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Ikhsan Nendi
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Journal of Digital Marketing and Search Engine Optimization
Published by Politeknik Siber Cerdika Internasional
ISSN : 30901634     EISSN : 30901634     DOI : https://doi.org/10.59261/jseo.v2i1
Core Subject : Science,
Computer Science & IT
The Journal of Digital Marketing and Search Engine Optimization (JSEO) is a peer-reviewed, open-access scholarly journal that provides a dedicated platform for the latest research and developments in digital marketing and SEO strategies. Published by Politeknik Siber Cerdika Internasional, JSEO aims to bridge academic theories and real-world marketing practices in the digital era. JSEO welcomes original research articles, case studies, theoretical frameworks, literature reviews, and practical reports from scholars, practitioners, and industry experts around the globe. Topics covered include, but are not limited to: - Search Engine Optimization (SEO) and Algorithm Updates - Content Marketing and Digital Branding - Social Media Strategies and Analytics - Paid Advertising (PPC) and Conversion Optimization - Consumer Behavior in Digital Environments - Mobile Marketing and Location-Based Services - Data-Driven Marketing and Marketing Automation - Influencer and Affiliate Marketing - E-commerce Marketing Techniques - AI and Machine Learning Applications in Marketing The journal is published biannually (two issues per year). It is committed to advancing the understanding of the digital marketing landscape by promoting interdisciplinary and cross-cultural studies that reflect global perspectives. All submissions undergo a rigorous double-blind peer review process to ensure quality and relevance. Accepted manuscripts are available freely online to encourage knowledge dissemination and academic exchange.
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Pemasaran
Articles 15 Documents
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AI-Driven SEO Models for Enhancing Digital Marketing Performance Sarfandi, Sarfandi
Journal of Digital Marketing and Search Engine Optimization Vol. 2 No. 2 (2025): Journal of Digital Marketing and Search Engine Optimization
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/w8bj0x41

Abstract

This study aims to examine the impact of AI-driven SEO models on digital marketing performance and to address the growing need for adaptive, data-driven optimization in an increasingly algorithmic search environment. The research adopts a quantitative design involving 18 organizations, utilizing Google Analytics and Search Console datasets, expert surveys, and AI-based predictive modeling outputs. Data were analyzed through descriptive statistics, regression analysis, mediation testing, and SEM-PLS to validate the structural relationships among AI-driven SEO, SEO performance, and digital marketing outcomes. The findings reveal that AI significantly improves keyword ranking stability, organic traffic, user engagement, and conversion metrics. Statistical results confirm strong direct effects of AI-driven SEO on marketing performance, with SEO performance acting as a substantial mediating variable. AI models such as XGBoost and Random Forest demonstrate high predictive accuracy, while automated semantic optimization greatly enhances content relevance and metadata quality. These results imply that AI-driven SEO provides organizations with strategic advantages by enhancing visibility, improving cost efficiency, and strengthening competitive positioning in digital markets. The originality of this study lies in developing and empirically validating an integrated AI-SEO performance model, offering a unified framework that has not been addressed in previous research.
Search Engine Algorithm Updates and Their Effects on Digital Content Performance Setiawan, Dedy; Nendi, Ikhsan
Journal of Digital Marketing and Search Engine Optimization Vol. 2 No. 2 (2025): Journal of Digital Marketing and Search Engine Optimization
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/n1kh3g82

Abstract

This research investigates search engine algorithm update effects on digital content performance during 2020-2025 using sequential explanatory mixed-method design. Analyzing 1,247 content pieces from 512 websites across 10 industry verticals through interrupted time series analysis, multiple regression, and machine learning classification, combined with 40 in-depth practitioner interviews. Findings reveal average organic traffic declined 14.2% post-update, creating trimodal distributions: 28% severe decline, 57% moderate fluctuation, and 15% significant growth. Temporal analysis identifies three phases: immediate shock (35.4% decline), volatile adjustment, and stabilization at 18% below baseline, with only 12.4% fully recovering. E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) emerges as strongest resilience predictor (B=0.386, p<0.001), alongside content depth and original research. Qualitative findings expose pervasive algorithm anxiety (92.5% practitioners) and paradigm shifts from technical optimization toward authentic expertise. The research provides evidence-based strategies emphasizing E-E-A-T implementation, content depth over volume, and strategic diversification. This study uniquely integrates quantitative performance patterns with practitioner phenomenology across 18 major updates, advancing understanding of algorithmic governance dynamics in digital content ecosystems.
Data-Driven SEO Techniques for Strengthening Digital Customer Acquisition Khoirunnisa, Siti
Journal of Digital Marketing and Search Engine Optimization Vol. 2 No. 2 (2025): Journal of Digital Marketing and Search Engine Optimization
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/y6ym6g60

