Judul: Pengembangan Sistem Kendali Robot Beroda dengan Metode Self-Organizing Map (SOM).
Abstrak: Self-organizing map (SOM) adalah salah satu teknik unsupervised learning yang dapat digunakan sebagai alternatif sistem kendali robot beroda yang biasanya menggunakan teknik supervised learning seperti backpropagation neural network (BPNN). Penelitian ini bertujuan untuk membandingkan kinerja SOM dengan kinerja BPNN dan membuat suatu mekanisme robot yang dapat memperbaiki kekurangan pada penelitian sebelumnya. Kontrol sistem yang digunakan adalah direct inverse control (DIC) yang bekerja dengan cara membangkitkan sinyal kendali berdasarkan lintasan (trajectory) yang telah ditentukan melalui proses inverse. Agar SOM dapat diterapkan pada sistem DIC dengan pemetaan input-output dinamis, maka diperlukan sedikit modifikasi pada algoritme asli SOM. Modifikasi tersebut menggunakan teknik vector-quantized temporal associative memory (VQTAM). Algoritme SOM menunjukkan kinerja yang lebih baik dibandingkan dengan algoritme BPNN karena menghasilkan eror yang lebih rendah dan hanya membutuhkan epoch sebesar 131 pada setiap jenis mapping neuron.
Keyword: direct inverse control, robot beroda, self-organizing map, sistem kendali

Judul: The Development of a Wheeled Robot Control System Using a Support Vector Regression Algorithm
Abstrak: Sistem kendali robot beroda mengatur pergerakan robot beroda dalam mencapai tujuannya. Umumnya, sistem kendali robot menggunakan direct inverse control yang memetakan lintasan yang diinginkan kembali ke sinyal kendali. Pada penelitian sebelumnya, telah berhasil dikembangkan sistem kendali robot beroda dengan algoritme self-organizing map (SOM). Namun, algoritme ini didasarkan pada pemetaan masukan-keluaran sehingga tidak dapat menghasilkan sinyal kendali baru di luar data yang dilatih. Penelitian ini bertujuan untuk mengatasi kekurangan penelitian sebelumnya dengan menggunakan algoritme baru yaitu support vector regression (SVR). Algoritme SVR bekerja sebagai sistem kendali yang memprediksi nilai sinyal kendali yang diperlukan untuk menggerakkan robot sesuai lintasan yang diinginkan. Sistem kendali SVR yang dikembangkan kemudian dibandingkan dengan sistem kendali SOM. Sistem kendali SVR menunjukkan kinerja yang lebih baik dengan menghasilkan NRMSE rata-rata terbaik sebesar 0,1166 dibandingkan dengan sistem kendali SOM yang menghasilkan NRMSE rata-rata terbaik sebesar 0,2005. Teknik correlation regressor chains juga terbukti meningkatkan kinerja sistem kendali SVR sebesar 22%. Data baru yang diperoleh telah digunakan untuk membangun sistem kendali SVR. Model kendali SVR yang dibangun dari data ini menunjukkan kinerja terbaik saat menggunakan metode CC1 dengan kernel RBF. Model terbaik ini memiliki nilai NRMSE sebesar 0,1095 yang lebih baik dari nilai NRMSE model SOM terbaik sebesar 0,2416 . Namun, pengujian model kendali pada lingkungan nyata masih menghasilkan penyimpangan yang tinggi antara pergerakan robot beroda terhadap jalur referensi., Wheeled robot control systems regulate the movement of a wheeled robot to achieve its goals. Typically, robot control systems use direct inverse control, which maps the desired path back to the control signal. In previous research, a wheeled robot control system with a self-organizing map (SOM) algorithm was successfully developed. However, this algorithm is based on input-output mapping and thus cannot produce a new control signal outside the trained data. This study aims to overcome the shortcomings of previous research using a new algorithm: support vector regression (SVR). The SVR algorithm works as a control system that predicts the value of the control signal that is required to move the robot based on the desired trajectory. The developed SVR control system is then compared to the previous SOM control system for wheeled robots. The SVR control system exhibits better performance by producing a best average NRMSE of 0,1166 compared to the SOM control system, which produces a best average NRMSE of 0,2005. The correlation regressor chains technique is also shown to improve the performance of the SVR control system by 22%. The newly acquired data has been used to build the SVR control system. The SVR control model built by this data shows the best performance when using the CC1 method with the RBF kernel. This best model has an NRMSE value of 0,1095 which is better than the NRMSE value of the best SOM model of 0,2416. However, real environmental testing of the control model still results in high deviation of wheeled robot movement against the reference path.
Keyword: control system, direct inverse control, self-organizing map, support vector regression, wheeled robot

Judul: Durability of Specific Immunoglobulin (Ig) Y Againts Influenza and Covid-19 in Sunscreen
Abstrak: The benefit of specific immunoglobulin Y (Ig Y) as an active ingredient in personal care products such as sunscreen has not been properly studied. Our study aims to evaluate the reliability of specific IgY against influenza and the COVID-19 virus in a sample sunscreen product. Specific IgY influenza and COVID-19 viruses were inserted in the sample sunscreen at two concentrations of 0.1% and 0.25%. Three treatments were observed: control (C) sunscreen without IgY, treatment 1 (T1) sunscreen with 0.1% IgY, and treatment 2 (T2) sunscreen with 0.25% IgY. Organoleptic observations were carried out to monitor changes in aroma, color, and texture, and sunscreen sampling was done every week for 6 weeks. The durability of IgY was evaluated using an enzyme-linked immunosorbent assay (ELISA) and a neutralization assay. Organoleptic observation from weeks 1 to 6 showed consistency in its aroma, color, and texture. Enzyme-linked immunosorbent assay and neutralization assay (NA) results of the T1 and T2 groups showed negative results except for sample T1 at week 5, which showed a positive result in the Influenza A ELISA. Based on the results, it seems that IgY concentrations of 0.1% or 0.25% were not optimal. However, this negative result might be due to low concentration of the IgY and the presence of beeswax, which inhibits IgY and causes it to dissolve in distilled water, thus preventing it from being detected in the assay.
Keyword: Immunoglobulin Y, Sunscreen, Influenza, COVID-19, Biological Activity