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All Journal G-Tech : Jurnal Teknologi Terapan
Oktavia Citra Resmi Rachmawati
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.
The Implementation of Agile Kanban in the Development of an IoT-Based Sugarcane Growth Monitoring System Sekar Sari; Oktavia Citra Resmi Rachmawati
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.
Identifying Financial Literacy and Asset Participation Segments among Young Adults in Indonesia Using K-Means Clustering Oktavia Citra Resmi Rachmawati; Kevin Ilham Apriandy; Kevin Harlis Oktaviano; Zakha Maisat Eka Darmawan
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10684

Abstract

Financial literacy is crucial in influencing asset ownership decisions among young adults; yet, the variability of financial literacy and asset involvement in Indonesia has not been adequately examined. This research seeks to categorize young Indonesian individuals based on financial literacy and asset participation using the K-Means clustering technique. The research employed a quantitative methodology, incorporating exploratory data analysis of a survey dataset comprising 952 participants and 13 variables related to financial literacy, asset involvement, demographic traits, economic education, and financial behavior. Missing values were addressed by group-based mode imputation for categorical variables and mean imputation for numerical variables, followed by encoding and data standardization utilizing StandardScaler. The ideal number of clusters was assessed by the Elbow Method, Silhouette Score, and Davies–Bouldin Index. Despite achieving the highest Silhouette Score at k = 2, the k = 9 model was chosen due to its lower Davies–Bouldin Index and its ability to enable more nuanced responder segmentation. The findings identified nine categories exhibiting varying levels of basic and advanced financial literacy, ranging from very low to very high. These findings offer significant insights for the formulation of targeted financial education initiatives and financial inclusion policies customized to the attributes of various young adult demographics.
Co-Authors Ashafidz Fauzan Dianta Darmawan, Zakha Maisat Eka Kevin Harlis Oktaviano Kevin Ilham Apriandy Kholid Fathoni Sekar Sari