Abstract

This study investigates the implementation of data-driven SEO techniques and their impact on customer acquisition performance across various organizational contexts. Employing a mixed-methods approach, the research combines quantitative survey data from 427 digital marketing practitioners with qualitative insights from 35 in-depth interviews. Data collection utilized stratified random sampling and structured interview protocols, while analysis employed multiple regression, ANOVA, and thematic analysis techniques. Four principal findings emerged: (1) adoption rates vary significantly by organization type, with technology firms and e-commerce companies showing substantially higher adoption (75.4% and 71.2%) compared to B2B organizations (42.7%); (2) analytics tool sophistication demonstrates strong positive correlation with customer acquisition performance, explaining up to 61.2% of variance in conversion rates; (3) balanced strategies integrating on-page and off-page SEO significantly outperform single-focus approaches, achieving 71.8% traffic growth; and (4) five critical success factors were identified including data literacy, cross-functional integration, adaptability, content quality emphasis, and strategic vision. This research advances theoretical understanding by integrating diffusion of innovation theory with dynamic capability perspective, while providing actionable frameworks for practitioners. The study's originality lies in quantifying SEO-performance relationships and revealing that effective implementation requires comprehensive organizational transformation beyond mere technology adoption.
Consumer Behavior Mapping Through Search Pattern Analysis in Digital Platform Muslim, Imam Safei
Journal of Digital Marketing and Search Engine Optimization Vol. 2 No. 2 (2025): Journal of Digital Marketing and Search Engine Optimization
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/3kp5ah14

Abstract

This study examines consumer behavior patterns through comprehensive search pattern analysis across three major e-commerce platforms. The research analyzed 127,543 search sessions from 12,847 unique users over six months using Latent Dirichlet Allocation (LDA) and K-means clustering. Data collection involved clickstream analysis, query pattern extraction, and behavioral tracking across mobile, desktop, and tablet devices. Statistical methods included hierarchical linear modeling, ANOVA, and chi-square tests. The analysis identified five distinct consumer segments: Exploratory Browsers (32.4%), Systematic Researchers (23.8%), Direct Purchasers (18.7%), Deal Seekers (15.3%), and Uncertain Seekers (9.8%). Results reveal significant behavioral variations across customer journey stages, with query length increasing from 2.84 to 4.89 words and brand mentions rising from 15.2% to 71.3% from awareness to retention stages. Mobile devices dominated usage (63.4%), with distinct behavioral patterns across demographics and temporal factors. These findings enable businesses to develop targeted marketing strategies, optimize user experience design, and implement personalized recommendation systems. This research contributes original insights by integrating quantitative behavioral analytics with qualitative thematic analysis, providing a comprehensive framework for understanding digital consumer decision-making processes in contemporary e-commerce environments.
Integration of Sustainability Keywords in Digital Marketing for Green Branding Respatiningsih, Hesti; Lestari, Tri Wahyu
Journal of Digital Marketing and Search Engine Optimization Vol. 2 No. 2 (2025): Journal of Digital Marketing and Search Engine Optimization
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/czattw53

Abstract

This study investigates how sustainability-oriented keywords can be strategically integrated into digital marketing to strengthen green branding performance among environmentally conscious consumers. The objective of this research is to identify the most effective sustainability keywords, examine their influence on brand perception, and develop a practical framework for optimizing keyword integration across digital marketing channels. Using a mixed-methods approach, the study combines keyword analytics from 1,200 online search queries with qualitative interviews conducted with 15 digital marketing professionals in sustainable business sectors. Findings reveal three dominant keyword clusters—eco-friendly, carbon-neutral, and sustainable materials—that significantly increase search visibility and positively influence perceptions of corporate environmental responsibility. The implications include offering practical guidance for marketers to align keyword strategies with sustainability values, enhancing brand authenticity and competitiveness. The originality of the research lies in presenting an empirically validated green-keyword integration framework that bridges SEO strategy with sustainability communication, a topic understudied in prior digital marketing literature.

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