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Abstrak :
Kematian yang disebabkan oleh kanker diperkirakan akan terus meningkat, terutama untuk kanker prostat. Penyakit ini adalah jenis kanker yang paling umum untuk pria di dunia. Jumlah kematian dapat dikurangi dengan deteksi dini menggunakan machine learning. Salah satunya adalah klasifikasi data kanker prostat. Data kanker yang digunakan memiliki berbagai fitur, tetapi tidak semua fitur adalah fitur penting. Dalam penelitian ini, kami menggunakan Support Vector Machine-Recursive Feature Elimination (SVM-RFE) dan One Dimensional Naïve Bayes Classifier (1-DBC) sebagai metode seleksi fitur. Dalam kedua metode itu akan mendapatkan peringkat untuk setiap fitur. Penggunaan kedua metode ini dalam klasifikasi data kanker prostat menghasilkan tingkat evaluasi yang tinggi. Kedua metode ini dapat menghasilkan tingkat akurasi 100%, precision 100%, dan recall 100% pada metode klasifikasi Random Forest. Dan menghasilkan tingkat akurasi 95%, precision 100%, dan recall 94,11% pada metode klasifikasi SVM. Dalam evaluasi tambahan, SVM-RFE memiliki running time lebih rendah dari 1-DBC.

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Death caused by cancer is expected to continue to increase, especially for prostate cancer. This disease is the most common type of cancer for men in the world. The number of deaths can be reduced by early detection using machine learning. One of them is the classification of prostate cancer data. Cancer data used has various features, but not all features are essential features. In this study, we use Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and One Dimensional Naïve Bayes Classifier (1-DBC) as a feature selection method. In both methods, it will get a rating for each feature. The use of these two methods in the classification of prostate cancer data produces a high level of evaluation. Both of these methods can produce 100% accuracy, 100% precision, and 100% recall in the Random Forest classification method. And it produces 95% accuracy, 100% precision, and 94.11% recall in the SVM classification method. In the additional evaluation, SVM-RFE has a running time lower than 1-DBC.

Abstrak :
Stylometry merupakan teknik analisa terhadap kepengarangan menggunakan statistik. Melalui stylometry, identitas kepengarangan dari suatu dokumen dapat dianalisis dengan tingkat akurasi yang tinggi. Hal ini menyebabkan adanya ancaman terhadap privasi penulis. Namun terdapat salah satu jenis metode dari stylometry yaitu penghapusan identitas kepengarangan yang dapat memberikan perlindungan privasi bagi penulis. Penelitian ini menggunakan metode penghapusan identitas kepengarangan yang diterapkan pada korpus Federalist Paper. Federalist Paper merupakan korpus terkenal yang telah banyak diteliti terutama pada metode identifikasi kepengarangan karena di dalam korpus tersebut terdapat 12 artikel yang tidak diketahui identitas penulisnya, salah satu metode identifikasinya adalah menggunakan algoritma Support Vector Machine. Melalui algoritma tersebut didapatkan identitas penulis dari artikel yang tidak diketahui pengarangnya dengan tingkat akurasi sebesar 86%. Tantangan dari metode penghapusan identitas kepengarangan adalah harus mampu mengubah gaya penulisan dengan tetap mempertahankan makna. Long-Short Term Memory (LSTM) merupakan algoritma berbasis Deep Learning yang mampu melakukan prediksi kata secara baik. Melalui model yang dibentuk dari algoritma LSTM, artikel-artikel dalam Federalist Paper diubah gaya penulisannya. Hasilnya, 30% dari artikel yang diklasifikasi dapat diubah identitas kepengarangannya dari satu penulis menjadi penulis lainnya. Tingkat kemiripan dokumen hasil ubahan berkisar antara 40-57% menandakan perubahan makna yang tidak signifikan dari dokumen aslinya. Hasil tersebut menyimpulkan bahwa metode yang diajukan mampu melakukan penghapusan identitas kepengarangan dengan baik. ......Stylometry is an authorship analysis technique using statistics. Through stylometry, authorship identity of a document can be analyzed with a high degree of accuracy. This causes a threat to the privacy of the author. But there is one type of method of stylometry, namely the elimination of authorship identity which can provide privacy protection for writers. This study uses the authorship method of eliminating the method applied to the Federalist Paper corpus. Federalist Paper is a well-known corpus that has been extensively studied especially in authorship identification methods because there are 12 disputed texts in the corpus, one of the identification methods is using the Support Vector Machine algorithm. Through this algorithm the author's identity of disputed text is obtained with an accuracy of 86%. The challenge of the authorship identity elimination method is that it must be able to change the writing style while maintaining its meaning. Long-Short Term Memory (LSTM) is a Deep Learning based algorithm that is able to predict words well. Through a model formed from the LSTM algorithm, the disputed articles in the Federalist Paper are changed in their writing style. As a result, 30% of classified articles can be changed from one author identity to another identity. The level of similarity of the changed documents ranges from 40-57%, which indicates a change in meaning that is not significant from the original document. These results conclude that the proposed method is able to perform authorship identity deletion properly.

