This study uses remote sensing technology that can provide information about the condition of the earth's surface area, fast, and spatially. The study area was in Karawang District, lying in the Northern part of West Java-Indonesia. We address a paddy growth stages classification using LANDSAT 8 image data obtained from multi-sensor remote sensing image taken in October 2015 to August 2016. This study pursues a fast and accurate classification of paddy growth stages by employing multiple regularizations learning on some deep learning methods such as DNN (Deep Neural Networks) and 1-D CNN (1-D Convolutional Neural Networks). The used regularizations are Fast Dropout, Dropout, and Batch Normalization. To evaluate the effectiveness, we also compared our method with other machine learning methods such as (Logistic Regression, SVM, Random Forest, and XGBoost). The data used are seven bands of LANDSAT-8 spectral data samples that correspond to paddy growth stages data obtained from i-Sky (eye in the sky) Innovation system. The growth stages are determined based on paddy crop phenology profile from time series of LANDSAT-8 images. The classification results show that MLP using multiple regularization Dropout and Batch Normalization achieves the highest accuracy for this dataset.
Deteksi object menjadi hal menarik untuk diteliti, namu deteksi object tidak lepas dari proses segmentasi untuk melepaskan background dengan area penting untuk dideteksi. Dalam peneltiian ini kami menggunakan segmentasi warna YCbCr dengan kluster warna 2 dan 3 dari metode K-Means pada 139 image dari dataset ImageClef2017. Images yang kami ambil memiliki karakteristik background yang kompleks sehingga membutuhkan operator-operator selain metode dari segmentasi warna seperti holes, filter dan openarea. Kami juga menggunakan pendekatan jarak dari Manhattan distance untuk mengkluster warna. Tujuan dari penelitian ini untuk mendapatkan nilai akurasi terbaik dari kluster-kluster yang kami teliti. Hasil yang kami peroleh adalah kluster 3 mendapatkan akurasi lebih baik dibandingkan kluster 2.
The purpose of this study is to produce information and communication technology/ICT-based calculus learning media that meets the valid, practical, and effective criteria. This type of research is development research. The development model used is the ADDIE model. According to Pribadi (2009: 125) one of the learning system design models with basic stages of learning system design that is simple and easy to learn is the ADDIE model. ADDIE stands for Analysis, Design, Development, Implementation, and Evaluation. The ADDIE model was developed by Reiser and Mollenda (Branch. 2009: 17-18). Validity data was obtained from validation results by material experts, media experts, and user lecturers. While practicality data is obtained from lecturers' decisions, student responses and the implementation of the learning process (lectures). For effectiveness analysis, it is done by determining the percentage of students who reach the minimum high category for the motivation questionnaire and determining the percentage of completeness of students' scores on classical learning achievement tests. Modules will be categorized as valid and practical if each review minimally is considered to the "good" category, whereas it is effective if students who achieve classical completeness of at least 80% for achievement tests and a minimum questionnaire assessment of the "good" category.
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