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All Journal Journal of Soft Computing Exploration
Much Aziz Muslim
Universitas Negeri Semarang, Indonesia
Computer Science & IT

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A new CNN model integrated in onion and garlic sorting robot to improve classification accuracy Apri Dwi Lestari; Atta Ullah Khan; Dwika Ananda Agustina Pertiwi; Much Aziz Muslim
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.
Comparison of gridsearchcv and bayesian hyperparameter optimization in random forest algorithm for diabetes prediction Rini Muzayanah; Dwika Ananda Agustina Pertiwi; Muazam Ali; Much Aziz Muslim
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.v5i1.308

Abstract

Diabetes Mellitus (DM) is a chronic disease whose complications have a significant impact on patients and the wider community. In its early stages, diabetes mellitus usually does not cause significant symptoms, but if it is detected too late and not handled properly, it can cause serious health problems. To overcome these problems, diabetes detection is one of the solutions used. In this research, diabetes detection was carried out using Random Forest with gridsearchcv and bayesian hyperparameter optimization. The research was carried out through the stages of study literature, model development using Kaggle Notebook, model testing, and results analysis. This study aims to compare GridSearchCV and Bayesian hyperparameter optimizations, then analyze the advantages and disadvantages of each optimization when applied to diabetes prediction using the Random Forest algorithm. From the research conducted, it was found that GridSearchCV and Bayesian hyperparameter optimization have their own advantages and disadvantages. The GridSearchCV hyperparameter excels in terms of accuracy of 0.74, although it takes longer for 338,416 seconds. On the other hand, Bayesian hyperparameter optimization has a lower accuracy rate than GridSearchCV optimization with a difference of 0.01, which is 0.73 and takes less time than GridSearchCV for 177,085 seconds.
Co-Authors Apri Dwi Lestari Atta Ullah Khan Dwika Ananda Agustina Pertiwi Muazam Ali Rini Muzayanah