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All Journal JURNAL FOKUS KONSELING Bunayya : Jurnal Pendidikan Anak FOKUS: Jurnal Kajian Keislaman dan Kemasyarakatan GUIDENA: Jurnal Ilmu Pendidikan, Psikologi, Bimbingan dan Konseling FIKRAH JOMSIGN: Journal of Multicultural Studies in Guidance and Counseling Menara Ilmu Jurnal Teknik Informatika C.I.T. Medicom Bulletin of Counseling and Psychotherapy Jurnal Al-Taujih : Bingkai Bimbingan dan Konseling Islami INSPIRASI (JURNAL ILMU-ILMU SOSIAL) An-Nuha : Jurnal Pendidikan Islam Inspirasi & Strategi (INSPIRAT) : Jurnal Kebijakan Publik & Bisnis Journal of Education Research Idarah Tarbawiyah: Journal of Management in Islamic Education Inovasi : Jurnal Sosial Humaniora dan Pendidikan Tsaqofah: Jurnal Penelitian Guru Indonesia Jurnal Penelitian Ilmu Pendidikan Indonesia Journal of Computer Science and Research TOFEDU: The Future of Education Journal International Journal of Islamic Studies Higher Education Indonesian Journal of Innovation Multidisipliner Research Tashdiq: Jurnal Kajian Agama dan Dakwah Indonesian Journal of Islamic Education INDOPEDIA (Inovasi Pembelajaran dan Pendidikan) Jurnal Pengabdian Masyarakat dan Riset Pendidikan Journal of International Multidisciplinary Research Ahlussunnah: Journal of Islamic Education TILA (Tarbiyah Islamiyah Lil Athfaal) QOUBA : Jurnal Pendidikan Indonesian Journal of Innovation Multidisipliner Research Acta Psychologia International Journal of Multidisciplinary Reseach Fikrah : Jurnal Pendidikan Agama Islam Variable Research Journal
Juliana Batubara
UIN Imam Bonjol Padang
Religion Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Journal : Jurnal Teknik Informatika C.I.T. Medicom

 1 
Dynamic Latent State Modeling for Predicting Public Behavior in Digital Ecosystems Panjaitan, Firta Sari; Batubara, Juliana
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 4 (2024): September: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

This study proposes a Dynamic Latent State Modeling (DLSM) framework to predict public behavior within rapidly evolving digital ecosystems. As online interactions grow increasingly complex shaped by algorithmic exposure, platform norms, and sociopolitical events traditional static models fail to capture the fluidity and nonlinearity of user behavior. Using a combination of Hidden Markov Models, state-space modeling, and probabilistic clustering, this research identifies latent behavioral states underlying observable digital activities such as posting frequency, sentiment shifts, network engagement, and information consumption patterns. Results reveal four major latent states Passive Observation, Selective Engagement, Active Participation, and Reactive Mobilization each corresponding to meaningful psychological and social modes of online behavior. Transition matrices demonstrate that users shift states in response to contextual triggers including emotional content exposure, social reinforcement, platform incentives, and external offline events. The DLSM framework outperforms baseline machine learning classifiers by capturing temporal dependencies and hidden motivational structures influencing online actions. The study offers important implications for digital governance, policy design, crisis communication, marketing strategy, and misinformation management, particularly in anticipating rapid escalations in public sentiment or mobilization. However, limitations include potential dataset biases, constraints on generalizability across platforms, and challenges in detecting synthetic or automated behavior (bots) embedded within user streams. Overall, the research contributes a robust, interpretable, and dynamic approach to understanding and predicting public behavior in complex digital environments.
A Unified Hybrid AHP, Utility, TOPSIS Decision Model for Enhancing Ranking Reliability in Complex Multi-Criteria Problems Sihotang, Jonhariono; Batubara, Juliana
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 1 (2025): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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This study proposes a unified mathematical framework that integrates the Analytic Hierarchy Process (AHP), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Utility Theory to enhance multi-criteria decision-making (MCDM) in complex environments. While AHP provides a structured mechanism for deriving criterion weights, TOPSIS offers an effective geometric ranking approach, and Utility Theory captures nonlinear preferences and risk attitudes. However, these methods often operate independently, resulting in inconsistent rankings and incomplete representation of decision-maker behavior. The proposed framework bridges these gaps by combining AHP-derived weights, utility-transformed criterion values, and TOPSIS proximity measures into an integrated decision function. A numerical case study illustrates the full application of the model, including weight calculation, utility transformation, ideal-solution analysis, and composite scoring. Results show that the unified model produces more stable and discriminative rankings than pure AHP, pure TOPSIS, or pure Utility Theory. Sensitivity and robustness analyses further demonstrate that the integrated approach maintains ranking consistency under variations in weights, normalization methods, and utility parameters. Comparative validation using Spearman correlation confirms strong agreement with established methods while improving resilience to uncertainty. Overall, this research contributes a comprehensive and theoretically grounded MCDM framework that better reflects human judgment, strengthens ranking reliability, and is adaptable to diverse decision contexts. The unified model offers a powerful tool for practitioners and researchers seeking more accurate and robust decision support in multi-criteria environments.
A Dynamic Decision-Making Model for Regional Governance Based on Adaptive Preference Learning Sihotang, Jonhariono; Batubara, Juliana
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 3 (2025): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

