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All Journal Indonesian Journal of Data and Science Edudikara: Jurnal Pendidikan dan Pembelajaran
Ananda, Rima
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Other

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PENINGKATAN KEDISIPLINAN DAN HASIL BELAJAR SISWA MELALUI PEMBELAJARAN TEMATIK DENGAN MODEL PICTURE AND PICTURE Ananda, Rima; Sudiyana, Benedictus; Siwi, Dwi Anggraeni
Edudikara: Jurnal Pendidikan dan Pembelajaran Vol 4 No 3 (2019)
Publisher : IPTPI Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32585/edudikara.v4i3.159

Abstract

Tujuan dari penelitian ini adalah 1) untuk meningkatkan kedisiplinan belajar siswa melalui pembelajaran tematik dengan model Picture and Picture pada siswa kelas II semester gasal SD Negeri 01 Tugu Kecamatan Jumantono Kabupaten Karanganyar Tahun Pelajaran 2019/2020, 2) untuk meningkatkan hasil belajar siswa melalui pembelajaran tematik dengan menggunakan model pembelajaran Picture and Picture pada siswa kelas II semester gasal SD Negeri 01 Tugu Kecamatan Jumantono Kabupaten Karanganyar Tahun Pelajaran 2019/2020, 3) mendeskripsikan penerapan model Picture and Picture untuk meningkatkan kedisiplinan dan hasil belajar siswa pada siswa kelas II semester gasal SD Negeri 01 Tugu Kecamatan Jumantono Kabupaten Karanganyar Tahun Pelajaran 2019/2020.
Performance Analysis of Random Forest and Naive Bayes Methods for Classifying Tomato Leaf Disease Datasets Ananda, Rima; Lilis Nur Hayati; Irawati
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.252

Abstract

Tomato productivity is often disrupted by diseases affecting tomato plants, such as early blight and late blight, which can significantly reduce crop yields. Early detection of these diseases is crucial to prevent greater losses. This study compares two machine learning-based classification methods, namely Random Forest and Naïve Bayes, in identifying diseases on tomato leaves. The dataset used consists of 1,255 images obtained from Kaggle, with the data divided into two classes: early blight with 627 images and late blight with 628 images, which then underwent preprocessing and data splitting with three ratio scenarios (70:30, 80:20, and 90:10) for training and testing. This study shows that it only achieved an accuracy of 76.98%, while the Random Forest method had the highest accuracy of 92.86% in the 90:10 data ratio scenario. Thus, the Random Forest method proves to be more effective in classifying tomato leaf diseases compared to Naïve Bayes. The implementation of this model can help farmers detect diseases more quickly and accurately, thereby increasing agricultural productivity.
Co-Authors Benedictus Sudiyana Irawati Lilis Nur Hayati Siwi, Dwi Anggraeni