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All Journal CESS (Journal of Computer Engineering, System and Science) Jurnal Informasi dan Teknologi JISA (Jurnal Informatika dan Sains) Jurnal Pengabdian kepada Masyarakat Jurnal Pengabdian Mandiri Jurnal Komputer Bisnis Open Access DRIVERset
Memet Sanjaya
Politeknik Takumi
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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Pelatihan Pembuatan Konten Media Sosial Menggunakan Teknologi Ai Untuk Mendapatkan Passive Income Bagi Siswa Smk di Cikarang Sanjaya, Memet
BALQIS : Journal of Business Innovation and Digital Marketing Vol. 1 No. 2 (2025): December 2025
Publisher : Program Studi Bisnis Digital - Fakultas Ekonomi dan Bisnis Islam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/balqis.v1i2.10484

Abstract

This study aims to analyze the improvement of digital literacy and AI-based content production skills among vocational high school (SMK) students through structured training. The research employed a quantitative approach using a one-group pretest–posttest design. A total of 33 students participated in the training, while 22 paired responses met the criteria for statistical analysis. The research instrument consisted of Likert-scale questionnaires and open-ended questions measuring content creation skills, AI knowledge, perceptions of the economic potential of digital content, and experience using AI tools. Data were analyzed using the Wilcoxon Signed Rank Test due to non-normal data distribution. The results indicate a statistically significant improvement across all measured indicators, with p-values < 0.001. These findings demonstrate that structured AI-based training is effective in enhancing digital literacy and preparing vocational students to produce economically valuable digital content. This study contributes empirical evidence to the development of AI-based digital literacy learning models in vocational education.
Optimizing Website Ranking Using Long-Tail Keywords and Internal Linking: A Case Study Memet Sanjaya; Rizaldi Putra; Deni Utama; Arif Prayoga
Jurnal Informasi dan Teknologi 2025, Vol. 7, No. 3
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.vi0.645

Abstract

This study proposes a structured and cost-effective Search Engine Optimization (SEO) strategy through the application of long-tail keyword targeting and internal linking. Conducted across three websites within the same corporate group, this research investigates how internal links and keyword mapping can collectively improve organic rankings. Each high-volume keyword was developed into long-tail variations and implemented into topic-specific articles, supported by contextual internal and cross-site linking. Website A, as the parent site, was used strategically to distribute link authority to other sites. In a two-month observation, keyword rankings showed significant improvements, particularly for underperforming domains. The results confirm that collaborative SEO using content relevance and internal link equity distribution can enhance visibility without relying on paid backlinks.
From Search to Discovery: Reframing the Consumer Journey in the AI era Memet Sanjaya
Jurnal Komputer Bisnis Vol 19 No 1 (2026)
Publisher : LPKIA

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The rapid development of artificial intelligence (AI), recommendation algorithms, and social commerce has significantly transformed how consumers discover products in digital environments. Although previous studies have extensively discussed consumer journeys, customer experiences, and AI-enabled marketing, limited attention has been given to explaining how AI reshapes the entry point of the consumer journey. This study aims to develop a conceptual framework explaining the transition from a search-driven consumer journey to a discovery-driven consumer journey. The study adopts a conceptual research design by synthesizing academic literature and recent digital industry reports from Think with Google, We Are Social, and DataReportal. The proposed framework identifies four major digital drivers—artificial intelligence, recommendation algorithms, social commerce platforms, and personalized content—that transform the entry point of consumer interactions from intentional information search to algorithmic product discovery. Furthermore, the framework demonstrates that contemporary consumer journeys have become increasingly platform-mediated, adaptive, and continuous through algorithmic feedback loops. This study contributes to consumer journey literature by proposing algorithmic product discovery as a new conceptual perspective while providing practical implications for organizations seeking to optimize digital marketing strategies in AI-driven environments.
Co-Authors Ade Oki Pebiansyah Adelia, Kinanti Dwi Arif Prayoga Berliana Berliana Berliana Chowal Jundy Kumoro Davina Clarissa Rabbani Deni Utama Deni Utama Elisabeth Padang Eria Eria Fathimah, Neng Siti Kinanti Dwi Adelia Kurnaesih Kurnaesih, Kurnaesih Lambok Rommy Sulaeman Neng Siti Fathimah Putri Nurazizah Reza Nugrahanto Rizaldi Putra Rizaldi Putra Rizaldi Putra Salsabilla Khoirun Nissa Tessalonika Audry Polnaya Utama, Deni