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All Journal Jurnal Online Mahasiswa (JOM) Bidang Matematika dan Ilmu Pengetahuan Alam Journal of Intelligent Systems Ensiklopedia of Journal Jurnal Riset Informatika Indonesian Journal of Business Intelligence (IJUBI) Jurnal Peduli Masyarakat Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Melek IT: Information Technology Journal Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Journal of Accounting Information System TRIAGE Journal of Computer Science Contributions (Jucosco) Research Journal of Social Economics Empowerment (ROSEE)
Indah Suryani
STMIK Nusa Mandiri
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Jurnal Riset Informatika

 1 
Comparison of Decision Tree, Naive Bayes and Random Forest Algorithm to get the Best Performance of Algorithm for Customer Credit Classification Suryani, Indah
Jurnal Riset Informatika Vol. 6 No. 3 (2024): June 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (818.138 KB)

Abstract

Credit is a potential income and the most significant business operation risk for a bank. Bad credit has become an ingrained problem in the banking world. Therefore, this research aims to classify customer data profiles who have the opportunity to be able to apply for a loan or not to reduce the risk of bad credit in the future by classifying using three commonly used data mining algorithms, namely the Decision Tree algorithm, Naïve Bayes and Random forest. The research was conducted using an experimental, descriptive method by testing the accuracy of the three methods to get the best performance. Based on the experiments' results, the accuracy performance with the confusion matrix was 73.20% for the Decision Tree algorithm, then the accuracy for the Naive Bayes algorithm was 74.4% and Random Forest was 77.4%. Meanwhile, performance evaluation is based on the Receiver Operating Characteristics (ROC) curve by looking at the resulting Area Under Curve (AUC) value of 0.717 for the Decision Tree algorithm, while Naive Bayes produces an AUC value of 0.741 and the largest is Random Forest at 0.796. So it can be concluded that the best performance of the classification carried out is the one that uses the Random Forest algorithm. Then, from the validation results using the T-Test of the three methods being compared, Random Forest produces a significant difference in the level of accuracy compared to the accuracy produced by the Decision Tree, namely with an alpha value of 0.031.
Implementation of Machine Learning Algorithms for Early Detection of Cervical Cancer Based on Behavioral Determinants Buani, Duwi Cahya Putri; Suryani, Indah
Jurnal Riset Informatika Vol. 5 No. 1 (2022): December 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (981.068 KB) | DOI: 10.34288/jri.v5i1.167

Abstract

Cervical cancer is a disease that affects women and has the highest mortality rate after breast cancer. Early detection of cervical cancer is critical at this time, so cervical cancer patients are decreasing. Many women, especially in Indonesia, are less concerned about the dangers of cervical cancer, even though if detected earlier, this disease will be easier to treat. One alternative for early detection can use machine learning algorithms. The machine learning algorithms used in this study are Naïve Bayes (NB), Logistic Regression (LR), Decision Tree (DT), SVM, and Random Forest. In this study, a random under-sampling method was employed, which had no uses in any prior research. This technique makes the accuracy of the five algorithms even better. The research results show that NB has an accuracy rate of 91.67%, LR has an accuracy rate of 87.5%, DT has an accuracy rate of 81.81%, SVM has an accuracy rate of 75%, and RF has the highest accuracy rate of 94.45%. This research shows that the best model is RF or Random Forest.
Co-Authors ', Mashadi A Gunawan Ade Christian Al Adzani, Muhammad Raulfie Anggraeini, Sita Arsa Pratama, Rakha Aslina, Aslina Aslina, Aslina Deppagoga, Wilson Benedict Duwi Cahya Putri Buani Endriana, E. Fina Ericka Darmawan Feri Catur Yuliani Fitri Latifah, Fitri Hani Harafani, Hani Herfan, Herfan Ibnu Rusdi Indah Purnamasari Lukman Hakim M. Natsir Maeza, Al Thorik Marsyah, Ulfy Muhammad Faisal Muhammad Ismail Muhammad Ridwan Nurkhayati, Sri Nurullah Sururi Afif Nurwahyuni Omar Pahlevi Perdana, Muhammad Rizky Priatna, Nico Purnomo, Azis Putri Samuel, Valencia Cornelia Raditya, Muhammad Rahma, Amelia Rahman, Muhammad Reynaldi, Faiz Octa Rizqul Mubarok, Salman Romi Satria Wahono Sandra Dewi Saraswati Supriyadi Supriyadi Suryani, Ulfa Tri Wahyono, Tri Ummu Radiyah, Ummu Verra Sofica Yazia, Velga Yeni Rusyani