar Production Forecasting System in PTPN XI Semboro Jember using Autoregressive Integrated Moving Average (ARIMA) Method Januar Adi Putra; Saiful Bukhori; Faishal Basbeth
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1988

Abstract

There is a lot of entrepreneurial competition in the production of goods or services in the world, especially in Indonesia, especially the production of staple goods, namely sugar. The problem that is often faced at Sugar Factory PTPN XI Semboro Jember is the lack of management that is neatly organized and efficient, which makes this company less working optimally. Often there is a lack and excess of sugar production which makes the sugar does not have the maximum value, the sugar has been damaged, and sales at a reduced price because the sugar is not as efficient as the initial product. From these various problems, it can reduce profits from the company. From these problems it can be concluded that the company needs a system that can organize the management of the company, and is able to forecast production in the future. In this research will make a forecasting system using the method of Autoregressive Integrated Moving Average (ARIMA), where this method is divided into three methods, namely the Autoregressive (AR) method, the Moving Average (MA) method, and the Autoregressive Integrated Moving Average (ARIMA) method, which preceded by checking stationary data, and modeling the Autoregressive Integrated Moving Average (ARIMA) method. Forecasting is done using production data for the previous 12 years from the company. The system is made to facilitate management that is less organized and displays predictions for the next production period. The results of this forecasting system are to determine the amount of production each year needed in this company. From the results of the ARIMA method modeling, the right ARIMA method is obtained by the ARIMA / AR (1,0,0), ARIMA / MA (0,0,1), and ARIMA (1,0,1) methods. The test results found that the average value of Mean Absolute Percentage Error (MAPE) in the Autoregressive (AR) method was 17%, the Moving Average (MA) method was 19%, and the Autoregressive Integrated Moving Average (ARIMA) method was 15%.
Sugar Production Forecasting System in PTPN XI Semboro Jember using Autoregressive Integrated Moving Average (ARIMA) Method Putra, Januar Adi; Bukhori, Saiful; Basbeth, Faishal
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1988

Abstract

There is a lot of entrepreneurial competition in the production of goods or services in the world, especially in Indonesia, especially the production of staple goods, namely sugar. The problem that is often faced at Sugar Factory PTPN XI Semboro Jember is the lack of management that is neatly organized and efficient, which makes this company less working optimally. Often there is a lack and excess of sugar production which makes the sugar does not have the maximum value, the sugar has been damaged, and sales at a reduced price because the sugar is not as efficient as the initial product. From these various problems, it can reduce profits from the company. From these problems it can be concluded that the company needs a system that can organize the management of the company, and is able to forecast production in the future. In this research will make a forecasting system using the method of Autoregressive Integrated Moving Average (ARIMA), where this method is divided into three methods, namely the Autoregressive (AR) method, the Moving Average (MA) method, and the Autoregressive Integrated Moving Average (ARIMA) method, which preceded by checking stationary data, and modeling the Autoregressive Integrated Moving Average (ARIMA) method. Forecasting is done using production data for the previous 12 years from the company. The system is made to facilitate management that is less organized and displays predictions for the next production period. The results of this forecasting system are to determine the amount of production each year needed in this company. From the results of the ARIMA method modeling, the right ARIMA method is obtained by the ARIMA / AR (1,0,0), ARIMA / MA (0,0,1), and ARIMA (1,0,1) methods. The test results found that the average value of Mean Absolute Percentage Error (MAPE) in the Autoregressive (AR) method was 17%, the Moving Average (MA) method was 19%, and the Autoregressive Integrated Moving Average (ARIMA) method was 15%.
Social Media Sentiment Analysis to Measure Community Response in the Millennial Road Safety Festival Program Using TF-IDF and Support Vector Machine Saiful Bukhori; Sonya Sulistyono; Antonius Cahya Prihandoko; Januar Adi Putra; Windi Eka Yulia Retnani; Umroh Makhmudah; Muhammad Noor Dwi Eldianto
Journal of Indonesia Road Safety Vol 3 No 2 (2020): Journal of Indonesia Road Safety
Publisher : Traffic Accident Research Center, Indonesia Traffic Police Corps and University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/korlantas-jirs.v3i2.16768

Abstract

This Sentiment Analysis is a combination of data mining and text mining. Sentiment Analysis itself is used to process various opinions that the public or experts have given through a variety of existing media. The argument is given to a product, service, or agency. Sentiment Analysis has three types of opinions: negative opinions, positive opinions, and neutral opinions. Based on the test results, the resulting model achieves the highest accuracy of 83.33% when using 80:20 scenario data, while the lowest accuracy of 80.00% is achieved when using the 60:40 scenario data. The higher the precision that will be obtained, whereas using less training data will be slightly unstable. ABSTRAK Sentiment Analysis merupakan perpaduan dari data mining dan teks mining, dimana Sentiment Analysis sendiri digunakan untuk mengolah berbagai macam opini yang telah diberikan oleh masyarakat atau para pakar melalui berbagai media yang ada, opini tersebut diberikan untuk sebuah produk, jasa maupun sebuah instansi. Pada Sentiment Analysis terdapat 3 jenis opini, yaitu opini negatif, opini positif dan opini netral. Berdasarkan hasil pengujian, model yang dihasilkan mencapai akurasi tertinggi yaitu 83,33% saat menggunakan data skenario 80:20, sedangkan akurasi terendah 80,00% dicapai ketika menggunakan skenario data 60:40 Skenario data dapat memengaruhi tingkat akurasi semakin banyak jumlah data pelatihan yang diberikan, semakin tinggi akurasi yang akan diperoleh, sedangkan jika menggunakan lebih sedikit data pelatihan, hasilnya akan sedikit tidak stabil.