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All Journal JURNAL PENDIDIKAN TAMBUSAI Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Aliyya, Farrel Rahma
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Computer Science & IT Education Social Sciences Other

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Pengujian Prasyarat Analisis Data Nilai Kelas: Uji Normalitas dan Uji Homogenitas Sonjaya, Rebina Putri; Aliyya, Farrel Rahma; Naufal, Syahandika; Nursalman, Muhammad
Jurnal Pendidikan Tambusai Vol. 9 No. 1 (2025)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

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Abstract

Pengujian prasyarat analisis data merupakan tahapan penting dalam analisis statistik untuk memastikan validitas hasil yang diperoleh, khususnya dalam pengolahan data nilai kelas. Penelitian ini membahas dua pengujian utama, yaitu uji normalitas dan uji homogenitas. Uji normalitas digunakan untuk memeriksa apakah data berdistribusi normal, dengan metode seperti Chi-Square, Liliefors, Shapiro-Wilk, dan Kolmogorov-Smirnov. Uji homogenitas variansi bertujuan memastikan keseragaman variansi antar kelompok data menggunakan teknik seperti Levene's test, Bartlett's test, Brown-Forsythe test, dan Fligner-Killeen test. Dalam konteks data nilai kelas, pengujian ini diterapkan untuk menentukan kesesuaian data dengan asumsi yang diperlukan dalam analisis variansi (ANOVA). Hasil analisis ini tidak hanya mendukung validitas statistik tetapi juga memberikan wawasan yang mendalam tentang efektivitas pembelajaran dan pencapaian siswa. Penelitian ini menggunakan pendekatan kualitatif berbasis studi pustaka untuk menggali pemahaman filosofis dan teoritis tentang pengujian prasyarat analisis data. Temuan ini menegaskan pentingnya pengujian prasyarat untuk mendukung keputusan berbasis data yang berkualitas di bidang pendidikan.
School Feasibility Analysis and Grade Improvement Strategies Using the Random Forest Algorithm Aliyya, Farrel Rahma; Farizi, Syahandhika Naufal; Riza, Lala Septem; Megasari, Rani; Nugraha, Eki; Wahyudin, Asep
JENTIK : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 4 No. 2 (2025): Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jentik.v4i2.475

Abstract

Background of Study: Educational disparities across Indonesian provinces persist, particularly in infrastructure, teacher quality, and dropout rates, necessitating data-driven analysis for equitable improvements.Aims: This study investigates school feasibility and proposes strategies to enhance provincial education performance using the Random Forest algorithm.Methods: Aggregated provincial education data covering student numbers, dropout rates, teacher qualifications, and classroom conditions were transformed into derivative indicators. A binary classification (Feasible/Not Feasible) based on national dropout median was applied. The model was developed using R with six systematic steps, including training and evaluation of a Random Forest model (ntree = 100, mtry = 3) using accuracy, sensitivity, and specificity.Result: The model accurately classified school feasibility. Key predictors included teacher quality, student-teacher ratios, and classroom conditions. Several provinces were identified as “Not Feasible.”Conclusion: Machine learning proves effective for education policy support. The study offers targeted recommendations such as improving infrastructure, enhancing teacher training, and reducing dropouts to promote equitable education in Indonesia.
Co-Authors Asep Wahyudin Eki Nugraha Farizi, Syahandhika Naufal Lala Septem Riza Naufal, Syahandika Nursalman, Muhammad Rani Megasari Sonjaya, Rebina Putri