Judul: Penerapan Metode Jaringan Saraf Tiruan Propagasi Balik Untuk Mendiagnosis Tingkat Keganasan Kanker Payudara
Abstrak: Medical imaging (Mammography) is very helpful in the breast cancer diagnostic. Mammography image analysis which is done manually requires expertise, experience, and subjective. A computer aided diagnostic system is helping radiologists to more objective, quicker, and more aqqurate detection of breast cancer. This research classifies benign and malignant breast cancer using backpropagation neural network (BpNN) with backpropagation method and gray level co-ocurence matrix (GLCM) feature extraction. Mammography image preprocessing methods Contrast Limited Adaptive Histogram Equalization (CLAHE) and Otsu’s N Segmentation. The object will be recognized cancer malignancy from spread of calcification in the breast. Feature extraction will be calculated at an angle of 00, 450, 900, dan 1350 and the number of hidden layer neurons tested were 4 and 8. The result is giving an average accuracy of 63.33% at angle of 90% and the number of hidden layer neurons is 4.
Keyword: Bogor Agricultural University (IPB), backpropagation, gray level co-occurrence matrix, reast cancer

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: Utilization of hydrolized duckweed (Lemna minor) meal as a soybean replacement in Nile tilapia (Oreochromis niloticus) diet,
Abstrak: Tilapia is a fast growing fish widely cultured in many tropical and sub tropical countries. The recent production figures reported our global production estimates for 2015 is 5,576.8 metric tons. In developing countries the major challenges facing aquaculture industry is high cost of production associated with fish feeds. Feed represents 40-70% of operating costs for aquaculture operations, increasing expenses and potential declining supplies soybean meal have led scientists to search for less expensive but compatible alternative protein sources for fish feed; most of which are based on animal or plant protein sources. The objectives of study was to determine the incubation period for hydrolysis of duckweed and to evaluate the use of hydrolized duckweed as aprotein source for the growth performance of Nile tilapia Three experiments were conducted to determine the composition and digestibility of hydrolyzed duckweed meal by sheep rumen liquor as the replacement of soybean meal in the diet of Nile tilapia. The first trial examined the effectivity of sheep rumen liquor enzyme to decrease crude fiber contents of (DWM), the added volume of sheep rumen liquor was 100 ml/kg DWM and then it was incubated for 0, 12, 24 hr . The second trial was to evaluate the apparent digestibility of hydrolyzed DWM for Nile Tilapia, apparent digestibility were determined using chromic oxide indicator added to both reference diet and test diets. The feed ingredients used in the trial were hydrolyzed DWM (HDWM) and unhydrolyzed DWM (DWM). Ten fish with an average weight of 18±0.36g were used in the trial and reared in the aquarium of size 30×40×50cm for 15 days; feces were collected from three replicates using a fecal collection column attached to the aquarium. Third trial was to evaluate the growth performance of Tilapia fed test diet with hydrolyzed Duckweed meal (HDWM). The experimental design was set according to completely randomized design with four treatments and three replicates of HDWM inclusion levels that is 0%, 15%, 30% and 45%. Nile tilapia fish with initial body weight of 18±0.36g was kept in 12 units aquarium size of 30×40×50cm at stocking density of 10fish/aquarium, the fish were fed two times a day at 08:00am and 16:00pm to apparent satiation for a period of 60 days. The results indicated that duckweed meal (DWM) hydrolyzed with 100 mL/kg and incubated for 24 hrs showed the lowest crude fiber contents of 13.68% compared to other treatments. Total digestibility of hydrolyzed duckweed (HDWM) meal was 87.3% compared with unhydrolyzed duckweed (DWM) which was 66.8%. Total final weight (TFW), survival rate (SR), specific growth rate (SGR), feed conversional rate (FCR) and protein retention (PR) shows growth performance at 15% inclusion. The results of this study showed that 100mL/kg sheep rumen liquor at incubation period of 24 hr can be suitable for hydrolysis of duckweed to reduce crude fiber also hydrolyzed duckweed can substitute soybean meal at an incubation rate of 15% in the diet of Nile tilapia and had the same growth and FCR with control diet
Keyword: Digestibility, Duckweed meal, Growth performance, Sheep rumen