Application of the Autoregressive Integrated Moving Average Exogenous (ARIMAX) with Calendar Variation Effect Method for Forecasting Chocolate Data in Indonesia and the United States

Forecasting chocolate consumption is required by producers in preparing the amount of production each month. The tradition of Valentine, Christmas and Eid al-Fitr which are closely related to chocolate makes it impossible to predict chocolate by using the Classical Time Series method. Especially for Eid al-Fitr, the determination follows the Hijri calendar and each year advances 10 days on the Masehi calendar, so that every three years Eid al-Fitr will occur in a different month. Based on this, the chocolate forecasting will show a variation calendar effect. The method used in modeling and forecasting chocolate in Indonesia and the United States is the ARIMAX (Autoregressive Integrated Moving Average Exogenous) method with Calendar Variation effect. As a comparison, modeling and forecasting are also carried out using the Naïve Trend Linear, Naïve Trend Exponential, Double Exponential Smoothing, Time Series Regression, and ARIMA methods. The ARIMAX method with Calendar Variation Effect produces a very precise MAPE value in predicting chocolate data in Indonesia and the United States. The resulting MAPE value is below 10 percent, so it can be concluded that this method has a very good ability in forecasting.

Power Business Intelligence in the Data Science Visualization Process to Forecast CPO Prices

Forecasting is one of the techniques in data mining by utilizing the data available in the data warehouse. With the development of science, forecasting techniques have also entered the computational field where the forecasting technique uses the artificial neural network (ANN) method. Where is the method for simple forecasting using the Time Series method. However, the ability to create data visualizations certainly hinders researchers from maximizing research results. Of course, with the development of the Power BI software, the data science process is more neatly presented in the form of visualization, where the data science process involves various fields so that in this paper the results of forecasting the price of crude palm oil (CPO) are presented for the development of the CPO business with the hope of implementing the Business Process. intelligence (BI) by involving ANN, namely the time series for forecasting. From the final results, accuracy in forecasting with time series involves 2 accuracy techniques, the first using MAPE and getting a result of 0.03214% and the second using MSE to get 962.91 results.

Evaluasi Produktivitas Bongkar Muat Petikemas pada PT. Berlian Jasa Terminal Indonesia (BJTI Port) di Tanjung Perak-Surabaya

Evaluation of loading and unloading productivity is an important thing that must be done because it minimizes the occurrence of obstacles in loading and unloading and helps determine the right steps to increase productivity in the following year. The purpose of this study is to determine the factors that affect the productivity of loading and unloading, calculate the percentage of productivity and determine the forecasting value of loading and unloading productivity for the next 10 years. This is expected to improve services with technological innovation and the development of related science. The method used was the time series method. The results were obtained from PT. BJTI in 2017 – 2020, the obstacles that occurred tended to decrease, so the percentage of productivity increased every year. Based on the forecasting results with the time series method, loading and unloading productivity in 2021 - 2030 showed a significant increase. In 2030 the total productivity of loading and unloading was obtained at 1,397,463 boxes. This showed that the total loading and unloading in 2030 did not affect the equipment's ability in the loading and unloading process because the equipment capacity was still sufficient to accommodate up to 2,242,560 boxes per year. Therefore, it was not necessary to add more HMC tools at Berlian Terminal. This was inversely proportional to the loading and unloading stack obstacles whose solution must be to add RTG heavy equipment so that the loading and unloading process would be faster.

A Novel Intuitionistic Fuzzy Time Series Method Based on Bootstrapped Combined Pi-Sigma Artificial Neural Network

Background: Intuitionistic fuzzy time series forecasting methods have been started to solve the forecasting problems in the literature. Intuitionistic fuzzy time series methods use both membership and non-membership values as auxiliary variables in their models. Because intuitionistic fuzzy sets take into consideration the hesitation margin and so the intuitionistic fuzzy time series models use more information than fuzzy time series models. The background of this study is about intuitionistic fuzzy time series forecasting methods. Objective: The study aims to propose a novel intuitionistic fuzzy time series method. It is expected that the proposed method will produce better forecasts than some selected benchmarks. Method: The proposed method uses bootstrapped combined Pi-Sigma artificial neural network and intuitionistic fuzzy c-means. The combined Pi-Sigma artificial neural network is proposed to model the intuitionistic fuzzy relations. Results and Conclusion: The proposed method is applied to different sets of SP&500 stock exchange time series. The proposed method can provide more accurate forecasts than established benchmarks for the SP&500 stock exchange time series. The most important contribution of the proposed method is that it creates statistical inference: probabilistic forecasting, confidence intervals and the empirical distribution of the forecasts. Moreover, the proposed method is better than the selected benchmarks for the SP&500 data set.

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

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.

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

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.