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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Education Curiosity
Izza, Mufidatul
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Computer Science & IT Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Other

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Peranan Lingkungan Sekolah terhadap Pembentukan Karakter Anak pada Mata Pelajaran PPKn di SDN Kayangan I Kecamatan Diwek Kabupaten Jombang Izza, Mufidatul; Rochmania, Desty Dwi
EduCurio: Education Curiosity Vol 3 No 3 (2025): April-Juli 2025
Publisher : Yayasan Pendidikan Tanggui Baimbaian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71456/ecu.v3i3.1248

Abstract

Penelitian ini membahas peranan lingkungan sekolah dalam pembentukan karakter anak pada mata pelajaran PPKn di SDN Kayangan I Kecamatan Diwek Kabupaten Jombang. Penelitian ini bertujuan untuk mengetahui bagaimana peranan lingkungan sekolah terhadap pembentukan karakter anak pada mata pelajaran PPKn. Metode yang digunakan adalah penelitian kualitatif dengan teknik pengumpulan data melalui observasi, wawancara, dan dokumentasi. Hasil penelitian menunjukkan bahwa lingkungan sekolah memiliki peranan signifikan dalam membentuk karakter anak dengan indikator disiplin, kejujuran, dan tanggung jawab. Guru dan kepala sekolah memiliki peran penting dalam menanamkan nilai-nilai karakter melalui keteladanan, peraturan sekolah, serta apresiasi terhadap perilaku positif siswa.
Detection of Sugarcane Leaf Disease Using Pre-Trained Feature Extraction and SVM Method Izza, Mufidatul; Lutfi, Moch.
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10626

Abstract

Sugarcane (Saccharum officinarum) is an important commodity in the sugar industry, but it is vulnerable to leaf diseases such as Red Rot, Rust, Yellow Leaf, and Mosaic, which can significantly reduce the quality and quantity of yields. Manual identification is time-consuming and prone to subjective errors, therefore an automatic detection method based on digital images is required. This study proposes a combination of VGG16 pre-trained as a feature extractor with Support Vector Machine (SVM) as a classifier. The dataset used is the Sugarcane Leaf Disease Dataset from Kaggle, consisting of 2,521 images of five classes, which were then balanced through augmentation in the form of rotation, zoom, and flipping to a total of 3,000 images (600 per class). The preprocessing stage includes resizing the images to 224×224 pixels and normalization using the preprocess_input function. Three model scenarios were tested, namely SVM, VGG16, and VGG16+SVM. Evaluation was carried out using two methods, namely an 80:20 train–test split and 10-fold cross-validation, with metrics of accuracy, precision, recall, F1-score, G-Mean, and AUC. The experimental results show that VGG16+SVM provides the best performance with an accuracy of 99.60% on the 80:20 scheme, while on 10-fold cross-validation the average accuracy is 80.76%. This value surpasses the baseline SVM and VGG16 + Softmax, proving that the integration of VGG16 feature extraction with SVM classification can produce stable and accurate performance. This research contributes to the development of image-based plant disease detection systems to support precision agriculture and fast decision-making.
Co-Authors Lutfi, Moch. Rochmania, Desty Dwi