Klasifikasi Kelas Kata (Part-Of-Speech Tagging) untuk Bahasa Madura Menggunakan Algoritme Viterbi

Firmansyah, Ilham; Adikara, Putra Pandu; Adinugroho, Sigit

doi:10.25126/jtiik.2021854483

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…Many previous studies have been conducted on TB classification using machine learning. For example [8], classified TB data into class 1 (positive) and class 2 (negative) using the Extreme Machine Learning (ELM) method.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of Tuberculosis Based on Chest X-Ray Images for Imbalance Data using SMOTE

Fadhlullah Kh.TQ,

Wahyono

2024

IJCDS

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This research delves into the challenge of dataset imbalance in classifying Chest X-Ray (CXR) images in the TBX11K dataset.To address this, the study employs Random Forest (RF) and XGBoost (XGB) methods, both with and without the Synthetic Minority Over-sampling Technique (SMOTE). The primary objective is to evaluate the impact of SMOTE on the performance of these models in classifying CXR images from the TBX11K dataset. This research applies SMOTE to the RF and XGB classification models to increase the number of minority class samples (TB positive) and address the imbalance with the majority class samples (TB negative). To ensure a comprehensive comparison, each model is assessed using a consistent set of evaluation metrics, including accuracy, precision, recall, and F1 score. The findings indicate that applying SMOTE to both RF and XGB models effectively mitigates class imbalance in the dataset. Specifically, the RF model without SMOTE achieves an accuracy of approximately 93.33%, while the RF model with SMOTE achieves an accuracy of 92.72%. On the other hand, the XGB model without SMOTE achieves an accuracy of 94.11%, and the XGB model with SMOTE reaches 94.33%. Although SMOTE enhances overall model performance, challenges persist in accurately predicting the minority classes 'altb' and 'ltb.' These challenges are attributed to the less representative features of these minority classes, which are difficult to overcome even with resampling techniques. Based on the experimental results, the XGB model with SMOTE emerges as the most optimal model for classifying TBX11K images. Despite the improved performance, further work is needed to enhance the prediction accuracy for minority classes, suggesting that additional techniques or more sophisticated models might be required to address this issue comprehensively.

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Section: Literature Reviewmentioning

confidence: 99%

Classification of Tuberculosis Based on Chest X-Ray Images for Imbalance Data using SMOTE

Fadhlullah Kh.TQ,

Wahyono

2024

IJCDS

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“…Pengisian data kosong dengan KNN menggunakan parameter K=2 telah meningkatkan nilai Silhouette Coefficient sebesar 3,4% jika dibandingkan dengan hasil clustering tanpa pengisian data kosong menggunakan KNN. Ini menunjukkan bahwa penerapan KNN secara efektif membantu mengatasi permasalahan data yang tidak lengkap pada dataset PMKS [2].…”

Section: Pendahuluanunclassified

Analisis Kinerja Algoritma K-Nearest Neighbor Imputation (KNNI) Untuk Missing Value Pada Klasifikasi Data Mining

Miraati Laia

2023

jieee

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At this time many researchers use datasets for research, in the dataset there is a lot of important information provided to make it easier for researchers to process the data, but there are obstacles when trying to process the data, namely some data values are lost, or even damaged (incompatible) with other values. The number of attributes and data samples in the dataset is unlimited which makes it difficult for researchers to find important information based on the goals of each researcher. The missing or damaged values are known as missing values and this often happens when researchers process data taken from datasets. When researchers classify a dataset, of course it is difficult to classify because if there is missing data, the classification process cannot be carried out completely. Of course this will greatly affect the results obtained and also the results of the accuracy of the process. The problem of missing data (missing value) must be resolved, one way to solve this problem is by using the K-Nearest Neighbor Imputation (K-NNI) algorithm. The KNNI algorithm is one of the data mining algorithms that can restore the missing value problem by performing pattern recognition of the nearest data from the missing data. The final result after applying KNNI in fixing the missing value is to obtain a weight value for each K closest observation of 51.6161 on K5. Datasets that experience missing values have been adjusted based on the weight values and the implementation of the KNNI algorithm with the rapidminer application. By returning data using the KNNI algorithm, it is hoped that it can help solve these problems so that the results obtained from the classification process are more accurate and the results of greater accuracy

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“…The goal is to adjust the input data or input with the output data or output. The method used in data normalization is Min-Max Normalization [11]. The data normalization is formulated as follows:…”

Section: Data Pre-processing and Post-processingmentioning

confidence: 99%

Application of Extreme Learning Machine Method on Stock Closing Price Forecasting Pt Aneka Tambang (Persero) TBK

Apriliyanti,

Satyahadewi,

Andani

2023

BAREKENG: J. Math. & App.

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Artificial neural networks are modeling methods that can capture complex input and output relationships. This method is widely used in forecasting and classification. However, in its application, there are some disadvantages in terms of low learning rate resulting in computational delay. Extreme Learning Machine (ELM) was introduced to overcome these problems. This method is believed to be able to produce more accurate forecasting results with a low level of forecasting error. In Indonesia, stocks are one of the most popular investments for investors. Stock prices tend to be volatile which is influenced by the amount of market supply and demand, so forecasting analysis is needed to minimize the risks that may occur. This research applies the ELM method to forecast the closing price of PT ANTM Tbk shares from January 1, 2018 - October 31, 2022. The data used is secondary data obtained from the official website https://id.investing.com. The ELM method is applied by dividing training data for ELM modeling and testing data used in the forecasting process. The model architecture of the ELM method uses a combination of inputs obtained from the PACF plot, hidden nodes with a range of 5-50, and one output layer. The results of this study show excellent forecasting accuracy in terms of forecasting. This is measured by the MAPE value of 0.0358. The architecture formed in the ELM process is one input, 31 hidden nodes, and one output. Forecasting the closing price of PT ANTM Tbk shares with 1-31-1 architecture produces a forecasting value that shows a low decline, but is quite stable.

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Klasifikasi Kelas Kata (Part-Of-Speech Tagging) untuk Bahasa Madura Menggunakan Algoritme Viterbi

Cited by 4 publications

References 3 publications

Classification of Tuberculosis Based on Chest X-Ray Images for Imbalance Data using SMOTE

Classification of Tuberculosis Based on Chest X-Ray Images for Imbalance Data using SMOTE

Analisis Kinerja Algoritma K-Nearest Neighbor Imputation (KNNI) Untuk Missing Value Pada Klasifikasi Data Mining

Application of Extreme Learning Machine Method on Stock Closing Price Forecasting Pt Aneka Tambang (Persero) TBK

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