C4.5 Classification Data Mining for Inventory Control

Data Mining is a process of exploring against large data to find patterns in decision making. One of the techniques in decision-making is classification. Classification is a technique in data mining by applying decision tree method to form data, algorithm C4.5 is algorithm that can be used to classify data in tree form. The system has been built that shows the results of good performance and minimal error in view of the system that is able to distinguish the anomaly traffic with normal traffic. Data mining inventory system applications can facilitate the control of inventory in the company to reduce production costs.

[1]  K. Sreenivasa Rao,et al.  Educational data mining for student placement prediction using machine learning algorithms , 2017 .

[2]  Iskandar Zulkarnain,et al.  Double hashing technique in closed hashing search process , 2017 .

[3]  S AlexDavid,et al.  Study of high yielding crops cultivation in India using data mining techniques , 2018 .

[4]  E P EPhzibah,et al.  Big data management with machine learning inscribed by domain knowledge for health care , 2017 .

[5]  Amutha Prabakar Muniyandi,et al.  Network Anomaly Detection by Cascading K-Means Clustering and C4.5 Decision Tree algorithm , 2012 .

[6]  G Nivedhitha,et al.  Data mining in personalized service of digital library , 2018 .

[7]  Ansari Saleh Ahmar,et al.  Forecasting of primary energy consumption data in the United States: A comparison between ARIMA and Holter-Winters models , 2017 .

[8]  Kasra Madadipouya,et al.  A Survey on Data Mining Algorithms and Techniques in Medicine , 2017 .

[9]  Andri Pranolo,et al.  Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO) , 2018 .

[10]  Iskandar Zulkarnain,et al.  A review: search visualization with Knuth Morris Pratt algorithm , 2017 .

[11]  Hemant Kumar Singh,et al.  Web Data Mining research: A survey , 2010, 2010 IEEE International Conference on Computational Intelligence and Computing Research.

[12]  Sachin Sharma,et al.  A study of frequent itemset mining techniques , 2017 .

[13]  Robbi Rahim,et al.  Impact of GDP Information Technology in Developing of Regional Central Business (Case 50 Airports IT City Development in Indonesia) , 2017 .

[14]  Robbi Rahim,et al.  Data Collision Prevention with Overflow Hashing Technique in Closed Hash Searching Process , 2017 .

[15]  A. Ahmar A Comparison of α-Sutte Indicator and ARIMA Methods in Renewable Energy Forecasting in Indonesia , 2018 .

[16]  Ansari Saleh Ahmar,et al.  Predicting movement of stock of “Y” using Sutte Indicator , 2017 .

[17]  Robbi Rahim,et al.  Research of Simple Multi-Attribute Rating Technique for Decision Support , 2017 .

[18]  Ping He,et al.  Fast C4.5 , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[19]  Mohammed Erritali,et al.  A comparative study of decision tree ID3 and C4.5 , 2014 .

[20]  Yu Zheng,et al.  Trajectory Data Mining , 2015, ACM Trans. Intell. Syst. Technol..

[21]  Robbi Rahim,et al.  Comparative Analysis of Membership Function on Mamdani Fuzzy Inference System for Decision Making , 2017 .

[22]  Natalia Silalahi,et al.  C4.5 Algorithm to Predict the Impact of the Earthquake , 2017 .