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All Journal Jurnal Teknik Informatika (JUTIF) Jurnal As-Syar’i: Jurnal Bimbingan & Konseling Keluarga
Nurhadi , Nurhadi
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Religion Humanities Computer Science & IT Education Law, Crime, Criminology & Criminal Justice Social Sciences

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Belis Gading dalam Perkawinan Adat Lamaholot Desa Leworaja: Telaah Normatif Empiris dalam Perspektif Hukum Islam Insafiyah Lamablawa, Nurmata; Fakhrurazi, Fakhrurazi; Alfarisi, Usman; Zakaria, Endang; Nurhadi , Nurhadi; Fatmakartika, Rini; Yumna, Laila
As-Syar i: Jurnal Bimbingan & Konseling Keluarga  Vol. 5 No. 3 (2023): As-Syar’i: Jurnal Bimbingan & Konseling Keluarga
Publisher : Institut Agama Islam Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/as.v5i3.9949

Abstract

This study examines the practice of ivory belis in Lamaholot traditional marriage in Leworaja Village, Lembata Regency, East Nusa Tenggara. Belis in the form of elephant ivory is a customary requirement that has social, cultural, and honorary values for Lamaholot women. However, this practice often burdens men economically and raises normative issues in the perspective of Islamic law which emphasizes the simplicity of dowry and the prohibition of burdening prospective husbands. This research uses a qualitative method with data collection techniques through in-depth interviews with traditional leaders, religious leaders, and local communities, as well as analysis of customary law documents and marriage fiqh. The results of the study show that ivory belis is seen as a symbol of self-esteem and kinship bonds, but in Islamic law this tradition cannot be positioned as a legal condition for marriage. Islam only requires a simple dowry which is the full right of the wife. Therefore, the practice of ivory belis requires reinterpretation in order to maintain the cultural values of the Lamaholot community while being in line with the principles of maqaṣid al-shari'ah which emphasizes convenience, justice, and benefits.    
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 Alfarisi, Usman Annisa, Tiko Nur Fakhrurazi, Fakhrurazi Fatmakartika, Rini Insafiyah Lamablawa, Nurmata Jasmir , Jasmir Yumna, Laila Zakaria, Endang