Judul: Klasifikasi Kanker Payudara Berdasarkan Citra Histopatologi Menggunakan Model Convolutional Neural Network
Abstrak: Kanker payudara menempati peringkat pertama sebagai jenis kanker yang paling umum diderita oleh perempuan di dunia. Kanker payudara memiliki kontribusi sebesar 11,7% dari total kasus baru kanker di dunia dan 16,6% di Indonesia secara keseluruhan yang tercatat pada tahun 2020. Pemeriksaan kanker umumnya menggunakan biopsi dengan melakukan pengambilan sampel jaringan yang dicurigai terdapat sel kanker. Penelitian ini bertujuan untuk membuat model convolutional neural network dalam memprediksi status kanker payudara pada citra histopatologi dengan performa paling baik. Data yang digunakan dalam penelitian ini bersumber dari dataset BreakHis yang terdiri atas 7909 citra histopatologi jaringan tumor payudara dengan 2 kelas yaitu, jinak dan ganas. Tahapan dalam penelitian ini meliputi praproses data, pembagian data, hyperparameter tuning, pelatihan model, dan evaluasi model. Hasil dari penelitian ini adalah satu buah model convolutional neural network dengan arsitektur DenseNet201 yang dapat memproses klasifikasi kanker payudara pada citra histopatologi. Model ini memiliki akurasi senilai 98,74%.
Keyword: breast cancer, convolutional neural network, histopathology

Judul: Classification of Histopathological Images of Breast Cancer with Methods CNN and Transfer Learning
Abstrak: Kanker payudara adalah kanker dengan jumlah kasus terbanyak pertama di dunia dan di Indonesia. Kanker payudara memiliki kontribusi sebesar 11,7% (2.261.419) dari total kasus baru kanker di dunia serta 16,6% (65,858) di Indonesia yang tercatat di tahun 2020. Pemeriksaan kanker umumnya menggunakan teknik biopsi dengan melakukan pengambilan sampel jaringan yang dicurigai terdapat sel kanker. Penelitian ini bertujuan untuk membuat model convolutional neural network dengan metode transfer learning dalam memprediksi status kanker payudara pada citra histopatologi. Data yang digunakan dalam penelitian ini bersumber dari dataset BreakHis yang terdiri atas 7,909 citra histopatologi jaringan tumor payudara dan terbagi menjadi dua kelompok utama, yaitu tumor jinak dan tumor ganas. Tahapan dalam penelitian ini meliputi praproses data, pembagian data, pelatihan model, dan evaluasi. Hasil dari penelitian ini adalah model CNN yang dapat membantu proses klasifikasi kanker payudara pada citra histopatologi. Penelitian ini menghasilkan dua model CNN berbeda dengan akurasi terbaik sebesar 96%., Breast cancer is the cancer with the highest number of incidences, both in Indonesia and in the world. Breast cancer contributes up to 11,7% (2,261,419) of the total new cases of cancer in the world and 16,6% (65,858) in Indonesia (recorded in 2020). Cancer examination generally uses a biopsy technique by taking tissue samples suspected of having cancer cells. This study aims to create a convolutional neural network model with transfer learning method in predicting breast cancer status on histopathological images. The data used in this study were sourced from the BreakHis dataset which consisted of 7,909 histopathological images of breast tumor tissue and was divided into two main groups, namely benign tumors and malignant tumors. The stages in this research include data preprocessing, data sharing, model training, and evaluation. The result of this research is a convolutional neural network model that can help classify breast cancer in histopathological images. Two different CNN models were created with the best accuracy of 96%.
Keyword: breast cancer, convolution neural network, histopathology, transfer  learning, cnn

Judul: Heterologous Expression Of Endo-Β-Xylanase From Bacillus Pumilus Strain Bcrs-01 In Escherichia Coli.
Abstrak: Xylanases are glycoside hydrolase family 11 which depolymerise xylan in the plant cell wall, the most excessive form of hemicellulose. Endo-β-xylanases (EC 3.2.1.8) are most important to breakdown β-1,4-glicoside bond of xylan to produce disaccaride xyloolygosaccaride (XOs) and xylose (monomer). Bovine rumen fluids have potential to obtain endo-β-xylanase produced of bacteria. The objectives of this study were to obtain xylanase producing bacteria and to express xynA gene encoding endo-β-xylanase from bacterium in Escherichia coli. Twenty nine isolate bacteria have been succesfully isolated from bovine rumen fluid in agar medium containing birchwood xylan. Isolate BCRS-01 bacterium was previously screened for the high production of xylanase. Based on 16S rRNA gene sequence analysis, BCRS-01 showed high similarity (99%) to Bacillus pumilus (accession number NR043241). The crude xylanase had optimal activity at 55oC, pH 7.0. Furthermore, the crude xylanase exhibited broad pH stability from 4.0 to 10.0 and remained more than 60% and 40% of its activity after incubation at pH 4.0 until 10.0, respectively for 60 min. It also showed a stability at temperature 25-50oC since it retained 80% and 50% of its activity when incubated at 50oC or 30 and 60 min, respectively. The xylanase activity was strongly inhibited by Ca2+ and Cu2+ at concentration 10 mM, and as little as 1 mM for Mn2+. Therefore, this xylanase has potentially used as additive feeds in animal feed industry. The DNA sequence xynA gene encoded endo-β-xylanase was amplified and cloned into the expression vector pET-15by under the control of T7 promoter. The recombinant protein had succesfully expressed in Escherichia coli BL21 (DE3) pLysS. A protein recombinant of approximately, 26.7 kDa was observed on the SDS-PAGE analysis. Crude extract of recombinant endo-β-xylanase demonstrated activity of enzyme at 56 U mL-1 using birchwood xylan as a substrate at 55oC for 10 min.
Keyword: Bacillus pumilus, bovine rumen fluid, endo-β-xylanase, heterologous expression.