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All Journal Journal of Computer Networks, Architecture and High Performance Computing Malcom: Indonesian Journal of Machine Learning and Computer Science
Saleh, Robby Febrianur
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Computer Science & IT Education

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Flood Prediction Using Support Vector Regression (Case Study of Floodgates in Jakarta) Azi, Amanda; Saleh, Robby Febrianur; Ardana, Wildan Muhammmad; Kusrini, Kusrini
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4360

Abstract

Flood can be interpreted as an event that occurs suddenly and quickly enough where the water discharge in the drainage channel cannot be accommodated, so that the blocked area causes the water discharge in the drainage channel in several surrounding areas to overflow and is one of the natural disasters that occurs at an unexpected time and cannot be prevented, because of this, a prediction must be made to detect floods for the next day. Flood prediction is a crucial aspect of disaster management and mitigation, particularly in flood-prone areas such as Jakarta, Indonesia. This study aims to leverage Support Vector Regression (SVR) to predict flood events by analyzing various environmental and hydrological factors that influence flooding. The primary data sources include historical wheater data, river water levels, floodgate positions in Jakarta. The data preprocessing involved cleaning, handling missing values, and normalizing the datasets to ensure compatibility with the SVR model. Feature selection was conducted to identify the most relevant predictors of flooding, such as wheater data, and river water levels. The dataset was then split into training and testing sets, maintaining an 80-20 ratio to ensure robust model validation. An SVR model with a radial basis function (RBF) kernel was trained on the standardized training data. The model's performance was evaluated using Root Mean Squared Error (RMSE) as the primary metric. The RMSE produced in this study was 0.112 with an R Square accuracy of 0.977. The results indicated that the SVR model could effectively predict flood events with a reasonable degree of accuracy, demonstrating its potential as a valuable tool in flood forecasting.
Analisis Perbandingan Kinerja Web Humas Infrastruktur On-Premise dan Cloud Computing dengan Load Balancer Round Robin: Comparative Analysis of Public Relations Web Performance of On-Premise and Cloud Computing Infrastructure with Round Robin Load Balancer Saleh, Robby Febrianur; Kusrini, Kusrini
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.2182

Abstract

Penelitian ini menganalisis perbandingan kinerja web humas untuk pelayanan publik antara infrastruktur on-premise dan cloud computing dengan load balancer round robin di RSUD Ratu Aji Putri Botung. Era digitalisasi mendorong rumah sakit mengoptimalkan sistem informasi termasuk web humas sebagai platform komunikasi dengan masyarakat. Metode penelitian menggunakan pendekatan eksperimental komparatif dengan pengujian beban menggunakan Apache JMeter pada tiga skenario: 50, 200, dan 2000 concurrent users. Parameter yang dianalisis meliputi response time, throughput, CPU utilization, memory usage, dan availability. Hasil penelitian menunjukkan cloud computing dengan load balancer round robin memberikan performa superior dengan response time excellent (215-293 ms) untuk semua skenario vs on-premise yang mengalami performance collapse hingga 111,969 ms pada 2000 users. CPU utilization cloud computing optimal (78-90%) dengan distribusi beban merata, sedangkan on-premise under-utilized (6-49%). Network traffic cloud computing consistent (354-356K bytes/sec) menunjukkan throughput predictable, sementara on-premise erratic (45-551K bytes/sec). Load balancer round robin terbukti highly effective dengan perfect success rate (100%) vs on-premise (99.3%). Cloud computing menunjukkan excellent scalability dan 497.8x lebih cepat pada extreme load. Penelitian merekomendasikan implementasi cloud computing untuk web humas rumah sakit guna meningkatkan kualitas pelayanan publik significantly
Co-Authors Ardana, Wildan Muhammmad Azi, Amanda Kusrini, Kusrini