Termitomyces genus, known as the termite mushroom, is an edible genus of mushroom that grows near termite nests. They are known for their flavor and high nutritional value. The species _Chlorophyllum molydites_, known locally as \"kulat asu\", also known as false parasol and the vomiter, can be most commonly found in eastern North America, California, as well as many subtropical countries around the world. They are mostly found growing near human populations. As a result, they are one of the most commonly misidentified species. The nature of the poisoning is mostly gastrointestinal, hence the nickname \"The Vomiter\" [7]. A concerning trend is the increasing number of fatalities due to the ingestion of toxic mushrooms, as more people participate in back-to-nature activities without adequate knowledge of fungi. As of 2024, there has been a lack of research studies regarding mushroom species recognition using machine learning in Malaysia specifically. Identifying mushroom species is crucial not only for medical providers to administer appropriate treatments to counteract toxins, but also to help the public gain more knowledge about fungi. Therefore, this project aims to address the lack of mushroom species recognition systems, specifically focusing on mushrooms found in Malaysia. Especially in recent years, back-to-nature activities such as hiking and jungle trekking have been gaining popularity rapidly. It is important for people to have some form of environmental education to be aware of the risks that come with ingesting unknown species of mushrooms. This project also aims to foster environmental stewardship among people. Environmental stewardship helps people to develop a lifelong appreciation and understanding of nature, its resources, and its biodiversity, as well as how to conserve and preserve the biodiversity of Malaysia. When identifying mushrooms for research purposes or for gastronomy purposes, experts tend to rely on traditional methods. This can potentially lead to human error and to loss of life in the worst-case scenario. Mushrooms tend to be identified based on the following characteristics: cap characteristics such as the texture and color, bruises on the mushroom, odor, characteristics of the gill such as the color, attachment, and spacing, stalk surface, veil color, and many more. However, examining the mushrooms visually can often lead to subjective interpretations that can prove fatal. Figure 1 is an example of how edible and poisonous mushrooms can look alike. There have been a few attempts at using machine learning to identify the edibility of mushrooms. The studies can be broken down into two parts: traditional machine learning and deep learning. One of the methods proposed is to use the K-Nearest Neighbor classifier with the Gaussian Naive Bayes algorithm [8]. Other methods include using other machine learning algorithms such as K-Nearest Neighbor as a standalone [9; 10], Support Vector  
Figure 1: Similar-looking mushrooms are hard to differentiate. (**a**) A picture of _Chlorophyllum molydites_, which is poisonous; (**b**) a picture of _Macrolepiota procera_, which is edible. Machine (SVM) [9; 10], and decision trees. In deep learning attempts, VGG16, Inception-v3 [9; 11], and MobileNet [12] have been used. The proposed algorithm in [8] is an ensemble of the Naive Bayes algorithm paired with K-Nearest Neighbor as the classifier. The dataset used was obtained from the UCI Machine Learning Repository and included only two classes: edible mushrooms and poisonous mushrooms. The data were split into 4208 entries for edible mushrooms and 3916 entries for poisonous mushrooms, for a total of 8124 entries. The feature extraction used was morphological features. The accuracy achieved with the Naive Bayes algorithm alone was 90.21%, which was then further improved to 100%. In a study conducted by [9], which used the K-Nearest Neighbor algorithm, the highest recorded accuracy for their attempt was 94.4% for their model trained on eigen features.