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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Mantik Jurnal Computer Science and Information Technology (CoSciTech) INFOSYS (INFORMATION SYSTEM) JOURNAL Jurnal PkM (Pengabdian kepada Masyarakat)
Fitri Handayani
Universitas Muhammadiyah Riau
Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences

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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

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Komparasi Algoritma Menggunakan Teknik Smote Dalam Melakukan Klasifikasi Penyakit Stroke Otak Fitri Handayani; Reny Medikawati Taufiq
Computer Science and Information Technology Vol 5 No 2 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v5i2.7439

Abstract

Stroke is a deadly disease. This can occur due to disturbances in brain function that occur suddenly, progressively and quickly. However, it is difficult to know the early symptoms of stroke. The application of data mining knowledge can be used to diagnose disease. This research was conducted to implement data mining in classifying brain stroke. The dataset used was obtained from Kaggle, totaling 4891 data. However, the dataset does not have a balanced amount of data for each class. To balance the data, the SMOTE technique is used which aims to increase accuracy. The application of the classification algorithms used, namely the Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) algorithms aims to determine the best algorithm performance. This research resulted in a comparison of the four algorithms which showed that the LR, RF and SVM algorithms produced the highest accuracy, precision, recall and f1-score values, namely 95% accuracy, 95% precision, 100% recall and 97% f1-score. The KNN algorithm produces lower accuracy, precision, recall and f1-score values, namely 90% accuracy, 95% precision, 85% recall and 90% f1-score.
Analisis Convolutional Neural Network LeNet-5 Dalam Klasifikasi Daun Mangga Fitri Handayani; Andi Sunyoto; Bayu Anugerah Putra
Computer Science and Information Technology Vol 5 No 3 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

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

Abstract

Mango is one of the agricultural productions. Like other agricultural crops, diseased mango leaves are a production problem. As a result, agricultural productivity decreases. This research aims to classify healthy or diseased mango leaves by developing a Convolutional Neural Network (CNN) based system with LeNet-5 feature extraction. The dataset used is sourced from Mendeley consisting of healthy leaf types totaling 265 images and diseased totaling 170 images. The data division used consists of 80% training data and 20% test data. The augmentation process is carried out to reduce over fitting. The results showed that the epoch process stopped at the 20th epoch and resulted in 93% accuracy. This shows that the CNN method for image classification can produce accurate accuracy in solving real-world problems.
Co-Authors Aldi, M Tri Andi Sunyoto Bayu Anugerah Putra Chan, Ridzky Dahriani Hakim Tanjung Eka Putra Fatma, Yulia Fina Nasari Fitriani, Aisyah Harun Mukhtar Hayami, Regiolina Munanda, Rizka Prastiwi, Adila Pramudiah Rahmad Firdaus Rahmawilda, Rahmawilda Suryanti, Anggi Aprilia Taufiq, Reny Medikawati