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All Journal POSITIF JISKa (Jurnal Informatika Sunan Kalijaga)
Kusuma, Dewa Adji
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Computer Science & IT Electrical & Electronics Engineering Library & Information Science

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ANALISIS KLASTERING DAMPAK LINGKUNGAN BERDASARKAN KONSUMSI ENERGI PERUSAHAAN BERBASIS INDUSTRI 4.0 MENGGUNAKAN METODE CRISP-DM Kusuma, Dewa Adji; Putro, Aditya Dwi
POSITIF : Jurnal Sistem dan Teknologi Informasi Vol 9 No 2 (2023): Positif : Jurnal Sistem dan Teknologi Informasi
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/positif.v9i2.2050

Abstract

The growth of energy consumption worldwide has experienced a significant increase in the past two decades. The increase in energy consumption in a company indicates that the company generates more carbon dioxide (CO2) emissions than usual. Excessive carbon emissions have a significant impact on human health and the environment. According to the World Health Organization (WHO), greenhouse gas emissions resulting from the extraction and combustion of fossil fuels are major contributors to climate change and air pollution. It is necessary to analyze what factors contribute to high carbon emissions. This study uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) method. The K-Means algorithm will be used to cluster the features that influence high carbon emissions. The feature selection process for K-Means uses Pearson correlation. The clustering model results in good evaluation scores using the Silhouette evaluation metric. Subset data 1 obtained a Silhouette score of 0.744, and subset data 2 obtained a Silhouette score of 0.7629. The evaluation results indicate that the K-Means model works quite well in creating clusters.
Perbandingan Random Forest dan Convolutional Neural Network dalam Memprediksi Peralihan Pelanggan Kusuma, Dewa Adji; Dewi, Atika Ratna; Wijaya, Andreas Rony
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 2 (2025): May 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.2.186-194

Abstract

The rapid growth of the telecommunications industry has increased competition among companies for customers. As a result, customers often switch to other services or terminate their subscriptions. Retaining customers is very important as it is 10 times cheaper than acquiring new customers. This study compares Random Forest (RF) and Convolutional Neural Network (CNN) algorithms in predicting customer switching, using Correlation-based Feature Selection (CFS) and Recursive Feature Elimination (RFE) for data partitioning. Model evaluation using Confusion Matrix and Area Under Curve (AUC). The evaluation results show that the performance of CNN models with optimization parameters is superior. Using the CFS dataset, the test data evaluation results yielded an accuracy of 98%, AUC of 0.96, precision of 99%, recall of 92%, and F1-score of 96%. The best tuning result for CNN is achieved with three combinations of filter and kernel sizes {[64, 7], [32, 3], [16, 2]} and a pool size of 2. A limitation of this research is determining how to compare the two algorithms being evaluated effectively. Both use different approaches, namely Supervised Learning and Deep Learning.
Co-Authors Andreas Rony Wijaya Atika Ratna Dewi Putro, Aditya Dwi