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All Journal Unri Conference Series: Community Engagement Jurnal Informatika: Jurnal Pengembangan IT
Ummi Sri Rahmadhani
Universitas Riau
Humanities Computer Science & IT

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Identifikasi stunting pada anak balita di Desa Rantau Mapesai Naila Fauza; Abdurrohman Abdurrohman; Ali Akbar Harahap; Ledy Monica; Lili Yani; Miftahul Jannah; Cici Mardila Purwanti; Syahrul Efendi Harahap; Ummi Sri Rahmadhani; Zulifa Febria
Unri Conference Series: Community Engagement Vol 3 (2021): Seminar Nasional Pemberdayaan Masyarakat
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/unricsce.3.673-679

Abstract

Stunting is a condition of failure to thrive in children due to malnutrition for a long time. Thus, the child is shorter than normal children his age and has a delay in thinking. Generally caused by food intake that is not in accordance with nutritional needs. Stunting is a major nutritional problem in Indonesia. Stunting can be caused by lack of nutritious food intake, exclusive breastfeeding, low birth weight, and a history of infection. Stunting can have an impact on motor and verbal development, as well as an increase in degenerative diseases. The purpose of this study is to increase public knowledge about the dangers of stunting and how to prevent it. This activity was carried out in Rantau Mapesai Village, Rengat District, Indragiri Hulu Regency. The method used in this service activity is a qualitative method. Data obtained from interviews in the form of parents' last education level and family income. The data collection technique was carried out by observing the environment of the area affected by stunting and documenting during activities at the Melur Posyandu. Next is a literature study to collect data related to stunting from previous journals.
Klasifikasi Jamur Berdasarkan Genus Dengan Menggunakan Metode CNN Ummi Sri Rahmadhani; Noveri Lysbetti Marpaung
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5229

Abstract

Mushrooms are plants that do not have true roots and leaves. There are many types of mushrooms that have been identified worldwide, with various shapes, sizes, and colors. Mushrooms have many benefits in the fields of economy, health, and others. One of the benefits of mushrooms is as a food source in Indonesia, but not all types can be consumed. To identify mushroom species, the concepts of Genus and species can be used. The concept of Genus is considered easier because it groups mushroom types based on similar morphological characteristics. Therefore, a model is needed to classify mushrooms based on consumable and toxic genera. The method used in this research is Convolution Neural Network (CNN) due to its good predictive results in image recognition. The model in the research utilizes three convolution layers, three MaxPooling layers, and two dropout layers. The use of dropout aims to reduce overfitting in the model. The research uses a dataset of 1200 images with a training and testing data ratio of 70:30, resulting in 840 training data and 360 testing data. The best accuracy achieved by this model is 89% for training and 82% for validation. Therefore, it can be concluded that the model is able to classify mushrooms based on Genus using the CNN method
Co-Authors Abdurrohman Abdurrohman Ali Akbar Harahap Cici Mardila Purwanti Ledy Monica Lili Yani Miftahul Jannah Naila Fauza Noveri Lysbetti M Syahrul Efendi Harahap Zulifa Febria