Teknologi robotika dewasa ini telah mengalami perkembangan yang pesat. Perkembangan teknologi robotika ini dapat membantu pekerjaan manusia. Salah satu pekerjaan manusia yang dapat dibantu oleh robot adalah memotong rumput. Pada penelitian yang dilakukan oleh Fajar Rinto Hadi Putra, Tri Kuntoro Priyambodo dan Jecky Yusakh Akay tentang robot dapat dikontrol pada jarak optimal tidak lebih dari 7,2 meter dan kecepatan mata pisau pemotong rumput tidak dapat dikontrol, sehingga baterai menjadi boros. Tujuan dari penelitian ini ialah merancang sebuah prototype robot pemotong rumput menggunakan wireless kontroler modul ESP32-CAM berbasis IoT dan actuator pemotong rumput dapat dikontrol melalui web browser. Pengujian dilakukan dengan menggunakan ESP32-CAM sebagai mikrokontroler, modul kamera OV2640 sebagai monitoring area rumput yang akan dipotong, modul wifi ESP32 sebagai koneksi antara robot dengan perangkat kontroler robot, dan motor brushless sebagai penggerak mata pisau pemotong rumput. Penggunaan motor brushless ini dapat dikontrol kecepatannya menggunakan modul ESC30A, sehingga dapat menghemat penggunaan daya baterai. Hasil kontroler robot dapat diakses dengan menggunakan ip address yang didapatkan dari acces point yang terhubung pada modul ESP32 CAM. Ip address dapat diakses melalui web browser pada perangkat laptop atau smartphone sehingga robot dapat dikontrol dengan jarak jauh. Berdasarkan hasil pengujian dapat disimpulkan bahwa robot pemotong rumput dapat memotong rumput dengan kontroler wifi dengan jarak kontrol 50 meter. Penggunaan motor brushless pada mata pisau pemotong rumput dapat kontrol kecepatan putaran aktuatornya, sehingga dapat menghemat penggunaan baterai sebesar 0,16V permenit dengan kecepatan maximal.
Rice is one of the global most critical harvests, and a great many people eat it as a staple eating routine. Different rice plant diseases harm, spread, and drastically reduce crop yields. In extreme situations, they may result in no grain harvest at all, posing a severe threat to food security. In this paper, to amplify the recognition ability for rice leaf disease (RLD) classification, we proposed hierarchical transfer learning (HTL) methods incorporating ensemble models containing two-step. In the first step, an ensemble combining MobileNet and DenseNet was addressed to tackle the diseased leaf problem. Consequently, DenseNet and XceptionNet were fused to identify three RLDs. Here, we compare our models with state-of-the-art deep learning models such as ResNet, DenseNet, InceptionNet, Xception, MobileNet, and EfficientNet. Our framework at top-notch with 89 % and 91 % for accuracy. In future works, RLD segmentation is suggested to pinpoint the illness and quantify the afflicted region.
Outlier detection is an important field of study because it is able to detect abnormal data distribution from a set of data. In the student graduation data, there are students with high semester GPA but do not graduate on time but students with low semester GPA can graduate on time. This study aims to detect outlier values in student graduation data for the 2020-2021 class. Factors (attributes) used in this study are Student Academic Support Credit Scores (AKPAM) and Social Studies from semester one to semester six. The dataset used is 1204 graduates. The outlier detection method used is One-Class Support Vector Machine (SVM). One-class SVM is a derivative of SVM method that detects outliers based on data outside the specified class. The results of outlier detection using One-Class SVM method with three types of kernels produce the following reference values: kernel 'rbf' n by 91.4%, kernel 'linear' by 90% and kernel 'poly' by 90%. After normalization using MinMaxScaler the reference value increased by 2% in each kernel. The results of testing the One-Class SVM method get an average 90.3%, thus it can be concluded that the One-Class SVM method is feasible to be used as an outlier detection method.
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