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All Journal Jurnal Pekommas Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi dan Sistem Komputer Compiler Journal of Dedicators Community Jurnal Penelitian Pers dan Komunikasi Pembangunan JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Berdaya Mandiri Indonesian Journal of Data and Science Jurnal Abdimas Kartika Wijayakusuma PELS (Procedia of Engineering and Life Science) Applied Science and Technology Research Journal Indonesian Journal on Data Science Teknomatika: Jurnal Informatika dan Komputer International Journal of Artificial Intelligence in Medical Issues
Aris Wahyu Murdiyanto
Universitas Jenderal Achmad Yani Yogyakarta
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SEO-Based Blog Content Pipeline Automation: Integrating Web Scraping and Generative AI for Digital Marketing Efficiency Aris Wahyu Murdiyanto; David Sulistiyantoro; Mukasi Wahyu Kurniawati
APPLIED SCIENCE AND TECHNOLOGY REASERCH JOURNAL Vol. 5 No. 1 (2026): Applied Science and Technology Research Journal
Publisher : Lembaga Penelitian dan Pengabdian Mayarakat (LPPM) Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/astro.v4i2.9454

Abstract

The consistent production of Search Engine Optimization (SEO) content remains a crucial challenge in digital marketing due to the inherent inefficiencies of manual workflows. This study aims to design, develop, and evaluate the technical feasibility of an end-to-end hybrid content automation pipeline architecture. The proposed system integrates deterministic web scraping (Selenium and BeautifulSoup) for data acquisition, Generative AI (OpenAI GPT) for text synthesis and On-Page SEO optimization, the Replicate API for visual asset generation, and the WordPress REST API for autonomous publication. Employing a Proof of Concept (PoC) method at Technology Readiness Level (TRL) 3, the system was tested across two scenarios representing varying Document Object Model (DOM) structural complexities. Empirical results demonstrate that on websites with standard HTML structures, the system successfully operated autonomously, improving computational time efficiency by 98.8% (reducing the production cycle from an estimated 195 minutes to 2.25 minutes per article). The generated content proved to optimally meet On-Page SEO indicators. However, objective evaluation also revealed technical vulnerabilities in dynamic websites utilizing Client-Side Rendering (CSR), where static scraper scripts failed to extract the text payload. This study concludes that integrating generative AI into the production pipeline offers massive SEO scalability, yet it necessitates a more adaptive data extraction mechanism to achieve universal system reliability.
A Comparative Study of Machine Learning Models for Stress Level Classification Using Social Media and Lifestyle Data M. Ikbal Siami; Aris Wahyu Murdiyanto; Sumiyatun
International Journal of Artificial Intelligence in Medical Issues Vol. 4 No. 1 (2026): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/r4d62a66

Abstract

The increasing use of social media and digital platforms has raised concerns regarding its potential relationship with sleep patterns, lifestyle behaviors, productivity, and psychological well-being. Stress is a common health-related issue that may be influenced by daily behavioral patterns, including screen time, social media usage, sleep duration, physical activity, and work or study habits. This study aims to develop and evaluate machine learning models for predicting stress levels based on non-invasive digital behavior and lifestyle indicators. The dataset used in this study consisted of 11,000 records with three stress level categories: Low, Medium, and High. The predictor variables included age, daily screen time, social media usage duration, sleep hours, exercise duration, study or work hours, productivity score, and the most frequently used social media platform. Several machine learning algorithms were evaluated, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, and Gradient Boosting. Model performance was assessed using accuracy, precision, recall, F1-score, confusion matrix analysis, and 5-fold stratified cross-validation. The experimental results showed that the overall classification performance was modest. The Decision Tree model achieved the best testing performance with an accuracy and macro F1-score of 0.3400, while Gradient Boosting achieved the highest cross-validation performance with a mean accuracy of 0.3480 and a mean macro F1-score of 0.3467. Feature importance analysis using Random Forest indicated that productivity score, sleep hours, study or work hours, social media hours, and daily screen time were the most influential variables. These findings suggest that digital behavior and lifestyle indicators may provide useful exploratory insights for stress-related analysis, although their predictive power remains limited. Therefore, the proposed approach is more suitable as an exploratory digital well-being assessment framework rather than a clinical diagnostic tool.
Co-Authors -, Purnawan Adri Priadana Adri Priadana Agung Purwanto Soedarbe Agung Satria Panca Ahmad Adita Shiddiq Ahmad Adita Shiddiq Ahmad Hanafi Ahmad Hanafi Alfun Roehatul Jannah Alfun Roehatul Jannah Almayanti Susillia Ningrum Alwiah, Izmy Angkotasan, Muhamad Arabi Rizki Arbintarso, Ellyawan Setyo Arif Himawan Arif Himawan Arif Himawan, Arif Aulia Puji Rahayu Bara Falah Adikaputra Catur Iswahyudi David Sulistiyantoro David Sulistiyantoro David Sulistiyantoro, David Sulistiyantoro Dewi, Tika Sari Dian Hafidh Zulfikar Dimas Pratama Jati Edhy Sutanta (Jurusan Teknik Informatika IST AKPRIND Yogyakarta) Fitriatul Hasanah Gerlan Haha Nusa Gilang Argya Dyaksa Haha Nusa, Gerlan Hamada Zein Hariyanto, Satriawan Dini Ida Ristiana Iqbal Hadi Subekti Iqbal Hadi Subekti Kadir Parewe, Andi Maulidinnawati Abdul Kharisma Kharisma Kusumaningtyas, Kartikadyota Latipah, Asslia Johar M Ikbal Siami M. Abu Amar Al Badawi Marausna, Gaguk Muhammad Habibi Muhammad Habibi Muhammad Luqman Bukhori Muhammad Rifqi Ma'arif Mukasi Wahyu Kurniawati Mukasi Wahyu Kurniawati Nafisa Alfi Sa'diya Naswin, Ahmad Nufia Alfi Rohyana Nufia Alfi Rohyana Nurcahyo, Raden Wisnu Nurul Fatimah Poetro, Bagus Satrio Waluyo Prasetiyo, Erwan Eko Puji Astuti, Nur Rochmah Dyah Purbobinuko, Zakharias Kurnia Purnawan Purnawan Putra, Fajri Profesio Putra, Ikbal Rizki Raden Wisnu Nurcahyo Risky Setyadi Putra Rosid, Ibnu Abdul Rudi Setiawan Samuel Kristiyana Septiyati Purwandari Siregar, Alda Cendekia Sisilia Endah Lestari, Sisilia Endah Sugeng Santoso Sumiyatun Suparni Setyowati Rahayu Surya Rizki Syahruddin, Fajar Tarigan, Thomas Edyson Umar Zaky Yulianto Prabowo, Fajar Zennul Mubarrok, Zennul Mubarrok