In this paper, we use chest x-ray images of Tuberculosis and Pneumonia to diagnose the patient using a convolutional neural network model. We use 4273 images of pneumonia, 1989 images of normal, and 394 images of tuberculosis. The data are divided into 80% as the training set and 20% as the testing set. We do the preprocessing steps to all of our images data, such as resize, converting RGB to grayscale, and Gaussian normalization. On the training dataset, the sampling technique used is undersampling and oversampling to balance each class. The best model was chosen based on the Area under Curve value i.e. the area under the curve of Receiver Operating Characteristics. This method shows that the best model obtains when trains the training dataset using oversampling. The Area under Curve value is 0.99 for tuberculosis and 0.98 for pneumonia. Therefore, this best model succeeds to identify 86% true for tuberculosis and 96% true for pneumonia.Keywords: chest X-ray images; tuberculosis; pneumonia; convolutional neural network. AbstrakPada penelitian ini memanfaatkan data citra chest x-ray penderita penyakit tuberculosis dan pneumonia. Model convolutional neural network digunakan untuk membantu mendiagnosis kedua penyakit ini. Data yang digunakan masing-masing sudah dilabeli sebanyak 4273 citra pneumonia, 1989 citra normal dan 394 citra tuberculosis. Data tersebut dibagi menjadi 80% himpunan data latih dan 20% data uji. Himpunan data tersebut telah melalui 3 tahap prepocessing yaitu resize citra, merubah citra RGB menjadi grayscale dan standarisasi gausian pada citra. Pada data latih dilakukan teknik sampling berupa undersampling dan oversampling data untuk menyeimbangkan data latih antar kelas. Model terbaik dipilih berdasarkan nilai Area under Curve yaitu luas daerah di bawah kurva Receiver Operating Chracteristics. Hasil menunjukkan bahwa model terbaik dihasilkan ketika dilatih menggunakan data latih hasil oversampling dengan nilai Area under Curve kelas tuberculosis sebesar 0,99 dan nilai Area under Curve kelas pneumonia sebesar 0,98. Oleh karena itu, model terbaik ini mampu mengindentifikasi sebanyak 86% penyakit tuberculosis dan 96% penyakit pneumonia.Kata Kunci: citra chest X-ray; penyakit infeksi paru; pengolahan citra digital Convolutional Neural Network.
As a country with the largest Muslim population in the world, the Lesbian, Gay, Bisexual, and Transgender (LGBT) issue in Indonesia has always been a hot topic to investigate. Social media such as Twitter is normally the main media where people normally discuss this LGBT topic. In this paper, we collect 18,552 tweets dated from 2015 up to 2018 to analyze the dynamics of the LGBT conversation among Indonesian peoples. In this research, we will explore the main topic of the LGBT conversation using Linear Discriminant Analysis (LDA). LDA is one of the most popular methods of soft clustering. This technique is effective to identify latent topic information (hidden) in a collection of big data using a bag of words approaches that treat every document as a vector of total words and is represented as a probability distribution on several topics. The result shows that there are seven main categories that people normally talked about regarding LGBT i.e. politics, religion, government, ethics, nationality, culture, and technology. Looking at the topic probability distributions on each semester we found that it is generally homogenous. An exception occurs during the government election period where politic tends to have a significantly higher probability. In other words, we have found that there is a tendency that LGBT issues are used in Indonesian politics.Keywords: LGBT; politics; topic modeling; twitter. AbstrakSebagai negara dengan penduduk muslim terbesar di dunia, isu mengenai Lesbian, Gay, Bisexual, dan Transgender (LGBT) di Indonesia adalah isu sensitif yang senantiasa menarik untuk diteliti. Media sosial seperti twitter adalah salah satu media yang biasa digunakan masyarakat untuk mendiskusikan tentang topik LGBT ini. Penelitian ini menggunakan 18.552 tweet tahun 2015 – 2018 dikumpulkan untuk melihat perbedaan pola perbincangan dari waktu ke waktu. Dalam penelitian ini, eksplorasi topik utama perbincangan LGBT dianalisis menggunakan metode Linear Discriminant Analysis (LDA). LDA adalah metode yang paling populer dalam soft clustering. Teknik ini efektif untuk mengidentifikasi informasi topik laten (tersembunyi) dalam koleksi dokumen besar menggunakan pendekatan bag of words yang memperlakukan setiap dokumen sebagai vektor jumlah kata dan direpresentasikan sebagai distribusi probabilitas atas beberapa topik, sementara setiap topik direpresentasikan sebagai distribusi probabilitas atas sejumlah kata. Hasil menunjukkan bahwa terdapat tujuh topik dominan yang sering muncul pada perbincangan tentang LGBT, yaitu politik, agama, pemerintahan, keasusilaan, kewarganegaraan, budaya dan teknologi. Pada kategori ini kemudian distribusi probabilitas topik dihitung dan dianalisa pada setiap semesternya. Hasilnya menunjukkan bahwa ada kecenderungan distribusi topik seragam, kecuali pada masa-masa pergantian pemerintahan dimana kategori politik cenderung meningkat secara signifikan. Dengan kata lain, ada kecenderungan bahwa isu LGBT dikaitkan dengan kehidupan perpolitikan di Indonesia.Kata kunci: LGBT, politik, topic modelling, twitter.
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