Prediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Algoritma C4.5 Pada Uin Syarif Hidayatullah Jakarta

Hasibuan, Tuhfatul Habibah; Mahdiana, Deni

doi:10.36080/skanika.v6i1.2976

Cited by 6 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So Naïve Bayes assumes that all features are independent of each other, and this is often a naive assumption. This algorithm is very suitable for modeling text classification, such as sentiment analysis or document classification (Hasibuan & Mahdiana, 2023).…”

Section: Algorithm C45mentioning

confidence: 99%

Comparison Of Naïve Bayes And Decision Trees In Determining The Best Manager Of Nurul Jadid Islamic Boarding School Based On Forward Selection

Dardiri

2024

SinkrOn

View full text Add to dashboard Cite

In an effort to find a solution for determining the best administrators, Islamic boarding school administrators try to determine the nominations for the best administrators using existing service data and knowledge. The process of determining nominations for the best administrators is less accurate, requiring computational methods to classify which administrators fall into the best category. In the context of data mining, classification is an important aspect. One of the classification models used is Naïve Bayes which focuses on class probability, and Decision Tree C4.5 which produces a decision tree to determine the priority of indicators that are most influential in predicting the best management. Both of these algorithms have their respective advantages. This research aims to analyze and compare the performance of the Naïve Bayes and Decision Tree classification algorithms. The comparative results of testing the Naïve Bayes and C4.5 algorithms in determining the nominations for the best administrators at the Nurul Jadid Paiton Probolinggo Islamic Boarding School on 455 administrator data tested in this study show that there is a fairly large comparison of accuracy. Naïve Bayes with Forward Selection has an accuracy rate of 91.21%, higher than Naïve Bayes itself whose accuracy results are only 87.64%. there is a difference of 3.57%. Likewise, the accuracy of C4.5 with Forward Selection has an accuracy rate of 90.99%, higher than C4.5 alone which has an accuracy rate of 90.11%. there is a difference of 0.88%. So in the comparison between 4 algorithm model trials, Naïve Bayes and Forward Selection had the most dominant accuracy with an accuracy result of 91.21%.

show abstract

Section: Algorithm C45mentioning

confidence: 99%

Comparison Of Naïve Bayes And Decision Trees In Determining The Best Manager Of Nurul Jadid Islamic Boarding School Based On Forward Selection

Dardiri

2024

SinkrOn

View full text Add to dashboard Cite

show abstract

“…Mahasiswa adalah salah satu pilar penting dalam siklus hidup suatu perguruan tinggi [11]. Salah satu indikator keberhasilan program studi dapat dilihat dari ketepatan masa studi mahasiswa [12]. Ketepatan masa studi mahasiswa merujuk pada waktu terjadwal yang harus ditempuh oleh mahasiswa dari saat mereka memulai program studi hingga lulus, sesuai dengan rentang waktu yang telah ditentukan oleh perguruan tinggi [13].…”

Section: Pendahuluanunclassified

Implementasi Algoritma Naive Bayes Classifier Menggunakan Feature Forward Selection Dalam Memprediksi Ketepatan Masa Studi Mahasiswa

Noliza Safitri

2023

JTIF

View full text Add to dashboard Cite

Students are one of the important pillars in the life cycle of a higher education institution. One indicator of the success of a study program can be seen from the accuracy of the student's study period. The accuracy of a student's study period refers to the time schedule that the student must achieve from entering the study program to graduating, according to the time span determined by the university. At the Diploma Three (D-III) level, it is said to graduate on time if you have completed the equivalent of three academic years of study and not graduate on time if you have completed more than three academic years. Study. This research aims to influence the quality of study programs, so that it is used as an assessment criterion for accreditation by (BAN-PT). The results of this research are presented in accordance with the research conducted. The data used in this research is academic data for students of the Informatics Management Study Program (D-III) Class 2020 – 2022. The categories used are gender, major, IPS 1 to IPS 4, credits, and GPA. The test results using 129 training data and 40 test data obtained an accuracy of 97.50% using the Naive Bayes method with forward selection. There were 12 students who made late predictions and 28 students made correct predictions. So it can be stated that the Naive Bayes process model is suitable for use in determining good decision results in terms of prediction and classification.

show abstract

“…Untuk memudahkan perusahaan untuk mencari faktor-faktor yang dapat meningkatkan hasil produksi dengan ini penulis melakukan penelitian dengan data mining menggunakan metode naïve bayes. [1]…”

Section: Pendahuluanunclassified

Analisa Prediksi Hasil Produksi Popok Bayi Metode Naïve Bayes

Widodo,

Fauziah,

Arwan Sulaeman

2023

bit

View full text Add to dashboard Cite

PT. Elleair Interantional Manufacturing Indonesia is a company engaged in the field of manufacturing baby diapers. With the increasing market demand causing an increase in the production process, what is often experienced is that there is often a lack of finish good product to meet consumer de mand due to delays in the production process. To make it easier for companies to look for factors that can increase production result, the authors coduct research with data mining using the naïve bayes method. In this study the training data and testing data were tested using the RapidMiner application with the naïve bayes algorithm where the tested data were 500 data. Testing is done by calculating the value of precision, recall, AUC dan accuracy using the RapidMiner Application and using Microsoft Excel and calculating the final probability of each class to calculate predictions of product result. With the naïve bayes method we can calculate predictions of production result based on data from the previus year as training data to anticipate shortages in production due to factors that can hider the production process. From the results of the analysis obtained factors that affect production result, namely, the number of materia used for the production of 318 data. The human error factor with the category of “No” as much as 305 data also influences because the less the occurrence of human error the production results are also high. Stop delivery factor with the category “No” as many as 299 data, with fewer cases of stop delivery, the more finish good product that can be sold

show abstract

Prediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Algoritma C4.5 Pada Uin Syarif Hidayatullah Jakarta

Cited by 6 publications

References 7 publications

Comparison Of Naïve Bayes And Decision Trees In Determining The Best Manager Of Nurul Jadid Islamic Boarding School Based On Forward Selection

Comparison Of Naïve Bayes And Decision Trees In Determining The Best Manager Of Nurul Jadid Islamic Boarding School Based On Forward Selection

Implementasi Algoritma Naive Bayes Classifier Menggunakan Feature Forward Selection Dalam Memprediksi Ketepatan Masa Studi Mahasiswa

Analisa Prediksi Hasil Produksi Popok Bayi Metode Naïve Bayes

Contact Info

Product

Resources

About