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All Journal Indonesian Applied Physics Letters Journal of Bio-molecule Research And Engineering
Ihsan, Anaqi Syaddad
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Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Electrical & Electronics Engineering Immunology & microbiology Materials Science & Nanotechnology Physics

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Elimination of Dengue Virus with Antiviral Compound and Appropriate Technology Farihah, Neni Isna; Wijayanti, Alvia Rachma; Sucipto, Teguh Hari; Putri, Deva Permata; Ihsan, Anaqi Syaddad; Fauziyah, Shifa; Saputri, Ratih Dewi; Damayanti, Mamik
Journal of Bio-Molecule Research and Engineering Vol 2 No 1 (2023)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jbiome.v2i1.44803

Abstract

Infection with the dengue virus by the Aedes aegypti mosquito vector is in the form of dengue hemorrhagic fever (DHF), which can cause a decrease in platelets and even death. The parasitic drug niclosamide, which is effective against dengue virus serotype 2 (DENV-2) is used to prevent further dengue virus infection. Many tests were carried out using inhibitors such as doxorubicin (SA-17), glycoside inhibitors in the form of deocynojirimycin (DNJ) and castanospermine (CSP), carbohydrate-binding agents (CBA), and the use of heparan sulfate aimed at inhibition of the adsorption process and replication process, as well as improper protein folding to prevent the conformation of virus merger. The elimination process can also be carried out using antiviral compounds found in the leaves of Psidium guajava and Carica papaya, which have inhibitory activities of 92.6% and 89.5%, respectively; propyl gallate, with a percent inhibition of dengue virus envelope protein serotype 2 of 53-9.85%; isobutyl gallate, with CC50 values of 167.19 g/mL and an inhibitory value (IC50) of 4.45; Cissampelos Pariera Linn methanol extract, with progressive inhibition as the Cipa extract concentration increased with an IC50 value of 6.1μg/ml Preventive methods are also carried out in several ways, namely by utilizing hydrophobic liquid in the form of silicone oil (low-viscosity polydimethylsiloxane, or L-PDMS), the use of eave tubes in home tubes inserted with insecticides, and utilizing ultrasound with a frequency of 100 kHz and 90 dB to repel mosquitoes carrying dengue virus vectors.
Speech Synthesis Based on EEG Signal for Speech Impaired Patients by Using bLSTM Recurrent Neural Network Yurianta, Abdufattah; Ihsan, Anaqi Syaddad; Jati, Arijal Ibnu; Rahma, Osmalina Nur; Pramulen, Aji Sapta
Indonesian Applied Physics Letters Vol. 3 No. 1 (2022): Indonesian Applied Physics Letters - June 2022
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v3i1.40257

Abstract

The disability rate in Indonesia is still relatively high and is one of the main health problems which reaches 30.38 million people or 14.2% of the Indonesian population. One of these types of disabilities is speech impairment. There are several possible causes for speech impairment, including the focal disturbance. This situation occurs because of disturbances in the vocal cords caused by injuries due to accidents and other conditions, such as throat cancer, which of course will reduce the productivity of the sufferer. Sign language can be used to communicate, but it still has limitations for normal individuals. In addition, speech synthesis using brain computer interface (BCI) based on electrocorticography (ECoG) has been developed. However, this method still has a weakness, namely invasive and allows the emergence of large enough scar tissue, so that it can reduce the quality of brain biopotential to be recorded. Therefore, a non-invasive EEG-based speech synthesis method was initiated. This method uses bLSTM as one of the components of the RNN model, so that it can construct syllables into words. This system consists of datasets, data filter programs, data segmentation programs, feature extraction programs, ANN and RNN deep learning model training programs, and text-to-speech programs. ANN and RNN form a 2-level deep learning. The testing accuracy and accuracy of the ANN are 26.04% and 20.83%, while the accuracy of the RNN is 81.25%. To improve these results, in the future, researchers can improve the data collection process and increase the number of the data, use the correct extraction feature, and compare several machine learning architectures, to produce optimal accuracy.
Co-Authors DAMAYANTI, MAMIK Farihah, Neni Isna Jati, Arijal Ibnu Osmalina Nur Rahma Pramulen, Aji Sapta Putri, Deva Permata Saputri, Ratih Dewi Shifa Fauziyah Teguh Hari Sucipto, Teguh Hari Wijayanti, Alvia Rachma Yurianta, Abdufattah