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All Journal EDUMATIC: Jurnal Pendidikan Informatika Infotek : Jurnal Informatika dan Teknologi
Oktalia Kumala Sari
Universitas Budi Luhur
Computer Science & IT Control & Systems Engineering Education Engineering

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Pendekatan Interpretable Machine Learning untuk Analisis Keberhasilan Kampanye Pemasaran Menggunakan CatBoost dan SHAP Aprilisa Arum Sari; Oktalia Kumala Sari
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 2 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i2.34715

Abstract

Predicting the success of digital marketing campaigns remains a significant challenge due to the complex interactions among various variables, such as budget allocation and advertising channel selection. This study aims to develop a marketing analytics model that achieves high predictive accuracy while also providing clear interpretability of the prediction results. The study uses the SalesMind Marketing Campaigns 2026 dataset, which simulates 3,478 digital marketing campaign records from 2026. The dataset consists of categorical variables such as ad_channel and campaign_type, as well as numerical variables including marketing_spend, impressions, and conversion_rate as the prediction target. The proposed approach applies Interpretable Machine Learning by combining the CatBoost algorithm to predict conversion rates and SHAP (SHapley Additive exPlanations) to analyze the contribution of each variable. Model optimization was performed using GridSearchCV, resulting in excellent performance with an RMSE of 0.0012, an MAE of 0.005, and a coefficient of determination (R²) of 99.12%. The analysis results indicate that budget allocation is the most dominant factor in improving conversion rates without showing indications of diminishing marginal effectiveness. In addition, the use of interactive platforms such as Meta and TikTok significantly contributes to campaign effectiveness. These findings contribute to providing an accurate and informative predictive model that can support strategic decision-making in digital marketing management more effectively.
Behaviorally Interpretable Transactional Features for Customer Segmentation Using K-Means in Grocery Retail Rendy Muhammad Aprizal; Oktalia Kumala Sari; Arif Bramantoro
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.34163

Abstract

Customer segmentation based on transactional data is widely used to understand purchasing behavior in retail. However, many existing studies tend to emphasize algorithm performance, with limited discussion on how transactional variables represent actual customer behavior. This study adopts a quantitative approach using transactional sales data from a grocery retail store (Toko Solo Latri), consisting of 10,000 item-level records collected during June 2025. The analysis follows the CRISP-DM framework, covering data understanding, preparation, modeling, and evaluation stages. Customer behavior is represented through several aggregated variables, including transaction frequency, total items purchased, and product diversity. The K-Means clustering algorithm is applied to group customers into meaningful segments. The number of clusters is determined using the Elbow Method and further evaluated using Silhouette analysis. The results reveal three distinct customer segments with different levels of purchase intensity and product diversity. The Silhouette Score of 0.464 indicates a moderate clustering structure. In addition, one-way ANOVA shows significant differences across the observed variables, with large effect sizes (η² ranging from 0.736 to 0.822). These findings suggest that constructing behavior-based transactional features can improve the interpretability of customer segmentation results.
Co-Authors Aprilisa Arum Sari Arif Bramantoro Rendy Muhammad Aprizal