Estimation of Gourami Supplies Using Gradient Boosting Decision Tree Method of XGBoost

TEM Journal ◽  
2021 ◽  
pp. 144-151
Author(s):  
I Made Sukarsa ◽  
Ngakan Nyoman Pandika Pinata ◽  
Ni Kadek Dwi Rusjayanthi ◽  
Ni Wayan Wisswani
Keyword(s):  
Decision Tree ◽  
Absolute Error ◽  
Feature Model ◽  
Gradient Boosting ◽  
Percentage Error ◽  
Transaction Data ◽  
Time Period ◽  
Decision Tree Method ◽  
The Right ◽  
Tree Method

The need for food supplies are very crucial in a food business, therefore it is necessary to estimate the right supplies to maximize profit. One of the methods to determine these is by looking for patterns and forecasting transaction data. The purpose of this research is to estimate the gourami supplies using transaction data to forecast using the gradient boosting decision tree method from XGBoost. The transaction data used comes from Restaurant X with a time period from 2016 to 2019. A measurement error rate of the model is achieved by using MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error). This study tried five XGBoost models with different features such as lag, rolling window, mean encoding, and mix. The results of this study indicate that the mixed feature model produces an accuracy of 97.54% with an MAE of 0.63 and a MAPE of 2.64%.

scholarly journals Estimating Surface Downward Shortwave Radiation over China Based on the Gradient Boosting Decision Tree Method

2018 ◽  
Vol 10 (2) ◽  
pp. 185 ◽  
Author(s):  
Lu Yang ◽  
Xiaotong Zhang ◽  
Shunlin Liang ◽  
Yunjun Yao ◽  
Kun Jia ◽  
...  
Keyword(s):  
Decision Tree ◽  
Shortwave Radiation ◽  
Gradient Boosting ◽  
Downward Shortwave Radiation ◽  
Decision Tree Method ◽  
Tree Method

scholarly journals Implementation of Data Mining Using C4.5 Algorithm for Predicting Customer Loyalty of PT. Pegadaian (Persero) Pati Area Office

2021 ◽  
Vol 2 (3) ◽  
pp. 64-68
Author(s):  
Ridlo Muttaqien ◽  
Musthofa Galih Pradana ◽  
Andri Pramuntadi
Keyword(s):  
Decision Tree ◽  
Customer Loyalty ◽  
Influential Factors ◽  
Marketing Strategies ◽  
Management Information ◽  
Experimental Application ◽  
C4.5 Algorithm ◽  
Decision Tree Method ◽  
The Right ◽  
Tree Method

PT Pegadaian (Persero) is engaged in the business of providing credit services with pawn, non-pawning and gold investment products. One of the right marketing strategies to survive today's high competition is to maintain customer loyalty, researchers use several data variables available in the MIS (Management Information System) in the form of customer transaction frequency, how many products are taken by customers, customer satisfaction and direct interviews. to predict customer loyalty of PT Pegadaian (Persero) by implementing the c4.5 algorithm. The c4.5 algorithm is the algorithm used to create a decision tree. Decision trees are a very powerful and well-known method of classification and prediction. The decision tree method converts very large facts into a decision tree that represents the rule. Rules can be easily understood in natural language. This study aims to determine the accuracy of the C4.5 algorithm to predict customer loyalty of PT Pegadaian (Persero) and the most influential factors in loyalty. The results of the experimental application of the c4.5 algorithm show that the level of accuracy generated in predicting customer loyalty is quite high, namely 89.94% in data testing 1 and 94% in data testing 2. The application of the c4.5 algorithm in predicting customer loyalty of PT Pegadaian (Persero) can be well applied.

scholarly journals Decision Tree dalam Menganalisis Data Pengunjung Wisata Danau Poso untuk Pengambilan Keputusan

2021 ◽  
Vol 2 (3) ◽  
pp. 261
Author(s):  
Fredryc Joshua Pa'o ◽  
Hendry Hendry
Keyword(s):  
Decision Tree ◽  
Classification System ◽  
Leaf Node ◽  
Test Results ◽  
Class Distribution ◽  
C4.5 Algorithm ◽  
Decision Tree Method ◽  
The Right ◽  
Data Transforming ◽  
Tree Method

This study uses a classification system in managing its data. In classification there are several methods provided, one of which is the decision tree method with the C4.5 algorithm this method means a decision tree where the structure is the same as a flowchart where each node signifies an attribute test, each branch presents the test results and the leaf node represents the class or class distribution. The data used is the data of Lake Poso Tourism visitors from 2009 to 2020, then the method used in this study is divided into several stages, namely the data being studied, analyzing the data, transforming data and designing a decision tree with the C4.5 algorithm. The results achieved from this study are that the number of visitors more than 28,984 has a description of "Much" which is dominated by local tourists, while the value with the name "Less" is in foreign tourists. This is one of the important points in determining the right strategy for developing tourism in Lake Poso.

Rancang Bangun Aplikasi Sistem Pakar untuk Menangani Masalah Kecantikan pada Wajah Menggunakan Metode Decision Tree

2014 ◽  
Vol 6 (1) ◽  
pp. 9-14
Author(s):  
Stefanie Sirapanji ◽  
Seng Hansun
Keyword(s):  
Decision Tree ◽  
Uneven Distribution ◽  
Dry Skin ◽  
Average Accuracy ◽  
Accuracy Level ◽  
Index Terms ◽  
Decision Tree Method ◽  
Tree Method

Beauty is a precious asset for everyone. Everyone wants to have a healthy face. Unfortunately, there are always those problems that pops out on its own. For example, acnes, freckles, wrinkles, dull, oily and dry skin. Therefore, nowadays, there are a lot of beauty clinics available to help those who wants to solve their beauty troubles. But, not everyone can enjoy the facilities of those beauty clinics, for example those in the suburbs. The uneven distribution of doctors and the expensive cost of treatments are some of the reasons. In this research, the system that could help the patients to find the solution of their beauty problems is built. The decision tree method is used to take decision based on the shown schematic. Based on the system’s experiment, the average accuracy level hits 100%. Index Terms–Acnes, Decision Tree, Dry Skin, Dull, Facial Problems, Freckles, Wrinkles, Oily Skin, Eexpert System.

Marketing Model Investigation of Cable Television Service Based on BP Neural Network and Decision Tree

2013 ◽  
Vol 774-776 ◽  
pp. 1757-1761
Author(s):  
Bing Xiang Liu ◽  
Xu Dong Wu ◽  
Ying Xi Li ◽  
Xie Wei Wang
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Bp Neural Network ◽  
Cable Television ◽  
Model Building ◽  
Television System ◽  
Marketing Model ◽  
Television Service ◽  
Decision Tree Method ◽  
Tree Method

This paper takes more than four hundred records of some cable television system for example, makes data mining according to users data record, uses BP neural network and decision tree method respectively to have model building and finds the best model fits for users to order press service. The results of the experiment validate the methods feasibility and validity.

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