Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Review Pendidikan dan Pengajaran (JRPP) Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam
Maharani, Desvita
Unknown Affiliation
Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
ANALISIS TINGKAT PEMAHAMAN KONSEP MELALUI PRAKTIKUM SIFAT-SIFAT CAHAYA DALAM PEMBELAJARAN IPA Maharani, Desvita; Cahyani, Bintang; Qotrunada, Talitha Ardelia; Wahyuni, Ranti; Wava, Ahmad Zainal; Ratnasari, Yuni
Jurnal Review Pendidikan dan Pengajaran Vol. 7 No. 3 (2024): Vol. 7 No. 3 (2024): Volume 7 No 3 Tahun 2024 (Special Issue)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jrpp.v7i3.30754

Abstract

Tujuan dari penelitian ini adalah untuk menentukan sifat-sifat cahaya seperti cahaya dapat merambat lurus, cahaya dapat dibiaskan, cahaya dapat menembus benda bening, dan cahaya dapat dipantulkan melalui praktikum. Teknik pengumpulan data yang digunakan adalah kualitatif deskriptif melalui pengamatan mendalam terhadap cahaya. Hasil penelitian menunjukkan bahwa, cahaya merambat lurus bergerak dalam garis lurus kecuali terpengaruh oleh media. Cahaya juga dapat dibiaskan ketika memasuki medium dengan kecepatan rambat yang berbeda, seperti air dan udara. Cahaya dapat menembus benda bening apabila material tersebut memiliki sifat transparan sehingga cahaya dapat melewatinya. Cahaya dapat memantul ketika mengenai permukaan yang halus dan mengkilap, mengikuti hukum refleksi di mana sudut datang sama dengan sudut pantul. Jadi, sifat-sifat ini tidak hanya memungkinkan cahaya untuk bergerak dalam garis lurus kecuali terpengaruh oleh medium, tetapi juga untuk berinteraksi dengan benda-benda seperti kaca dan air dengan cara yang dapat diprediksi.
IMPACT ANALYSIS OF DATA IMBALANCE HANDLING USING EDITED NEAREST NEIGHBORS IN THE CLASSIFICATION OF ABO₃ PEROVSKITE OXIDE BANDGAP MATERIALS Rizqi, Aliyah Zahratu; Akrom, Muhamad; Maharani, Desvita; Ramadhani, Johana Oktavia
SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam Vol. 15 No. 1 (2026): Volume 15 Nomor 1 (Maret 2026)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat151113292026

Abstract

The classification of bandgap types in perovskite ABO₃ materials presents a significant challenge due to overlapping feature distributions and class imbalance between direct and indirect bandgap classes. This study investigates the impact of data cleaning using the Edited Nearest Neighbour (ENN) method on the performance of multiple machine learning classifiers, including Multilayer Perceptron (MLP), Gradient Boosting, CatBoost, and Extra Trees. Model evaluation focuses on accuracy and F1 macro to capture both overall performance and class wise balance. Under baseline conditions, the best performing model achieved a test accuracy of 0.8934 with an F1 macro of 0.7783, indicating strong predictive capability but limited sensitivity to the minority class. After applying ENN exclusively to the training data, the class distribution became more balanced, resulting in improved recall for the minority class across several models. However, this improvement was accompanied by a reduction in overall accuracy. The highest F1 macro under ENN was 0.7632, achieved by the Gradient Boosting model, demonstrating enhanced class balance despite a lower accuracy of 0.8646. Cross validation results confirm that ENN does not significantly increase performance stability but effectively refines decision boundaries by removing ambiguous majority class samples. These findings highlight the trade off between accuracy and class fairness and emphasize that model selection should be guided by the evaluation objective. For imbalanced bandgap classification tasks, F1 macro oriented evaluation combined with ensemble based models provides a more reliable and equitable predictive framework.
Co-Authors Cahyani, Bintang Muhamad Akrom Qotrunada, Talitha Ardelia Ramadhani, Johana Oktavia Rizqi, Aliyah Zahratu Wahyuni, Ranti Wava, Ahmad Zainal Yuni Ratnasari