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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Malcom: Indonesian Journal of Machine Learning and Computer Science
Farida Try Puspa Siregar
STMIK Amik Riau
Computer Science & IT

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Komparasi Algoritma K-NN, Naive Bayes dan SVM untuk Prediksi Kelulusan Mahasiswa Tingkat Akhir: Comparison of K-NN, Naive Bayes and SVM Algorithms for Final-Year Student Graduation Prediction Aulia Putri; Cindy Syaficha Hardiana; Elma Novfuja; Farida Try Puspa Siregar; Rahmaddeni Rahmaddeni; Yulia Fatma; Refni Wahyuni
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 3 No. 1 (2023): MALCOM April 2023
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v3i1.610

Abstract

Mahasiswa tingkat akhir adalah seorang pelajar yang sedang berjuang demi mendapat gelar sarjana dan memilih tujuan hidup dengan tugas yang baru seperti pekerjaan yang sesuai dengan minat dan bakatnya. Untuk mendapatkan tingkat kelulusan dengan baik dan tepat waktu. Mahasiswa sangat bergantung pada pengaruh dari faktor dalam dan luar kampus. Pemilihan dan penentuan data yang digunakan, diambil dari data publik. Dengan 379 orang mahasiswa tahap akhir sebagai responden. Pengujian ini membandingkan algoritma K-NN, NBC, dan SVM yang lebih baik menyelesaikan masalah terkait prediksi tingkat kelulusan mahasiswa pascasarjana. Berdasarkan perbandingan algoritma tersebut dengan teknik splitting data, didapatkan bahwa Algoritma K-NN (K-Nearest Neighbor) memiliki rata-rata lebih tinggi dibandingkan (NBC) Naïve Bayes Classifier dan SVM (Support Vector Machine) untuk prediksi kelulusan mahasiswa tingkat akhir dengan akurasi 87,8%, presisi 87,8%, dan recall 84%.
Evaluation of Support Vector Machine, Naive Bayes, Decision Tree, and Gradient Boosting Algorithms for Sentiment Analysis on ChatGPT Twitter Dataset Salsabila Rabbani; Dea Safitri; Farida Try Puspa Siregar; Rahmaddeni Rahmaddeni; Lusiana Efrizoni
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.24662

Abstract

ChatGPT is a language model employed to produce text and engage in conversation with users. It serves as a tool for generating text and facilitating interactions in a conversational manner. The model was designed to provide relevant and useful responses based on the context of the ongoing conversation. By the increasing popularity of using ChatGPT, it makes it difficult for users to classify responses about the use of ChatGPT. Therefore, sentiment classification of ChatGPT is carried out. The dataset used is sourced from the kaggle website with a total of 20,000 data. The classification methods used in this research include Support Vector Machine (SVM), Naïve Bayes, Decision Tree, and Gradient Boosting. Through the research results, the Support Vector Machine algorithm had the highest accuracy value with 80% compared to other methods, when the data is divided by a ratio of 90:10. This research is expected to help developers and service providers to improve ChatGPT and understand user responses better.
Co-Authors Aulia Putri Cindy Syaficha Hardiana Dea Safitri Efrizoni, Lusiana Elma Novfuja Rahmaddeni Rahmaddeni Refni Wahyuni Salsabila Rabbani Yulia Fatma