Abstrak :
[ABSTRAK
Kanker payudara adalah tumor ganas yang tumbuh akibat pertumbuhan sel-sel jaringan yang tidak normal pada jaringan payudara. Kanker payudara pada wanita merupakan penyakit yang kini paling banyak diderita dibandingkan jenis kanker lainnya. Cara yang dilakukan agar penyakit ini tidak memiliki kesempatan untuk menyebar adalah dengan mendeteksinya sedini mungkin dengan menggunakan mammografi.

Pada penelitian ini penulis telah merancang suatu sistem yang menggunakan komputer untuk mendeteksi dan mengklasifikasi kanker payudara pada citra mammogram. Citra mammogram yang digunakan adalah citra mammogram dari Mommographic Image Analysis Society (MIAS) yang terdiri dari 322 citra. Pengolahan awal citra pada sistem ini menggunakan metode Otsu Thresholding, pendeteksian tepi dengan menggunakan metode Canny, dan metode dilasi. Ciri yang digunakan pada sistem ini adalah Gray Level Co-occurrence Matrix (GLCM) dan Discrete Wavelet Transform (DWT). Metode pengklasifikasian yang digunakan pada penelitian ini adalah Support Vector Machine (SVM). Sistem memiliki ketahanan yang baik terhadap noise salt and pepper pada nilai noise tertentu pada tiap jenis citra mammogram yang digunakan. Tingkat keakuratan berkisar 80% pada saat diberi noise sebesar -16dB pada citra mammogram jinak dan ganas. Keakuratan sistem juga teruji cukup baik untuk jumlah data latih yang hanya sebesar 70% dimana tingkat keakuratan pendeteksian dan pengklasifikasian adalah sebesar 80,6%.

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ABSTRACT
Breast cancer is a malignant tumor that grows as a result of the growth of tissue cells that are not normal in the breast tissue. Breast cancer in women is a disease that is now the most common cancer than other types. How that is done so that the disease does not have a chance to spread is to detect it as early as possible by using mammography.

In this study, the authors have designed a system that uses a computer to detect and classify breast cancer on a mammogram image. Mammogram image has been taken from Mommographic Image Analysis Society (MIAS) which consists of 322 images. Initial processing images on this system using Otsu Thresholding, edge detection using Canny method, and the method of dilation. Features used in this system is the Gray Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT). Claassification method was used in this study is Support Vector Machine (SVM).

The system has good resistance to salt and pepper noise on certain noise value for each type of mammogram image are used. The accuracy range was 80% when given the noise of -16dB on mammogram images of benign and malignant. The accuracy of the system was also tested well enough for the amount of training data that only 70% where the level of detection and classification accuracy is 80,6 %.;Breast cancer is a malignant tumor that grows as a result of the growth of tissue cells that are not normal in the breast tissue. Breast cancer in women is a disease that is now the most common cancer than other types. How that is done so that the disease does not have a chance to spread is to detect it as early as possible by using mammography. In this study, the authors have designed a system that uses a computer to detect and classify breast cancer on a mammogram image. Mammogram image has been taken from Mommographic Image Analysis Society (MIAS) which consists of 322 images. Initial processing images on this system using Otsu Thresholding, edge detection using Canny method, and the method of dilation. Features used in this system is the Gray Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT). Claassification method was used in this study is Support Vector Machine (SVM). The system has good resistance to salt and pepper noise on certain noise value for each type of mammogram image are used. The accuracy range was 80% when given the noise of -16dB on mammogram images of benign and malignant. The accuracy of the system was also tested well enough for the amount of training data that only 70% where the level of detection and classification accuracy is 80,6 %., Breast cancer is a malignant tumor that grows as a result of the growth of tissue cells that are not normal in the breast tissue. Breast cancer in women is a disease that is now the most common cancer than other types. How that is done so that the disease does not have a chance to spread is to detect it as early as possible by using mammography. In this study, the authors have designed a system that uses a computer to detect and classify breast cancer on a mammogram image. Mammogram image has been taken from Mommographic Image Analysis Society (MIAS) which consists of 322 images. Initial processing images on this system using Otsu Thresholding, edge detection using Canny method, and the method of dilation. Features used in this system is the Gray Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT). Claassification method was used in this study is Support Vector Machine (SVM). The system has good resistance to salt and pepper noise on certain noise value for each type of mammogram image are used. The accuracy range was 80% when given the noise of -16dB on mammogram images of benign and malignant. The accuracy of the system was also tested well enough for the amount of training data that only 70% where the level of detection and classification accuracy is 80,6 %.]

