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All Journal Journal of Soft Computing Exploration Journal of Student Research Exploration Journal of Electronics Technology Exploration (JOeTEx)
Lestari, Apri Dwi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Journal : Journal of Soft Computing Exploration

 1 
Application of pest detection on vegetable crops using the cnn algorithm as a smart farm innovation to realize food security in the 4.0 era Lestari, Apri Dwi; Nur Afan syarifudin; Yopi Julia Nurriski
Journal of Soft Computing Exploration Vol. 3 No. 2 (2022): September 2022
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v3i2.72

Abstract

Pests and diseases are one of the factors that become obstacles in the cultivation of vegetables because they can cause a decrease in the quality and quantity of production. The more varied types of pests have different impacts on crops, so if farmers incorrectly identify the class of pests, the treatment will be ineffective. Therefore, we need a technology that can classify the types of pests on vegetable crops to maintain the quality and quality of the product as well as the abundant harvest. The classification model of pests on vegetables using the deep learning method using the Convolutional Neural Network (CNN) algorithm with a high level of accuracy is the solution to this problem. The application of artificial intelligence in the agricultural sector also supports smart agriculture in Indonesia. Based on the research that has been carried out, the application of pest classification on vegetable crops made by applying the CNN model using the Inception V3 - k-fold cross-validation method has a test accuracy rate of 99%, meaning that the application can perform pest classification correctly.
An expert system on diagnosis of mental diseases Jain, Somay; Aggarwal, Mukul; Singhal, Yash; Lestari, Apri Dwi
Journal of Soft Computing Exploration Vol. 4 No. 1 (2023): March 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i1.100

Abstract

Mental disorder is one of the most serious problems in today's time. Mental disorders can be classified into different sub-disorders according to changes in human behavior and mental condition. According to reports one out of seven people suffered from mental disorders. In this research paper, our main emphasis is to build an expert system that diagnoses people based on their symptoms, so people can diagnose themselves early before going to the doctor. Expert Systems are one of the most important applications in artificial intelligence that solves complex problems without human help. We provide different rules, facts, and relationships among different symptoms in our knowledge base, from which users can query their problems and get their results. We used SWI-prolog to build an expert system. There are a few types of disorders, such as mental disorders, neurodevelopmental disorders, eating disorders, etc.
A new CNN model integrated in onion and garlic sorting robot to improve classification accuracy Lestari, Apri Dwi; Khan, Atta Ullah; Pertiwi, Dwika Ananda Agustina; Muslim, Much Aziz
Journal of Soft Computing Exploration Vol. 5 No. 1 (2024): March 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i1.304

Abstract

The profit share of the vegetable market, which is quite large in the agricultural industry, needs to be equipped with the ability to classify types of vegetables quickly and accurately. Some vegetables have a similar shape, such as onions and garlic, which can lead to misidentification of these types of vegetables. Through the use of computer vision and machine learning, vegetables, especially onions, can be classified based on the characteristics of shape, size, and color. In classifying shallot and garlic images, the CNN model was developed using 4 convolutional layers, with each layer having a kernel matrix of 2x2 and a total of 914,242 train parameters. The activation function on the convolutional layer uses ReLu and the activation function on the output layer is softmax. Model accuracy on training data is 0.9833 with a loss value of 0.762.
Co-Authors Aggarwal, Mukul Husyen Ramadhan, Farhan Jain, Somay Khan, Atta Ullah Much Aziz Muslim Muzayanah, Rini Nur Afan syarifudin Pertiwi, Dwika Ananda Agustina Singhal, Yash Yopi Julia Nurriski