Indonesia is a country with a tropical climate so that fruit and vegetable plants can grow easily in Indonesia.Fruits have many good nutrients such as vitamins, proteins and others. But the fruit also has a period where the fruit is said to be fresh fruit.During this time there are still many fruit supplier companies that send fruit unfit for consumption due to lack of accuracy in the process of sorting the fruit when the fruit is taken from the plantation and the entry of other fruit into an improper packaging. Thus, it makes detecting food spoilage from the production stage to consumption is very important. We propose a design of computer vision-based technique usingdeep learning with the Convolutional Neural Network (CNN) model to detect fruit freshness. The specially designed CNN model is then evaluated with public datasets of fruits fresh and rotten for classification derived from Kaggle.
Fruits are usually used as complementary foods because they contain good nutrients such as protein and vitamins. In addition to having good content, it turns out that there are potentially harmful microorganisms contained in fruits caused by decay. Currently, many artificial intelligence (AI) techniques have been proposed in research related to fruit freshness. Deep learning is one of its most prominent types in similar studies. As deep learning typically requires a lot of computation power, it usually consumes a lot of electricity. This is an important concern, especially for agribusiness companies that require AI implementations. Based on these problems, we propose to build a convolutional neural network (CNN) model consisting of six layers to detect fruit freshness and save energy. The CNN model we built uses electrical power ranging from 55 to 73 Watts during the training process and 20 to 27 Watts during the testing process. For accuracy, the result is 98.64%. However, compared to previous studies with the MobileNetV2 model, our model only excels in several aspects, such as recall in fresh banana and fresh oranges, recall and F1-score in Rotten Banana.
Lansia adalah suatu proses alami yang tidak dapat dihindari oleh setiap individu pada fase akhir dari perkembangan manusia. Tingginya jumlah lansia maka permasalahan yang dihadapi oleh lansia juga semakin tinggi, terutama dalam mengalami tingkat ansietas yang tinggi disebabkan oleh berbagai faktor seperti memikirkan umur yang semakin lanjut sehingga berdampak pada ketidakmampuan dalam memenuhi kebutuhan dan memikirkan masalah yang terjadi pada keluarga. Seiring dengan mengalami tingkat ansietas yang tinggi maka berdampak pada kualitas dan pola tidur lansia yang terdiri dari dua aspek yaitu baik dan tidak baik sehingga mengakibatkan berbagai macam kemungkinan lansia mengalami gangguan kesehatan. Tujuan penelitian ini untuk mengetahui Hubungan Ansietas Terhadap kualitas Tidur Pada Lansia Hipertensi di Puskesmas Pekan Heran Kecamatan Rengat Barat Kabupaten Indragiri Hulu. Jenis penelitian yang digunakan dalam penelitian ini adalah kuantitatif dengan deskriptif korelasional dengan menggunakan rancangan cross sectional. Jumlah sampel sebanyak 108 responden dengan teknik pengambilan sampel dalam penelitian ini adalah Purposive sampling. Hasil penelitian diperoleh ansietas pada lansia sebagian besar pada kategori sedang sebanyak 41 responden (38,0%). Kualitas tidur lansia hipertensi sebagian besar pada kategori baik sebanyak 61 responden (56,5%). Ansietas memiliki hubungan dengan kualitas tidur lansia Hipertensi dimana terdapat p value 0,000. Tenaga kesehatan di Puskesmas disarankan untuk dapat menggali dukungan keluarga pada lansia sehingga dapat membantu mempertahankan kualitas tidur pada lansia hipertensi.
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