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All Journal Jurnal Informatika Global Indo-MathEdu Intellectuals Journal
Putri, Basmallah Ramadhani Aisyah
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Computer Science & IT Mathematics

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PENERAPAN DAN PERBANDINGAN ALGORITMA NAÏVE BAYES DAN K-NEAREST NEIGHBOR DALAM ANALISIS SENTIMEN TERHADAP KEPUASAN PENGGUNA APLIKASI FLO Putri, Basmallah Ramadhani Aisyah; Fitrianti
Jurnal Ilmiah Informatika Global Vol. 16 No. 2: August 2025
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v16i2.5471

Abstract

Digital technology is developing rapidly and has a wide and significant impact on the health sector, including through the presence of health monitoring applications such as the Flo application. This application is designed to help women track their menstrual cycles, fertile periods, and pregnancy. As an application that is personal and used routinely, user satisfaction is an important factor that determines the quality and sustainability of services. Sentiment analysis is needed to explore user views and preferences for this application. This study aims to analyze 18215 user reviews of the Flo application from the Google Play Store to classify sentiment, using web scraping techniques as a data retrieval method. Naïve Bayes and K-Nearest Neighbor are used as classification algorithms in data analysis. Data are analyzed through several stages, namely preprocessing, sentiment classification, model evaluation, and interpretation of results. The results showed that 93.1% of reviews were positive and 6.9% of reviews were negative. In terms of performance, the Naïve Bayes algorithm showed the best results with an accuracy value of 99%, precision 100%, recall 98%, and f-measure 99%, and without False Positive errors. Meanwhile, the K-Nearest Neighbor algorithm obtained an accuracy of 95%, precision of 97%, recall of 90%, and f-measure of 93%. The results of the study showed that the Naïve Bayes algorithm was more effective in analyzing the sentiment of Flo application user reviews.
Klasifikasi Tingkat Prestasi Mahasiswa Pada Mata Kuliah Penambangan Data Menggunakan Naïve Bayes Putri, Basmallah Ramadhani Aisyah; Mursidan, Almurozy; Wardhana, Indra Sari Kusuma
Indo-MathEdu Intellectuals Journal Vol. 7 No. 1 (2026): Indo-MathEdu Intellectuals Journal
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/imeij.v7i1.5050

Abstract

This study aims to classify the academic achievement levels of students in Data Mining courses using the Naïve Bayes algorithm and to evaluate the performance of the resulting classification model. This study uses a quantitative approach with academic data from 190 students, including assignment scores, mid-term exam scores, and final exam scores. The classification process was carried out by applying the Naïve Bayes algorithm, while model evaluation was performed using accuracy metrics, classification reports, and confusion matrices. The test results showed that the Naïve Bayes model produced an accuracy rate of 73.68%. Based on the classification report, classes B and B+ showed the best performance with recall values of 1.00 and f1-scores of 0.87 and 0.95, respectively. Confusion matrix analysis showed that most of the data in classes B and B+ were classified correctly. The results of this study indicate that the Naïve Bayes algorithm is quite effective in classifying students' academic achievement levels and has the potential to be used as an academic evaluation tool in learning decision-making.
Co-Authors Almurozy Mursidan FITRIANTI Wardhana, Indra Sari Kusuma