output
20212025
most citedGLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning

16 citations

5 papers

cs.DC2025

SPARS: A Reinforcement Learning-Enabled Simulator for Power Management in HPC Job Scheduling

Muhammad Alfian Amrizal, Raka Satya Prasasta, Santana Yuda Pradata +3

High-performance computing (HPC) systems consume enormous amounts of energy, with idle nodes as a major source of energy waste. Powering down idle nodes can mitigate this problem,…

cs.DC2025★ 6 cited

Improving the Efficiency of a Deep Reinforcement Learning-Based Power Management System for HPC Clusters Using Curriculum Learning

Thomas Budiarjo, Santana Yuda Pradata, Kadek Gemilang Santiyuda +3

High energy consumption remains a key challenge in high-performance computing (HPC) systems, which often feature hundreds or thousands of nodes drawing substantial power even in id…

cs.CV2024★ 16 cited

GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning

Florentina Tatrin Kurniati, Daniel HF Manongga, Eko Sediyono +2

In the era of modern technology, object detection using the Gray Level Co-occurrence Matrix (GLCM) extraction method plays a crucial role in object recognition processes. It finds…

cs.CV2023★ 1 cited

Object Classification Model Using Ensemble Learning with Gray-Level Co-Occurrence Matrix and Histogram Extraction

Florentina Tatrin Kurniati, Daniel HF Manongga, Eko Sediyono +2

In the field of object classification, identification based on object variations is a challenge in itself. Variations include shape, size, color, and texture, these can cause probl…

cs.SD2021

Feature extraction with mel scale separation method on noise audio recordings

Roy Rudolf Huizen, Florentina Tatrin Kurniati

This paper focuses on improving the accuracy of noise audio recordings. High-quality audio recording, extraction using the mel frequency cepstral coefficients (MFCC) method produce…