Judul: Penambatan Molekuler Xantin Oksidase oleh Senyawa Aktif Sirih Merah (Piper crocatum) sebagai Pengobatan Alternatif Asam Urat
Abstrak: Produksi asam urat berlebih di dalam tubuh dapat mengendap di persendian dan menimbulkan rasa nyeri dan kaku sehingga sendi mengalami pembengkakan. Penghambatan enzim xantin oksidase diketahui mampu menjadi jalan terapi dalam menghambat produksi asam urat. Penelitian ini bertujuan mengidentifikasi interaksi molekuler xantin oksidase oleh senyawa aktif sirih merah yang berpotensi dalam menghambat aktivitas enzim xantin oksidase sebagai pengobatan alternatif asam urat. Penelitian dilakukan dengan metode in silico yaitu penambatan terarah (site directed docking). Ukuran pusat penambatan yang digunakan dalam penelitian ini sebesar x=26,7853; y= 9,9800; z= 113,3917 dengan nilai dimensi x=14 ; y=14 ; z=18. Potensi inhibisi terbaik dimiliki oleh sofalkon, asam protokatekuat, dan 2-(3,4-dimetoksi fenil)-6-etoksi-7- metoksi-1-naftol dengan nilai ∆G dan Ki yang lebih baik dibanding allopurinol serta memiliki interaksi residu yang cukup kuat terhadap sisi aktif xantin oksidase, Excess uric acid production in the body can settle in the joints and cause pain and stiffness si that the joints experience swelling. Inhibitoin of the xanthine oxidase enzym is known to be abe to be a therapeuticroute in inhibiting uric acid production. This research aims to identify the molecular interactions of xanthine oxidase by red betel compounds which had the potential to inhibit the activity of xanthine oxidase enzyme as an alternative treatment for gout. The research was conducted using in silico method by site-directed docking. The size of the center retardation used in this research was x= 26,7853; y= 9,9800; z= 113,3917 with dimension value x= 14; y= 14; z= 18. The best inhibition potential was sofalcone, protocatechuic acid, and 2-(3,4-dimethoxy phenyl)-6-ethoxy-7- methoxy-1-naphtol with better ∆G and Ki values than allopurinol and had a fairly strong residual interaction with the active site of xanthine oxidase.
Keyword: molecular docking, red betel, uric acid, xanthine oxidase

Judul: Penapisan Senyawa Aktif Daun Sirsak, Putri Malu, Alang-alang dan Pandan Wangi sebagai Inhibitor Xantina Oksidase secara in Silico
Abstrak: Gout adalah penyakit kronis yang disebabkan oleh pengendapan kristal natrium urat. Salah satu cara mengatasi penyakit ini adalah dengan menurunkan kadar asam urat melalui penghambatan kinerja enzim xantina oksidase (XO). Allopurinol yang merupakan analog purina adalah salah satu obat penghambat XO komersial. Upaya mencari senyawa baru yang berpotensi dapat menurunkan kadar asam urat dari tumbuhan berkhasiat obat telah lama dilakukan melalui isolasi senyawa murni dan turunannya sebagai bahan dasar pembuatan obat modern. Penelitian ini bertujuan mendapatkan informasi tanaman herbal yang berpotensi dapat menghambat kinerja XO dengan melakukan penapisan senyawa aktif yang berasal dari daun sirsak, putri malu, alang-alang, dan pandan wangi sebagai inhibitor enzim XO secara in silico dengan metode penapisan virtual dan penambatan molekuler. Parameter yang digunakan adalah energi ikatan (ΔG), konstanta inhibisi (Ki), dan ikatan yang terbentuk. Hasil penelitian menunjukkan senyawa (E,E)-pandanamina dari pandan wangi berpotensi paling tinggi sebagai inhibitor enzim XO dengan ΔG sebesar -9,28 kkal/mol, Ki 0,16 µM, dan nilai binding site similarity (%BSS) sebesar 88,89%. Selanjutnya, senyawa aktif ini dapat diisolasi dari daun pandan wangi untuk dijadikan kandidat obat pada pengobatan penyakit gout., Gout is a chronic disease caused by the deposition of sodium urate crystals. One of the treatments for this disease is to reduce uric acid levels through the inhibition of the enzyme xanthine oxidase (XO). Allopurinol which an analog of purines is one of the commercially available XO inhibitor drugs. Various studies have been carried out to find new potential compounds to reduce uric acid levels from medicinal plants, which have long been carried out through the isolation of pure compounds and their derivatives as basic ingredients for making modern medicines. This study aims to obtain the information on potential herbal plants to inhibit XO activity by screening active compounds derived from soursop leaves, mimosa, cogongrass, and pandanus as XO enzyme inhibitors in silico by virtual screening and molecular docking methods. The parameters analyzed are bond energy (ΔG), inhibition constant (Ki), and the bonds formed. The results of this study indicated that (E,E)-pandanamine from pandanus had the highest potential as XO enzyme inhibitor with ΔG of -9,28 kcal/mol, Ki of 0,16 µM, and the value of binding site similarity (% BSS) is 88,89%. Furthermore, this active compound can be isolated from pandanus leaves to be used as a drug candidate in the treatment of gout.
Keyword: gout, uric acid, virtual screening, molecular docking, xanthine oxidse

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.
Keyword: