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All Journal Techno.Com: Jurnal Teknologi Informasi International Journal of Artificial Intelligence Research Technomedia Journal Aptisi Transactions on Management Aptisi Transactions on Technopreneurship (ATT) ADI Journal on Recent Innovation (AJRI) ADI Bisnis Digital Interdisiplin (ABDI Jurnal) ADI Pengabdian kepada Masyarakat Jurnal (ADIMAS Jurnal) ProBisnis : Jurnal Manajemen International Journal of Cyber and IT Service Management (IJCITSM) Blockchain Frontier Technology (BFRONT) Journal of Computer Science and Technology Application
Adam Faturahman
Universitas Raharja
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Strategi Digital Content Experience Pada Instagram Terhadap Brand Attachment Konsumen Startupreneur F&B Tuti Nurhaeni; Nuke Puji Lestari Santoso; Refa Azka; Adam Faturahman
ProBisnis : Jurnal Manajemen Vol. 17 No. 03 (2026): June: Management Science
Publisher : Lembaga Riset, Publikasi dan Konsultasi JONHARIONO

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Abstract

Perkembangan media sosial, khususnya Instagram, telah mendorong pelaku bisnis startupreneur food and beverage (F&B) untuk memanfaatkan konten digital sebagai sarana membangun hubungan dengan konsumen. Namun, banyak bisnis F&B masih berfokus pada promosi produk sehingga belum mampu menciptakan pengalaman digital yang menarik dan membangun keterikatan emosional konsumen terhadap merek. Selain itu, penelitian mengenai peran Brand Defence dalam hubungan antara Digital Content Experience dan Brand Attachment masih terbatas. Penelitian ini bertujuan untuk menganalisis pengaruh Digital Content Experience terhadap Brand Attachment serta menguji peran Brand Defence sebagai variabel intervening pada konsumen startupreneur F&B di Instagram. Penelitian menggunakan pendekatan kuantitatif dengan metode survei terhadap 106 responden yang dipilih menggunakan teknik purposive sampling. Data dikumpulkan melalui kuesioner online dan dianalisis menggunakan metode Partial Least Square-Structural Equation Modeling (PLS-SEM) dengan bantuan SmartPLS. Hasil penelitian menunjukkan bahwa seluruh konstruk memenuhi kriteria validitas dan reliabilitas dengan nilai Cronbach’s Alpha masing-masing sebesar 0,975 untuk Digital Content Experience, 0,929 untuk Brand Defence, dan 0,941 untuk Brand Attachment. Pengujian hipotesis menunjukkan bahwa Digital Content Experience berpengaruh positif dan signifikan terhadap Brand Defence (β=0,933; p<0,001) dan Brand Attachment (β=0,584; p=0,010). Sebaliknya, Brand Defence tidak berpengaruh signifikan terhadap Brand Attachment (β=0,329; p=0,150). Nilai R² sebesar 0,870 pada Brand Defence dan 0,807 pada Brand Attachment menunjukkan kemampuan model yang kuat. Penelitian ini menyimpulkan bahwa Digital Content Experience merupakan faktor utama dalam membangun Brand Attachment konsumen pada bisnis startupreneur F&B melalui Instagram.
Pengaruh Content Marketing Dan Emotional Terhadap Customer Engagement Pada Instagram Startupreneur Adam Faturahman; Untung Rahardja; Reyhan Algiffary Gunawan; Fitra Putri Oganda; Nuke Puji Lestari Santoso
ProBisnis : Jurnal Manajemen Vol. 17 No. 03 (2026): June: Management Science
Publisher : Lembaga Riset, Publikasi dan Konsultasi JONHARIONO

