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Endaryono
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Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Mathematics Medicine & Pharmacology

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Perhitungan Korelasi dan Regresi dalam Penentuan Koefisien Muai Volume Beberapa Minyak Nabati Endaryono; Nurfidiah Dwitiyanti
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 11 No 1 (2023): VOLUME 11 NO 1 TAHUN 2023
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v11i1.23798

Abstract

Dalam peneilitian ini dilakukan proses perhitungan penentuan nilai koefisien muai ruang beberapa minyak nabati dengan perhitungan teori korelasi dan regresi. Data yang digunakan data sekunder yang bersumber dari International Journal of  Food Properties, Shreya N. Sahasrabudhe, dkk, tahun 2017. Dalam data sekunder terdapat nilai densitas beberapa minyak nabati yang memiliki massa 69,67 gram pada rentang suhu mulai 220C sampai 2000C. Dari data densitas dihitung perubahan volume setiap perubahan temperatur. Selanjutnya dilakukan perhitungan korelasi dan regresi. Berdasarkan hasil perhitungan didapatkan nilai koefisien muai volume beberapa minyak nabati yaitu minyak jagung (corn oil) adalah 7,5978 x 10-4/0C, minyak zaitun (olive oil) 7,4997 x 10-4/0C, dan minyak kedelai (soyben oil) sebesar 7,4785 x 10-4/0C. Sebagai bahan perbandingan, hasil percobaan yang dilakukan Meta Yantidewi  tahun 2018 di Laboratorium Elektronika Dasar dan Instrumentasi Jurusan Fisika, FMIPA Unesa bahwa nilai koefisien valume minyak nabati sebesar (7,2 ± 0,2) x 10-4/0C.
Predicting Student Final Grades Using Random Forest Algorithms and Linear Regression Mahyudi, Mahyudi; Endaryono; Ristiawan, Rifki
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.618

Abstract

The increasing adoption of intelligent systems in higher education has encouraged the use of data-driven approaches to predict students’ academic performance. Accurate prediction models are essential to support early intervention and informed academic decision-making. This study aims to conduct a comparative analysis between Random Forest and Linear Regression algorithms in predicting students’ final academic scores. The dataset consists of assessment components, including quiz scores, assignment scores, and midterm examination (UTS) scores, which are used as predictor variables. The data were divided into training and testing sets with a ratio of 80:20. Model performance was evaluated using accuracy, error metrics, and feature importance analysis. The experimental results indicate that Random Forest outperforms Linear Regression in terms of predictive accuracy and robustness. Furthermore, both models consistently identify midterm examination scores as the most influential factor affecting students’ final performance. These findings demonstrate that ensemble-based learning methods are more suitable for academic performance prediction and can serve as a reliable foundation for intelligent academic support systems in higher education.
Co-Authors Mahyudi Mahyudi, Mahyudi Nurfidiah Dwitiyanti Rifki Ristiawan