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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Abdi Insani Journal of Innovation Information Technology and Application (JINITA) Journal of Computer and Information System (J-CIS) SainsTech Innovation Journal Jurnal Ilmiah Edutic : Pendidikan dan Informatika IJMST SainsTech Innovation Journal
Ahmad Fatoni Dwi Putra
Universitas Qamarul Huda Badaruddin Bagu
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics

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Performance Analysis of K-Nearest Neighbors and Naive Bayes Algorithms in Stunting Risk Classification in Toddlers Using Public Dataset Nurhikmayani; Syahrani Lonang; Ahmad Fatoni Dwi Putra
SainsTech Innovation Journal Vol. 9 No. 1 (2026): SIJ VOLUME 9 NOMOR 1 TAHUN 2026
Publisher : LPPM Universitas Qamarul Huda Badaruddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37824/sij.v9i1.2026.1354

Abstract

Stunting is a chronic nutritional problem in toddlers that can affect physical growth, cognitive development, and children's health quality in the future. This study aims to analyze and compare the performance of the K-Nearest Neighbors (KNN) and Naïve Bayes algorithms in classifying stunting risk in toddlers using a public dataset from Kaggle. The research was conducted through several stages, including data preprocessing, data cleaning, normalization using the Min-Max method, data balancing using SMOTE-ENN, splitting training and testing data, and parameter optimization using Grid Search. Model evaluation was carried out using a Confusion Matrix with accuracy, precision, Recall, F1-score, ROC Curve, and AUC Score metrics. The results showed that the KNN algorithm performed better than the Naïve Bayes algorithm in classifying stunting risk. The KNN algorithm produced higher accuracy, precision, Recall, and F1-score values, as well as more optimal ROC-AUC values for each classification class. Based on these evaluation results, the KNN algorithm was considered more effective and stable in detecting stunting risk in toddlers compared to the Naïve Bayes algorithm. Therefore, the KNN algorithm can be used as an effective method to support early stunting risk detection based on Machine Learning.
Performance Analysis of K-Nearest Neighbors and Naive Bayes Algorithms in Stunting Risk Classification in Toddlers Using Public Dataset Nurhikmayani; Syahrani Lonang; Ahmad Fatoni Dwi Putra
SainsTech Innovation Journal Vol. 9 No. 1 (2026): SIJ VOLUME 9 NOMOR 1 TAHUN 2026
Publisher : LPPM Universitas Qamarul Huda Badaruddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37824/sij.v9i1.2026.1354

Abstract

Stunting is a chronic nutritional problem in toddlers that can affect physical growth, cognitive development, and children's health quality in the future. This study aims to analyze and compare the performance of the K-Nearest Neighbors (KNN) and Naïve Bayes algorithms in classifying stunting risk in toddlers using a public dataset from Kaggle. The research was conducted through several stages, including data preprocessing, data cleaning, normalization using the Min-Max method, data balancing using SMOTE-ENN, splitting training and testing data, and parameter optimization using Grid Search. Model evaluation was carried out using a Confusion Matrix with accuracy, precision, Recall, F1-score, ROC Curve, and AUC Score metrics. The results showed that the KNN algorithm performed better than the Naïve Bayes algorithm in classifying stunting risk. The KNN algorithm produced higher accuracy, precision, Recall, and F1-score values, as well as more optimal ROC-AUC values for each classification class. Based on these evaluation results, the KNN algorithm was considered more effective and stable in detecting stunting risk in toddlers compared to the Naïve Bayes algorithm. Therefore, the KNN algorithm can be used as an effective method to support early stunting risk detection based on Machine Learning.
Sentiment Analysis of Positive and Negative User Reviews for TikTok and YouTube Applications on the Google Play Store Using Naïve Bayes and Support Vector Machine (SVM) Algorithms RAKYATOL HASANAH; Syahrani Lonang; Ahmad Fatoni Dwi Putra
SainsTech Innovation Journal Vol. 9 No. 1 (2026): SIJ VOLUME 9 NOMOR 1 TAHUN 2026
Publisher : LPPM Universitas Qamarul Huda Badaruddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37824/sij.v9i1.2026.1358

Abstract

The development of digital technology has increased the use of social media applications such as TikTok and YouTube, resulting in a large number of user reviews on the Google Play Store. These reviews can be utilized to determine user satisfaction through sentiment analysis. This study aims to analyze the sentiment of user reviews on TikTok and YouTube applications using the Naïve Bayes and Support Vector Machine (SVM) algorithms, as well as to compare the performance of both algorithms. The research data were obtained through a web scraping process consisting of 20,000 reviews, including 10,000 TikTok reviews and 10,000 YouTube reviews. The data then underwent preprocessing, sentiment labeling, splitting into training and testing datasets, and classification using the Naïve Bayes and Support Vector Machine (SVM) algorithms. Model evaluation was conducted using a confusion matrix with accuracy, precision, recall, and F1-score as evaluation metrics. The results showed that the Naïve Bayes algorithm outperformed SVM. For the TikTok application, Naïve Bayes achieved an accuracy of 80.30%, precision of 80.20%, recall of 80.30%, and F1-score of 80.20%, while SVM achieved an accuracy of 78.70%, precision of 78.60%, recall of 78.70%, and F1-score of 78.50%. For the YouTube application, Naïve Bayes achieved an accuracy of 78.40%, precision of 78.00%, recall of 78.40%, and F1-score of 77.90%, while SVM achieved an accuracy of 77.50%, precision of 77.20%, recall of 77.50%, and F1-score of 76.70%. Based on these results, the Naïve Bayes algorithm demonstrated better performance in classifying user review sentiments on TikTok and YouTube applications.
Co-Authors Adipta, Marazaenal Afriansyah, M. Anggarista, Randa Arsan Kumala Jaya Asmaul Husna RS Asmaul Husna RS Asno Azzawagama Firdaus G. Tangaro, Diana May Glaiza Grancho, Bernadine Hadi San, Ahmad Syahrul Heri Wijayanto I Gede Putu Wirarama Wedashwara Wirawan Joni Saputra Joni Saputra Kumoro, Danang Tejo Mindi Richia Putri Mindi Richia Putri Muhajir Yunus Muhamad Nizam Azmi Muhamad Yamin Muhammad Ari Rifqi Muhammad Ari Rifqi Nuraqilla Waidha Bintang Grendis Nurhikmayani Parhanudin Parhan Pratama, Ramadhana Agung RAKYATOL HASANAH Sa'adati, Yuan Satria Utama Satrio Budi Santoso Sumarno Wijaya Sumiati Syahrani Lonang Syaidatussalihah Syuhada, Fahmi Syuhada, Fahmi Syuhada Talirongan, Florence Jean B. Yuan Sa'adati