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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

Aplikasi Penghitung Kendaraan Pada Jalur Pantura Menggunakan Blob Deteksi Dan Kalman Filter A Sumarudin; Darsih Darsih; Iryanto Iryanto; Adi Suheryadi
Journal of Applied Informatics and Computing Vol 3 No 1 (2019): Juli 2019
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1164.935 KB) | DOI: 10.30871/jaic.v3i1.1050

Abstract

Jalur puntara jawa merupakan jalan raya wilayah utara jawa membentang dari merak sampai ke banyuwangi. Jalur pantura ini merupakan jalur padat yang dilalui oleh kendaraan baik roda 4 maupun lebih. Beban jalan pantura per tahun mencapai 1 juta ton. Selain itu juga dengan beban ini merupakan penyumbang penggunaan BBM (bahan bakar minyak) yang lebih sebesar 42 ribu kiloliter dan mengakibatkan pemberi emisi udara cukup banyak sebesar 350 ton per hari. Dengan demikian semakin banyak kendaraan yang melintas jalur pantura, sehingga perlu diketahui tentang jumlah kendaraan yang melintas dijalur pantura secara otomatis oleh sistem. Data ini dapat dijadikan rujukan beban jalan pantura secara tepat. Pada penelitian ini, menggunakan beberapa algoritma dari blob detection dengan menggunakan library openCV dan kalman filter. Hasil dari deteksi dengan menggunakan bolb deteksi masih terdapat beberapa error yang cukup besar dan menggunakan kalman filter didapatkan 78.81 %.
Hyperparameter Tuning on Graph Neural Network for the Classification of SARS-CoV-2 Inhibitors Himawan, Salamet Nur; Sohiburoyyan, Robieth; Iryanto, Iryanto
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6735

Abstract

COVID-19 is caused by the SARS-CoV-2 virus, which results in a range of symptoms, from mild to severe, and can lead to fatalities. As of October 2023, WHO has recorded 771 cases of COVID-19 globally. Various efforts have been made to control the spread of the virus, including vaccination, isolation measures, and intensive medical care. The emergence of new SARS-CoV-2 variants has led to the ongoing evolution of virus transmission. Continued research is essential to understand this virus and develop strategies to address the pandemic. Inhibitors of SARS-CoV-2 play a crucial role in the vaccine development process. Inhibitors can impede the virus's development, helping reduce disease severity and control the pandemic. The classification of inhibitors is expected to serve as a foundation for selecting compounds that can be developed into vaccines. This research develops a Graph Neural Network model for inhibitor classification and uses the random search method for hyperparameter tuning. Graph Neural Networks are chosen due to their excellent performance in modelling graph data. This study demonstrates the success of hyperparameter tuning in improving the performance of the Graph Neural Network for accurate classification of SARS-CoV-2 inhibitors.