One of the main causes of death is cancer. The most common cancer in women is breast cancer. This disease if it be known early could be overcome and even prevented. Data classification techniques could be used to predict which patients had breast cancer and not with some parameters. Using the Neural Network method and Rapid Miner 9.0 tools aims to predict breast cancer diagnosis and then produced an accuracy value of 71,83%, precision 81,08% and recall of 69,17% with AUC of 0,806 which means that the classification was good so that patients with parameters there could be predicted which ones were breast cancer patients and which were not, so this pattern could be used as a benchmark for diagnosis so that it could be detected earlier and was expected to reduce the number of deaths from breast cancer.
Memiliki keturunan yang sehat, normal dan tidak beresiko bukan hal mudah didapatkan, pada kondisi tertentu melahirkan secara normal bukan solusi terbaik, operasi sesar bisa menjadi salah satu opsi yang dianggap relatif aman sejauh ini. Namun, karena sesar merupakan operasi besar, besar pula risikonya. Maka perlu pertimbangan yang matang mengenai metode melahirkan dengan normal atau operasi sesar. Penelitian ini bertujuan untuk mengklasifikasi harus menggunakan metode sesar atau tidak dengan memperhitungkan parameter yang ada yaitu diantaranya Age, Delivery Time, Delivery, Blood, Heart sehingga dapat memprediksi keselamatan ibu dan bayi dalam proses lahiran dengan menggunakan metode Neural Network dengan 80 dataset caesarian, training cycles 200, learning rate 0.01 dan momentum 0.9 dan menghasilkan akurasi sebesar 71,25% dan dengan nilai AUC (Area Under Curve) sebesar 0,721 yang artinya mendapat status fair classification.
This study discusses the application of data mining to predict student competencies using the decision tree method. In this study applying data mining to predict student competency using the decision tree method. This research was conducted to predict student learning outcomes based on report card grades semester 1, semester 2 semester 3 and semester 4. Data were then managed using Rapid Miner to facilitate predicting student competencies. The study was conducted at Multicomp SMK which has 3 majors namely Hospitality Accommodation, Online Business and Marketing and Multimedia. Research using data from students in each department includes class X and class XI. The application of data mining is used to predict student competencies by using a decision tree and C 4.5 algorithm as a support as well as a comparison to determine the competency of students of Multicomp Depok Vocational School based on both methods. This method is able to measure the ability of students appropriately and be able to provide an understanding at a certain level according to the needs of Indonesian education has a pattern and learning strategy based on students' reasoning abilities. Students are expected to be able to analyze a problem well and find the right solution. students are not accompanied by an adequate education system or curriculum. Teacher competencies that are not evenly distributed in various schools and governments are felt to be very lacking in realizing reasoning based education systems.
<p class="Abstrak">Salah satu ukuran terpenting saat awal persalinan adalah keakuratan prediksi berat lahir. Dengan menggunakan metode prediksi yang tepat, perkiraan ekstrim berat lahir bayi dapat dideteksi lebih atau kurang sehingga beberapa tindakan pencegahan dapat dilakukan sebelum persalinan. Di sisi lain, untuk meningkatkan akurasi suatu proses pembelajaran, dibutuhkan suatu prediksi yang akurat untuk masalah yang dihadapi atau dengan menggunakan gabungan beberapa metode. Penelitian bertujuan untuk prediksi berat lahir bayi menggunakan metode <em>Deep Learning autoencoder</em> untuk memprediksi berat lahir bayi. Salah satu tantangan dalam pengembangan sistem prediksi berat lahir bayi adalah datanya yang berdimensi tinggi. Teknik konvensional untuk <em>feature reduction</em> seperti <em>principal component analysis</em> (PCA), mengasumsikan <em>orthogonality</em> atau independensi antar komponen prinsipal nya. Dengan <em>autoencoder</em>, asumsi tersebut tidak ada. Sehingga <em>autoencoder</em> dapat memodelkan korelasi antar fitur. Dengan melakukan variasi parameter pada <em>autoencoder</em>, performa terbaik diperoleh adalah MSE 0.002, MAE 0.029, R<sup>2</sup> 0.991 dengan <em>autoencoder</em> dengan 4 lapisan <em>hidden layer encoder</em> dan <em>decoder</em>. Ini lebih baik dibandingkan PCA.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p><em>One of the most important measurements at the onset of labor is the accuracy of the prediction of birth weight. By using precise prediction methods extreme estimates of baby birth weight can be detected more or less so that some precautions can be taken before delivery. On the other hand, to improve the accuracy of a learning process, an accurate prediction is needed for the problem at hand or by using a combination of several methods. This study aims to predict baby birth weight using the Deep Learning autoencoder method to predict baby birth weight. One of the challenges in developing a predictive system for infant birth weight is the high dimensional data. Conventional techniques for feature reduction, such as principal component analysis (PCA), assume orthogonality or independence between the principal components. With an autoencoder, that assumption doesn't exist. So that the autoencoder can model the correlation between features. By varying the parameters of the autoencoder, the best performance is MSE 0.002, MAE 0.029, R2 0.991 with an autoencoder with 4 hidden layer encoder and decoder layers. This is better than PCA.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>
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