Judul: Pengembangan proses produksi bioinsektisidaoleh Bacillus thuringiensis subsp. israelensis secara curah menggunakan substrat onggok
Abstrak: The research was aimedat developing the technique of producing bioinsectiside from Bacillus thuringiensis subsp. israelensis with specific objectives (1) to obtain the basis of calculation to scale up the bioreactor scale from 2 liters to 13 liters with "onggok" medium and batch system (2) to obtain information on the characteristics of bioinsectiside producer and (3) to find out description on feasibility and financial analysis of the bioinsectiside production at industrial scale
Keyword: 

Judul: Study of scale up for bioinsecticides production from Bacillus thuringiensis aizawai using liquid waste industrial tofu and coconut water substrate
Abstrak: Bacillus thuringiensis aizawai is one type of bacteria which often used in the production of microbial bioinsecticide because this patties very effective in controlling larvae of Lepidoptera and Diptera, especially the leaf caterpillar pests of cabbage and other vegetables. The use of biopesticides in Indonesia is still rarely because bioinsecticide marketed in Indonesia is still an import product so that price is relatively expensive. This problem can be overcome with producing bioinsecticide contain active Bacillus thuringiensis aizawai using local raw materials such as liquid waste industrial tofu and coconut water. The objectives of this research was to study the scale up based on the optimum conditions for growth of Bacillus thuringiensis subsp. aizawai in pilot and industrial scale in the production of bioinsecticide microbial use of liquid waste of industrial tofu and coconut water. Carbon-nitrogen ratio in this research is (7:1), with 80 : 20 composition of liquid waste of industrial tofu and coconut water, the starter added 10% (v/v), cultivation time during 72 hours and using fermentors 3 and 40 liters. The highest toxicity of the product can be obtained through bioassay test to larvae Croccidolomia pavonana and the highest toxicity levels is at 48 hours fermentation with LC50 value of 0.01 mg/L. The result showed that by 0.01 mg/L bioinsecticide microbial could kill 50% of the target insect. Range of pH value from 5.25.-7.23, and the highest total plate count (TPC) is 1.44 x 107 CFU/ml in the fermentor with a capacity of 40 litres. The highest viable spore count (VSC) is 8.03 x 105 spores/ml in fermentor with a capacity of 40 litres with cultivation 72 hours. The calculation of the scale up on industrial scale fermentor 10,000 L showed that the needs of Pg/V are 0.0256 HP/m3 per second, a rate of aeration: 0.27 VVM and agitation speed: 0.47 rps. Based on the similarity of geometry fermentor 10,000 litres, fermentor has tank diameter: 2.09 m and impeller diameter: 0.85 m with a working volume of 7,000 litres.
Keyword: 

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