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All Journal SMARTICS Journal Teknomatika (Jurnal Teknologi dan Informatika) 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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
Model Prediktif Kerusakan Jalan Berbasis Tren Pertumbuhan Kendaraan Melalui Analisis Data Historis Provinsi Sumatera Selatan Tahun 2015–2024 Kharisma Elan; Rijalul Fikri; Terttiaavini
Teknomatika Jurnal Teknomatika, Vol.16 No.01 Maret 2026
Publisher : Institut Teknologi dan Bisnis PalComTech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61423/k441ah56

Abstract

Infrastruktur jalan merupakan komponen penting dalam mendukung pertumbuhan ekonomi daerah dan mobilitas logistik. Di Provinsi Sumatera Selatan, percepatan kerusakan jalan menjadi permasalahan serius seiring dengan pesatnya pertumbuhan jumlah kendaraan bermotor, khususnya kendaraan berat yang memberikan beban struktural tinggi pada perkerasan jalan. Penelitian ini bertujuan membangun model prediktif kerusakan jalan berbasis tren pertumbuhan kendaraan menggunakan data historis periode 2015–2024. Dataset yang digunakan mencakup data tahunan volume kendaraan berdasarkan jenis serta data kondisi jalan yang diklasifikasikan sebagai rusak dan rusak berat pada seluruh kabupaten/kota. Tahapan pra-pemrosesan meliputi pembersihan data, encoding variabel kategorikal, dan seleksi fitur. Peramalan pertumbuhan kendaraan dilakukan menggunakan regresi linier, sedangkan prediksi kerusakan jalan menggunakan algoritma Random Forest Regression. Evaluasi model dengan Mean Absolute Error (MAE) dan R-squared (R²) menunjukkan kinerja yang sangat baik, dengan nilai R² sebesar 0,9194 untuk jalan rusak dan 0,8899 untuk jalan rusak berat. Hasil penelitian menegaskan adanya hubungan kuat antara pertumbuhan kendaraan dan tingkat kerusakan jalan, serta pentingnya penerapan strategi pemeliharaan preventif.
Co-Authors Kharisma Elan Lesfandra Marshanda Amalia Vega Rijalul Fikri Saputra, Tedy Setiawan Via Kris Savitri