Ensemble Classifier untuk Klasifikasi Kanker Payudara

Khadijah, Khadijah; Kusumaningrum, Retno

doi:10.25299/itjrd.2019.vol4(1).3540

Cited by 4 publications

(5 citation statements)

References 8 publications

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“…The determination of the optimal weight will lead to the right prediction results [25]. In MLP, a standard Sigmoid function is used where the weighting amount of a number of inputs and biases are entered into the activation level through the transfer function to produce output, and the units are arranged in a feed-forward topology layer called feed forward neural network (FFNN) [26]. When there is more than one hidden layer, the output of the hidden layer is inserted into the next hidden layer and a separate weight is used for summation to each subsequent layer.…”

Section: Multi-layer Perceptron Classifier (Mlp)mentioning

confidence: 99%

Sentiment Analysis for the Brazilian Anesthesiologist Using Multi-Layer Perceptron Classifier and Random Forest Methods

Asian

Rosita

Mantoro

2022

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Sexual harassment is defined as giving sexual attention both verbally, either in speech or writing, and physically to victims who are predominantly women, On July 13, 2022, there was a tweet featuring a video of sexual harassment that made it trend in various countries. The video irritated Twitter users and made various comments resulting in various sentiments that can be analyzed using sentiment analysis. The purpose of this study is to see what the public thinks about the sexual harassment case of Brazilian anesthesiologist. Besides the sentiment analysis, another aim of this study is to see how objective are those sentiments based on their polarity. This study uses a comparison of two methods in sentiment analysis, namely Multi-Layer Perceptron Classifier and Random Forest, and labeling automatically using TextBlob. This results in 94.44% accuracy, 94.44% precision, 92% recall and 93% f1_score. For MLP Classifier and accuracy 96.42%, precision 94.44%, recall 96.66% and f1_score 95.56% for Random Forest. Sentiment polarity score from the TextBlob is -0.5 and subjectivity is 0.4 which indicates that most statements are negative and subjective score is 0.4, which means those sentiments are subjective in nature.

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Section: Multi-layer Perceptron Classifier (Mlp)mentioning

confidence: 99%

Sentiment Analysis for the Brazilian Anesthesiologist Using Multi-Layer Perceptron Classifier and Random Forest Methods

Asian

Rosita

Mantoro

2022

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“…Akurasi merupakan perhitungan dengan dilakukannya perbandingan antara jumlah data yang diklasifikasikan secara benar dengan jumlah keseluruhan data [28]. Tingginya nilai akurasi menunjukkan bahwa sistem memiliki kinerja yang baik.…”

Section: B) Pengukuran Hasil Klasifikasiunclassified

“…Sensitivitas merupakan perhitungan dengan dilakukannya perbandingan antara jumlah data di kelas positif yang diklasifikasikan secara benar ke kelas positif 𝑇𝑃 dengan jumlah data yang seharusnya dikelas positif [28]. Sensitivitas dilakukan pada persamaan 10 : 𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑎𝑠 = 𝑇𝑃 𝑇𝑃+𝐹𝑁 (10) Spesifisitas merupakan perhitungan dengan dilakukannya perbandingan antara jumlah data di kelas negatif yang diklasifikasikan ke kelas negatif 𝑇𝑁 dengan jumlah data yang seharusnya dikelas negatif [28]. Spesifisitas dilakukan pada persamaan 11: 𝑆𝑝𝑒𝑠𝑖𝑓𝑖𝑠𝑖𝑡𝑎𝑠 = 𝑇𝑁 𝐹𝑃+𝑇𝑁 (11) Geometri mean (G-mean) merupakan perhitungan yang bertujuan untuk mengevaluasi kinerja algoritma klasifikasi yang pada umumnya sering digunakan pada permasalahan kelas dataset yang tidak seimbang.…”

Section: B) Pengukuran Hasil Klasifikasiunclassified

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Klasifikasi Gangguan Tidur REM Behaviour Disorder Berdasarkan Sinyal EEG menggunakan Machine Learning

