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: Cervical Cancer Histopathology Image Classification with Transfer Learning
Abstrak: Kanker mulut rahim (serviks) adalah kanker dengan jumlah kasus terbanyak kelima pada wanita di seluruh dunia dan merupakan masalah kesehatan perempuan di Indonesia dengan angka kejadian dan kematiannya yang tinggi. Tes skrining pap smear manual yang berfungsi sebagai pencegahan dan deteksi awal kanker serviks masih rawan akan terjadinya kesalahan observasi. Penelitian ini bertujuan untuk membuat model convolutional neural network dengan metode transfer learning untuk identifikasi kanker serviks pada citra pap smear. Data yang digunakan pada penelitian ini adalah dataset open source SIPaKMeD yang mengandung 4049 citra sel serviks normal, abnormal, dan jinak. Penelitian ini dilakukan melalui beberapa tahapan yaitu praproses data, pembagian data, hyperparameter tuning, pembuatan model prediksi, dan evaluasi model. Penelitian ini menghasilkan tiga model CNN berbeda dengan akurasi terbaik sebesar 99,04%., Cervical cancer is a cancer with the fifth highest number of cases in women worldwide and is a health problem in Indonesia with high incidence and death rate. Manual pap smear screening tests that function as prevention and early detection of cervical cancer are still prone to observation errors. This study aims to create a convolutional neural network with transfer learning method to identify cervical cancer on pap smear images. The data used in this study are the open source dataset SIPaKMeD that contains 4049 normal, abnormal, and benign cervical cell images. The study was carried out through several stages which are data preprocessing, data splitting, hyperparameter tuning, prediction model making, and model evaluation. Three different CNN models were made, having the best accuracy of 99.04%.
Keyword: cervical cancer, convolutional neural network, feature concatenation, pap smear, transfer learning

Judul: Pengaruh Konsentrasi Katalis Methyl Ester Sulfonic Acid Terhadap Sifat Fisikokimia Gliserol Ester Oleat yang Dihasilkan
Abstrak: Glycerol is a by-product from palm olein biodiesel industry which could be value-added to obtain various products. One of the alternatives is to modify glycerol become glycerol ester oleat. Before processing, glycerol was first refined using 5% (v/v) phosphoric acid 85%. The refinery showed that glycerol content was increased from 49% to 80%. Glycerol was then esterified with oleic acid and catalyst methyl ester sulfonic acid (MESA) to obtain glycerol ester. Esterification was conducted at 240°C and nitrogen atmosphere using 200 rpm of mixing for 90 minutes. The best catalyst concentration used in esterification process is at 0,5% and resulting glycerol ester oleat with acid number 48,07 mg KOH/g sample, density of 0,9124 g/cm3, viscosity 68,32 cP (25°C), kinematic viscosity 36 cSt (40°C), flash point 230°C and pour point -15°C.
Keyword: glycerol ester, glycerol