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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Pengabdian kepada Masyarakat Nusantara Prosiding SeNTIK STI&K
Himawan, Salamet Nur
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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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.
Prediction of Nile Tilapia (Oreochromis niloticus) Harvest Yield in Brackishwater Pond Aquaculture Using XGBoost Himawan, Salamet Nur; Wisnu, Arif; Nugraha, Nur Budi
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11378

Abstract

Nile tilapia aquaculture is one of the aquaculture subsectors with significant development potential. However, the productivity of Nile tilapia cultured in brackishwater ponds is often constrained by variability in technical factors such as the number of fingerlings stocked, pond area, stocking density, land status, planting season, and feed quantity. To address these challenges, a predictive model based on machine learning was developed. Data were collected through field observations and interviews with Nile tilapia farmers in Wanantara, Sindang, Indramayu. The data were then processed using label encoding and normalization techniques. The dataset was divided into 80% for training and 20% for testing. XGBoost, Random Forest, and Support Vector Regression algorithms were trained using hyperparameter tuning and five-fold cross-validation, and evaluated using RMSE and R² metrics. The results show that XGBoost achieved the best performance (R² = 0.9798 and RMSE = 442.05 kg), followed by Random Forest (R² = 0.955 and RMSE = 679.742 kg) and SVR (R² = 0.888 and RMSE = 1065.367 kg).
Co-Authors Iryanto Iryanto Kurnia Adi Cahyanto Nugraha, Nur Budi Pamungkas, Kiki Adi Rendi Rendi Rendi, Rendi Robieth Sohiburoyyan Sitanggang, Filemon Sohiburroyan, Robieth Suheryadi, Adi Wati, Vera Wisnu, Arif