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Jurnal Riset Informatika
Published by KresnaMedia Publisher
ISSN : 26561743     EISSN : 26561735     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal Riset Informatika, merupakan Jurnal yang diterbitkan oleh Kresnamedia Publisher. Jurnal Riset Informatika, berawal diperuntukan menampung paper-paper ilmiah yang dibuat oleh peneliti dan dosen-dosen program studi Sistem Informasi dan Teknik Informatika.
Arjuna Subject : -
Articles 442 Documents
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PRODUCT SALES PREDICTION USING XGBOOST WITH FEATURE IMPORTANCE ANALYSIS FOR ADVERTISING MEDIA EVALUATION Wilsen Grivin Mokodaser; Tonny Irianto Soewignyo; Fanny Soewignyo
Jurnal Riset Informatika Vol. 8 No. 3 (2026): Juni 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i3.537

Abstract

Product sales prediction plays a crucial role in supporting data-driven marketing strategies and optimizing advertising expenditures. Although previous studies have demonstrated the effectiveness of machine learning techniques for sales forecasting, most of them primarily focus on prediction accuracy and provide limited insights into the contribution of individual advertising channels to sales performance. This limitation reduces the interpretability and practical value of predictive models for business decision-making. Therefore, this study proposes a product sales prediction framework using Linear Regression as a baseline model and XGBoost Regression combined with Feature Importance Analysis for advertising media evaluation. The novelty of this study lies in integrating predictive modeling and interpretable analysis within a single framework, enabling both accurate sales prediction and the identification of influential advertising factors. Hyperparameter optimization and five-fold cross validation were employed to improve model reliability and robustness. Experimental results show that Linear Regression outperformed XGBoost, achieving an R² score close to 1.0, while XGBoost achieved an R² score of 0.953 with a mean cross-validation R² score of 0.950, indicating stable predictive performance. Feature Importance Analysis revealed that Affiliate Marketing was the most influential factor, followed by Billboards and Social Media. These findings contribute to marketing analytics by providing interpretable insights that support advertising budget optimization and more effective data-driven business decision-making.
MACHINE LEARNING APPROACH FOR TRANSFORMER CONDITION ASSESSMENT USING K-MEANS CLUSTERING AND MULTI-CLASSIFIER MODELS Zulfiana Safitri Majid; Andarini Asri; Musfirah Putri Lukman; Wisna Saputri Alfira WS; Auliya Nabila
Jurnal Riset Informatika Vol. 8 No. 3 (2026): Juni 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i3.539

Abstract

Transformers play a critical role in power systems, yet their degradation is often difficult to detect due to complex influencing factors. Conventional diagnostic methods, such as Dissolved Gas Analysis (DGA), are time-consuming and rely heavily on expert interpretation. This study proposes a machine learning approach for transformer condition assessment by combining clustering and classification techniques. K-Means clustering is first applied to identify patterns in transformer condition data without prior labeling, with the optimal number of clusters determined as three using the Elbow Method. The resulting clusters are then used as pseudo-labels to train multiple classification models, including KNN, Decision Tree, SVM, Gradient Boosting, Extra Trees, and Voting Classifier. The results show that all models achieve high performance, with accuracy above 94%. Ensemble methods, particularly Gradient Boosting and Voting Classifier, achieve the best performance with an accuracy of 98.30%. These findings demonstrate that the proposed approach effectively improves transformer condition assessment and supports faster and more reliable maintenance decision-making.

Page 45 of 45 | Total Record : 442

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