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All Journal Idealita: Jurnal Pendidikan dan Sosial Keagamaan Indonesian Journal of Applied Technology and Innovation Science
Mustika, Dinda
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Religion Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences

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Problematika Pembelajaran IPS di MI/SD Abidin, Zainal; Mustika, Dinda
IDEALITA: Jurnal Pendidikan dan Sosial Keagamaan Vol. 5 No. 2 (2025): September
Publisher : LP2M Sekolah Tinggi Agama Islam Al Falah ( STAIFA ) Pamekasan

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Abstract

Pembelajaran IPS di MI/SD memiliki peran penting dalam membentuk karakter dan pengetahuan siswa tentang bangsa dan lingkungannya. Namun, dalam praktiknya, pembelajaran IPS di MI/SD masih dihadapkan pada berbagai problematika. Problematika tersebut meliputi: Kurangnya minat siswa: Siswa seringkali menganggap IPS sebagai mata pelajaran yang membosankan dan kurang menarik. Metode pembelajaran yang kurang variatif: Metode pembelajaran yang monoton dan kurang kreatif membuat siswa mudah jenuh dan kurang aktif dalam belajar. Keterbatasan sumber belajar: Keterbatasan buku, alat peraga, dan media pembelajaran lain membuat pembelajaran IPS kurang efektif. Keterampilan guru: Beberapa guru IPS di MI/SD belum memiliki keterampilan yang memadai dalam mengelola pembelajaran IPS yang efektif dan menarik.Pentingnya mengatasi problematika pembelajaran IPS di MI/SD: Meningkatkan minat dan motivasi belajar siswa. Meningkatkan kualitas pembelajaran IPS dengan metode yang lebih variatif dan kreatif. Meningkatkan ketersediaan dan kualitas sumber belajar. Meningkatkan keterampilan guru dalam mengelola pembelajaran IPS.
Prediction of Fetal Health Using Machine Learning Algorithms Mustika, Dinda; Putri, Rindiani Suhadi; Alhady, M. Naufal Dzaky; Khairunnisa, Kharisma Ummi; Mahmudah, Arifah Nur
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 3 No. 1 (2026): IJATIS February 2026
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v3i1.2496

Abstract

This study evaluates several machine learning algorithms for predicting fetal health conditions using cardiotocography (CTG) data. The dataset contains 2,126 records with 22 numerical features obtained from Kaggle and is classified into three categories: normal, suspect, and pathological. Four classification models Decision Tree, Random Forest, Support Vector Machine, and Logistic Regression were implemented and evaluated using two data split scenarios (80:20 and 70:30). Model performance was assessed using precision, recall, and F1-score. The results show that Random Forest achieves the best performance with an F1-score of 91% in both split scenarios, indicating stable and accurate classification compared with other models. The contribution of this study is to provide a comparative evaluation of classical machine learning algorithms for CTG-based fetal health prediction. The findings can support the development of decision-support tools to help medical personnel detect and monitor fetal health risks early.
Co-Authors Alhady, M. Naufal Dzaky Khairunnisa, Kharisma Ummi Mahmudah, Arifah Nur Putri, Rindiani Suhadi Zainal Abidin