Algoritma Klasifikasi Naive Bayes Dan Support Vector Machine Dalam Layanan Komplain Mahasiswa

Abstract

Service in the world of education is an important element for the creation of an academic atmosphere that is conducive to the implementation of a successful teaching and learning process. The process of service to students there is a tendency to be implemented not following the minimum service standards that must be provided to students so that students tend to complain about the services provided. Submission of criticism, complaints, input, or suggestions for dissatisfaction and problems that exist in the university environment is still very limited. Complaints can be constructive if submitted to the right place and party. In this research the data processing of email complaints from students conducted at the academic student body (students.bsi.ac.id). Student complaint data that will be processed is data in the form of * .xls complaint file. Before text data is analyzed using text mining methods, the pre-processing text needs to be done including tokenizing, case folding, stopwords, and stemming. After pre-processing, the classification method is then performed in classifying each complaint category and dividing the status into two parts, namely complaint and not complaint so that the status becomes a normal condition in text mining research. The purpose of this study is to obtain the most accurate algorithm in the classification of student complaints and can find out the results of the classification of the Naïve Bayes algorithm method and Support vector Machine used and compared. In this study, the results of testing by measuring the performance of these two algorithms using Cross-Validation, Confusion Matrix, and ROC Curves. The obtained Support vector Machine algorithm has the highest accuracy value compared to Naïve Bayes. AUC value = 0.922. for the Support vector machine method using the student academic data collection dataset (students.bsi.ac.id) has 84.45%, from the Naïve Bayes algorithm has an accuracy rate of about 69.75% and AUC value = 0.679.

Analisis Sentimen Mahasiswa terhadap Kuesioner Penilaian Pelayanan Sekretariat FTKI Universitas Nasional dengan menggunakan Metode Simple Multi Attribute Rating Technique (SMART) dan Simple Additive Weighting (SAW)

“…Untuk meningkatkan pelayanan maka diperlukan penilaian dengan melakukan analisis sentimen untuk mengetahui respon mahasiswa agar dapat dijadikan evaluasi untuk staff sekretariat ftki. Pada jurnal tentang Algoritma Klasifikasi Naive Bayes Dan Support Vector Machine Dalam Layanan Komplain Mahasiswa dapat disimpulkan bahwa hasil tingkat akurasi yang didapatkan algoritma Support Vector Machine lebih unggul yaitu 84,45% dibanding dengan Naive Bayes 69.75% [1].…”

Analisis Sentimen Zoom Cloud Meetings di Play Store Menggunakan Naïve Bayes dan Support Vector Machine

“…Kelebihan dari Support Vector Machine (SVM) adalah : mempunyai kemampuan generalisasi yang tinggi, mampu menghasilkan model klasifikasi yang baik meskipun dilatih dengan himpunan data yang relatif sedikit hanya dengan pengaturan parameter yang sederhana, SVM memiliki konsep dan formulasi yang jelas dengan sedikit parameter yang harus diatur, relatif mudah diimplementasikan karena penentuan SVM dapat dirumuskan dalam masalah QP (Quadratic Programming) [13].…”

Analysis of User Reviews for the PeduliLindungi Application on Google Play Using the Support Vector Machine and Naive Bayes Algorithm Based on Particle Swarm Optimization

“…The important purpose of text mining is to get highquality information from text. This is usually done by extracting knowledge and information from patterns in text documents [15]. It is also done through finding patterns and trends in ways such as statistical pattern learning, topic, and statistical language modeling.…”