Judul: Analysis of Text Similarity to Goal Standard Using Levenshtein Distance and Cosine Similarity Algorithm (Case Study: Data Result of Company X Survey)
Abstrak: Text mining merupakan teknik dalam pengolahan data teks untuk mengekstrak informasi tertentu. Salah satu contoh text mining adalah mengukur nilai kemiripan antara dua string. Algoritme Levenshtein Distance dapat digunakan untuk mengukur kemiripan teks dengan menghitung jarak perbedaan antara dua string. Algoritme lain yang digunakan adalah algoritme Cosine Similarity. Algoritme Cosine Similarity melakukan pengukuran dengan membandingkan jarak antara dua string yang dinyatakan dalam dua buah vektor. Penerapan algoritme ini juga dilakukan dengan berbagai teknik text pre-processing yang dapat memberikan hasil yang lebih maksimal. Kedua algoritme tersebut digunakan dalam penelitian ini untuk mengukur kemiripan teks antara dua dataset. Data yang digunakan yaitu, data hasil survei dengan master data pada database Perusahaan X. Data hasil survei berjumlah 24.659 respon sedangkan pada database Perusahaan X berjumlah 725 daftar nama program kelas online. Objek yang diukur kemiripannya ialah nama program kelas online pada data hasil survei dengan database Perusahaan X. Nilai kemiripan yang dihasilkan digunakan untuk mengidentifikasi daftar nama program kelas online yang diikuti peserta pada hasil survei. Hasil identifikasi dari 24.659 respon hasil survei diperoleh 322 daftar nama program kelas online dengan algoritme Levenshtein Distance dan 327 daftar nama program kelas online dengan algoritme Cosine Similarity., Text mining is a technique in processing text data to extract certain information. One example of text mining is measuring the similarity value between two strings. Levenshtein Distance algorithm can be used to measure the similarity of text by calculating the distance difference between two strings. Another algorithm used is the Cosine Similarity algorithm. The Cosine Similarity algorithm performs measurements by comparing the distance between two strings expressed in two vectors. The application of this algorithm is also carried out with various text pre-processing techniques that can provide maximum results. The two algorithms are used in this study to measure the similarity of the text between the two datasets. The data used are survey results data with master data in Company X database. Data from survey results totaled 24,659 responses, while in Company X database there were 725 lists of online class program names. The object whose similarity is measured is the name of the online class program in the survey data with the Company X database. The similarity value generated is used to identify the list of names for the online class program that participants participated in the survey results. The identification results from 24,659 responses to the survey results obtained 322 lists of names for online class programs with the Levenshtein Distance algorithm and 327 lists of names for online class programs with the Cosine Similarity algorithm.
Keyword: cosine similarity, kemiripan teks, levensthein distance, text mining, cosine similarity, text similarity, levensthein distance, text mining

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: Ornithonyssus Bursa (Berlese, 1888) Famili Macronyssidae yang Menyerang Unggas Dan Penaggulangannnya
Abstrak: Ornithonyssus bursa adalah tungau penghisap darah pada unggas di daerah tropis dan subtropis. Tungau ini relatif kecil, dengan panjang tubuh antara 700-1000 mikron dan lebarnya 250-490 mikron. Tubuhnya bulat berwarna coklat kekuningan sebelum menghisap darh dan berwarna merah setelah menghisap darah dari induk semangnya.
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