Abstrak :
Penelitian ini melakukan studi mengenai sistem identifikasi pembicara berbahasa Indonesia menggunakan SVM. Parameter sistem terdiri atas silence removal, PCA, nilai rata-rata dan varians MFCC. Ujicoba menggunakan data berita berbahasa Indonesia dari televisi dan radio yang disegmen dalam 5, 10, 15 detik dengan jumlah data 26 jam (715 pembicara). Hasil penelitian ini menunjukkan ketepatan pengenalan pembicara sebesar 94-98% untuk kombinasi parameter silence removal dan rata-rata MFCC dengan akurasi terbaik pada segmen waktu 10 detik. Namun dengan bertambahnya jumlah pembicara, ketepatan pengenalan cenderung berkurang. Penelitian ini dapat dikembangkan untuk sistem perolehan informasi data speech berdasarkan siapa yang berbicara dalam suatu sesi data.

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This research studies speaker identification system for Indonesian speech based on SVM. Parameters of this system are silence removal, PCA, average and varians values of MFCC. The experiments use 26 hours (715 speakers) Indonesian broadcast news from radio and television segmented into 5, 10, 15 seconds. The results achieve 94-98% identification accuracy for combination of parameters silence removal and average of MFCC. The best accuracy comes from 10 seconds time segment. However, the accuracy falls when the number of speakers increases. This study could be used for speech retrieval system based on who speaks in a speech session.

Abstrak :
Komputasi intelejensia yang digunakan dalam masalah klasifikasi pola dapat digolongkan menjadi dua bagian, yaitu yang berbasis pada Neural Network dan yang berbasis pada Pembelajaran Statistika (Statistical Learning). Pembelajaran yang berbasis statistika, pertama kali ditemukan oleh Vapnik pada dekade tujuh-puluhan. Untuk masalah klasifikasi pola Vapnik mengembangkan metode hyperplane optimal separation, atau dikenal juga dengan nama metode Support Vector Machines (SVM). Pada awalnya SVM dirancang hanya untuk menyelesaikan masalah klasifikasi biner, yaitu dari data-data yang ada, diklasifikasikan menjadi dua kelas. Untuk mengklasifikasikan data yang terdiri dari lebih dari dua kelas, metode SVM tidak dapat langsung digunakan. Ada beberapa metode yang dapat digunakan untuk menyelesaikan masalah klasifikasi multikelas SVM yaitu: metode One-vs-One dan metode One-vs-Rest. Kedua metode ini merupakan perluasan dari klasifikasi biner SVM. Kedua metode tersebut akan dibahas di artikel ini dan akan dilihat kinerjanya dalam mengklasifikasikan aroma. Data aroma yang digunakan dalam percobaaan ini terdiri dari 3 jenis aroma, masing-masing aroma terdiri atas 6 kelas. Pembagian kelas ini berdasarkan pada konsentrasi alkohol yang dicampurkan pada masing-masing aroma. Misalkan untuk aroma A, terdapat 6 jenis aroma A dengan kandungan alkohol : 0%, 15%, 25%, 30%, 45% dan 75%. Kinerja dari kedua metode diukur berdasarkan kemampuan untuk mengenal dan mengklasifikasikan aroma, dengan tepat dan sesuai dengan jenis atau kelas, dari data yang diberikan.

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Aroma classification using one-vs-one and one-vs-rest methods. Computational Intelligence used in pattern classification problem can be divided into two different parts, one based on Neural Network and the other based on Statistical Learning. The Statistical Learning discovered by Vapnik on 70-est decade. For the pattern classification, Vapnik developed hyperplane optimal separation, which is known as Support Vector Machines Method (SVM). In the beginning, SVM was designed only to solve binary classification problem, where data existing are classified into two classes. To classify data whose consist of more than two classes, the SVM method can not directly be used. There are several methods can be used to solve SVM multiclasses classification problem, they are One-vs-One Method and One-vs-Rest Method. Both of this methods are the extension of SVM binary classification, they will be discussed in this article so that we can see their performance in aroma classification process. Data of aroma used in this experiment is consisted of three classes of aroma, each of them has six classes. The division of this class is based on alcohol concentration mixed into each of those aromas. For example, for aroma A, there are six kinds of aroma A with different alcohol concentration: 0%, 15%, 25%, 30%, 45% and 75%. The performance of these methods is measured based on their ability to recognize and classify aroma, precisely and match with the right class or variety of data existed.

