Ratio Interval-Frequency Density with Modifications to the Weighted Fuzzy Time Series

The improvement of plantation forecasting accuracy, particularly with regard to coffee production, was an essential aspect of earth observations for the purpose of informing plantation management alternatives. These decisions included strategic and tactical decisions on supply chain operations and financial decisions. Many research initiatives have used a variety of methodologies to the forecasting of plantation areas and related industries, such as coffee production. One of these methods was known as the fuzzy time series (FTS) technique. This  study combined ratio-interval and frequency density to get universe of discourse and partition followed by adopted weighted and modified that weighted. The first step was defined universe of discourse using ratio-interval algorithm. The second step was partition the universe of discourse using ratio-interval algorithm followed by frequency density partitioning. The third step was fuzzyfication. The fourth step built fuzzy logic relationship (FLR) and fuzzy logic relationship group (FLRG). The fifth step was adopted the modification weighted. The last step was defuzzyfication. The  models evaluated  by  average  forecasting  error  rate  (AFER)  and  compared  with  existing methods.  AFER  value  1.24%  for  proposed method

Triangular Fuzzy Time Series for Two Factors High-order based on Interval Variations

Fuzzy time series (FTS) firstly introduced by Song and Chissom has been developed to forecast such as enrollment data, stock index, air pollution, etc. In forecasting FTS data several authors define universe of discourse using coefficient values with any integer or real number as a substitute. This study focuses on interval variation in order to get better evaluation. Coefficient values analyzed and compared in unequal partition intervals and equal partition intervals with base and triangular fuzzy membership functions applied in two factors high-order. The study implemented in the Shen-hu stock index data. The models evaluated by average forecasting error rate (AFER) and compared with existing methods. AFER value 0.28% for Shen-hu stock index daily data. Based on the result, this research can be used as a reference to determine the better interval and degree membership value in the fuzzy time series.

The cross-association relation based on intervals ratio in fuzzy time series

The fuzzy time series (FTS) is a forecasting model based on linguistic values. This forecasting method was developed in recent years after the existing ones were insufficiently accurate. Furthermore, this research modified the accuracy of existing methods for determining and the partitioning universe of discourse, fuzzy logic relationship (FLR), and variation historical data using intervals ratio, cross association relationship, and rubber production Indonesia data, respectively. The modifed steps start with the intervals ratio to partition the determined universe discourse. Then the triangular fuzzy sets were built, allowing fuzzification. After this, the FLR are built based on the cross association relationship, leading to defuzzification. The average forecasting error rate (AFER) was used to compare the modified results and the existing methods. Additionally, the simulations were conducted using rubber production Indonesia data from 2000-2020. With an AFER result of 4.77%<10%, the modification accuracy has a smaller error than previous methods, indicating  very good forecasting criteria. In addition, the coefficient values of D1 and D2 were automatically obtained from the intervals ratio algorithm. The future works modified the partitioning of the universe of discourse using frequency density to eliminate unused partition intervals

Identification of South Sumatra Province’s Local Wisdom as Science Literacy Objects

The ability of Indonesian students to master physics is still low. Based on PISA in 2018, Indonesia ranked at 62th out of 72 participating countries. It was much lower than other Asian countries. Therefore, this problem should be overcome. One of the existing sources of physics literacy is local wisdom. The local wisdom can be used as a direct source of physics studies for students to increase their literacy skills. Therefore, in this paper, the authors discussed some local wisdoms in South Sumatra Province that potentially used as physics literacy objects for students in their school. South Sumatra has many local wisdoms potentially used as real objects in physics studies for students. Some of the local wisdom as otok-otok boat and roasted kemplang can be used as the object of heat transfer studies (thermal), and the traditional houses of Limas and Baghi can be used as media for learning force, mass, load and modulus of elasticity. The objects of literacy studies are sources of study that can be used by teachers in teaching physics. Through local wisdom-based literacy, it is certainly expected to improve Indonesia's PISA ranking. Not only to improve PISA ranking, but it is also increase the superiority of the nation's future generations

PERAMALAN PRODUKSI KARET INDONESIA MENGGUNAKAN FUZZY TIME SERIES DUA FAKTOR ORDE TINGGI RELASI PANJANG BERDASARKAN RASIO INTERVAL

The fuzzy time series method for forecasting continues to develop over time. This research discusses fuzzy time series, which considers two factors for high order using interval partitioning based on interval ratio with long relation construction for getting different accuracy in forecasting between combination method and existing method. The first step is the formation of the universe of speech. Second, divide the universe of discourse into several intervals using interval ratios. Third, fuzzification. Fourth, build fuzzy logic relations and fuzzy logic relation groups, and fifth, defuzzification. The previous methods would be compared with the fuzzy logic relation construction result. The simulation used Indonesian rubber production data for 2000-2020. The results and errors were tested using the average forecasting error rate (AFER). AFER value of the forecasting method is 1.863% obtained. Keywords: Forecasting, fuzzy time series, long relation MSC2020: 62M10, 62M20, 62M86, 03E7

Car insurance segmentation prediction based on the most influential features using random forest and stacking ensemble learning

In addition to financial transaction services, the Bank also provides insurance services by conducting regular campaigns to attract new customers such as car insurance based on market segmentation, which is one of the main aspects of marketing used in financial services based on demographic data. One way to analyze the market is to predict the likely target market based on the campaign's target demographic data. Therefore, this study aims to find the best classification method for predicting campaign targets using historical data from 4000 customers of a bank in the United States. The market segmentation analysis process uses the best feature selection and ensemble learning. The best feature selection is selected using important features for Random Forest. The ensemble learning used is a stacking model consisting of the basic model of Logistic Regression, Support Vector Classifier, Gradient Boosting, Extra Tree, Bagging, Adaboost, Gaussian Naive Bayes, MLP, XBoost, LGBM, KNeighbors, Decision Tree, and Random Forest. The accuracy results of the stacking model can exceed the accuracy of the basic model with an accuracy rate of 78.80%