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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab The Indonesian Journal of Computer Science Research
Arsellina Milka Martin
Universitas Amikom Yogyakarta
Computer Science & IT Engineering

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Studi Kinerja Algoritma K-Nearest Neighbors (KNN) untuk Klasifikasi Pasien Diabetes Erni Seniwati; Edelweiss Rinjani Bawana; Peni Febrian Kristami; Arsellina Milka Martin; Ninik Tri Hartanti
The Indonesian Journal of Computer Science Research Vol. 4 No. 2 (2025): Juli
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v4i2.219

Abstract

Penyakit diabetes merupakan salah satu penyakit kronis yang jumlah penderitanya terus meningkat setiap tahun termasuk di Indonesia. Deteksi dini dan klasifikasi yang akurat sangat penting untuk membantu proses diagnosis dan penanganan yang tepat sehingga meminimalkan resiko komplikasi. Penelitian ini bertujuan untuk membangun model klasifikasi pasien diabetes serta mengkaji kinerja algoritma K-Nearest Neighbor (KNN) dalam melakukan klasifikasi pasien diabetes berdasarkan data medis. Dataset yang digunakan dalam penelitian ini adalah Pima Indians Diabetes Dataset, yang berisi informasi kesehatan pasien seperti kehamilan (Pregnancies), tingkat glukosa (Glucose), tekanan darah (Blood Pressure), kadar insulin (Insulin), nilai BMI (BMI), usia (Age) dan status diagnosa pasien (Outcome). Proses penelitian mencakup 6 tahapan kegiatan yang dilakukan. Pada penelitian ini menghasilkan parameter nilai k=8 adalah nilai k optimal. Evaluasi performa model menggunakan confusion matrix yang menghasilkan akurasi yang menghasilkan 0.83 atau 83%, presisi (0.78), recall (0.61) dan F1-score (0.69). Model juga diimplementasikan secara interaktif menggunakan Jupyter Notebook serta penggunaan Streamlit sebagai userinterface sehingga memungkinkan pengguna dapat melakukan konsultasi dengan memasukkan data medis dan sekaligus mendapatkan hasil prediksi. Hasil pengujian menunjukkan bahwa algoritma KNN mampu memberikan performa yang cukup baik dalam mengklasifikasikan pasien yang terkena diabetes dan tidak terkena diabetes.
PENERAPAN ALGORITMA NAÏVE BAYES DENGAN TEKNIK SMOTE UNTUK KLASIFIKASI SENTIMEN KURSUS ONLINE SKILL ACADEMY: APPLICATION OF THE NAÏVE BAYES ALGORITHM WITH SMOTE TECHNIQUE FOR SENTIMENT CLASSIFICATION OF SKILL ACADEMY ONLINE COURSES Arsellina Milka Martin; Arif Nur Rohman
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7545

Abstract

The rapid growth of online learning has led to the emergence of various e-learning platforms, including Skill Academy. However, not all courses are able to maintain learner engagement, partly due to discrepancies between user expectations and the quality of the provided materials. This study aims to classify user sentiment toward course titles by applying the Multinomial Naïve Bayes algorithm combined with the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Data were collected through web scraping from five main public course pages, including information on course titles, prices, ratings, number of raters, release dates, and topic categories. Sentiment labels were assigned based on rating values, where ratings ≥ 4.0 were categorized as positive and ratings < 4.0 as negative. Text feature extraction was performed using the TF-IDF method. The experimental results show that the model developed without SMOTE achieved an accuracy of 89.36% but completely failed to identify the negative class, as indicated by a recall value of 0%. After applying SMOTE to the training data, the recall for the negative class increased significantly to 64% demonstrating a substantial improvement in the model’s ability to detect previously overlooked negative sentiment. Although a slight decrease in accuracy was observed in several testing scenarios, the improvement in recall and F1-score for the minority class represents the primary contribution of this study. These findings confirm that SMOTE is effective in mitigating class imbalance and enhances sentiment analysis performance for short text data on online course platforms.
Co-Authors Arif Nur Rohman Edelweiss Rinjani Bawana Erni Seniwati NINIK TRI HARTANTI Peni Febrian Kristami