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All Journal CommIT (Communication & Information Technology) JOIV : International Journal on Informatics Visualization G-Tech : Jurnal Teknologi Terapan GUYUB: Journal of Community Engagement
Rachmawati, Oktavia Citra Resmi
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Journal : G-Tech : Jurnal Teknologi Terapan

 1 
Comparison of Machine Learning Classification Methods for Weather Prediction: A Performance Analysis Darmawan, Zakha Maisat Eka; Dianta, Ashafidz Fauzan; Fathoni, Kholid; Rachmawati, Oktavia Citra Resmi; Apriandy, Kevin Ilham
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.
The Implementation of Agile Kanban in the Development of an IoT-Based Sugarcane Growth Monitoring System Sari, Sekar; Rachmawati, Oktavia Citra Resmi
G-Tech: Jurnal Teknologi Terapan Vol 9 No 4 (2025): G-Tech, Vol. 9 No. 4 October 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i4.7845

Abstract

This research stems from the urgent demand for modernisation of sugarcane farming in Indonesia, which faces challenges such as declining productivity due to climate change, limited cultivation technology, and weather uncertainty. The main problem is the absence of a real-time environmental monitoring system that can support farmers in making timely and accurate cultivation decisions. The objective of this study is to develop an IoT-based Sugarcane Growth Monitoring System equipped with four sensors—temperature, humidity, air pressure, and light intensity—using the Agile Kanban project management method. The methodology consists of literature study, planning, implementation, and analysis, carried out iteratively with the aid of a Kanban Board to structure and monitor progress. The results demonstrate that the system successfully integrates hardware, software, and user interfaces to deliver real-time environmental data. At the same time, Agile Kanban proves effective in managing the complexity of the development process. This research contributes not only academically, by showing the applicability of Agile Kanban in agricultural IoT projects, but also practically, by providing sugarcane farmers with decision-making support tools that can enhance efficiency, reduce resource waste, and improve cultivation productivity.
Co-Authors Afifah, Izza Nur Ali Ridho Barakbah APRIANDY, KEVIN ILHAM Arna Fariza Ashafidz Fauzan Dianta, Ashafidz Fauzan Bima Sena Bayu Dewantara Dadet Pramadihanto Darmawan, Zakha Maisat Eka Fathoni, Kholid Hamida, Silfiana Nur Huda, Achmad Thorikul Idris Winarno iwan Syarif Rudi Kurniawan Sari, Sekar Sesulihatien, Wahjoe Tjatur Setiawardhana, Setiawardhana Susanti, Puspasari Tessy Badriyah, Tessy Tita Karlita Tri Harsono