Algorithm Implementations Naïve Bayes, Random Forest. C4.5 on Online Gaming for Learning Achievement Predictions

Gata, Windu; Basri, Hasan; Hidayat, Rais; Patras, Yuyun Elizabeth; Baharuddin, Baharuddin; Fatmasari, Rhini; Tohari, Siswanto; Wardhani, Nia Kusuma

doi:10.2991/icream-18.2019.1

Cited by 4 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Disarankan bahwa algoritma Naive Bayes efektif untuk prediksi cacat perangkat lunak. Dalam sebuah penelitian oleh [23], algoritma Naive Bayes mencapai akurasi 69,18% dan nilai AUC 0,771, yang menunjukkan kinerja klasifikasinya. Implementasi ini bertujuan untuk memanfaatkan kekuatan Naive Bayes dalam prediksi cacat perangkat lunak, terutama dalam konteks dataset JM1, untuk meningkatkan akurasi dan kemampuan prediksi.…”

Section: Naive Bayes Classification Untuk Prediksi Cacat Perangkat Lunakunclassified

Naive Bayes Classification Untuk Prediksi Cacat Perangkat Lunak

Hari Agus Prastyo,

Suhartono,

Faisal

et al. 2024

jipi. jurnal. ilmiah. penelitian. dan. pembelajaran. informatik

View full text Add to dashboard Cite

Cacat perangkat lunak atau bug merupakan bagian yang tak terhindarkan dari pengembangan perangkat lunak dan dapat memiliki dampak signifikan terhadap keandalan dan kinerja aplikasi perangkat lunak. Penelitian ini bertujuan untuk meningkatkan prediksi dan pemantauan cacat perangkat lunak dalam konteks pengembangan perangkat lunak. Kestabilan sistem dan pencegahan cacat perangkat lunak menjadi hal yang sangat krusial dalam industri perangkat lunak. Untuk mencapai tujuan tersebut, berbagai tindakan proaktif seperti pengujian perangkat lunak yang ketat, pemeliharaan rutin, dan pemantauan sistem secara berkelanjutan perlu diterapkan. Masalah utama yang menjadi fokus dalam penelitian ini adalah kurangnya prediksi yang efisien terkait dengan cacat perangkat lunak selama proses pengembangan. Dalam penelitian ini, metode Naive Bayes Classification dimanfaatkan untuk mengatasi tantangan tersebut. Hasil uji coba yang dilakukan terhadap seluruh dataset menunjukkan bahwa metode Naive Bayes mampu menghasilkan klasifikasi dengan tingkat akurasi yang sangat tinggi, mencapai nilai 0,98%. Hasil ini mengindikasikan bahwa metode ini berpotensi menjadi alat yang sangat efektif dalam memprediksi cacat perangkat lunak dan mencegahnya selama proses pengembangan perangkat lunak. Selain itu, melalui analisis regresi linear, model ini memiliki nilai "Intercept" (intersepsi) sebesar -0.09359968647139849 dan koefisien "Coef" sebesar 0.00761893. "Intercept" mencerminkan titik awal atau baseline dalam prediksi model ini, sementara koefisien "Coef" mengindikasikan sejauh mana perubahan dalam variabel independen berpengaruh terhadap prediksi variabel dependen. Hasil penelitian ini memiliki dampak yang signifikan dalam konteks implementasi metode Naive Bayes untuk analisis prediksi cacat perangkat lunak, terutama dalam penggunaan bahasa pemrograman Python dengan bantuan Google Colab. Implementasi metode ini dapat membantu dalam memitigasi risiko dan meningkatkan kualitas perangkat lunak selama proses pengembangan

show abstract

Section: Naive Bayes Classification Untuk Prediksi Cacat Perangkat Lunakunclassified

