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All Journal Jurnal Teknik Informatika (JUTIF) Jurnal Manajemen Sistem Informasi
Jasmir , Jasmir
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Computer Science & IT

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Sistem Pendukung Keputusan Keluarga Penerima Manfaat Bantuan Langsung Tunai Dana Desa (BLT-DD) Kusuma Safitri, Lestari; Jasmir , Jasmir
Jurnal Manajemen Sistem Informasi Vol 8 No 4 (2023): MANAJEMEN SISTEM INFORMASI
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jurnalmsi.2023.8.4.1519

Abstract

Village Fund Direct Cash Assistance is the government's effort to stabilize the economy of the poor in Indonesia, especially in villages. collectively and as social protection for the community. The purpose of this study was to design and apply a decision support system to help determine a decision for the Village Head of Bukit Tempurung to determine the prospective beneficiary families of the Village Fund Direct Cash Assistance (BLT-DD). This study uses a research method using SAW (Simple Addictive Weight). This research will produce a ranking of each alternative. Based on the research that has been done, there are still difficulties because the assessment is still done subjectively. This study uses UML (Unified Modeling Language) tools in the form of use case diagrams and activity diagrams. With this decision support system it can help minimize errors, because the determination is based on predetermined criteria and has gone through a calculation process so that the assessment is objective
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.
Co-Authors Annisa, Tiko Nur Kusuma Safitri, Lestari Nurhadi , Nurhadi