Judul: Aktivitas Antioksidan Ekstrak Daun Papaya (Carica papaya (L). Var Kalina) dengan Perlakuan Tanah Lempung.
Abstrak: Penambahan tanah lempung pada proses perebusan daun pepaya bertujuan mengurangi rasa pahit, namun belum diketahui dapat mempengaruhi aktivitasnya sebagai antioksidan. Oleh karena itu, penelitian ini bertujuan menguji aktivitas antioksidan dan komponen bioaktif daun papaya berdasarkan cara pengolahan. Aktivitas antioksidan ditentukan dengan metode DPPH dan diukur dengan spektrofotometer pada panjang gelombang 517 nm. Hasil penelitian menunjukkan ekstrak air dan etanol daun papaya yang telah diberi perlakuan tergolong memiliki aktivitas antioksidan yang sangat lemah, aktivitas antioksidan yang paling tinggi terdapat pada perebusan selama tiga menit dinyatakan dalam bentuk IC50, yaitu 528.495 ppm ekstrak etanol. Aktivitas antioksidan ini ditimbulkan oleh kandungan metabolit sekunder pada daun papaya, yaitu flavonoid dan fenolik. Hasil analisis menunjukkan bahwa tidak ada pengaruh aktivitas antioksidan, fenolik, dan flavonoid total daun papaya yang direbus dengan penambahan tanah lempung (P>0.05).
Keyword: Antioksidan, daun papaya, fenolik, flavonoid, tanah lempung

Judul: Pengaruh Penambahan Antioksidan dan Aktivator Untuk Mempertahankan Aktivitas Proteolitik Getah Pepaya Selama Penyimpanan
Abstrak: Indonesia is the fifth largest producers of papaya in the world. To increase its value, this raw material should be processed further to many kind products include papain. The Indonesian contribution of papain in international market is still low. Crystalline of papain can be made directly from fresh papaya latex. This method is considered more effective than when it is prepared from crude papain. The quality of pure papain relied on the quality of the papaya latex as raw material. With the appropriate handling, the proteolytic activity of the enzymes of the latex can be maintained during storage.
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Judul: Klasifikasi genre musik menggunakan Learning Vector Quantization (LVQ)
Abstrak: Radio stations and music television have a milion of music tapes. A lot of musical genres create a problem when people wants to determine the right genre of a new kind of music. To classify the musical genre is not an easy task, because the musical genre is really difficult to standardization. Automatic musical genre classification can assist the human role in that process and help people to searching for the song acording to the genre that people want. This research using Mel Frequency Coefficient Cepstrum (MFCC) to obtain feature extraction. Learning Vector Quantization (LVQ), one kind of artificial neural network used for classification method. The number of genres that are used were four kind of musical genre, that is rock, classic, keroncong, and jazz with four different duration that is 5 second, 10 second, 20 second and 25 second. This research using k- fold cross validation to distribute dataset for training and testing set with the number of folds as much as 2 until 10 fold. This research succesfully implemented MFCC feature exraction and classification using LVQ. Based on this research, the accuracy of the classification using Learning Vector Quantization reaches 93,75% for the four type musical genre. The highest accuracy value was obtain from the experiments with a duration of 10 second and the number of fold 4. Training time for each duration is 30 minute for 5 second music duration, 45 minute for 10 second music duration 120 minute for 20 second music duration and 150 minute for 25 second music duration.
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