Computational thinking (CT) is an essential skill in the twenty-first century. The computational physics course (CPC) is one subject that is designed to support students in the practice of CT. Many studies show that the worksheets could be a solution in a CPC as a scaffold to achieve the CT objectives both online and offline. The study aims to develop the worksheet and integrate it with CT in a computational physics course. This study applied the research and development (R & D) method with the ADDIE model approach. In the results, the evaluation test from the experts reached a very good interpretation score based on the learning media expert (96%), the teaching material expert (95%), and the pedagogy experts (92%). So that this media is declared feasible to be used in the CPC. Furthermore, after the experimental study of students who took the computational physics course (n = 31), the study showed that the modified course could significantly improve student skills regarding overall CT (p value <0.05). However, this research also found that cooperative learning as part of CT had no improvement (p value >0.05). The experiment was conducted amid the COVID 19 pandemic wherein the students could only study at home for the whole semester. These findings indicate that the pandemic has impacted the collaborative skills of students on the course.

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Penelitian ini menganalisis pengaruh penerapan beberapa jenis algoritma preprocessing untuk mencari karakteristik segmen abnormal yang tampak pada citra mamografi. Mamografi merupakan pemeriksaan radiografi khusus payudara. Penerapan algoritma preprocessing yang terdiri dari metode filtering, contrast enhancement, sharpening, dan smoothing diharapkan dapat mengurangi noise dan meningkatkan kontras citra mamografi serta membantu ahli radiologi untuk melakukan diagnosis pada citra. Pada penelitian ini akan digunakan dua algoritma filtering yaitu median filter dan gaussian filter. Selain itu digunakan dua algoritma contrast enhancement yaitu global histogram equalization dan CLAHE (Contrast Limited Adaptive Histogram Equalization). Nilai piksel rata-rata segmen abnormal berkisar antara 206.9-213.3 dan rasio sumbu minor/mayor segmen abnormal berkisar antara 0.5-0.7.Pemilihan jenis metode filter (median filter dan gaussian filter) tidak mempengaruhi hasil nilai piksel rata-rata maupun rasio sumbu minor/mayor dan ukuran segmen abnormal, namun pemilihan jenis metode peningkatan kontras (CLAHE dan global histogram equalization) menghasilkan segmen abnormal dengan ukuran yang berbeda. Metode global histogram equalization menghasilkan segmen abnormal yang tidak dapat dibedakan dengan sekitarnya sehingga hasil ekstrasi segmen terlalu besar.

Combination of four filters namely gaussian filter, median filter, wiener filter, average filter were tested using two contrast enhancement techniques, intensity adjustment and histogram equalization, were tested to improve the quality of kidney ultrasound images. The research was conducted using 40 images, consist of 9 normal images, 17 hydronerfosis images, and 14 kidney stones images. The measured image quality were PSNR (Peak Signal to Noise Ratio) and MSE (Mean Square Error). The higher the PSNR and the lower the MSE, the better the image quality. Visual evaluation through questionnaires to clinicians has also been carried out to assess the visualization of the kidney and its abnormalities. The results of PSNR and MSE calculations showed that every image processings methods combinations produce different results in each image categories. However, whether in normal, hydronefrosis, or kindey stone categories, the combination of filters with image adjustment method gave the highest PSNR and lowest MSE. Meanwhile, the results of the visual evaluation from the clinicians showed that the best image enhancement technique in improving the visualization of abnormalities in kidney ultrasound images (Hydronephrosis and kidney stones) was the combination of a wiener filter with intensity adjustment, in accordance with the results of the PSNR measurement.

Studi ini bertujuan untuk mengetahui selisih nilai piksel Liver-kidney berdasarkan derajat perlemakan hati, perbandingan selisih nilai piksel Liver-kidney berdasarkan derajat perlemakan hati, dan membandingkan hasil analisis selisih nilai piksel Liver-kidney dengan diagnosis klinisi. Penelitian ini menggunakan 80 citra ultrasonografi dengan klinis Non-Alcoholic Fatty Liver Disease pada setiap derajat perlemakan hati dan citra normal, dan analisis nilai piksel dilakukan dengan software Matlab 2013a. Sebelum pengamatan nilai piksel, citra USG diproses dengan preprocessing terlebih dahulu. Adapun preprocessing memanfaatkan algoritma filtering berupa filter Gaussian dan filter Wiener. Hasil penelitian menunjukkan selisih nilai piksel semakin besar sesuai dengan derajat perlemakan hati. Penggunaan filter Gaussian dalam menentukan selisih nilai piksel liver-kidney lebih baik dibandingkan filter Wiener. Selain itu, penentuan selisih nilai piksel liver-kidney lebih sensitif pada citra kategori severe dibandingkan derajat perlemakan hati lainnya.