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All Journal Bioedukasi: Jurnal Pendidikan Biologi Jurnal Teknik Informatika (JUTIF)
Anshari, Muhammad Ridha
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Education

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HASIL BELAJAR PESERTA DIDIK KELAS XI MIPA SMAN 4 BANJARMASIN PADA PEMBELAJARAN KONSEP SISTEM KOORDINASI PADA MANUSIA Anshari, Muhammad Ridha; Noorhidayati, Noorhidayati; Amintarti, Sri
BIOEDUKASI: Jurnal Pendidikan Biologi Vol 14, No 2 (2023): BIOEDUKASI, NOVEMBER 2023
Publisher : UNIVERSITAS MUHAMMADIYAH METRO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/bioedukasi.v14i2.8594

Abstract

The application of the Problem-Based Learning (PBL) model in biology learning is relevant to the goals of 21st-century learning. The concept of Coordinating Systems in Humans must be known and understood by everyone, especially students. This study aims to describe the effect of applying the PBL model to the learning of the Coordinating System concept on the learning outcomes of students in class XI MIPA at Senior hight school  4 Banjarmasin. This study used a quasi-experimental method with the nonequivalent control group research design. The research sample was class XI MIPA 2 as the experimental class and class XI MIPA 4 as the control class for 2 meetings. The research data were obtained from the results of the pretest and posttest, LKPD. Data analysis used the Wilcoxon Signed Ranks Test at α = 0.05. The results showed (1) the application of the PBL learning model to learning the Coordination System concept in Humans had a significant effect on the learning outcomes of class XI MIPA 4 SMAN 4 Banjarmasin (2) Cognitive learning outcomes were in a good category (3) Affective learning outcomes of character behavior and social behavior are included in the good to very good category (4) psychomotor learning outcomes are good to very good category.
Performance Comparison of AdaBoost, LightGBM, and CatBoost for Parkinson's Disease Classification Using ADASYN Balancing Anshari, Muhammad Ridha; Saragih, Triando Hamonangan; Muliadi, Muliadi; Kartini, Dwi; Indriani, Fatma; Rozaq, Hasri Akbar Awal; Yıldız, Oktay
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4726

Abstract

Parkinson's disease is a neurodegenerative condition identified by the decline of neurons that produce dopamine, causing motor symptoms such as tremors and muscle stiffness. Early diagnosis is challenging as there is no definitive laboratory test. This study aims to improve the accuracy of Parkinson's diagnosis using voice recordings with machine learning algorithms, such as AdaBoost, LightGBM, and CatBoost. The dataset used is Parkinson's Disease Detection from Kaggle, consisting of 195 records with 22 attributes. The data was normalized with Min-Max normalization, and class imbalance was resolved with ADASYN. Results show that ADASYN-LightGBM and ADASYN-CatBoost have the best performance with 96.92% accuracy, 97.10% precision, 96.92% recall, and 96.92% F1 score. This improvement suggests that combining boosting methods and data balancing techniques can improve the accuracy of Parkinson's diagnosis. These results demonstrate the effectiveness of ADASYN in addressing data imbalance and improving the performance of boosting algorithms for medical classification problems. The findings contribute to the development of intelligent diagnostic systems in the field of medical informatics and computer science. These findings are essential for developing more accurate and efficient diagnostic tools, supporting early diagnosis and better management of Parkinson's disease.
Co-Authors Dwi Kartini, Dwi Fatma Indriani Muliadi Noorhidayati Noorhidayati Rozaq, Hasri Akbar Awal Saragih, Triando Hamonangan Sri Amintarti YILDIZ, Oktay