Implementation of Data Mining to Predict Student Study Period with Decision Tree Algorithm (C4.5)
Graduating on time is what every student wants to accomplish in college. Students of Prof. Dr. Hamka Muhammadiyah University are one of those who have this dream. Based on 2020 graduates data from the Tracer Study, 60% said the university had a high enough impact on improving competence. This data...
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| Format: | Article |
| Language: | English |
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LPPM ISB Atma Luhur
2024-02-01
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| Series: | Jurnal Sisfokom |
| Subjects: | |
| Online Access: | https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/1943 |
| _version_ | 1797225863912095744 |
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| author | Kirana Alyssa Putri Dimas Febriawan Firman Noor Hasan |
| author_facet | Kirana Alyssa Putri Dimas Febriawan Firman Noor Hasan |
| author_sort | Kirana Alyssa Putri |
| collection | DOAJ |
| description | Graduating on time is what every student wants to accomplish in college. Students of Prof. Dr. Hamka Muhammadiyah University are one of those who have this dream. Based on 2020 graduates data from the Tracer Study, 60% said the university had a high enough impact on improving competence. This data indicates that university needs to evaluate improvement of academic quality. Often, students have difficulty finding information about important factors that support achieving timely graduation. A prediction analysis is needed to provide information about the student's graduation study period. For this analysis, data mining is implemented using the classification function of the decision tree (C4.5) algorithm with RapidMiner tools. The methodology for implementing data mining follows the stages of Knowledge Discovery In Database (KDD), beginning with data collection, preprocessing, transformation, data mining, and evaluation. The research findings consist of visualization and decision tree rules that reveal GPA as the most influential factor in determining a student's study period.There is other information, namely, students graduated on time (less than equal to 4 years) amounted to 170 or 54.5% and students did not graduate on time (more than 4 years) amounted to 142 or 45.6%. Testing the performance of decision tree (C4.5) utilizing confusion matrix through RapidMiner tools, resulted in accuracy reaching 83.87%, with precision of 87.50% and recall of 91.18%. Provides evidence that the decision tree algorithm (C4.5) has optimal performance to provide valuable information about predicting student graduation in order to increase student enrollment with the right study period. |
| first_indexed | 2024-03-08T00:45:45Z |
| format | Article |
| id | doaj.art-0b48ffbe5f2b4780814a7fe123b5de15 |
| institution | Directory Open Access Journal |
| issn | 2301-7988 2581-0588 |
| language | English |
| last_indexed | 2024-04-24T14:15:47Z |
| publishDate | 2024-02-01 |
| publisher | LPPM ISB Atma Luhur |
| record_format | Article |
| series | Jurnal Sisfokom |
| spelling | doaj.art-0b48ffbe5f2b4780814a7fe123b5de152024-04-03T08:40:45ZengLPPM ISB Atma LuhurJurnal Sisfokom2301-79882581-05882024-02-01131313910.32736/sisfokom.v13i1.1943835Implementation of Data Mining to Predict Student Study Period with Decision Tree Algorithm (C4.5)Kirana Alyssa Putri0Dimas Febriawan1Firman Noor Hasan2Informatics Engineering, FTII Prof. DR. Hamka Muhammadiyah UniversityInformatics Engineering, FTII Prof. DR. Hamka Muhammadiyah UniversityInformatics Engineering, FTII Prof. DR. Hamka Muhammadiyah UniversityGraduating on time is what every student wants to accomplish in college. Students of Prof. Dr. Hamka Muhammadiyah University are one of those who have this dream. Based on 2020 graduates data from the Tracer Study, 60% said the university had a high enough impact on improving competence. This data indicates that university needs to evaluate improvement of academic quality. Often, students have difficulty finding information about important factors that support achieving timely graduation. A prediction analysis is needed to provide information about the student's graduation study period. For this analysis, data mining is implemented using the classification function of the decision tree (C4.5) algorithm with RapidMiner tools. The methodology for implementing data mining follows the stages of Knowledge Discovery In Database (KDD), beginning with data collection, preprocessing, transformation, data mining, and evaluation. The research findings consist of visualization and decision tree rules that reveal GPA as the most influential factor in determining a student's study period.There is other information, namely, students graduated on time (less than equal to 4 years) amounted to 170 or 54.5% and students did not graduate on time (more than 4 years) amounted to 142 or 45.6%. Testing the performance of decision tree (C4.5) utilizing confusion matrix through RapidMiner tools, resulted in accuracy reaching 83.87%, with precision of 87.50% and recall of 91.18%. Provides evidence that the decision tree algorithm (C4.5) has optimal performance to provide valuable information about predicting student graduation in order to increase student enrollment with the right study period.https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/1943decision treec4.5 algorithmpredictionstudy periodrapidminer |
| spellingShingle | Kirana Alyssa Putri Dimas Febriawan Firman Noor Hasan Implementation of Data Mining to Predict Student Study Period with Decision Tree Algorithm (C4.5) Jurnal Sisfokom decision tree c4.5 algorithm prediction study period rapidminer |
| title | Implementation of Data Mining to Predict Student Study Period with Decision Tree Algorithm (C4.5) |
| title_full | Implementation of Data Mining to Predict Student Study Period with Decision Tree Algorithm (C4.5) |
| title_fullStr | Implementation of Data Mining to Predict Student Study Period with Decision Tree Algorithm (C4.5) |
| title_full_unstemmed | Implementation of Data Mining to Predict Student Study Period with Decision Tree Algorithm (C4.5) |
| title_short | Implementation of Data Mining to Predict Student Study Period with Decision Tree Algorithm (C4.5) |
| title_sort | implementation of data mining to predict student study period with decision tree algorithm c4 5 |
| topic | decision tree c4.5 algorithm prediction study period rapidminer |
| url | https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/1943 |
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