Judul: Prediksi Interaksi Antar Protein pada Indonesian Jamu Herbs (IJAH) Berbasis Sekuen Protein Menggunakan Deep Learning dan Autocovariance
Abstrak: Jamu adalah obat herbal khas Indonesia yang diracik dari tumbuh-tumbuhan. Salah satu tools dalam perkembangan jamu di Indosesia adalah Indonesian Jamu Herbs (IJAH) analytics. Sistem ini memanfaatkan data tanaman, protein, senyawa, penyakit, dan interaksi antar protein dan senyawa dari basis data yang tersebar di internet, seperti STRING, Swiss-Prot, dan Uniprot. Penelitian ini melakukan prediksi interaksi antar protein pada basis data IJAH berbasis sekuen protein menggunakan algoritma Deep Learning. Metode yang digunakan adalah Stack Autoencoder dan Softmax Classifier untuk membuat model klasifikasi dan Autocovariance untuk ekstraksi fitur.Model terbaik dengan akurasi sebesar 92.13% diperoleh pada model dengan struktur satu lapisan hidden menggunakan 400 neuron pada lapisan hidden-nya. Model ini mampu menghasilkan nilai akurasi yang cukup bagus dalam memprediksi interaksi antar protein pada basis data IJAH
Keyword: IJAH, protein, sequence protein, stack autoencoder, autocovariance

Judul: Prediksi Interaksi Protein-Protein Berbasis Sekuens pada Indonesia Jamu-Herbs Menggunakan Stacked-Autoencoder dan Conjoint Triad
Abstrak: Masyarakat Indonesia sudah mengenal obat herbal yang bernama jamu. Umumnya, formula jamu diperoleh secara empiris dan diwariskan dari generasi ke generasi. Interaksi protein-protein (protein-protein interaction, PPI) dari jamu memiliki peran penting dalam mempengaruhi proses biologis untuk penyembuhan. Analisis PPI perlu dilakukan untuk mempelajari bagaimana protein berinteraksi dengan protein lainnya. Data tentang interaksi protein masih terbatas. Karenanya menemukan interaksi protein menjadi lebih sulit. Sehingga metode prediksi masih diperlukan untuk menghasilkan PPI. Penelitian ini bertujuan untuk menghasilkan model prediksi interaksi protein berbasis sekuens pada pangkalan data Indonesia Jamu-Herbs dengan Stacked-Autoencoder sebagai arsitektur, Softmax sebagai classifier, dan Conjoint Triad sebagai metode ekstraksi fitur. Model ini dengan satu, dua, dan tiga autoencoder layer memperoleh akurasi masing-masing 95.58%, 95.56%, dan 95.60%. Studi ini menyimpulkan bahwa Stacked-Autoencoder yang tergabung dengan Softmax classifier (termasuk autoencoder tunggal) memperoleh hasil yang sangat baik. Walaupun begitu, penambahan jumlah layer tidak mempengaruhi peningkatan akurasi.
Keyword: autoencoder, deep learning, protein-protein interaction, stackedautoencode

Judul: Enhancement of photon absorbtion on BaxSr1-xTiO3 thin films semiconductor using photonic crystals.
Abstrak: In this research, has been conducted BaxSr1-xTiO3 (BST) thin films manufacturing for mole fraction x = 0.25, 0.35, 0.45, and 0.55; and concentration of 1 molar using chemical solution depositon (CSD) method. The materials, which are used in this research, are barium acetate [Ba(CH3COO)2, 99%], strontium acetate [Sr(CH3COO)2, 99%], titanium isopropoxide [Ti(C12O4H28), 97.999%], gallium trioxide [Ga2O3], 2-methoxyethanol [H3COOCH2CH2OH, 99%] and 1 x 1 cm of transparent conductive oxide (TCO) glass as substrate. Two BST thin films are made that for BST thin films which are doped by 10% galium (BGST) as p-type layer and pure BST solution as n-type. BST solution is made by reacting barium acetate [Ba(CH3COO)2, 99%], strontium acetate [Sr(CH3COO)2, 99%] and titanium isopropoxide [Ti(C12O4H28)] into 2-methoxyethanol solvent [H3COOCH2CH2OH, 99%] in accordance with mass gram for each x value. While, BGST solution is made in the same way as BST solution, plus adding gallium at 10% of the mass of BST. Once all materials are reacted, then vibrated using ultrasonic for 90 minutes, and followed by coating BGST / BST on the TCO substrate. Once the layer is formed, then annealed for 15 hours at temperature of 500 °C with increasing rate of 1.67 oC/minute. Characterizations of BST thin films with or without photonic crystals include the absorbance and transmittance in the visible light spectrum, as well as the electrical photoconductivity characterizations. From BST thin films without photonic crystals indicate that our thin films have light absorption area on almost the entire range of the visible light spectrum even to the infrared range with absorption value over than 67.6%, in addition, it can be seen that the maximum absorbance of BST thin films for all mole fractions lie in blue to green light spectrum region, 457 nm ≤ λ ≤ 570 nm and the minimum in yellow to red light spectrum region, 570 nm ≤ λ ≤ 678 nm. The largest absorbance is found at x = 0.25 with average absorbance value of 92.04% and maximum and minimum absorbance peaks at wavelength of 500.89 and 657.72 nm, respectively. At x = 0.35, average absorbance value decreased to 83.55% with maximum and minimum peaks at 505.73 and 613.68 nm, respectively. Then increased to 91.16% at x = 0.45 with maximum and minimum peaks at 506.04 and 618.56 nm, respectively. At x = 0.55, average absorbance value decreased to 80.12% with maximum and minimum peaks at 507.85 and 608.79 nm, respectively. Moreover, it can be seen that the addition of barium mole fraction leads to wider maximum absorbance or minimum transmittance peaks and narrower minimum absorbance or maximum transmittance peaks in the same range of the green and blue & red light spectrums, respectively. BST thin films with photonic crystals show increasing in absorption for all x values in the range of 3.04 into 13.33%. At x = 0.25, average absorbance percentage increased 3.96% from 92.4 into 95.68%, for x = 0.35 average percentage increased 7.07% into 89.45 from 83.55%. Furthermore, average absorbance of BST thin films at x = 0.45 increased 3.04% from 91.16 into 93.93% and x = 0.55 has
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