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All Journal INOVTEK Polbeng - Seri Informatika Jurnal Manajemen dan Bisnis (Performa)
Patah Herwanto
Ekuitas University
Computer Science & IT

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Bridging Theory and Prediction: A Hybrid Explainable SEM–Machine Learning Approach to Consumer Purchase Intention Sussy Susanti; Patah Herwanto; Henny Utarsih
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/d0jnct08

Abstract

The growing use of Instagram as a visual and interactive marketing platform has intensified scholarly interest in how social media content shapes consumer purchase intention. However, most prior studies have relied either on theory-driven Structural Equation Modeling (SEM) or data-driven machine learning, with limited integration between causal explanation, predictive evaluation, and model interpretability. This study addresses this methodological gap by proposing a hybrid explainable SEM–machine learning framework that combines PLS-SEM, XGBoost, and SHAP to examine the relationship between social media content, brand image, and purchase intention. Data were collected from 500 Indonesian Instagram users exposed to fashion and lifestyle brand-related content. The PLS-SEM results show that social media content significantly affects brand image (β = 0.581, p < 0.001), while brand image significantly influences purchase intention (β = 0.511, p < 0.001). Brand image also significantly mediates the relationship between social media content and purchase intention, with a significant indirect effect (β = 0.297; 95% BC-CI: 0.241–0.356). In the predictive stage, Linear Regression and tuned XGBoost demonstrated stable generalization, with test R² values of 0.288 and 0.277, respectively, while Random Forest showed overfitting with a negative test R². SHAP analysis revealed that brand image was the strongest predictive feature (mean |SHAP| = 0.302), followed by social media content (0.268), indicating that brand image plays a more prominent role in forecasting purchase intention. The findings contribute theoretically by reinforcing brand image as a key mediating mechanism, methodologically by integrating validated latent constructs into explainable machine learning, and practically by offering digital marketers a dual-lens approach that combines structural explanation with predictive importance.
Comprehensive Analysis: A Review of Loan Origination Systems from Information Systems and Regulatory Perspectives R. Ali Fajar Saleh; Patah Herwanto; Harmansyah Nasution
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/s2302h34

Abstract

The Loan Origination System (LOS) has become a key infrastructure in Indonesia’s digital mortgage lending ecosystem, where technological innovation increasingly intersects with regulatory governance. This study examines LOS through an integrated perspective that bridges information systems architecture and legal-regulatory frameworks. Using a qualitative normative-analytical approach grounded in systematic document analysis (2021–2026) and thematic synthesis, the research identifies a triple-layer compliance gap: a regulatory gap in technical specification, an implementation gap between regulatory intent and system design, and a legal defensibility gap concerning evidentiary robustness. The study proposes a conceptual legal-by-design framework integrating technical security, regulatory alignment, and evidentiary considerations within a unified architectural model. Rather than offering a validated industry standard, the framework serves as an analytical proposal to inform future empirical research and institutional system development.
HARNESSING AI-DRIVEN CONSUMER SEGMENTATION STRATEGIES TO ENHANCE SALES PERFORMANCE ON SHOPEE Rani Apriliani Aditya; Ari Purno Wahyu Wibowo; Patah Herwanto; Zeni Gustira; Fasya Islami Ramadhan
Jurnal Manajemen dan Bisnis Performa Vol. 23 No.2 (2026)
Publisher : UPT Publikasi Ilmiah UNISBA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/performa.v23i2.10436

Abstract

Growing adoption of digital platforms has made Artificial Intelligence (AI) a central driver of competitive strategy in e-commerce. Despite this widespread use, studies that simultaneously evaluate AI’s contribution to consumer segmentation precision and sales outcomes remain scarce, especially within the Indonesian market. This research examines how AI shapes segmentation quality and, in turn, whether improved segmentation translates into stronger sales results on the Shopee platform. Using a quantitative-associative design, structured questionnaires were administered to 120 purposively selected active Shopee users. The data were processed through multiple linear regression, partial and simultaneous significance tests, goodness-of-fit measurement (R²), and mediation testing via path analysis (Hayes PROCESS macro, Model 4, with 5,000 bootstrap resamples) in SPSS. Findings confirm that AI adoption meaningfully enhances the precision of consumer grouping, and that well-executed segmentation in turn lifts sales outcomes. Beyond this indirect channel, AI also produces a statistically significant direct effect on sales performance. These results enrich the body of knowledge on technology-driven marketing and offer actionable direction for e-commerce managers seeking to harness AI as a lever for sharper targeting and sustained revenue growth.
Co-Authors Fasya Islami Ramadhan Harmansyah Nasution Henny Utarsih R. Ali Fajar Saleh Rani Apriliani Aditya Sussy Susanti Zeni Gustira