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Abstract

Perkembangan media sosial, khususnya Instagram, telah mendorong pelaku startupreneur memanfaatkan pemasaran digital untuk meningkatkan customer engagement. Namun, masih banyak akun bisnis yang memiliki tingkat interaksi pelanggan yang rendah. Penelitian ini bertujuan untuk menganalisis pengaruh emotional attachment terhadap content marketing, pengaruh content marketing terhadap customer engagement, serta pengaruh emotional attachment terhadap customer engagement pada Instagram Startupreneur. Penelitian menggunakan metode kuantitatif dengan pendekatan asosiatif. Data diperoleh melalui penyebaran kuesioner kepada 109 responden yang merupakan pengguna aktif Instagram dan pernah berinteraksi dengan akun Instagram Startupreneur. Teknik pengambilan sampel menggunakan purposive sampling, sedangkan analisis data dilakukan menggunakan Structural Equation Modeling-Partial Least Square (SEM-PLS) dengan bantuan SmartPLS. Hasil pengujian menunjukkan bahwa seluruh indikator memenuhi kriteria validitas dan reliabilitas, dengan nilai Cronbach’s Alpha pada variabel Content Marketing sebesar 0,820, Customer Engagement sebesar 0,741, dan Emotional Attachment sebesar 0,825. Hasil pengujian hipotesis menunjukkan bahwa Emotional Attachment berpengaruh positif dan signifikan terhadap Content Marketing (β = 0,733; p = 0,000), Content Marketing berpengaruh positif dan signifikan terhadap Customer Engagement (β = 0,563; p = 0,000), serta Emotional Attachment berpengaruh positif dan signifikan terhadap Customer Engagement (β = 0,315; p = 0,001). Nilai R² sebesar 0,537 pada Content Marketing dan 0,676 pada Customer Engagement menunjukkan bahwa model memiliki kemampuan yang cukup baik dalam menjelaskan hubungan antar variabel. Dengan demikian, emotional attachment dan content marketing merupakan faktor penting dalam meningkatkan customer engagement pada Instagram Startupreneur.
AI-Driven Big Data Solutions for Personalized Healthcare: Analyzing Patient Data to Improve Treatment Outcomes Ageng Setiani Rafika; Adam Faturahman; Bintang Nandana Henry; Firdaus Dwi Yulian; Mohammed Hassan
CORISINTA Vol 2 No 1 (2025): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i1.61

Abstract

The advent of AI-driven big data solutions has transformed personalized healthcare by enabling the analysis of vast and complex patient datasets to optimize treatment outcomes. This study aims to evaluate the effectiveness of AI models in improving healthcare delivery through enhanced diagnostic accuracy, reduced processing times, and personalized treatment plans. The research utilizes AI models to process extensive patient data from electronic health records, wearable devices, and genetic information. The results show an impressive accuracy rate of 93%, a 25% reduction in diagnostic errors, and significant improvements in patient outcomes, including 72% of patients receiving more accurate diagnoses and 65% experiencing faster recovery. A comparison with traditional methods highlights the advantages of AI in scalability, efficiency, and reliability, offering a clear improvement over existing healthcare approaches. However, challenges such as data bias, ethical concerns, and scalability need to be addressed to en- sure the responsible application of AI in healthcare systems. In conclusion, this research provides valuable insights for healthcare organizations that aim to implement AI-driven solutions, fostering the advancement of patient care and encouraging innovation in the industry. The findings suggest that AI-powered big data solutions have the potential to revolutionize healthcare, improving diagnostic precision and treatment personalization, ultimately enhancing patient satisfaction and outcomes.
Big Data Governance Framework for Trustworthy Artificial Intelligence Decision Systems Adam Faturahman; Alfri Adiwijaya; Ardivan Avandi; Nanda Septiani; Kristina Vaher
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/kp2fkq63

