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All Journal JURNAL INSTEK (Informatika Sains dan Teknologi) Science and Education Journal
Dwi Astuti, Erna
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Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Social Sciences

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Peningkatan Keterampilan Menulis Dialog Bahasa Inggris Melalui Model Pembelajaran Diskusi Kelompok Pada Peserta Didik Kelas VIII F SMP Negeri 1 Tegalrejo Dwi Astuti, Erna
Science and Education Journal (SICEDU) Vol. 2 No. 1 (2023): Science and Education Journal 2023
Publisher : Faculty Of Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/sicedu.v2i1.83

Abstract

Salah satu permasalahan dalam pembelajaran Bahasa Inggris adalah lemahnya kemampuan peserta didik berbicara baik lisan maupun tulis dalam menerapkan pengetahuan dan ketrampilan. Untuk menyelesaikan masalah dalam kehidupan nyata terutama dalam keterampilan menulis dialog dimungkinkan dapat membantu kemampuan berbicara dari tulis kemudian lisan  Untuk mengatasi masalah tersebut, dilakukan penelitian tindakan kelas (PTK) menggunakan model diskusi kelompok. Tujuan penelitian ini adalah untuk meningkatkan hasil belajar peserta didik kelas VIII F SMP Negeri 1 Tegalrejo Tahun Pelajaran 2022/2023 menggunakan model pembelajaran diskusi kelompok. Subjek penelitian adalah peserta didik berjumlah 30 orang, sumber data      penelitian diperoleh dari data kualitatif berupa data hasil observasi aktivitas guru dan      peserta didik serta data kuantitatif berupa data tes hasil belajar peserta didik. Hasil penelitian siklus  I diperoleh peserta didik yang tuntas secara individu sebanyak 22 peserta didik dengan ketuntasan klasikal sebesar 73 %. Sedangkan hasil penelitian siklus II mengalami peningkatan dengan jumlah peserta didik yang tuntas secara individu sebanyak 30 peserta didik dengan presentase ketuntasan 100 %. Berdasarkan hasil penelitian tersebut, pembelajaran menggunakan model pembelajaran diskusi kelompok dapat meningkatkan hasil     belajar peserta didik kelas VIII F SMP Negeri 1 Tegalrejo Tahun Pelajaran 2022/2023.
Comparative Analysis of Random Forest, SVM-RBF, and XGBoost with SHAP Interpretation for Google Earth Engine-Based Landslide Susceptibility Classification in Wonosobo Regency Amaliya, Mizana; Dwi Astuti, Erna; Mahmudati, Rina
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 11 No 2 (2026): OCTOBER
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v11i2.68406

Abstract

Landslides constitute the predominant disaster type in Wonosobo Regency, Central Java, with 190 recorded events (62% of all disaster incidents) in 2024. Prior studies have reported inconsistent performance among machine learning algorithms due to variations in geographic context, dataset characteristics, feature selection, and evaluation frameworks. A review of the literature reveals a lack of comparative evaluations of Random Forest (RF), Support Vector Machine with Radial Basis Function kernel (SVM-RBF), and XGBoost that integrate Google Earth Engine (GEE), SHAP interpretability analysis, and three-class susceptibility classification specifically for Wonosobo Regency. This study aims to evaluate and compare the performance of these three algorithms in mapping landslide susceptibility using geospatial data. A total of 642 sample points (321 landslide and 321 non-landslide locations) were analyzed using eight conditioning factors extracted via GEE, seven of which were retained following Variance Inflation Factor (VIF) testing. Model performance was evaluated using accuracy, Cohen’s Kappa, Matthews Correlation Coefficient (MCC), and AUC-ROC with 10-fold stratified cross-validation. XGBoost achieved the highest performance on the study dataset, with an accuracy of 93.02%, an AUC-ROC of 0.974, and a cross-validation accuracy of 91.0% ± 3.1%. SHAP analysis indicated that rainfall made the largest contribution to the model predictions, highlighting its importance in the model’s decision-making process. Classification of prediction probabilities at the sample points using the Jenks Natural Breaks method yielded a susceptibility distribution of 49.2% Low, 6.4% Moderate, and 44.4% High.
Co-Authors Amaliya, Mizana Mahmudati, Rina