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All Journal Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial Jurnal Manajemen Informasi dan Administrasi Kesehatan
Rizmayanti, Ade Irma
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Computer Science & IT Economics, Econometrics & Finance Health Professions Medicine & Pharmacology Public Health Social Sciences

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Penerapan Data Mining Untuk Memprediksi Kompetensi Siswa Menggunakan Metode Decission Tree ( Studi Kasus SMK Multicomp Depok ) Rizmayanti, Ade Irma; Hidayati, Nadiyah; Nugraha, Fitra Septia; Gata, Windu
Swabumi Vol 9, No 1 (2021): Volume 9 Nomor 1 Tahun 2021
Publisher : Universitas Bina Sarana Informatika Kota Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/swabumi.v9i1.8363

Abstract

Abstract- This study discusses the application of data mining to predict student competencies using the decision tree method. In this study applying data mining to predict student competency using the decision tree method. This research was conducted to predict student learning outcomes based on report card grades semester 1, semester 2 semester 3 and semester 4. Data were then managed using Rapid Miner to facilitate predicting student competencies. The study was conducted at Multicomp SMK which has 3 majors namely Hospitality Accommodation, Online Business and Marketing and Multimedia. Research using data from students in each department includes class X and class XI. The application of data mining is used to predict student competencies by using a decision tree and C 4.5 algorithm as a support as well as a comparison to determine the competency of students of Multicomp Depok Vocational School based on both methods. This method is able to measure the ability of students appropriately and be able to provide an understanding at a certain level according to the needs of Indonesian education has a pattern and learning strategy based on students' reasoning abilities. Students are expected to be able to analyze a problem well and find the right solution. students are not accompanied by an adequate education system or curriculum. Teacher competencies that are not evenly distributed in various schools and governments are felt to be very lacking in realizing reasoning based education systems.
Analisis Rekam Medis dengan Metode Data Mining untuk Memprediksi Faktor Risiko Stunting dalam Kesehatan Masyarakat Cholifatul Izza, Nurril; Rizmayanti, Ade Irma
Jurnal Manajemen Informasi dan Administrasi Kesehatan Vol. 7 No. 1 (2024): JMIAK
Publisher : Program Studi D3 Rekam Medis dan Informasi Kesehatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32585/jmiak.v7i1.5192

Abstract

Stunting adalah kondisi serius yang disebabkan oleh malnutrisi berkepanjangan dan infeksi berulang, menghambat pertumbuhan fisik dan perkembangan kognitif anak-anak. Kondisi ini mempengaruhi jutaan anak di seluruh dunia, terutama di negara-negara berkembang, dan memiliki dampak jangka panjang terhadap kesehatan, pendidikan, dan potensi ekonomi mereka. Dalam penelitian ini, data rekam medis terhadap kejadian stunting dianalisis dengan menggunakan 121.000 dataset. Data tersebut diklasifikasikan ke dalam empat status gizi: normal, stunted, tinggi, dan severely stunted, berdasarkan empat variabel utama yaitu usia, jenis kelamin, tinggi badan sebagai label, dan status gizi sebagai variabel target. Model Random forest digunakan untuk menguji keakuratan model dalam memprediksi status gizi anak-anak. Hasil analisis menunjukkan bahwa model ini sangat akurat, dengan nilai rata-rata akurasi sebesar 0,9990 setelah dilakukan 10 kali cross-validation. Temuan ini menunjukkan bahwa Random forest merupakan model yang efektif dalam memprediksi status gizi anak-anak dan dapat digunakan untuk membantu intervensi kesehatan yang lebih tepat sasaran. Penelitian ini juga menyoroti pentingnya penggunaan data rekam medis yang komprehensif dalam pemantauan kesehatan populasi anak-anak
Co-Authors Cholifatul Izza, Nurril Gata, Windu Nadiyah Hidayati Nugraha, Fitra Septia