Classification of Favorite Book Borrowing Data at the STIKOM CKI Library Using the Decision Tree Algorithm
DOI:
https://doi.org/10.55606/jeei.v2i1.3663Keywords:
Classification, Decision Tree Algorithm, Favorite Books, Library Information System, Data ProcessingAbstract
The library on the STIKOM CKI campus as a means of providing information and has a complete collection of learning media books, but the data processing system for borrowing and returning favorite books in the library is currently still manual, that is, all data collection processes are written on book cards, although it is quite good but the process is rather slow and requires quite a long time because in the process of searching the data must be checked per page one by one so that the data processing is less effective and efficient. To overcome this, it is necessary to develop an application using the decision tree algorithm method which can make it easier to collect borrowing data and return favorite books that are more effective and efficient and display integrated output of student reports that have not returned so that data processing is more accurate and can speed up officer performance. library. Submitting a favorite book lending classification application can make it easier to access loans and returns anywhere and anytime. So that data processing is more accurate and can speed up librarian performance.
References
1. Sutoyo, I. (2018). Implementation of Decision Tree Algorithm for Student Data Classification. PILAR Nusa Mandiri , *14*(2), 217–224.
2. Nasrullah, AH (2021). Implementation of the Decision Tree Algorithm for Classifying Best-Selling Products. Scientific Journal of Computer Science , *7*(2), 45–51.
3. Ahmad Zam Zami, ONGD (2022). Classification of Nutritional Conditions of Infants Under Five Years Old at the Melati Integrated Health Post Using the Decision Tree Algorithm. Journal of Computer Systems and Informatics (JSON) , *3*(3), 305–310.
4. Maulidah, M., Gunawan, W., & Chandra, RA (2020). Decision Tree Classification Algorithm for Book Recommendations Based on Book Categories. JOURNAL OF ECONOMICS AND BUSINESS SCIENTIFIC , *13*(2), 89–96.
5. Qadrini, L., Sirajuddin, A., & Aulia, A. (2021). Decision Tree and Adaboost in Classifying Recipients of Social Assistance Programs. Journal of Research Innovation , *2*(17), 1959–1965.
6. Irawan, Y. (2021). Application of the C4.5 Decision Tree Algorithm to Predict the Eligibility of Prospective Donors to Donate Blood Using Data Mining Classification. JTIM: Journal of Information Technology and Multimedia , *2*(4), 181–189.
7. Pamungkas, YW, & Qomariyah, AD (2017). Snake Bite Image Classification Using Regionprops and Decision Tree Algorithm. Journal Speed Center for Engineering and Education Research , *9*(2), 1–7.
8. Robianto, SH, & Ramadhan, UR (2021). Application of the Decision Tree Method to Classify Orange Fruit Quality Based on Color and Size Features. Journal of Computers and Applications , *9*(1), 76–86.
9. Nursyahfitri, R., Maulana, AN, & Firdaus, YU (2021). Classification of Drug Type Determination Using Decision Tree Algorithm. JIP (Jurnal Informatika Polinema) , *7*(3), 53–60.
10. Sang, AI, & Syafrudin, E. (2021). Data Mining Analysis for Classification of DKI Jakarta Air Quality Data Using Decision Tree Algorithms and Support Vector Machines. e-Proceeding of Engineering , *8*(5), 8954–8963.
11. Pambudi, RE, & Fadlilah, S. (2020). Classification of Stroke Disease Using Decision Tree C Algorithm. 45. JOURNAL OF TECHNIQUES , *16*(2), 221–226.
12. Pane, FN (2020). Design and Construction of a Web-Based Kraepelin Test Application Using the Bubble Sort Method. JOINTECS (Journal of Information Technology and Computer Science) , *5*(1), 41–48.
13. Yulita, L., Suryadi, A., & Ningsih, S. (2022). Genetic Algorithm Optimization in Predicting Student Reading Interest at the SMK Negeri 1 Gantar Library Using the Decision Tree Method. Journal of Practical Computer Science (JPCS) , *2*(1), 15–23.
14. Rahmianti, I. (2022). Analysis of the Feasibility of Providing Cooperative Credit Using the Decision Tree Data Mining Method. JIRE (Journal of Informatics & Electronic Engineering) , *5*(2), 153–161.
15. Partogi, Y. (2021). Designing the Decision Tree Method for the STMIK Kuwera Library System. JOURNAL OF INFORMATION SYSTEMS AND TECHNOLOGY (SINTEK) , *1*(2), 20–26.
16. Santoso, P., & Sari, R. (2020). Application of Decision Tree Classification Method and C4.5 Algorithm in Predicting Mega Auto Finance Credit Customer Criteria. JURIKOM (Journal of Computer Research) , *7*(2), 200–206.
17. Gunawan, I., Santoso, S., & Himawan, T. (2019). The Use of Bubble Sort Algorithm to Determine Student Achievement Grades. SISTEMASI (Journal of Information Systems) , *8*(2), 296–304.
18. Hana, FM (2020). Classification of Diabetes Patients Using the C4 Decision Tree Algorithm. 5. Journal of Computer Systems and Artificial Intelligence , *4*(1), 32–39.
19. Cynthia, EP, & Iskandar, E. (2018). Implementation of the Bubble Sort Algorithm in Library Systems Using the CodeIgniter Framework. Journal of Information Systems and Informatics Engineering Research (JURASIK) , *3*, 1–13.
20. Orpa, EPK, Fendrik, E., & Tinus, R. (2019). Prediction Model for the Initial Study Period of Students Using the Decision Tree C4 Algorithm. 5. JUSTIN (Journal of Information Systems and Technology) , *7*(4), 272–278.
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