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Journal : Borobudur Informatics Review

Using Data Mining with C4.5 Algorithm for Student Department Selection at MTs N Kaliangkrik Ardhin Primadewi; Faruq Ardana Kurniawan; Emilya Ully Artha
Borobudur Informatics Review Vol 1 No 1 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/binr.4989

Abstract

Psychological tests can determine the characteristics of behavior, personality, attitudes, interests, motivation, attention, perceptions, thinking power, intelligence, fantasies of students. MTs N Kaliangkrik routinely conducts tests for the selection of majors on its students assisted by Pelita Harapan Bangsa Magelang. In the implementation of the test for students at MTs N Kaliangkrik, processing and calculating the score still used Ms. Excel which requires extra time to recap and know the test results and the school needs to recap the existing results. The system developed applies data mining using the C4.5 Algorithm to predict the selection of majors. The test that is used as system input is the grade IX test score of MTs N Kaliangkrik which includes verbal, non-verbal, general intelligence, language knowledge, definite knowledge, general knowledge, and qualitative power tests. The accuracy of the similarity in the system reaches 80% (good) so that the system is suitable for use as a prediction tool for selecting majors in other schools.
Prediction of material requirements for network construction using apriori algorithm Layli Nur'Aini; Mukhtar Hanafi; Emilya Ully Artha
Borobudur Informatics Review Vol 2 No 1 (2022): Vol 2 No 1 (2022)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/binr.5824

Abstract

PT XYZ is one of the providers of fixed broadband development services spread throughout Indonesia. What often happens is the lack of material availability and the long pre-order process, thus hampering projects that have been agreed upon for completion. The a priori algorithm is one of the data mining algorithms in the association method, which is looking for relationships between interrelated items. This research uses the help of RStudio software. This prediction is expected to help to prepare material stock in the warehouse so as to prevent material vacancies in the warehouse. The results of this study are in the form of a rule that meets the minimum support value (a measure that shows how much dominance an item/item set is from the entire transaction) of 27.84%, minimum confident (a measure that shows the relationship between 2 items conditionally) of 27.84%. 84.48% and the lift ratio (a measure to see whether or not the association rules are formed) is > 1.