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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 Abdi Insani 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 Journal of Soft Computing Exploration
Aris Wahyu Murdiyanto
Department Of Information System, Universitas Jenderal Achmad Yani Yogyakarta, Indonesia
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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.
Enhancing YOLO performance with attention module for plastic and non-plastic waste detection on water surfaces Adri Priadana; Aris Wahyu Murdiyanto; Muhammad Ichwandar Akrianto; Heru Cahyono
Journal of Soft Computing Exploration Vol. 7 No. 2 (2026): June 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i2.46

Abstract

The rapid accumulation of plastic waste in aquatic environments poses serious threats to ecosystems, water management systems, and human health. This growing concern creates an urgent need for efficient and accurate detection methods. To address this challenge, this work proposes an approach to enhance YOLO performance by integrating attention modules for plastic and non-plastic waste detection on water surfaces. A comprehensive evaluation is conducted on the Plastic on Water dataset, considering detection accuracy, computational complexity, and inference speed. The results identify YOLO11n as the most effective baseline, achieving a mean Average Precision (mAP) of 96.3% with 2,590,230 parameters, 6.4 GFLOPs, and an inference speed of 18.58 FPS. To further improve performance, several attention modules are integrated into the YOLO11n architecture. Among them, the Convolutional Block Attention Module (CBAM) yields the best performance, achieving an mAP of 96.7% with 2,598,520 parameters and 6.5 GFLOPs, while maintaining real-time performance at 18.26 FPS. The results demonstrate improved detection capability, particularly for small and less prominent objects, with negligible additional computational cost. These findings highlight the effectiveness of attention mechanisms, especially CBAM, in enhancing lightweight object detection models for real-time aquatic waste monitoring.
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/ijaimi.v4i1.437

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.
PENERAPAN POMPA AIR TENAGA SURYA UNTUK PENYEDIAAN AIR BERSIH DI PADUKUHAN GONDANG, KALURAHAN DONOKERTO, SLEMAN Gaguk Marausna; Aris Wahyu Murdiyanto; Muhammad Fa’iz Alfatih; Ikbal Rizki Putra; Muhammad Dzaki Nurrosyid; Muhammad Akbar Ardani
Jurnal Abdi Insani Vol 13 No 5 (2026): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v13i5.3319

Abstract

Air bersih merupakan kebutuhan mendasar yang menopang kesehatan dan kesejahteraan masyarakat. Di Padukuhan Gondang, Kalurahan Donokerto, Kecamatan Turi, Sleman, warga menghadapi keterbatasan pasokan air bersih terutama pada musim kemarau. Kondisi ini diperburuk oleh belum adanya sistem distribusi yang efisien sehingga akses terhadap air bersih tidak terjamin. Oleh karena itu, diperlukan solusi berbasis energi terbarukan yang mampu menyediakan layanan air bersih secara berkelanjutan. Kegiatan ini bertujuan menyediakan sistem penyediaan air bersih yang layak dan berkelanjutan melalui penerapan teknologi tepat guna berbasis energi surya, sekaligus memperkuat kapasitas masyarakat dalam pengelolaan sistem. Metode pelaksanaan meliputi survei lapangan untuk identifikasi kebutuhan, sosialisasi teknologi kepada warga, instalasi sistem pompa air tenaga surya, serta pendampingan teknis. Evaluasi dilakukan melalui pemantauan kinerja sistem dan pengumpulan umpan balik masyarakat. Hasil kegiatan menunjukkan bahwa sistem pompa air tenaga surya berkapasitas 1500 Wp mampu menyediakan pasokan air bersih secara stabil dengan efisiensi energi yang baik. Debit air yang dihasilkan mencukupi kebutuhan rumah tangga warga sepanjang tahun. Melalui sosialisasi dan pelatihan, masyarakat memperoleh peningkatan kapasitas teknis dalam mengoperasikan serta memelihara sistem secara mandiri. Pembentukan kelompok kerja lokal memperkuat kelembagaan pengelolaan air bersih di tingkat komunitas. Program ini berdampak positif terhadap kualitas hidup warga dan mendukung pencapaian SDG 6 dan SDG 7 di wilayah pedesaan. Penerapan sistem pompa air tenaga surya terbukti efektif meningkatkan akses air bersih dan memperkuat kemandirian masyarakat dalam pengelolaan sumber daya.
Zero-Shot Detection of IndoT5-Synthesized Indonesian Scientific Abstracts Using mDeBERTa v3 Aldo Syahputra; Aris Wahyu Murdiyanto; Ulfi Saidata Aesyi
Indonesian Journal of Data and Science Vol. 7 No. 2 (2026): Indonesian Journal of Data and Science
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i2.457

Abstract

 Introduction: Distinguishing human-written scientific abstracts from AI-synthesized text remains challenging, particularly when machine-generated language appears fluent and formally structured. This study evaluates mDeBERTa v3 in a zero-shot Natural Language Inference (NLI) setting for detecting Indonesian scientific abstracts specifically synthesized using IndoT5-base-paraphrase. Method: A balanced dataset of 2,274 abstracts comprising 1,137 human-written abstracts from SINTA 3 journals and 1,137 IndoT5-synthesized counterparts was analyzed. Seven linguistic features were examined using the Mann–Whitney U test, followed by zero-shot mDeBERTa v3 classification using one-, three-, and five-aspect NLI instruction scenarios. A Random Forest classifier using the same linguistic features was included as a supervised baseline. Results and Discussion: All seven linguistic features differed significantly between classes (p < 0.001), with AI texts showing substantially higher sentence-length variation than human texts. The targeted one-aspect NLI scenario achieved the highest recall of 76.52% but only 53.52% accuracy because 790 human abstracts were misclassified as AI. Increasing instruction complexity further reduced recall. In contrast, Random Forest achieved 91.21% accuracy and an F1-score of 0.9130, confirming that the identified linguistic anomalies are strong learnable signals. Conclusion: Zero-shot mDeBERTa v3 can detect generator-specific structural artifacts but remains insufficiently precise for standalone academic-integrity screening and should be supplemented by supervised methods and human review.
Co-Authors -, Purnawan Adri Priadana Adri Priadana Adri Priadana Agung Purwanto Soedarbe Agung Satria Panca Ahmad Adita Shiddiq Ahmad Adita Shiddiq Ahmad Hanafi Ahmad Hanafi Aldo Syahputra 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 Gaguk Marausna Gerlan Haha Nusa Gilang Argya Dyaksa Haha Nusa, Gerlan Hamada Zein Heru Cahyono Ibnu Abdul Rosid Ida Ristiana Ikbal Rizki Putra 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 Akbar Ardani Muhammad Dzaki Nurrosyid Muhammad Fa’iz Alfatih Muhammad Habibi Muhammad Habibi Muhammad Ichwandar Akrianto 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 Puspita, Kori Putra, Fajri Profesio Putra, Ikbal Rizki Raden Wisnu Nurcahyo Risky Setyadi Putra Rudi Setiawan Samuel Kristiyana Satriawan Dini Hariyanto Septiyati Purwandari Siregar, Alda Cendekia Sisilia Endah Lestari, Sisilia Endah Sugeng Santoso Sumiyatun Suparni Setyowati Rahayu Surya Rizki Syahruddin, Fajar Tarigan, Thomas Edyson Ulfi Saidata Aesyi Umar Zaky Yulianto Prabowo, Fajar Zennul Mubarrok