Implementasi Algoritma Convolutional Neural Network (CNN) Untuk Klasifikasi Daun Dengan Metode Data Mining SEMMA Menggunakan Keras

Suwitono, Yosia Amado; Kaunang, Fergie Joanda

doi:10.31603/komtika.v6i2.8054

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…In classifying data using the SVM method, the kernel function K(xi, xd) is used. The kernel function that will be used as in formula (1) as follows [23]:…”

Section: Methodsmentioning

confidence: 99%

Comparative SVM and Decision Tree Algorithm in Identifying the Eligibility of KIP Scholarship Awardee

Asriyanik,

Pambudi

2023

conferenceseries

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Scholarship selection process has specific rules, but if the number of applicants exceeds the quota, a selection process is needed. Based on the observation of a university in Sukabumi, the selection for KIP scholarship has not yet had a standard method. Several methods can be used to assist the selection process, such as classification based on historical data of applicants. The algorithms used for classification include Decision Tree (DT) and Support Vector Machine (SVM). The research process uses SEMMA (Sample, Explore, Modify, Model, Assess) method. Dataset for KIP scholarship awardee from 2021-2022 consist of 519 samples with 16 attributes. From the exploration results, the most important features for model modeling are Status DTKS, Status P3KE, Father's income, mother's income, combined income, and performance. These attributes are converted into numerical data to facilitate model fitting. The K-Fold Cross-Validation results for the Decision Tree model in the case of KIP Scholarship classification yield an accuracy of 78.44% for the entire test dataset, a precision of 0.73107, indicating that 73.11% of the predictions are true, a recall (sensitivity) of 78.45%, and an F1 score of 73.20%. The results for the SVM model are an accuracy of 80.17%, a precision of 84.44%, and a recall of 80.17%.

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“…In classifying data using the SVM method, the kernel function K(xi, xd) is used. The kernel function that will be used as in formula (1) as follows [23]:…”

Section: Methodsmentioning

confidence: 99%

Comparative SVM and Decision Tree Algorithm in Identifying the Eligibility of KIP Scholarship Awardee

Asriyanik,

Pambudi

2023

conferenceseries

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“…Proses Data Mining ada beberapa macam diantaranya KDD(Knowledge Data Discovery), CRISP-DM(Cross Industry Standard for Data Mining), SEMMA(Sample, Explore, Modify, Model, Assess) [3], dan pada penelitian ini menggunakan Proses KDD.…”

Section: Iunclassified

Komparasi Metode Apriori dan FP-Growth Data Mining Untuk Mengetahui Pola Penjualan

Purwati,

Pedliyansah,

Kurniawan

et al. 2023

JPIT

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Sales data is generally still rarely used, as well as the Perfume Corner shop just piling up in the database, even though there are problems experienced by the store regarding sales data for the best-selling products and to increase the number of sales of subsequent perfume products, so that the store can survive and develop even better. The algorithm that can be used to manage sales data to overcome this problem is Apriori. The research method used in this research is the KDD (Knowledge Discovery in Database) process. This research produces a high frequency pattern for itemsets with a minimum support value of 20% resulting in products that become The Most Tree Items namely Jo Malone 82.49%, Zarra 28.25%, and Zwitsal 20.34%. While the association rules formed from the value of Min. Supp 20% and Min. Conf 80%, get a combination of 2 itemsets, namely Jo Malone and Zarra. Whereas for the combination of 3 itemsets, namely Jo Malone, Zarra and Baccarte with valid and strong status, it is proven by a lift value greater than 1, therefore the association rules are very appropriate to be used.

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“…As a result, the proposed stacking ensemble technique significantly improves accuracy compared to other existing ML-based techniques if all kinds of feature sets are present. However, among the various models investigated to categorize PCOS and non-PCOS patients, the stacking ensemble model with the classifier 'Gradient Boosting' as a meta learner outperformed the others with 95.7% accuracy while making use of the top 25 features selected using Principal Component Analysis feature selection (PCA) [2], [10].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Polycystic Ovarian Syndrome Classification Using Convolutional Neural Network

Nurdiawan,

Susana,

Faqih

2024

jitk

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Polycystic Ovarian Syndrome (PCOS) is the main cause of infertility in women. This condition results in abnormal hormone levels. Women who experience this syndrome will have irregular hormone levels and experience irregular menstrual cycles as well, thereby affecting the reproductive system. Symptoms that arise as a result of the increase in these hormones can be seen from the growth of hair on the legs, weight gain which results in not being ideal, irregular menstruation, unusual acne growth, and oily skin. The problem of Polycystic Ovarian Syndrome can cause disturbances in ovulation and cause infertility in women. Urgency This research requires a classification that has good accuracy in diagnosing early to minimize the rate of pregnancy failure. The aim of the research is to be able to model early detection of Polycystic Ovarian Syndrome with high accuracy so that it can help the health team in detecting Polycystic Ovarian Syndrome or not having Polycystic Ovarian Syndrome. The research stage has 3 stages including the first stage of identifying problems and collecting datasets from Telkom University dataverse in the form of images and literature reviews of various sources. The second stage is Pre Processing of image data, Data Training, modeling design by managing image data and classifying using the Convolutional Neural Network Algorithm deep learning model and testing. The third stage is evaluating the test results and discussing the results of accuracy in determining the status of Normal Polycystic Ovarian Syndrome or PCOS. The results of training and validation on the ovarian xray image dataset using the CNN architecture that has been made, 40 iterations (epochs), and 4 step_per_epochs show an accuracy value of 0.8947 or 89.47% and a loss value of 0.2684.

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Implementasi Algoritma Convolutional Neural Network (CNN) Untuk Klasifikasi Daun Dengan Metode Data Mining SEMMA Menggunakan Keras

Cited by 4 publications

References 4 publications

Comparative SVM and Decision Tree Algorithm in Identifying the Eligibility of KIP Scholarship Awardee

Comparative SVM and Decision Tree Algorithm in Identifying the Eligibility of KIP Scholarship Awardee

Komparasi Metode Apriori dan FP-Growth Data Mining Untuk Mengetahui Pola Penjualan

Deep Learning for Polycystic Ovarian Syndrome Classification Using Convolutional Neural Network

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