Judul: COVID-19 Pneumonia Identification Based  Computed Tomography Imaging Using Deep Transfer Learning
Abstrak: Dunia sedang dilanda pandemi COVID-19 yang menyebabkan pneumonia hingga kematian pada penderitanya. Karena tingkat penyebaran virus SARS-CoV 2 yang tinggi, maka pemberian tes harus dilakukan secara cepat dan masif agar penderita dapat diisolasi sesegera mungkin. Pendekatan standar yang digunakan untuk mendeteksi COVID-19 saat ini adalah RT-PCR. Namun, terdapat beberapa kekurangan seperti sensitivitasnya hanya sebesar 60-70% dan biayanya relatif mahal. Penelitian ini bertujuan untuk membangun metode pendeteksian alternatif COVID-19 berbasis citra Computed Tomography menggunakan teknik deep transfer learning. Transfer learning dilakukan menggunakan pre-trained model DenseNet-201. Pembuatan model terdiri dari beberapa tahapan yaitu praproses data, pembagian data, augmentasi data, hyperparameter tuning, pembangunan model klasifikasi deep transfer learning, dan evaluasi. Model yang telah dibangun memiliki performa yang cukup baik dalam mengidentifikasi paru-paru dengan pneumonia COVID-19 dan paru-paru sehat dengan akurasi sebesar 93,41%, presisi sebesar 94,19%, sensitivitas (recall) sebesar 93,10%, dan spesifisitas sebesar 93,75%., The world is being stricken by the COVID-19 pandemic, which can cause pneumonia, even the worst case is death. Due to the high level of spread of the SARS-CoV-2 virus, rapid and massive tests must be done. The infected people need to be isolated as soon as possible. The gold standard used to detect COVID-19 is RT-PCR. However, there are some drawbacks, this method only has 60-70% sensitivity, and has a relatively high cost. This study aims to develop an alternative detection method for COVID-19 based on Computed Tomography images using deep transfer learning techniques. Transfer learning is carried out using the pre-trained DenseNet-201 model. The process consists of several stages; data preprocessing, data splitting, data augmentation, hyperparameter tuning, developing a deep transfer learning classification model, and evaluation. The model that has been built has a fairly good performance in identifying lungs with pneumonia COVID-19 and healthy lungs with accuracy 93,41%, precision 94,19%, sensitivity (recall) 93,10%, and specificity of 93,75%.
Keyword: computed tomography, covid-19, densenet-201, transfer learning

Judul: Ekstraksi Fitur Klinis Citra CT Scan Penderita COVID-19 Menggunakan Metode Texture Analysis
Abstrak: Pada akhir tahun 2019, muncul penyakit COVID-19 yang disebabkan oleh coronavirus. Penyakit ini kemudian dinyatakan sebagai pandemi oleh WHO pada akhir bulan Januari 2020. Metode standar yang disarankan oleh panduan diagnosis COVID-19 adalah menggunakan RT-PCR dan analisis citra. Namun, kedua metode ini masih belum cukup optimal dikarenakan waktu yang dibutuhkan untuk melakukan identifikasi COVID-19 cukup lama, yaitu 4-6 jam menggunakan RTPCR dan 21,5 menit menggunakan analisis citra. Selain itu, sensitivitas yang dihasilkan menggunakan metode RT-PCR masih cukup rendah, yaitu 71%. Penelitian ini bertujuan untuk menerapkan algoritme texture analysis untuk melakukan identifikasi COVID-19 berbasiskan citra computed tomography. Fitur klinis yang didapatkan pada texture analysis kemudian diklasifikasikan menggunakan metode support vector machine. Hasil klasifikasi yang didapatkan cukup baik dengan nilai precision 86%, recall (sensitivitas) 99%, dan accuracy 91%. Diharapkan metode ini dapat membantu tenaga medis dalam melakukan proses identifikasi COVID-19., At the end of 2019, the COVID-19 disease caused by the coronavirus emerged. The disease was later declared a pandemic by WHO at the end of January 2020. The standard method recommended by the COVID-19 diagnosis guide is to use RT-PCR and image analysis. However, these two methods are still not optimal enough because the time needed to identify COVID-19 is quite long, i.e. 4-6 hours using RT-PCR and 21.5 minutes using image analysis. In addition, the sensitivity produced using the RT-PCR method is still quite low, i.e. 71%. This study aims to apply a texture analysis algorithm to identify COVID-19 based on computed tomography images. The clinical features obtained in the texture analysis are then classified using the support vector machine method. The classification results obtained are quite good with 86% precision, 99% recall (sensitivity), and 91% accuracy. It is hoped that this method can help medical personnel in carrying out the COVID-19 identification process.
Keyword: computed tomography, COVID-19, identification, texture analysis

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