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All Journal G-Tech : Jurnal Teknologi Terapan
Kevin Ilham Apriandy
Politeknik Internasional Tamansiswa Mojokerto, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering

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Comparison of Machine Learning Classification Methods for Weather Prediction: A Performance Analysis Zakha Maisat Eka Darmawan; Ashafidz Fauzan Dianta; Kholid Fathoni; Oktavia Citra Resmi Rachmawati; Kevin Ilham Apriandy
G-Tech: Jurnal Teknologi Terapan Vol 9 No 2 (2025): G-Tech, Vol. 9 No. 2 April 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i2.6649

Abstract

Weather classification is crucial in various sectors, including agriculture, transportation, and disaster management. Accurate weather prediction can help mitigate risks and improve decision-making in these fields. However, classifying weather conditions remains challenging due to the complex and dynamic nature of meteorological data. This study aims to compare different machine learning classification methods to determine the most effective model for weather classification. The research employs a structured methodology consisting of seven key steps: literature study, data understanding, exploratory data analysis, data preparation, modeling, evaluation, and hyperparameter tuning. The study used Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Gradient Boosting, AdaBoost, and Extra Trees to identify the best-performing classifier. Model evaluation was conducted using accuracy, precision, recall, and F1-score. The results indicate that Gradient Boosting achieved the highest performance, surpassing other models with an accuracy of 90.15%. To optimize the model further, hyperparameter tuning was conducted using GridSearchCV, and feature selection was done using SelectKBest. This process resulted in an improved accuracy of 90.22%, demonstrating the effectiveness of model optimization.
Design and Implementation of a Web-Based Visual Search System for MSME E-Commerce Using the Flask Framework Kevin Harlis Oktaviano; Kevin Ilham Apriandy; M. Sholahudin Sunardiyanta
G-Tech: Jurnal Teknologi Terapan Vol 10 No 1 (2026): G-Tech, Vol. 10 No. 1 January 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i1.8942

Abstract

This research presents the design and implementation of an end-to-end web-based visual search system for MSME e-commerce using the Flask framework and a VGG16-based convolutional neural network. The system addresses two critical challenges commonly faced by MSME digital platforms: product tagging errors during product uploads by sellers and limitations of text-based search for customers. A dual-model architecture is implemented, consisting of a visual search module for similarity-based image retrieval and a backend classification module for automatic product categorization. The system is evaluated using a locally collected MSME product image dataset from the Tapal Kuda region, achieving a classification accuracy of 89.17% and visual search performance with a macro precision of 0.85, macro recall of 1.0, and macro F1-score of 0.91. To support real-time deployment, visual features are pre-extracted and stored, enabling efficient query processing with response times under 2 seconds during concurrent usage testing. The results demonstrate that the proposed system provides effective and practical visual search functionality within a localized MSME context while maintaining feasible computational requirements, making it suitable for deployment in resource-constrained MSME environments.
Co-Authors Ashafidz Fauzan Dianta Darmawan, Zakha Maisat Eka Kevin Harlis Oktaviano Kholid Fathoni M. Sholahudin Sunardiyanta Oktavia Citra Resmi Rachmawati