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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Fraction: Jurnal Teori dan Terapan Matematika
Nuzla Af'idatur Robbaniyyah
Universitas Mataram
Mathematics

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LAND COVER CHANGE ANALYSIS IN TERONG TAWAH USING GOOGLE EARTH ENGINE Muhammad Adriyansyah; Fitrah Ramadhan; Dimas Indrawardi; Nuzla Af'idatur Robbaniyyah; Kurnia Ulfa; Muhammad Rijal Alfian
Fraction: Jurnal Teori dan Terapan Matematika Vol. 6 No. 1 (2026): FRACTION: Jurnal Teori dan Aplikasi Matematika
Publisher : Jurusan Matematika, Fakultas Teknik, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/fraction.v6i1.111

Abstract

This study analyzes land cover changes in Terong Tawah Village, West Lombok using Google Earth Engine (GEE) as the main platform. Landsat 8 satellite imagery data for 2015, 2018, 2022, and 2023 was used to classify five types of land cover: forests, settlements, fields, vacant land, and roads. The methods applied include data preprocessing, classification using the Random Forest algorithm, and spatial change analysis. The results show significant trends, such as an increase in settlement area and a decrease in vegetation. Validation of classification accuracy results in an overall accuracy value of above 93% per year. This study demonstrates the effectiveness and accuracy of GEE in analyzing land cover changes and provides important insights into development dynamics in Terong Tawah Village.
Breast Cancer Classification Model Using Decision Tree Algorithm Nuzla Af'idatur Robbaniyyah; Ismi Asmawati; Syamsul Bahri
Jurnal Matematika, Statistika dan Komputasi Vol. 22 No. 3 (2026): May 2026
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v22i3.49472

Abstract

Cancer is a disease characterized by the presence of abnormal cells or tissues that grow rapidly, uncontrollably, can spread to other parts of the patient's body and it can also sometimes be malignant. According to the International Agency for Research on Cancer, in 2024 breast cancer will rank second in terms of the highest number of cases and fourth as the leading cause of death globally. The objective of this study is to apply the Classification and Regression Tree (CART) decission tree algorithm to a breast cancer classification model based on patient medical records. The model developed has a specificity of 95.77%, recall, precision, and F1-Score of 93.02%, and accuracy of 94.74%. The model was evaluated using a confusion matrix to measure its performance. Thus, the CART algorithm can be applied in classification models, and the resulting model is considered optimal as it achieves percentages within the 90%-100% range for all performance evaluation metrics.  
Co-Authors Dimas Indrawardi Fitrah Ramadhan Ismi Asmawati Kurnia Ulfa Muhammad Adriyansyah Muhammad Rijal Alfian Syamsul Bahri