Judul: Pendeteksian Penjiplakan Kode Program C dengan Bisecting K-means
Abstrak: Plagiarism of source codes assignments is a widespread problem in academic institutions. Manual plagiarism detection is time and energy consuming. Therefore, a system that can help detecting this plagiarism is needed. The detection can be done by grouping similar source codes based on their structure. This method is used in previous research by using automatic K-means iterations algorithm. That algorithm, although produced decent clusters, had a long execution time. The purpose of this research is to improve the time efficiency and clusters result quality by using bisecting K-means algorithm. The results showed a significant improvement in execution time from 11.68 seconds to 6.64 seconds. Bisecting K-means also produced fewer clusters with slightly better Rand Index than K-means iterations. Furthermore, experiments using 2-gram to 6-gram showed that 4-gram resulted in the best performance
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Judul: Pendeteksian Kemiripan Kode Program C dengan Algoritme KMedoids
Abstrak: Practice of plagiarism on program codes is more common and easier to do. Manual detection of plagiarism takes a lot of time and effort. The detection can be done by grouping program codes that have similar structures. This study intends to apply K-medoids algorithm on 4 C code program datasets to find similarities of code program and analyze clustering results. The experimental results show that the best clustering in dataset1 (If-Else Condition and Looping While) was obtained at k=10 with an average of dissimilarity 2.655, and 18.9% of students have the same group. In dataset2 (Looping While), the best clustering was obtained at k=9 with an average of dissimilarity 2.227, and 32.6% student assignments are in the same group. For dataset3, the assignments are divided into two clusters with an average of dissimilarty 0.719, and 87% of students assignments are in the same cluster. The best c lustering result on the dataset4 was obtained at k=6 with an average of dissimalrity 3.199, and 61% of students assignments are in the same group. The accuracy rate from clustering results is 93.28%
Keyword: similarity detection, K-Medoids algorithm, clustering, Bogor Agricultural University (IPB)

Judul: Durability of Specific Immunoglobulin (Ig) Y Againts Influenza and Covid-19 in Sunscreen
Abstrak: The benefit of specific immunoglobulin Y (Ig Y) as an active ingredient in personal care products such as sunscreen has not been properly studied. Our study aims to evaluate the reliability of specific IgY against influenza and the COVID-19 virus in a sample sunscreen product. Specific IgY influenza and COVID-19 viruses were inserted in the sample sunscreen at two concentrations of 0.1% and 0.25%. Three treatments were observed: control (C) sunscreen without IgY, treatment 1 (T1) sunscreen with 0.1% IgY, and treatment 2 (T2) sunscreen with 0.25% IgY. Organoleptic observations were carried out to monitor changes in aroma, color, and texture, and sunscreen sampling was done every week for 6 weeks. The durability of IgY was evaluated using an enzyme-linked immunosorbent assay (ELISA) and a neutralization assay. Organoleptic observation from weeks 1 to 6 showed consistency in its aroma, color, and texture. Enzyme-linked immunosorbent assay and neutralization assay (NA) results of the T1 and T2 groups showed negative results except for sample T1 at week 5, which showed a positive result in the Influenza A ELISA. Based on the results, it seems that IgY concentrations of 0.1% or 0.25% were not optimal. However, this negative result might be due to low concentration of the IgY and the presence of beeswax, which inhibits IgY and causes it to dissolve in distilled water, thus preventing it from being detected in the assay.
Keyword: Immunoglobulin Y, Sunscreen, Influenza, COVID-19, Biological Activity