Abstrak :
ABSTRAK
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Diabetes merupakan penyakit kronis yang terjadi ketika terdapat peningkatan kadar glukosa dalam darah karena tubuh tidak dapat atau tidak cukup menghasilkan hormon insulin atau tidak dapat menggunakan insulin secara efektif. Umumnya untuk mendeteksi penyakit diabetes adalah dengan tes kadar gula darah atau hemoglobin HbA1c yang dilakukan oleh praktisi medis. Pada penelitian ini, dibangun sistem prediksi penyakit diabetes berbasis iridologi atau melalui citra mata, menggunakan machine learning. Sistem yang dikembangkan terdiri dari instrumen akuisisi citra mata dan algoritma pengolahan citra. Metode GLCM (Gray Level Co-Occurence Matrix) digunakan untuk proses ekstraksi ciri, dengan tujuan untuk mendapatkan ciri tekstur pada citra. Metode SVM (Support Vector Machine) dan kNN (k Nearest Neighbor) digunakan untuk proses klasifikasi kelas diabetes dan non-diabetes. Hasil klasifikasi kemudian dilakukan proses validasi dengan menggunakan metode k-fold cross validation. Hasil yang diperoleh menunjukkan bahwa metode kNN memiliki performa yang lebih baik dibandingkan dengan metode SVM. Performa terbaik didapatkan saat variasi kombinasi ukuran area segmentasi 30×360 dengan jarak antar tetangga 30 pixel. Tingkat akurasi yang diapatkan dari pengujian sebesar 79,6%, dengan nilai misclassification rate (MR) 20,4%, false positive rate (FPR) 20,6%, false negative rate (FNR) 20%, sensitivity 87,1%, dan specificity 70,0%.

 

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ABSTRACT

Diabetes is a chronic disease that occurs when there is an increase in glucose levels in the blood because the body cannot produce enough of the hormone insulin or cannot use insulin effectively. Generally, to detect diabetes is by pengujian blood sugar levels or hemoglobin HbA1c carried out by medical practitioners. In this study, a diabetes prediction system based on iridology or through eye images was constructed using machine learning. The developed system consists of eye image acquisition instruments and image processing algorithms. The GLCM (Gray Level Co-Occurence Matrix) method is used for feature extraction processes, with the aim of obtaining texture characteristics in the image. The SVM (Support Vector Machine) and kNN (k Nearest Neighbor) methods are used to classify diabetic and non-diabetic classes. The classification results are then validated by using the k-fold cross validation method. The results show that kNN method has better performance compared to the SVM method. The best performance is when size of the segmentation area 30×360 pixel with the distance between neighbors 20 pixel. The results show that the accuracy from pengujian is 79.6%, misclassification rate (MR) 20.4%, false positive rate (FPR) 20.6%, false negative rate (FNR) 20.0%, sensitivity 87.1%, and specificity 70.0%.

 

Abstrak :
There are many information which can be processed in many emails. Clasisification is a way to organize the informations which are be in the emails.....

Abstrak :
Diabetes is one of the most serious health challenges in both developed and developing countries. Early detection and accurate diagnosis of diabetes can reduce the risk of complications. In recent years, the use of machine learning in predicting disease has gradually increased. A promising classification technique in machine learning is the use of support vector machines in combination with radial basis function kernels (SVM-RBF). In this study, we used SVM-RBF to predict diabetes. The study used a Pima Indian diabetes dataset from the University of California, Irvine (UCI) Machine Learning Repository. The subjects were female and ≥ 21 years of age at the time of the index examination. Our experiment design used 10-fold cross-validation. Confusion matrix and ROC were used to calculate performance evaluation. Based on the experimental results, the study demonstrated that SVM-RBF shows promise in aiding diagnosis of Pima Indian diabetes disease in the early stage.

