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All Journal Jurnal Buana Informatika Biogenesis: Jurnal Ilmiah Biologi JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL ILMIAH INFORMATIKA Journal of Computer System and Informatics (JoSYC) Journal of Information System and Technology (JOINT) Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Ilmiah Teknologi dan Komputer (JITTER) J-SIGN (Journal Of Informatics, Information System, And Artificial Intelligence) JoDENS: Journal of Digital Ecosystem for Natural Sustainability JUTEKOM - Jurnal Teknologi dan Ilmu Komputer
Purnajaya, Akhmad Rezki
Universitas Universal
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Immunology & microbiology Industrial & Manufacturing Engineering Mechanical Engineering

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Ant Colony Optimization for Prediction of Compound-Protein Interactions Akhmad Rezki Purnajaya
Journal of Applied Informatics and Computing Vol 3 No 2 (2019): Desember 2019
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (952.028 KB) | DOI: 10.30871/jaic.v3i2.1639

Abstract

The prediction of Compound-Protein Interactions (CPI) is an essential step in drug-target analysis for developing new drugs. Therefore, it needs a good incentive to develop a faster and more effective method to predicting the interaction between compound and protein. Predicting the unobserved link of CPI can be done with Ant Colony Optimization for Link Prediction (ACO_LP) algorithms. Each ant selects its path according to the pheromone value and the heuristic information in the link. The path passed by the ant is evaluated and the pheromone information on each link is updated according to the quality of the path. The pheromones on each link are used as the final value of similarity between nodes. The ACO_LP are tested on benchmark CPI data: Nuclear Receptor, G-Protein Coupled Receptor (GPCR), Ion Channel, and Enzyme. Result show that the accuracy values for Nuclear Receptor, GPCR, Ion Channel, and Enzyme dataset are 0.62, 0.62, 0.74, and 0.79 respectively. The results indicate that ACO_LP has good accuracy for prediction of CPI.
Perbandingan Performa Teknik Sampling Data untuk Klasifikasi Pasien Terinfeksi Covid-19 Menggunakan Rontgen Dada Akhmad Rezki Purnajaya; Fuad Dwi Hanggara
Journal of Applied Informatics and Computing Vol 5 No 1 (2021): July 2021
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v5i1.3010

Abstract

The COVID-19 virus became a virus that was deadly and shocked the world. One of the consequences caused by the COVID-19 virus is a respiratory infection. The solution put forward for this problem is with a prediction of the COVID-19 virus infection. This prediction was made based on the classification of chest X-ray data. One challenging issue in this field is the imbalance on the amount of data between infected chest X-rays and uninfected chest X-rays. The result of imbalanced data is data classification that ignores classes with fewer data. To overcome this problem, the data sampling technique becomes a mechanism to make the data balanced. For this reason, several data sampling techniques will be evaluated in this study. Data sampling techniques include Random Undersampling (RUS), Random Oversampling (ROS), Combination of Over-Undersampling (COUS), Synthetic Minority Over-sampling Technique (SMOTE), and Tomek Link (T-Link). This study also uses the Support Vector Machines (SVM) data classification, because it has high accuracy. Furthermore, the evaluation is carried out by selecting the highest accuracy and Area Under Curve (AUC). The best sampling technique found was SMOTE with an accuracy value of 99% and an AUC value of 99.32%. The SMOTE technique is the best data sampling technique for the classification of COVID-19 chest x-ray data.
Co-Authors ., Very Alvaro, Charlos Charles Charles Darmawan, Justin Eva Tesvara Fatma Indriani Fery Gunawan Fuad Dwi Hanggara Gunawan, Fery Ilwan Syafrinal Jaden Gil Salim Jaya, Mega Joni Irwansyah Kaharuddin Limuel, Patrick Medria Kusuma Dewi Hardhienata Milson Nestelrody Muhammad Reza Faisal, Muhammad Reza Noverio, Oscar Pangestu, Amora Antonio Patrick Limuel Pernando, Yonky Raymond Erz Saragih Rika Jelita Roza, Yuni Saragih, Raymond Erz Steffany Tayanto, Vincent Vincent Tayanto Vinxencius Lieputra Williams Williams Wisnu Ananta Kusuma Yamin, Valerian Yodi Yuni Roza