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
Keyword: 

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: Duodenal Histopathology of Pediatric Rat (Rattus norvergicus)&#160;Fed with Orange Juice (Citrus sinensis)
Abstrak: This research investigates the impact of orange juice consumption on the histological structure of the duodenum, a critical site for nutrient absorption and digestion. Focusing on pediatric animals, orange juice (with a pH of 3) was administered to post-weaning rats aged 4 weeks to 6 weeks at dosages ranging from 0g/kg BW to 20g/kg BW over two weeks. Duodenal histopathology was assessed, focusing on parameters such as the quantity of crypts of Lieberkuhn, as well as the length, height, width, and depth of villi (measured in µm). Anatomical pathology examination included measurements of the length and width of the duodenum (also in µm), as well as the color mean (OD). The research findings indicate no significant changes in histopathology or anatomy pathology of the duodenum. This suggests that young individuals can adapt to acidic challenges without compromising gastrointestinal health. These findings offer reassurance regarding the short-term consumption of acidic orange juice (with a pH of 3) with minimal risks to intestinal integrity, both during growth and development.
Keyword: acidic changes