Article Per Year (5 Year)
p-Index From 2021 - 2026
1.405
P-Index
This Author published in this journals
All Journal Epsilon: Jurnal Matematika Murni dan Terapan Indonesian Journal of Artificial Intelligence and Data Mining BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) Jurnal Pengabdian Sumber Daya Manusia RAGAM: Journal of Statistics and Its Application
Dewi Anggraini
Department Of Statistics, Faculty Of Mathematics And Natural Sciences, Universitas Lambung Mangkurat, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
Application of Categorical Boosting Model in Classifying Diseases of Tomato Leaves Fitria Rahmah; Selvi Annisa; Dewi Anggraini
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 1 (2026): March 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Tomatoes are a strategic horticultural commodity whose productivity is often hampered by leaf diseases, particularly early blight and late blight. Manual identification through visual inspection is often inaccurate due to the similarity of symptoms between diseases. This study aims to improve the performance of tomato leaf disease classification using machine learning by overcoming the limitations of previous research by Ningsih et al., which focused solely on disease classes and did not include healthy leaf samples, thereby risking the model failing to recognize normal plant conditions. The proposed methodology integrates the VGG16 architecture as a feature extractor with the Categorical Boosting (CatBoost) algorithm as a classifier. The dataset sourced from Kaggle was cleaned and resized to 224x224 pixels, resulting in 3,285 images. The experimental results show that integrating VGG16 with CatBoost achieves good performance. The accuracy score achieved is 93.1%, while the F1 scores achieved are 90.2% (healthy leaves), 90.3% (early blight), and 98.6% (late blight). Compared to the research by Ningsih et al., this approach not only expands the scope of classification by including the healthy leaf class, but also shows better accuracy in identifying the health conditions of tomato plants.
HYBRID METHOD IMPLEMENTATION: FUZZY DECISION TREE IN THE CLASSIFICATION OF GENDER INEQUALITY Siti Fatimah Marliany Irawan; Dewi Anggraini; Selvi Annisa
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0509-0522

Abstract

The classification of continuous data using the C4.5 decision tree algorithm requires prior discretization based on the calculation of cut points, a process that can be time-consuming and potentially introduce bias. These limitations may negatively impact both the computational efficiency and the classification accuracy of the decision tree model. This study proposes a hybrid method that integrates fuzzy logic with decision tree techniques in the classification process of continuous data types. Fuzzy logic is utilized to manage uncertainty in data variables and enhance flexibility in processing continuous values, while the decision tree plays a role in providing a structured and rule-based framework for decision-making. This proposed method is applied to gender inequality data, encompassing aspects of reproductive health, education and empowerment, and employment across 166 countries worldwide. The results demonstrate that the fuzzy decision tree method, which was constructed using the C4.5 algorithm, achieved a classification accuracy of 91%, while the C4.5 decision tree method without fuzzy only achieved a classification accuracy of 77%. The fuzzy decision tree method successfully improved the classification accuracy by 14%. Additionally, the fuzzy decision tree exhibited more stable and balanced performance in classifying data into four target categories. Therefore, this approach offers an effective and comprehensive alternative for classifying gender inequality, with the potential to support data-driven and targeted policy-making.
Co-Authors Akhriyandi Wijanarta Aldianur Khairani Aprida Siska Lestia Azkia Azkia Dewi Sri Susanti Dhika Nugraha Dini Hidayati Dwidjo Susilo Faisal Faisal Fitria Rahmah Fuad Muhajirin Farid Lestia, Aprida Siska Muhammad Shiddieq Adjie Yudhistira Nabila Septiani Nur Salam Sekar Wahyuningtias Selvi Annisa Selvi Annisa Siti Fatimah Marliany Irawan Siti Jubaidah Syifa Ajeria Thaibatun Nissa Winda Adinda Tanjung Yeni Rahkmawati Yuana Sukmawaty Zakiah Zakiah