Machine learning with multistage classifiers for identification of of ectoparasite infected mud crab genus &lt;i&gt;Scylla&lt;/i&gt;

Ali, Rozniza; Yusro, Muhamad Munawarar; Hitam, Muhammad Suzuri; Abdullah, Mhd Ikhwanuddin

doi:10.12928/telkomnika.v19i2.16724

Cited by 6 publications

(6 citation statements)

References 24 publications

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“…Classification is the process of identifying an object and can be in the form of data (Pattnaik & Parvathi, 2022) (Elmannai & Al-Garni, 2021). The classification that will be carried out is by grouping data based on the class of each data (Ali, Yusro, Hitam, & Ikhwanuddin, 2021). By using this method, the author can carry out a data classification of people who are interested in Telkomsel cards.…”

Section: Methodsmentioning

confidence: 99%

Analysis of Public Interest in Telkomsel Cards Using the Decision Tree Method

Cantika,

Yanris,

Hasibuan

2023

SinkrOn

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SIM card (Subscriber Identification Module) card is a physical electronic device that is the integrated circuit of the internet. Sim cards are used by the public as a place to store quotas for internet, phone calls and SMS. There are many types of SIM cards that are used by the public, such as Telkomsel cards, XL cards, Exis cards and Smartfren cards. There are some people who are interested and use Telkomsel cards, because the network is good. But there are some people who don't use Telkomsel cards, because the quota price is quite expensive. Therefore, the Penlus will make research about people's interest in Telkomsel cards. This study aims to determine the amount of public interest in the Telkomsel card. To conduct this research, the authors used 42 community data which would be classified using the decision tree method. The data used by the author was obtained by distributing a questionnaire to the public. After classifying using the decision tree method, the result is that the people who are interested in the Telkomsel card are 33 people who are interested in the Telkomsel card (for the representation results it is 78.5%) and the results obtained are that the people who are not interested in the Telkomsel card are 9 people (for its representation results of 21.4%). From the results of the study, many people are interested in Telkomsel cards, even though the internet, call and SMS quota prices are quite expensive.

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Section: Methodsmentioning

confidence: 99%

Analysis of Public Interest in Telkomsel Cards Using the Decision Tree Method

Cantika,

Yanris,

Hasibuan

2023

SinkrOn

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“…Machine Learning involves specialized algorithms for analyzing datasets to predict trends, categorize information, or establish distinctions [15].…”

Section: Machine Learningmentioning

confidence: 99%

Machine Learning Algorithms for Forecasting and Categorizing Euro-to-Dollar Exchange Rates

El Mahjouby,

Taj Bennani,

Lamrini

et al. 2024

IEEE Access

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Forecasting changes in foreign exchange rates is a well-explored and widely recognized area within finance. Numerous research endeavors have delved into the utilization of methods in machine learning to analyze and predict movements in the foreign exchange market. This work employed several machine learning techniques such as adaboost, logistic regression, gradient boosting, random forest classifier, bagging, Gaussian naïve Bayes, extreme gradient boosting classifier, decision tree classifier, and our approach. Our objective is to predict the most advantageous times for purchasing and selling the euro about the dollar. We integrated a range of technical indicators into the training dataset to enhance the precision of our techniques and strategy. The outcomes of our experiment demonstrate that our approach outperforms alternative methods, achieving superior prediction performance. Our methodology yielded an accuracy of 0.948.

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“…Today and in the future, algorithms in processing datasets are becoming increasingly necessary knowledge in many fields [21]. In machine learning, we can perform multistage classifiers such as logistic regression (LR), k-nearest neighbors (kNN), Gaussian Naive Bayes (GNB), support vector machine (SVM), and linear discriminant analysis (LDA) [22]. Group classification problems can be solved using classification algorithms such as SVM [23].…”

Section: Introductionmentioning

confidence: 99%

Prediction of linear model on stunting prevalence with machine learning approach

Mambang

Marleny²,

Zulfadhilah³

2023

Bulletin EEI

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An increase in the number of residents should be anticipated including in the health sector, especially the problem of stunting. Stunting in children disrupts height and lack of absorption of nutrients. Information and data drive change in many areas such as health, entertainment, economics, business, and other strategic areas. The stages carried out in this study are initiating, developing linear models, and making prediction results on linear machine learning models. The results of testing with the scikit-learn linear model with a minimum variable of 19 get the best test results, namely the polynomial regression with pipeline model with mean absolute percentage error (MAPE) 0.02, root mean square error (RMSE) 3.32, and coefficient of determination (R2) 1,00. Testing with the scikit-learn linear model with a maximum variable of 48 gets the best test results, namely the polynomial regression with pipeline model with MAPE 0.00, RMSE 3.79 and R2 1.00. Testing with the scikit-learn linear model with an average variable of 32 gets the best test results, namely the polynomial regression model with MAPE 0.01, RMSE 3.32, and R2 1.00. The results of testing with the scikit-learn linear model with the minimum, maximum, and average variables get the best test results, namely the polynomial regression with pipeline model.

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Machine learning with multistage classifiers for identification of of ectoparasite infected mud crab genus <i>Scylla</i>

Cited by 6 publications

References 24 publications

Analysis of Public Interest in Telkomsel Cards Using the Decision Tree Method

Analysis of Public Interest in Telkomsel Cards Using the Decision Tree Method

Machine Learning Algorithms for Forecasting and Categorizing Euro-to-Dollar Exchange Rates

Prediction of linear model on stunting prevalence with machine learning approach

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