Judul: Pengelompokan kode program c berdasarkan kemiripan struktur menggunakan metode hierarchical agglomerative clustering
Abstrak: Tujuan penelitian ini adalah mengimplementasikan algoritme hierarchical agglomerative clustering untuk membantu pendeteksian penjiplakan pada kode program c secara otomatis dan membandingkan hasil penelitian dengan penelitian sebelumnya menggunakan metode iterasi k-means otomatis dan bisecting k-means. Data yang digunakan dalam penelitian ini sebanyak 386 kode program c yang terdiri atas 4 dataset. Metode yang digunakan single linkage, complete linkage, dan average linkage. Hasil clustering yang dilakukan menunjukkan eksekusi waktu metode single linkage sebesar 1.7220 detik dengan jumlah cluster 14 dan ri sebesar 0.9706, eksekusi waktu metode complete linkage sebesar 1.7500 detik dengan jumlah cluster 10 dan ri sebesar 0.8560, dan eksekusi waktu metode average linkage sebesar 1.7400 detik dengan jumlah cluster 14 dan ri sebesar 0.9546. Hasil penelitian ini menunjukkan adanya perbaikan waktu eksekusi clustering dari penelitian sebelumnya yang menggunakan metode iterasi k-means sebesar 8.41 detik dan metode bisecting k-means sebesar 4.69 detik
Keyword: hierarchical clustering, kode program c, pendeteksi penjiplakan

Judul: Pendeteksian Penjiplakan Kode Program C dengan K-Means
Abstrak: The practice of plagiarism in programming assignments can be easily done by students. Thus, a system that can detect this practice, i.e., by grouping similar programs into a cluster, is needed. K-Means clustering is a method of flat clustering that has been used to determine similar codes based on the structure of the source codes. The objective of this research is to make the checking process automatic. This research uses a sample of 92 source codes in C comprising 9 groups based on the source code structure similarity. In this study we conducted two experiments, i.e., by determining the number of clusters (manual) and without determining the number of clusters (automatic). The results indicate that the manual experiment resulted in a higher Rand Index (91.35%) and a faster execution time. The system is able to perform automatic clustering process with a Rand Index of 90.63%. The automated clustering does not have significant difference of Rand index, implying the performance is good enough for the clustering without determining the K value first
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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