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All Journal Techno.Com: Jurnal Teknologi Informasi CESS (Journal of Computer Engineering, System and Science) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Penelitian Pendidikan IPA (JPPIPA) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Journals of Ners Community Jurnal Teknologi dan Informasi JSI (Jurnal sistem Informasi) Universitas Suryadarma JSAI (Journal Scientific and Applied Informatics) Jurnal Teknologi Dan Sistem Informasi Bisnis Prosiding National Conference for Community Service Project Jurnal Sistem Informasi, Teknologi Informasi, dan Edukasi Sistem Informasi (JUST-SI) Telcomatics Journal of Information System and Technology (JOINT) Journal of Computer and Information System (J-CIS) INOVTEK Polbeng - Seri Informatika
Sama, Hendi
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Nursing Physics

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A SYSTEMATIC LITERATURE REVIEW: BIG DATA IN SMART CITY DEVELOPMENT Sama, Hendi; Ulfa, Tasya Selvia; Yulianto, Andik
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7441

Abstract

The modernization of information technology has generated a large volume of data known as Big Data, which plays an essential role in supporting data-driven decision-making. With regard to Smart City development, Big Data contributes to enhancing the effective, efficient, and environmentally friendly public services. However, the utilization of Big Data in Indonesia still faces several challenges, including insufficient supporting infrastructure, limited technical expertise, and issues related to data security and privacy. This study aims to analyze the role of Big Data in Smart City development, identify the most frequently used technologies, and examine the challenges in implementing Big Data within Smart City initiatives. This study adopts the Systematic Literature Review (SLR) approach, following a structured study selection process, 40 articles were initially retrieved and evaluated, with 25 studies ultimately satisfying the methodological criteria for inclusion in the final synthesis. The results of the analysis indicate that Cloud Computing, Big Data,  Artificial Intelligence (AI) and Internet of Things (IoT) are the most dominant technological components in Smart City implementation. Furthermore, the study emphasizes that the success of Smart City initiatives is contingent not merely upon technological progress but also on human resource readiness, data quality, and information protection. This research contributes to providing a strategic foundation for policy development and implementation planning of Smart Cities in Indonesia, particularly in strengthening data governance and national digital capacity building to support sustainable urban innovation.
Pengembangan Chatbot Generatif untuk Manajemen Aktivitas Mahasiswa di Perguruan Tinggi Aklani, Syaeful Anas; Jason, Delvin; Sama, Hendi
Techno.Com Vol. 25 No. 1 (2026): February 2026
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v25i1.15081

Abstract

Penelitian ini bertujuan mengembangkan dan mengevaluasi chatbot berbasis kecerdasan buatan generatif untuk manajemen aktivitas mahasiswa dengan arsitektur server-side context injection. Pendekatan yang digunakan menggabungkan dua tahapan: (1) eksperimen A/B yang membandingkan baseline prompt dan engineered prompt, serta (2) survei penerimaan pengguna berdasarkan model Unified Theory of Acceptance and Use of Technology (UTAUT) yang diperluas dengan konstruk AI Credibility Assurance (AICA). Hasil eksperimen terhadap 100 kueri menunjukkan bahwa engineered prompt secara signifikan meningkatkan relevansi, kelengkapan, dan kegunaan keluaran (p < 0.001), serta mengindikasikan tren peningkatan akurasi faktual dan kepatuhan struktur JSON. Survei terhadap 321 responden memperlihatkan bahwa seluruh konstruk UTAUT yang diperluas reliabel dan model pengukuran memiliki kecocokan yang sangat baik. Analisis model struktural SEM menunjukkan bahwa Facilitating Conditions dan Performance Expectancy merupakan kontributor utama terhadap Behavioral Intention, diikuti oleh Effort Expectancy, Social Influence, dan AICA, seluruh jalur pengaruh bersifat positif dan signifikan. Temuan ini mengindikasikan bahwa desain prompt yang tepat dan pengelolaan konteks yang baik berpotensi meningkatkan kualitas keluaran sekaligus mendukung terbentuknya kepercayaan dan niat adopsi pengguna di lingkungan pendidikan tinggi.   Kata Kunci - Chatbot Generatif, Prompt Engineering, Injeksi Konteks Sisi-Server, UTAUT, AI Credibility Assurance (AICA).
Behavioral Manipulation In Big Data Implementation: Systematic Literature Review Sama, Hendi; Siahaan, Mangapul; Vanessa, Nancy
Techno.Com Vol. 25 No. 1 (2026): February 2026
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v25i1.15099

Abstract

This study investigated the phenomenon of behavioral manipulation in big data implementation through a systematic literature review of thirty peer-reviewed articles published between 2020 and 2025. The objective of the review was to provide a comprehensive understanding of the mechanisms, impacts, and mitigation strategies related to the use of big data for influencing human behavior. The review was conducted following the PRISMA 2020 framework, ensuring transparency and reproducibility in the selection and evaluation process. Out of an initial 250 records identified across major academic databases, 30 studies were ultimately included based on predefined inclusion and exclusion criteria. The analysis revealed that behavioral manipulation was primarily executed through algorithmic recommendation systems, dynamic pricing models, deceptive interface design, and data-driven persuasion techniques. The reviewed studies indicated that such practices compromised individual autonomy, shaped consumer and political decisions, and contributed to psychological strain and social inequality. The findings also highlighted the paradox of algorithmic transparency, showing that disclosure without user comprehension could legitimize manipulation rather than reduce it. Furthermore, evidence suggested that emerging interventions, such as dynamic consent mechanisms and independent algorithmic audits, showed potential in restoring trust and protecting user rights, although their implementation remained limited. Approximately 83.3% of the reviewed studies concluded that behavioral manipulation through big data is a multidimensional challenge requiring an integrated response that combines technical safeguards, ethical design, adaptive regulation, and enhanced digital literacy.   Keywords - Behavioral manipulation, Big data implementation, Decision making
Co-Authors ., Henly Aboagye, Osei Enoch Adi Agus Yanto Andik Yulianto Christian, Yefta Claudia Erica, Cindy Deli Djayadhinata, Djayadhinata Eryc, Eryc Firmansyah, Muhamad Dody Gautama Wijaya Gumolung, Randy Hendi Che Hilton, Chelsea Jason, Delvin Jerry Khomali, Carlos Justin Liang, Suwarno Lius, Kevin Muhamad Dody Firmansyah Mungkap Mangapul Siahaan Nasution, Dimas Firmansyah Permata, Dwi Ninda Prasetyo, Stefanus Eko Putra, Edi Yulianto Putra, Edy Yulianto Ricky Fernando Sabariman Sabariman Sabariman Sabariman Salsabila, Nanda Selina Stefanie Syaeful Anas Aklani, Syaeful Syahputra, Bayu Ting, Vincent Vannesse Titoni, Erica Tjahyadi, Surya Tony Jack Tan Ding Tony Wibowo, Tony tukino, tukino Ulfa, Tasya Selvia Vanessa, Nancy Vendryan WAN, Jed Wan Zhang, Wesley Zulkarnain Zulkarnain Zulkarnain