Kasus-Kasus Keracunan Jamur Liar Di Indonesia

Putra, Ivan Permana

doi:10.22435/jek.v20i3.4943

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…Hingga saat ini informasi tersebut utamanya masih mengandalkan surat kabar cetak ataupun elektronik. Selama 1 dekade (2010-2020) terdapat 76 kasus (550 korban) keracunan jamur liar di Indonesia, dan 9 kasus kematian (Putra 2020c). Beberapa diantaranya diduga diakibatkan oleh jamur liar (non-merang) yang tumbuh pada jerami, namun tidak dilaporkan sampai menyebabkan kematian.…”

Section: Pendahuluanunclassified

Potensi dan Toksisitas Coprinus sp. Liar pada Jerami Padi di Indonesia

Amelya¹,

Putra

Hermawan³

et al. 2023

j. quagga

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Jamur merupakan bahan nutraceutical dengan kegunaan dan kandungan nutrisi yang beragam. Jamur liar yang tumbuh di sawah merupakan salah satu alternatif bahan konsumsi oleh sebagian masyarakat Indonesia. Umumnya, masyarakat lokal tersebut merambah jamur jerami (Volvariella volvacea). Namun, beberapa kelompok jamur coprinoid seperti Coprinus spp. juga terdistribusi pada relung yang sama. Bagi non-mikolog atau orang yang telah berpengalaman, tubuh buah muda dari kedua jamur tersebut seringkali sulit dibedakan. Hal ini disebabkan karena morfolonya yang mirip. Selain bermanfaat untuk kesehatan, beberapa spesies dari Coprinus juga memiliki kandungan racun. Selama 2010-2020, terdapat 11 kasus keracunan (48 orang) setelah mengkonsumsi jamur jerami liar di Indonesia. Coprinus merupakan jamur yang cosmopolitan sehingga sangat mudah ditemukan di Indonesia. Jamur ini bisa dimakan hanya padafase mudanya ketika lamelanya belum menghitam atau tudungnya belum meluruh. Keracunan yang muncul diduga disebabkan oleh senyawa coprine dari jamur ini. Informasi terkait karakter identifikasi sederhana dan resiko toksisitas dari jamur Coprinus liar perlu disampaikan ke mayarakat guna mencegah terjadinya keracunan jamur liar di Indonesia.

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Section: Pendahuluanunclassified

Potensi dan Toksisitas Coprinus sp. Liar pada Jerami Padi di Indonesia

Amelya¹,

Putra

Hermawan³

et al. 2023

j. quagga

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“…Sebanyak 550 orang menjadi korban dan 9 diantaranya meninggal dunia akibat keracunan tersebut. Puncak kasus keracunan terjadi pada tahun 2014, dan kasus-kasus keacunan tercatat berasal dari beberapa daerah besar yaitu Sumatra, Jawa, Kalimantan, Sulawesi, dan Nusa Tenggara (NTB dan NTT) [4] [5]. Selama periode tahun 2010 sampai dengan tahun 2020 di Indonesia terdapat 7 kasus keracunan yang disebabkan oleh salah mengidentifikasi jenis jamur Inocybe sebagai Termitomyces.…”

Section: Pendahuluanunclassified

“…Dari 31 korban, satu orang meninggal dengan rentang usia 2-80 tahun. Penyebaran kasus keracunan hanya terjadi di 5 provinsi, dan sebagian besar kasus terjadi di Pulau Jawa [5]. Berdasarkan permasalahan yang ada maka dirasa perlu untuk melakukan penelitian yang bisa mengklasifikasikan jamur beracun dan tidak beracun dengan memanfaatkan teknologi data mining.…”

Section: Pendahuluanunclassified

Perbandingan Jarak Metrik pada Klasifikasi Jamur Beracun Menggunakan Algoritma K-Nearest Neighbor (K-NN)

Suarisman,

Nazir,

Syafria

et al. 2023

JoSYC

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Mushrooms are organisms from the kingdom fungi that have a fleshy body structure and can be consumed, but there are some species of mushrooms that are not safe to eat and have specific characteristics, so distinguishing between edible and poisonous mushrooms can be tricky due to the almost identical appearance of various mushroom species. Errors in identifying edible mushrooms can impact the health of consumers who consume the mushrooms. Evaluating the performance of various methods on a dataset is a key step in determining the most suitable classification method. This research is about how to measure the performance of classification methods on toxic mushroom datasets using the K-Nearest Neighbor algorithm with several metrics such as euclidean, manhattan and minkowski, which is a method for classifying new data based on proximity to existing training data. The results obtained in this study with several distance metrics can be concluded that the accuracy value of the manhattan metric is better than the euclidean and minkowski metrics. Because the manhattan metric gets the highest accuracy result of 99% with K = 100 and the lowest 82% with K = 3000, while the euclidean metric gets accuracy results with a value of 98% with K = 100 and 72% with K = 3000, and the minkowski metric gets accuracy results with a value of 96% at K = 100 and 64% at K = 3000.

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“…Some cases of death due to mushrooms poisoning also happened in Indonesia in the last 10 years. There were 76 cases with total of 550 victims, 9 of whom died [7]. With these many dangers caused by poisonous mushrooms, we need a system to classify whether a mushroom is poisonous or not.…”

Section: Introductionmentioning

confidence: 99%

The Improvement of Artificial Neural Network Accuracy Using Principle Component Analysis Approach

Hermawan,

Wibowo,

Setiawan Wijaya

2022

matrik

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An important problem in a classification system is how to get good accuracy results. A way to increase the accuracy of a classifier system is to improve the number of input data attributes. Improving the number of input data attributes can be done using the Principal Component Analysis (PCA) method. The aim of this research is to reduce the number of input data attributes to increase the accuracy in a mushroom classification system. The research method used in this study started from collecting datasets from Kaggle.com related to mushroom-classification, then the data visualization process was carried out using pie charts then a dimension reduction process was carried out to reduce the number of variables using the PCA method. The next step is the training and testing of the artificial neural network. The architecture of artificial neural network used is backward error propagation with the number of hidden layers as much as 2 layers with the number of cells as many as 3 and 2. The training data used is 80%, while the testing data is 20%. Based on the test results, obtained an accuracy of 100% with 150,000 iterations and using 11 input variables from 22 existing input variables. By adding Principal Component Analysis part of the development that can improve the accuracy and performance of Artificial Neural Networks

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Kasus-Kasus Keracunan Jamur Liar Di Indonesia

Cited by 3 publications

References 17 publications

Potensi dan Toksisitas Coprinus sp. Liar pada Jerami Padi di Indonesia

Potensi dan Toksisitas Coprinus sp. Liar pada Jerami Padi di Indonesia

Perbandingan Jarak Metrik pada Klasifikasi Jamur Beracun Menggunakan Algoritma K-Nearest Neighbor (K-NN)

The Improvement of Artificial Neural Network Accuracy Using Principle Component Analysis Approach

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