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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Teknik Informatika (JUTIF)
Annisa, Tiko Nur
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Computer Science & IT Engineering

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Comparison of ANOVA and Chi-Square Feature Selection Methods to Improve Machine Learning Performance in Anemia Classification Annisa, Tiko Nur; Jasmir , Jasmir; Nurhadi , Nurhadi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5017

Abstract

Anemia is a prevalent hematological condition marked by decreased hemoglobin concentration in the blood, which can lead to serious health complications if undetected. Although machine learning has shown potential in supporting early diagnosis, its effectiveness is often hindered by irrelevant or excessive features. This study investigates the impact of ANOVA and Chi-Square feature selection methods in improving the effectiveness of three distinct machine learning models algorithms, Naive Bayes, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) for anemia classification. Using a Kaggle dataset consisting of 15,300 instances and 25 features, the evaluation of each model was conducted with reference to its accuracy, precision, recall, and F1-score, both before and after applying feature selection. Experimental results show a substantial improvement in classification performance after feature selection, with the SVM + ANOVA combination achieving the highest accuracy of 94.61%. In contrast, models without feature selection performed below 90%, highlighting the need for appropriate feature reduction techniques. This study contributes a comparative analysis framework for medical data classification, emphasizing the role of statistical feature selection in optimizing model accuracy. Its novelty lies in demonstrating consistent performance improvement across algorithms using real-world anemia data and providing evidence that ANOVA and Chi-Square can significantly enhance model generalization in medical diagnostic contexts.
HYBRIDIZATION OF FASTTEXT-BLSTM AND BERT FOR ENHANCED SENTIMENT ANALYSIS ON SOCIAL MEDIA TEXTS Jasmir; Rosario, Maria; Irawan, Irawan; Siswanto, Agus; Annisa, Tiko Nur
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7488

Abstract

The development of internet technology and social media has driven the increasing use of sentiment analysis to understand public opinion. This study aims to improve the classification performance of sentiment analysis by proposing a hybrid model that combines FastText-BLSTM and BERT. The dataset used consists of 900 Indonesian-language Netflix app user reviews obtained through crawling using Google Play Scraper. The research stages include text preprocessing, feature extraction using FastText and BERT, and classification using BLSTM, which are then combined in a concatenation layer to produce a richer feature representation. Experimental results show that the FastText-BLSTM-BERT hybrid model provides the best performance with an accuracy of 94.22%, a precision of 95.98%, a recall of 95.68%, and an F1-score of 95.83%. This achievement is superior to the single models of FastText-BLSTM and BERT. The main novelty of this research lies in the integration of contextual embeddings from BERT with subword-level semantic and sequential representations from FastText-BLSTM, which has not been extensively explored in prior studies on Indonesian sentiment analysis. This hybridization demonstrates significant improvement in model generalization and robustness for low-resource language texts
An Adaptive Feature-Aware Hybrid Resampling Strategy for Imbalanced Diabetes Classification with Integrated Balanced Index Evaluation Jasmir, Jasmir; Pahlevi, Riza; Gunardi, Gunardi; Rohaini, Eni; Annisa, Tiko Nur
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7418

Abstract

Class imbalance remains a critical challenge in medical data classification, particularly in diabetes prediction, as it significantly degrades minority-class sensitivity. This study proposes an Adaptive Feature-Aware Hybrid Resampling Strategy (AHRS) that dynamically integrates oversampling and undersampling based on Imbalance Ratio (IR) and Feature Importance (FI). Unlike conventional static resampling methods, AHRS iteratively adjusts class distribution while preserving informative feature structures. In addition, this study introduces the Integrated Balanced Index (IBI), a bounded composite metric integrating precision, recall, and specificity to provide a fairer evaluation of classification performance on imbalanced medical datasets. The proposed approach was evaluated using the Pima Indian Diabetes Dataset (768 instances) with K-Nearest Neighbor, Naïve Bayes, and Random Forest classifiers under 5-fold stratified cross-validation. Experimental results demonstrate that AHRS consistently outperforms SMOTE, Random Oversampling, and Tomek Links, achieving accuracy improvements of 5–7% and recall gains of up to 10%. Random Forest combined with AHRS achieved the highest IBI score of 0.90, indicating strong balance between sensitivity and specificity. The findings suggest that adaptive, feature-aware resampling combined with balanced evaluation metrics provides a reliable and interpretable framework for fair medical classification systems and Clinical Decision Support Systems (CDSS).
Co-Authors Agus Siswanto Gunardi Gunardi Irawan Irawan Jasmir Jasmir , Jasmir Jasmir, Jasmir Nurhadi , Nurhadi Riza Pahlevi, Riza Rohaini, Eni Rosario, Maria