Utami

2020

JSC

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REM Behavior Disorder (RBD) is a sleep disorder characterized by the loss of normal muscle atony (loss of paralysis) during Rapid Eye Movement (REM) sleep, where sufferers act on dreams that can result in physical injury to individuals or their sleep partners. REM is a sleep stage characterized by cessation of eye movement, a decrease in body temperature, slow heart rate and no muscle activity in several parts of the body. One of the methods used to detect RBD is Electroencephalography (EEG). EEG is a method of recording or capturing electrical activity in the brain. The dataset used was sourced from PhysioNet.org which consisted of 2 classes, namely normal class and RBD class which were taken from 26 subjects with 6 normal subjects and 20 RBD subjects. This research was conducted to classify RBD sleep disorders based on EEG signals using the ELM algorithm and it is expected to determine the best algorithm for classifying RBD sleep disorders using the ELM algorithm which will be compared with the SVM and backpropagation algorithms based on the EEG signal in terms of the resulting accuracy value and also the time required. to create a model in the algorithm classification process. Classification of RBD sleep disorders based on EEG signals begins with data pre-processing, feature extraction and classification. Data pre-processing includes signal splitting per 30 seconds and data smoothing. The feature extraction process uses Discrete Wavelet Transformation. The RBD classification process based on EEG signals uses the Extreme Learning Machine (ELM) algorithm with the binary sigmoid activation function. Prior to the training process on the ELM algorithm, undersampling was first carried out to overcome the imbalance in the number of classes. Evaluation of the classification results is done by using k-fold cross-validation. The classification results of RBD sleep disorders based on EEG signals using the ELM algorithm show that the ELM algorithm can classify RBD and non-RBD sleep disorders based on EEG signals with an average accuracy value of 70.71% ± 5.44. The comparison result states that the backpropagation algorithm has the best average accuracy in RBD classification based on EEG signals, reaching 83.81% ± 1.40. However, based on the computation of time, the ELM algorithm is superior in the speed of the RBD classification process based on EEG signals, reaching 0.04 ± 0.06 seconds compared to the Support Vector Machine (SVM) algorithm and backpropagation.

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“…Berdasarkan data Globocan Tahun 2018, Indonesia berada pada peringkat 8 di Asia Tenggara dan 23 di Asia dalam hal jumlah penyakit kanker. Kanker payudara merupakan jenis kanker yang banyak diderita oleh kaum wanita di Indonesia dengan jumlah kejadian 42,1 per 100.000 orang dan jumlah kematian rata-rata 17 per 100.000 orang [5]. Prevalensi kanker bergerak cepat di negara berkembang karena semakin meningkatnya gaya hidup yang dapat memicu penyakit ini, seperti merokok, kurang beraktivitas secara fisik, dan melakukan diet "westernized" [6].…”

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Klasifikasi Pasien Kanker Payudara Menggunakan Metode Support Vector Machine dengan Backward Elimination

Resmiati¹,

Arifin²

2021

SISTEMASI

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Kanker payudara merupakan tumor ganas yang tumbuh pada sel-sel payudara dan dapat menyebar di antara jaringan atau organ di sekitar payudara dan berpindah ke bagian tubuh lainnya. Jika deteksi kanker dilakukan sejak dini, memungkinkan dilakukan penanganan yang lebih baik dan timbulnya sel-sel kanker dapat diatasi dengan segera dan dihentikan penyebarannya. Untuk membantu meningkatkan kemampuan pendeteksian otomatis dapat digunakan teknik machine learning dengan metode klasifikasi. Salah satu metode klasifikasi yang dapat digunakan yaitu metode Support Vector Machine. Pada penelitian ini, metode Support Vector Machine diterapkan pada Breast Cancer Coimbra Data Set. Penerapan Backward Elimination bertujuan untuk mengoptimalkan performa suatu model dengan sistem kerja pemilihan mundur dan memilih atribut yang paling relevan pada proses klasifikasi. Hasil penelitian klasifikasi pasien kanker payudara menggunakan metode Support Vector Machine menghasilkan nilai akurasi sebesar 65,22% dan nilai AUC sebesar 0,700 yang termasuk ke dalam kategori Fair Classification. Sedangkan hasil penelitian klasifikasi pasien kanker payudara menggunakan metode Support Vector Machine dengan Backward Elimination menghasilkan nilai akurasi sebesar 95,65% dan nilai AUC sebesar 1,000 yang termasuk ke dalam kategori Excellent Classification.

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Ensemble Classifier untuk Klasifikasi Kanker Payudara

Cited by 4 publications

References 8 publications

Sentiment Analysis for the Brazilian Anesthesiologist Using Multi-Layer Perceptron Classifier and Random Forest Methods

Sentiment Analysis for the Brazilian Anesthesiologist Using Multi-Layer Perceptron Classifier and Random Forest Methods

Klasifikasi Gangguan Tidur REM Behaviour Disorder Berdasarkan Sinyal EEG menggunakan Machine Learning

Klasifikasi Pasien Kanker Payudara Menggunakan Metode Support Vector Machine dengan Backward Elimination

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