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All Journal International Journal of Advances in Data and Information Systems Journal of Applied Data Sciences
Utama, I Komang Ram Pramartha
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Enhancing Federated Learning Performance through Adaptive Client Optimization with Hyperparameter Tuning Putra, Made Adi Paramartha; Utama, I Komang Ram Pramartha; Utami, Nengah Widya; Putra, I Gede Juliana Eka
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.251

Abstract

The effectiveness of Industrial Internet of Things (IIoT) systems requires a robust fault detection mechanism, a task effectively accomplished by leveraging Artificial Intelligence (AI). However, the current centralized learning approach proves inadequate. In response to this limitation, Federated Learning (FL) enables decentralized training, ensuring the protection of individual data. The traditional FL settings are not sufficient to provide an effective learning process, which needs to be refined. This paper introduces an Adaptive Distributed Client Training (ADCT) mechanism designed to optimize performance for each FL participant, thereby establishing an efficient and resilient system. The proposed ADCT utilizes two parameters, namely the accuracy threshold and grid search step, to find the optimal hyperparameter for each client in a specific number of federation rounds. The evaluation results, conducted using the MNIST and FMNIST datasets in non-IID settings, indicate that the proposed ADCT enhances the F1-score by up to 37.13% compared to state-of-the-art methods.
Implementation of Business Intelligence and Data Mining in Money Changer Transaction Analysis (Case Study of PT. Gemilang Artha Valindo) Utama, I Komang Ram Pramartha; Putra, Made Adi Paramartha; Purnama, I Nyoman
International Journal of Advances in Data and Information Systems Vol. 6 No. 1 (2025): April 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i1.1355

Abstract

This study aimed to implement Business Intelligence (BI) and Data Mining for analyzing currency exchange transactions at PT. Gemilang Artha Valindo to support data-driven decision-making. Transaction data was analyzed using Power BI to generate visualizations, including a pie chart for transaction frequency by currency type, a bar chart for the number of buy and sell transactions per currency, and a line chart for monthly average exchange rate fluctuations. The pie chart indicated that the AUD currency dominated transactions, contributing 51.95% of the total. The bar chart revealed that AUD buy transactions accounted for 63.22% of total AUD transactions, while the line chart showed that GBP and EUR had the highest average exchange rates, reaching Rp20,835 and Rp17,700, respectively. The exchange rate prediction process utilized three algorithms: Linear Regression, K-Nearest Neighbors (KNN), and Random Forest. Their performances were evaluated using Root Mean Squared Error (RMSE). The Random Forest algorithm produced the most accurate predictions with the lowest RMSE value of 134.63, followed by KNN and Linear Regression. These findings highlight the importance of leveraging BI and Data Mining to transform transaction data into valuable insights, enabling more informed business decisions.
Co-Authors Nyoman Purnama, Nyoman Putra, I Gede Juliana Eka Putra, Made Adi Paramartha Utami, Nengah Widya