Abstrak :
Diabetes is one of the most serious health challenges in both developed and developing countries. Early detection and accurate diagnosis of diabetes can reduce the risk of complications. In recent years, the use of machine learning in predicting disease has gradually increased. A promising classification technique in machine learning is the use of support vector machines in combination with radial basis function kernels (SVM-RBF). In this study, we used SVM-RBF to predict diabetes. The study used a Pima Indian diabetes dataset from the University of California, Irvine (UCI) Machine Learning Repository. The subjects were female and ? 21 years of age at the time of the index examination. Our experiment design used 10-fold cross-validation. Confusion matrix and ROC were used to calculate performance evaluation. Based on the experimental results, the study demonstrated that SVM-RBF shows promise in aiding diagnosis of Pima Indian diabetes disease in the early stage.

Abstrak :
ABSTRAK
Secara visual, sulit untuk membedakan antara perokok dan bukan perokok bahkan untuk dokter atau dokter gigi yang berpengalaman. Salah satu cara yang paling obyektif untuk mengenali lidah perokok adalah dengan menggunakan alat seperti kamera. Penelitian yang relevan menemukan bahwa kelainan pada permukaan lidah dapat ditangkap oleh kamera HS pada rentang spektrum 650 - 900 nm. Sistem yang diusulkan terdiri dari dua bagian, perangkat keras dan perangkat lunak. Perangkat keras terdiri dari rangka aluminium, slider, sebuah sumber cahaya halogen dan kamera HS dengan rentang spektral antara 400-1000 nm yang terhubung ke komputer. Sistem dilengkapi oleh perangkat lunak pengolah citra hiperspektral yang dirancang untuk mendeteksi lidah perokok. Nilai reflektansi permukaan lidah diekstraksi dari citra lidah responden yang sebelumnya dikoreksi dengan menggunakan referensi citra hiperspektral gelap dan terang. Merata-ratakan data reflektansi spektral disetiap region lidah dilakukan untuk mengubah fitur yang ada menjadi ruang dimensi yang lebih kecil. Principal Component Analysis PCA digunakan untuk menghitung dan memilih subset fitur yang akan digunakan sebagai input oleh pengklasifikasi. Support vector machine SVM digunakan sebagai model klasifikasi citra karena kinerjanya sangat baik untuk memilih separator hyperplane terbaik di antara dua kelas yang berbeda. Sejumlah sampel citra lidah diakuisisi, diolah dan diklasifikasikan sebagai lidah perokok dan bukan perokok oleh sebuah sistem pengukuran hiperspektral. Evaluasi hasil sistem diperiksa menggunkan confusion matriks dengan menjadikan false positive rate FPR , false negative rate FNR , sensitivity dan specificity sebagai parameter kehandalan sistem. Validasi terhadap hasil pengukuran dilakukan menggunakan metode k-fold cross validation dengan rata-rata error klasifikasi SVM sebagai parameter akurasi sistem prediksi. Sistem deteksi perokok untuk mengidentifikasi smoker rsquo;s melanosis ini berhasil mengklasifikasi lidah perokok dan bukan perokok dengan akurasi yang baik.Kata kunci: Hiperspektral, SVM, Fingerprint, Lidah, Perokok.

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ABSTRACT
Visually, it is difficult to diffrentiate between smoker and non smoker tongue even for an experienced doctor or dentist. One of the most objective way to acknowledge the smoker tongue is by using tools such as camera. The relevant research found that lession on tongue surface possible to be captured by hiperspektral camera in spectral range 650 ndash 900 nm. The proposed system contains of two parts, hardware and software. The hardware consists of workbench, slider, a halogen light source and hyperspectral camera with spectral range between 400 1000 nm connected to personal computer. The system complemented with hiperspektral image processing software built up especially to analyse the smoker tongue. The reflectance values of tongue surface was extracted from respondent tongue image that previously corrected using white and dark hiperspektral image references. Averaging all of spectral data have been done to transform the existing features into a lower dimensional space. The principal component analysis PCA was used to compute and select the features subset which will be used as an input by the classifier. The support vector machine SVM classifier is used as image classification model since it perform excellent to choose the best hyperplane separator between two difference classes. A number of samples of the tongue image were acquired, processed and classified as smokers and non smokers tongue by a hyperspectral measurement system. The evaluation of system result is checked using confusion matrix by making false positive rate FPR , false negative rate FNR , sensitivity and specificity as system reliability parameters. Validation of the measurement results was done using k fold cross validation method with average error classification SVM as parameter of system prediction accuration. Smoker detection system to identify smoker rsquo s melanosis is successfully classify the tongue of smokers and non smokers with good accuracy.Keywords Hiperspektral, Reflectance, Smoker, Tongue, Diagnosis, SVM, PCA