Naive Bayes Classification Untuk Prediksi Cacat Perangkat Lunak

Hari Agus Prastyo,

Suhartono,

Faisal

et al. 2024

jipi. jurnal. ilmiah. penelitian. dan. pembelajaran. informatik

View full text Add to dashboard Cite

show abstract

“…The Bayes method, which uses the likelihood function as a prerequisite, is an effective machine learning technique based on data training. According to Gata et al, [13], a statistic on the classification that can be used to foretell the possibility that a group will have members is the Naive Bayes Classifier. The Bayesian classification, which is based on the Bayes theorem, is named after Thomas Bayes, a mathematician, and minister of the Presbyterian Church in the United Kingdom [14].…”

Section: Naïve Bayes Algorithmmentioning

confidence: 99%

Performance Evaluation of College Students’ Google Classroom Engagement Using Data Mining Techniques

Palmares,

Abara-Palmares,

T. Arroyo

et al. 2023

TEM Journal

View full text Add to dashboard Cite

The purpose of this study is to assess how well college students use Google Classroom as a useful and informative teaching and learning tool. The survey method was utilized in the study to measure student involvement in Google Classroom. This study's sample population included 292 college students from Northern Negros State College of Science and Technology. Algorithms such as Random Forest (RF), C4.5, and Naive Bayes (NB) were utilized with three of the most crucial techniques, such as 60% split, training set, and 8-fold cross-validation, for performing analysis on the student data. After analyzing different metrics for performance (Correctly Classified Instances, FP Rate, ROC Area, F-Measure, TP Rate, Recall, Precision, Time taken to build model, Mean Absolute Error, Root Mean Squared Error, Root Relative Squared Error, Relative Absolute Error) by various algorithms for data mining, the researchers determined which algorithm performs better than others on the student dataset gathered, allowing the researchers to make a recommendation for future improvement in students' Google Classroom engagement.

show abstract

“…There is a visible difference in accuracy between the Naïve Bayes with a random forest of 2.84%. The difference between the Naïve Bayes with C4.5 is 3.53% [17].…”

Section: Introductionmentioning

confidence: 96%

Comparison of the Performance Results of C4.5 and Random Forest Algorithm in Data Mining to Predict Childbirth Process

Muhasshanah¹,

Tohir²,

Ningsih³

et al. 2023

CommIT (Communication and Information Technology) Journal

View full text Add to dashboard Cite

Technology advancements in the world of information have made it easier for many people to process data. Data mining is a process of mining more valuable information from large data sets. The research aims to determine the difference between the C.45 and random forest algorithms in data mining to predict the childbirth process of pregnant women. It compares the accuracy of the performance results of the C4.5 and random forest algorithms to predict the delivery process for pregnant women. Then, experimental research is conducted to classify the childbirth process in Situbondo, Indonesia, by applying the C.45 and the random forest algorithm in the data mining. The decision tree J48 algorithm is used for the C4.5 algorithm in the research. Both algorithms are compared for their error classification and accuracy level. The research uses 1,000 data for training and 200 data for testing. The results show the accuracy of implementing the C4.5 and random forest algorithms with data mining using 10-fold cross-validation, generating 96% and 95% as correctly classified data. Then, the Relative Absolute Error for both algorithms has the same result. It is 15%. The C4.5 algorithm has a better result than the random forest algorithm by comparing the performance results. Further research can add more data to improve the accuracy of the analysis results by using another algorithm.

show abstract

Algorithm Implementations Naïve Bayes, Random Forest. C4.5 on Online Gaming for Learning Achievement Predictions

Cited by 4 publications

References 6 publications

Naive Bayes Classification Untuk Prediksi Cacat Perangkat Lunak

Naive Bayes Classification Untuk Prediksi Cacat Perangkat Lunak

Performance Evaluation of College Students’ Google Classroom Engagement Using Data Mining Techniques

Comparison of the Performance Results of C4.5 and Random Forest Algorithm in Data Mining to Predict Childbirth Process

Contact Info

Product

Resources

About