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All Journal SMARTICS Journal OKTAL : Jurnal Ilmu Komputer dan Sains
Terttiaavini
Universitas Indo Global Mandiri
Astronomy Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Social Sciences

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Penerapan Clustering Menggunakan Metode K-Means Untuk Penggunaan E-Learning Di Dunia Marshanda Amalia Vega; Via Kris Savitri; Terttiaavini
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 2 No 05 (2023): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

This research describes the application of the K-means Clustering method to analyze e-learning user data. E-learning is a form of learning that uses electronic-based media. The main objective of this research is to cluster e-learning users based on the similarity of certain attributes and find patterns in the data. The research steps include collecting e-learning user data from keaglee website, from January 2004 to October 2021, cleaning the data to ensure accuracy and consistency, and applying clustering algorithm. This algorithm divides data into groups based on similarities. In this study, the data was divided into three groups using a value of k = 3. Through testing with the davies bouldin method, the best results were found in the 9th cluster with a centroid of 1,279. This cluster has similar e-learning user characteristics. K-means Clustering method successfully analyzes e-learning user data simply, efficiently, and easily interpreted. Grouping e-learning users based on similar attributes can be done using this method. This research can be the basis for further development in the use of clustering methods in e-learning.
Prediksi Keberlanjutan UMKM Menggunakan Pseudo-Labeling Berbasis Composite Index dan Model Ensemble Machine Learning Terttiaavini; Tedy Setiawan Saputra; Lesfandra
SMARTICS Journal Vol 11 No 2 (2025): Journal SMARTICS (Oktober 2025)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v11i2.13760

Abstract

SME sustainability plays a crucial role in strengthening local and national economies; however, many enterprises face high risks due to limited access to capital, demand volatility, and disparities in operational capacity. This study aims to develop an SME sustainability prediction model using composite index–based pseudo-labeling and ensemble machine learning. Pseudo-labels are constructed from six key indicators revenue, profit, operating costs, average production volume, business scale, and number of employees and categorized into three sustainability classes. The dataset consists of 400 SMEs operating in Palembang City, with Random Forest based feature selection employed to identify the most relevant variables. The evaluated base models include Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Gradient Boosting, while ensemble approaches comprise Bagging, Boosting, and Stacking. The results indicate that Logistic Regression achieves perfect accuracy (100%) on the test set, suggesting potential overfitting, whereas the Stacking ensemble provides more stable predictions with an F1-score of 0.918. Statistical validation using the Friedman and Wilcoxon tests confirms the superior performance of ensemble models compared to single learners. The contributions of this study include an objective pseudo-labeling method for unlabeled datasets and the development of a robust predictive model that can support policymakers and SME stakeholders in identifying sustainability risks and implementing evidence-based interventions.
Co-Authors Lesfandra Marshanda Amalia Vega Saputra, Tedy Setiawan Via Kris Savitri