This research develops a dynamic decision-making model for regional governance based on adaptive preference learning to address the limitations of traditional static policy frameworks. The study integrates decision theory, reinforcement learning, Bayesian preference modeling, and multi-criteria decision-making (MCDM) into a unified system capable of capturing evolving stakeholder preferences and responding to rapidly changing socio-economic conditions. The model consists of four core components data input layer, preference learning engine, policy decision module, and real-time feedback system which collectively enable continuous updating of decision parameters and ongoing evaluation of policy outcomes. Using a mixed-method approach that combines stakeholder surveys, historical governance data, performance indicators, and computational simulations, the study demonstrates that the adaptive model significantly improves decision accuracy, responsiveness, and alignment with citizen needs. The system’s dynamic feedback loops allow policies to be refined in real time, enhancing predictive capability and reducing the risks associated with rigid or outdated policy assumptions. Results show that the model outperforms traditional governance approaches in terms of decision efficiency, data-driven fairness, and the ability to anticipate emerging issues. Although challenges remain such as data sparsity, computational complexity, infrastructure limitations, and potential resistance from policymakers the findings highlight the model’s practical value for modern regional governance. The research contributes theoretically by advancing the application of adaptive learning in public policy decision-making and practically by offering a framework that supports faster, smarter, and more citizen-centric governance. Overall, the study underscores the potential of adaptive preference learning to transform regional decision-making in increasingly complex and uncertain environments.
Co-Authors Afnibar Ahmad Fadly Ahmad Sabri Ahmad, Arbi Salam Akhyar, Muaddyl Alesha, Aisyah Asnur, Latifah Asriatuzzeky, Asriatuzzeky Azima, Rizqiyul Azmiyah, Azmiyah Bahyu, Adri Chahnia, Juwi Daharnis Daharnis Deliana, Nurfarida Delliani, Nurfarida E, Yuli Fazni Eko Susanto Elfa, Febriyoli Farid, Abdul Fauza Masyhudi, Fauza ferbiani Feri, Dores okta Fitriza, Rozi Gusril Gusril Hanum, Ardina Harmonedi Hasnah, Radhiatul Hayati, Rifdha Illahi, Rilci Kurnia Insani Herlando, Husnul Irfansyah Irmayanti, Azizah Putri Jihana, Nabilah Julfi, Alfath Khadijah Khadijah Marhamah Marhamah Maulana, Febrian Milya Sari Misra Misra Muaddyl Akhyar Muhammad Hidayat Muhammad Syahril Nana Sepriyanti Nirmala, Zilfadlia Nurfarida Deliani Olivia Olivia Padila, Cisia Panjaitan, Firta Panjaitan, Firta Sari Putri, Mella Ika Ramadhoni Aulia Gusli Rehani rehani REMISWAL REMISWAL Rishan, Muhamad Rivdya Eliza sa'adah, nailus Sihotang, Jonhariono Silvia, Elvi Simbolon , Romasinta Sinta, Aulia Sinta, Roma Siregar, Nurul Hidayah Syahrani P, Shancy Syamsi Syamsi Ummah, khaira Uswatun Hasanah Zainal Asril Zaky, Muhammad Zulvia Trinova