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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control)
Zuraida Abal Abas
Universiti Teknikal Malaysia Melaka (UTeM)
Computer Science & IT Engineering

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Ensemble machine learning algorithm optimization of bankruptcy prediction of bank Bambang Siswoyo; Zuraida Abal Abas; Ahmad Naim Che Pee; Rita Komalasari; Nano Suyatna
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp679-686

Abstract

The ensemble consists of a single set of individually trained models, the predictions of which are combined when classifying new cases, in building a good classification model requires the diversity of a single model. The algorithm, logistic regression, support vector machine, random forest, and neural network are single models as alternative sources of diversity information. Previous research has shown that ensembles are more accurate than single models. Single model and modified ensemble bagging model are some of the techniques we will study in this paper. We experimented with the banking industry’s financial ratios. The results of his observations are: First, an ensemble is always more accurate than a single model. Second, we observe that modified ensemble bagging models show improved classification model performance on balanced datasets, as they can adjust behavior and make them more suitable for relatively small datasets. The accuracy rate is 97% in the bagging ensemble learning model, an increase in the accuracy level of up to 16% compared to other models that use unbalanced datasets.
Hybrid optimization algorithm for resource-efficient and data-driven performance in agricultural IoT Depa Ramachandraiah Kumar Raja; Zuraida Abal Abas; Goshtu Hemanth Kumar; Chakana Ravindra Murthy; Venappagari Eswari
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 1: February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i1.26361

Abstract

The agricultural sector is undergoing a significant transformation with the adoption of the agricultural internet of things (IoT), yet it faces persistent challenges in optimizing resource efficiency and data-driven performance due to limitations in current optimization algorithms. This research assesses the effectiveness of four prominent algorithms such as ant colony optimization (ACO), genetic algorithms (GA), particle swarm optimization (PSO), and artificial bee colony (ABC) in addressing these challenges within agricultural IoT (AIoT). Introducing a novel hybrid optimization algorithm (HOA), we aim to overcome these limitations by prioritizing both resource efficiency and data-driven performance. Through a thorough evaluation, HOA demonstrates its superiority in enhancing both aspects, thereby establishing itself as a compelling solution for AIoT applications. The introduction of HOA sets the stage for sustainable, cost-effective, and data-driven precision agriculture, significantly enhancing resource efficiency and data accuracy within the IoT network.
Co-Authors Ahmad Naim Che Pee Bambang Siswoyo Chakana Ravindra Murthy Depa Ramachandraiah Kumar Raja Goshtu Hemanth Kumar Komalasari, Rita Suyatna, Nano Venappagari Eswari