Abstract

The rapid adoption of Artificial Intelligence (AI) decision systems has increased organizational dependence on large-scale data, making big data governance a critical requirement for ensuring reliable and responsible decision-making. Although AI systems are often evaluated based on predictive accuracy and computational performance, their trustworthiness is strongly influenced by the quality, security, privacy, traceability, and fairness of the data used throughout the AI lifecycle. This study aims to develop a Big Data Governance Framework for Trustworthy AI Decision Systems by integrating key governance dimensions with trustworthy AI requirements. A qualitative conceptual framework development approach was employed, supported by structured literature review, thematic synthesis, and design science research principles. Relevant literature on big data governance, trustworthy AI, data quality, privacy, security, explainability, accountability, fairness, and AI decision systems was reviewed to identify recurring concepts and research gaps. The results show that trustworthy AI decision systems require seven core governance dimensions: data quality governance, security and privacy governance, metadata and data lineage, bias and fairness control, explainability support, accountability mechanisms, and continuous monitoring. These dimensions strengthen trustworthy AI capabilities, including reliability, transparency, explainability, fairness, privacy preservation, security, robustness, and auditability. The proposed framework demonstrates that trustworthy AI is not only determined by algorithmic performance but also by strong data governance across the AI lifecycle. This study concludes that effective big data governance can improve decision accuracy, traceability, accountability, risk reduction, and stakeholder trust in AI-based decision systems.
Strategic Digital Marketing in F&B Startupreneur Using the AISAS Model Nugroho Prihantoni Wibowo; Adam Faturahman; Muhamad Yusup; Nuke Puji Lestari Santoso; Abdullah Arif Kamal
Technomedia Journal Vol 11 No 1 (2026): June
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/tmj.v11i1.2660

Abstract

This research is motivated by the phenomenon of accelerated development of digital technology that has fundamentally transformed consumer behavior and the competitive landscape of marketing strategies in the Food and Beverage (F&B) industry, especially in the rice bowl startup business model that has a very high dependence on the digital platform ecosystem. The purpose of this study is to analyze in depth the influence of digital marketing strategies through the AISAS (Attention, Interest, Search, Action, Share) model framework on consumer purchasing decisions in the digital era. The method used is an explanatory quantitative approach by collecting data through distributing questionnaires to 133 respondents who have experience consuming rice bowl products, then analyzed using the Partial Least Squares Structural Equation Modeling (PLS- SEM) technique through Smart PLS software. The results of the study revealed that the AISAS model has a very significant and strong influence on purchasing decisions with an R² value of 0.978, which means that 97.8% of the variation in purchasing decisions can be accurately explained by these variables. Partially, the Attention variable emerged as the most dominant factor, followed by Interest and Search, while the Share variable showed a negative relationship of -0.169, indicating that content sharing behavior does not automatically drive an increase in direct purchasing decisions. In conclusion, strengthening visual appeal and easy access to information are the main keys to marketing success, although social engagement or experience sharing strategies still require in-depth evaluation to improve their effectiveness.
Co-Authors Abdullah Arif Kamal Alfiah Khoirunisa Alfri Adiwijaya April Lansonia Ardivan Avandi Bintang Nandana Henry Chandra Lukita Dede Cahyadi Dhiyah Ayu Rini Kusumawardhani Ellen Dolan Erick Febriyanto Firdaus Dwi Yulian Fitra Putri Oganda Harahap, Eka Purnama Hendriyanti Haryani Jett Lee Wilson Kenita Zelina Kristina Vaher Leli Nirmalasari Mitra Trima Dessincer Putri Mochamad Heru Riza Chakim Mohammed Hassan Muhamad Yusup Nanda Septiani Ninda Lutfiani Nugroho Prihantoni Wibowo Nuke Puji Lestari Santoso Qurotul Aini Rafika, Ageng Setiani Rakhmansyah, Mohamad Refa Azka Reyhan Algiffary Gunawan Sandy Kosasi Sella Avionita Shofiyul millah Sri Lestari Pujiastuti, Sri Lestari Steven Harazaki Lase Sudaryono Sudaryono Sulis Setiawati Syifa Muthiatunnisa Tuti Nurhaeni Tuti Nurhaeni Untung Rahardja