Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal Building of Informatics, Technology and Science
Nirwana Hendrastuty
Universitas Teknokrat Indonesia, Bandar Lampung
Computer Science & IT

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Perbandingan Kinerja XGBoost dan Naive Bayes dalam Analisis Sentimen Komentar TikTok Terhadap Ibu Kota Nusantara (IKN) pada Data Tidak Seimbang Novi Purnamasari; Nirwana Hendrastuty
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9488

Abstract

The growth of social media has generated diverse public responses regarding the development of Indonesia’s new capital city, Ibu Kota Nusantara (IKN), particularly on TikTok, a platform with high user interaction. This study aims to compare the performance of Naive Bayes and eXtreme Gradient Boosting (XGBoost) algorithms in sentiment analysis of TikTok comments related to IKN development under imbalanced data conditions. The dataset consists of 1,132 comments that underwent preprocessing, including case folding, text cleaning, tokenization, normalization, and stemming. Feature extraction was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method, generating 1,926 features to represent word importance. The classification process used an 80:20 split for training and testing data. The results show that Naive Bayes achieved an accuracy of 61.23%, while XGBoost obtained a slightly higher accuracy of 62.11%. XGBoost improved recall in the negative class (from 0.21 to 0.40) and neutral class (from 0.11 to 0.26), although the improvement remains limited. The difference in accuracy between the models is relatively small and does not indicate a significant overall performance improvement. This study is limited by the relatively small dataset size and imbalanced class distribution, which may affect data representativeness and model generalization. Therefore, the results are not yet optimal for broader real-world applications.
Evaluasi Validitas Model Machine learning pada Klasifikasi Stunting Berbasis Data Antropometri dan Hubungan Deterministik Turwan Aldi Putra; Nirwana Hendrastuty
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9584

Abstract

Stunting is a chronic nutritional problem among infants and toddlers that affects children’s growth and development. Various studies have utilized machine learning for nutritional status classification based on anthropometric data; however, the validity of the resulting models has rarely been examined. This study aims to evaluate the validity of machine learning models in classifying stunting status using the XGBoost, Random Forest, and Naïve Bayes algorithms. The dataset consists of 120,999 anthropometric records of infants, with age, gender, and height as features, and nutritional status as the target variable. The research process included preprocessing, data transformation, and model evaluation using the k-fold cross-validation method with accuracy, precision, recall, and F1-score metrics. The results showed that Random Forest and XGBoost achieved very high accuracy, at 99.91% and 99.08%, respectively, while Naïve Bayes reached only 55%. This stark difference in performance indicates that ensemble-based models are capable of capturing very strong patterns in the data, while Naïve Bayes struggles due to the interdependence among features. Furthermore, the high accuracy of certain models suggests a deterministic relationship between features and labels, which could potentially make the models less robust against data containing measurement errors or noise.
Perbandingan Kinerja Naive Bayes dan SVM dalam Analisis Sentimen Program Makanan Bergizi Gratis (MBG) sebagai Pendukung Pengambilan Keputusan Vebi Adeka Putra; Nirwana Hendrastuty
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10032

Abstract

The Free Nutritious Meal Program is one of the Indonesian government programs aimed at improving community nutritional quality and reducing stunting rates. The program has generated various publik responses expressed through media social platforms, particularly in YouTube comment sections. This study was conducted to analyze publik sentiment toward the Free Nutritious Meal Program (MBG) and to compare the performance of the Naive Bayes and Support Vector Machine (SVM) algorithms in classifying sentiment from YouTube user comments. The research data were obtained through a YouTube comment scraping process and then processed through several preprocessing stages, including cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Furthermore, feature weighting was performed using the TF-IDF method, and data labeling was carried out using a lexicon-based approach. The sentiment classification process employed the Naive Bayes and Support Vector Machine (SVM) algorithms, while model evaluation was conducted using confusion matrix, accuracy, precision, precision, and f1-score metrics. The results showed that the Support Vector Machine (SVM) algorithm achieved better performance than Naive Bayes. The SVM algorithm obtained an accuracy of 77.4%, precision of 78.4%, precision of 77.4%, and f1-score of 77.6%, whereas the Naive Bayes algorithm achieved an accuracy of 70.5%, precision of 74.4%, precision of 70.5%, and f1-score of 67.7%. The main contribution of this study is the comparative evaluation of Naive Bayes and Support Vector Machine (SVM) for classifying public sentiment from YouTube comments related to the MBG program, providing empirical evidence on the most effective classification approach for supporting social media–based public opinion analysis of government policies.
Co-Authors Novi Purnamasari Turwan Aldi Putra Vebi Adeka Putra