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All Journal JUTIK : Jurnal Teknologi Informasi dan Komputer Natural: Jurnal Ilmiah Pendidikan IPA
Fandi Chriswantoro Putro
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Engineering Social Sciences

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PENGARUH MODEL PEMBELAJARAN OUTDOOR LEARNING TERHADAP HASIL BELAJAR IPA SISWA KELAS VII SMP NEGERI 5 BANGUNTAPAN Fandi Chriswantoro Putro
N A T U R A L: Jurnal Ilmiah Pendidikan IPA Vol 3 No 2 (2016)
Publisher : Universitas Sarjanawiyata Tamansiswa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30738/natural.v3i2.1831

Abstract

This study aims to identify trends descriptive learning outcomes Science of class VII Banguntapan 5 Junior High School academic year 2015/2016 are learning to use the Outdoor Learning models and Expository models. Comparatively aims to know is there any difference in learning outcomes Science of class VII Banguntapan 5 Junior High School academic year 2015/2016 between the learning model and the Outdoor Learning and expository models. This type of research is a Quasi-Experiment. Descriptive research results showed that the tendency of science learning achievement of class VII Banguntapan 5 Junior High School academic year 2015/2016 were learning to use the model included in the category of Outdoor Learning is very high and learning using expository models included in the high category. Comparatively, there are significant differences Science learning outcomes of students of class VII Banguntapan 5 Junior High School academic year 2015/2016 between learning using Outdoor Learning models and expository models. Means no influence learning Outdoor Learning models the learning outcomes of students of class VII Science.
KLASIFIKASI PRESTASI AKADEMIK PESERTA DIDIK DENGAN METODE MACHINE LEARNING DI SMP X Fandi Chriswantoro Putro; Ahmad Chusyairi; Cian Ramadhona Hassolthine
Jurnal Teknologi Informasi dan Komputer Vol. 11 No. 1 (2025): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi April 2025
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v11i1.3751

Abstract

Machine learning (ML) is a field of science that focuses on designing and developing algorithmic models to create behavior based on available data. Academic achievement is a metric used for the assessment of quality educational institutions. By using academic data of students in SMP X and machine learning classification algorithms such as Random Forest, Naïve Bayes, k-Nearest Neighbors (k-NN), and Support Vector Machine (SVM), so that this research can classify the academic achievement of students in SMP X optimally seen from the comparison of the best accuracy rate among classification algorithms. The accuracy of an algorithm is a measure of how precisely it classifies a sample. Evaluation results are compared in the form of validation accuracy and standard deviation. The comparison is done to determine the best algorithm based on accuracy and stability. The results showed that the SVM algorithm has the highest validation accuracy with a value of 0.987410 which shows the best performance in predicting classes and the lowest standard deviation value of 0.005132 which shows a more stable and consistent performance, compared to other algorithms. This indicates that SVM excels in predicting the correct class with stable performance. Based on the results and analysis, it is concluded that the selected SVM algorithm is used to develop a classification model of students' academic achievement in the form of a python program that is still simple but has high accuracy, stable and consistent. This program has become a tool for SMP X in identifying students' academic achievement and as a material for reporting students' learning outcomes to parents.
Co-Authors Chusyairi, Ahmad Cian Ramadhona Hassolthine