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All Journal Syntax Literate: Jurnal Ilmiah Indonesia Musamus Journal Of Research Information and Communication Technology REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Sustainable: Jurnal Kajian Mutu Pendidikan Nusantara Hasana Journal Brilliance: Research of Artificial Intelligence Jurnal Minfo Polgan (JMP) Batara Wisnu : Indonesian Journal of Community Services Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Informatika dan Teknologi Informasi JSITP Jurnal Indonesia Sosial Teknologi Jurnal Publikasi Teknik Informatika (JUPTI) Jurnal Ekonomi, Teknologi dan Bisnis Jurnal Sains dan Ilmu Terapan JURSIMA Jurnal Akademik Pengabdian Masyarakat Svarga Pena: Jurnal Pengabdian Kepada Masyarakat
Nugroho Adhi Santoso
STMIK YMI Tegal
Agriculture, Biological Sciences & Forestry Humanities Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Nursing Social Sciences Other

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Journal : Jurnal Sains dan Ilmu Terapan

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Prediksi Nilai Akhir Semester Siswa Menggunakan Algoritma Random Forest Slamet Kusworo; Nugroho Adhi Santoso; Rifki Dwi Kurniawan
Jurnal Sains dan Ilmu Terapan Vol. 7 No. 2 (2024): Jurnal Sains dan Ilmu Terapan
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/jsit.v7i2.918

Abstract

Students' end-of-semester grades show the results of the learning process that has been carried out for 1 semester, therefore accurate evaluation is very important to determine the extent to which learning objectives have been achieved properly. This research aims to create an appropriate prediction model to project students' final semester grades before the assessment period so that it can help teachers to identify at-risk students and provide appropriate treatment. This research uses the random forest algorithm and uses a dataset of student grades. The Random Forest model was successfully obtained through training and model evaluation using test data, showing an accuracy of 93.33%. The use of the random forest algorithm to predict students' final semester grades can be the right way to produce high accuracy for the benefit of educators in handling their students.
Co-Authors Aang Alim Murtopo Abdul Rochim Ahmad Zulfikri Anik Mutaqin Anik Muttaqin Ardi Supratman Arif Rohman Hakim Bangkit Indarmawan Nugroho Bayu Aji Santoso Chalimatus Solikhah Fajrul Falah Fikri Zain Hidayatullah Gunawan Gunawan Gunawan Gunawan Gunawan Gunawan Intan Mayla Faiza Isni Azmi Khofifah Indah Hasanah Khofifah Indah Hasanah Kusworo Adi Maezun Nafis Maezun Nafis Marzuqi Marstyo Ardiansyah Moh. Jamaludin Nurul Fadilah Nurul Fadilah Oky Dwi Nurhayati Pingky Septiana Ananda Rifki Dwi Kurniawan Rifki Dwi Kurniawan Sarif Surorejo Slamet Kusworo Suryadi Budiutomo Syefudin Syefudin Syefudin Windi Setiawati Wresti Andriani Zaenul Arif Zaenul Arif Zainul Arif Zakaria Zuldhan Ibnu Pangestu