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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Mantik Jurnal Teknik Informatika C.I.T. Medicom INFOKUM International Journal of Basic and Applied Science Instal : Jurnal Komputer Jurnal Info Sains : Informatika dan Sains INTERNATIONAL JOURNAL OF MECHANICAL COMPUTATIONAL AND MANUFACTURING RESEARCH Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA) International Journal of Enterprise Modelling Vertex Law and Economics Jurnal Pengabdian Masyarakat Nauli
Jonhariono Sihotang
Universitas Putra Abadi Langkat, Sumatera Utara, Indonesia
Aerospace Engineering Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation

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A Foundational Model for Data-Driven Decision Systems Using Probabilistic Preference Structures Sihotang, Jonhariono; Batubara, J
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 4 (2025): Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol17.2025.1396.pp207-217

Abstract

This research proposes a foundational model for data-driven decision systems based on probabilistic preference structures, addressing the limitations of traditional deterministic and utility-based approaches. The model integrates probability theory, Bayesian inference, and decision theory to represent preferences as flexible probability distributions capable of capturing uncertainty, partial orderings, and multi-attribute trade-offs. A set of novel algorithms is introduced for learning and estimating latent probabilistic preferences from noisy, incomplete, and heterogeneous data sources. These learned preference structures are embedded within an optimization framework that combines Bayesian updating with Markov decision processes, enabling the system to generate optimal decisions under uncertainty. Experimental evaluations conducted across synthetic and real-world datasets demonstrate significant improvements in accuracy, robustness, stability, and decision quality compared to existing preference modeling methods. The unified framework also enhances explainability by quantifying uncertainty and providing interpretable probabilistic outputs. The research makes theoretical contributions by establishing a mathematical ontology for probabilistic preferences, methodological contributions through the development of scalable inference and decision algorithms, and practical contributions by enabling reliable decision-making in environments characterized by inconsistent or probabilistic data. Overall, the results validate the proposed framework as a comprehensive and flexible foundation for next-generation intelligent decision systems, offering improved adaptability, reliability, and transparency in complex real-world applications.
Effectiveness of Ultrasonic Frequencies on the Behavior and Migration Patterns of Rice Field Rats (Rattus argentiventer) Sihotang, Hengki Tamando; Sihotang, Jonhariono; Simbolon, Romasinta
International Journal of Enterprise Modelling Vol. 16 No. 3 (2022): Sep: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/int.jo.emod.v16i3.163

Abstract

Rat infestation by Rattus argentiventer remains a serious problem in irrigated rice fields, causing significant yield losses and threatening sustainable rice production. Conventional control methods rely heavily on chemical rodenticides, which pose environmental risks and show declining long-term effectiveness. Ultrasonic deterrent technology has been proposed as an alternative; however, its effectiveness in open-field agricultural environments remains inconsistent and poorly understood. This study aims to analyze the behavioral and migration responses of rice field rats to different ultrasonic frequency ranges to clarify the mechanisms underlying ultrasonic deterrence. A field-based experimental design was applied using paired treatment and control plots, with ultrasonic frequencies ranging from 20 to 40 kHz. Rat activity and movement were monitored through camera traps and motion sensors, and spatial behavior was analyzed using activity reduction rates, migration distance, and path deviation indices. The results indicate a clear frequency-dependent response, with ultrasonic exposure at 30–35 kHz producing the strongest avoidance behavior and directional displacement. These findings suggest that ultrasonic deterrence primarily induces spatial displacement rather than population elimination and provide important implications for the development of adaptive ultrasonic–IoT systems to support smart and sustainable pest management in rice agriculture.
Adaptive Scheduling Model of Ultrasonic Frequencies Based on Environmental Data for Rice Field Rat Pest Control Sihotang, Hengki Tamando; A, Galih Prakoso Rizky; Sihotang, Jonhariono; Simbolon, Romasinta
International Journal of Enterprise Modelling Vol. 19 No. 3 (2025): September: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/int.jo.emod.v19i3.164

Abstract

Rat infestation remains a major constraint to rice production, causing significant yield losses and threatening food security in many rice-growing regions. Although ultrasonic deterrent systems have been promoted as an environmentally friendly alternative to chemical rodenticides, their effectiveness is often inconsistent due to static frequency emission and rapid behavioral habituation. This study proposes an adaptive scheduling model for ultrasonic frequencies based on real-time environmental data to enhance long-term deterrence effectiveness. The model integrates environmental sensing, stochastic frequency selection, and habituation-aware control within a context-aware scheduling framework. Environmental data were acquired using field-deployed sensors, while the adaptive algorithm dynamically adjusted ultrasonic frequency, emission duration, and interval. Field evaluations compared the proposed system with static ultrasonic control. Results demonstrate sustained spectral diversity, reduced habituation, and significant decreases in rat activity and crop damage, alongside improved energy efficiency. These findings highlight the potential of adaptive ultrasonic control as a scalable and sustainable solution for smart agriculture, supporting chemical-free pest management and precision rice farming.
New Method for Identification and Response to Infectious Disease Patterns Based on Comprehensive Health Service Data Desi Vinsensia; Siskawati Amri; Jonhariono Sihotang; Hengki Tamando Sihotang
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.4000

Abstract

Infectious diseases continue to pose a major threat to global public health and require early detection and effective response strategies. Despite advances in information technology and data analysis, the full potential of health data in identifying disease patterns and trends remains underutilised. This study aims to propose a comprehensive new mathematical model (new method) that utilises health data to identify infectious disease patterns and trends by exploring the potential of data-driven care approaches in addressing public health challenges associated with infectious diseases. The research methods used are exploratory data collection and analytical model development. The research results obtained mathematical models and algorithms that consider data of period, time, patterns, and trends of dangerous diseases, statistical analysis, and recommendations. Data visualisation and in-depth analysis were conducted in the research to improve the ability to respond to infectious disease threats and provide better decision-making solutions in improving outbreak response, as well as improving preparedness in addressing public health challenges. This research contributes to health practitioners and decision-makers.
Co-Authors A, Galih Prakoso Rizky Aisyah Alesha Amran Manalu Batubara, J Blackhurst, Alcívar Colicchia, Cagno Stone Dea Okviar Egano Napitupulu Desi Vinsensia Erwin Setiawan Panjaitan Firta Sari Panjaitan Fristi Riandari Hengki Tamando Sihotang Indradewa, Rhian Judijanto, Loso Juliana Batubara Juliana Batubara Lemos, Sgarbossa Carlo Manalu, Amran Panjaitan, Firta Sari Patrisius Michaud Felix Marsoit Roma Sinta Simbolon Roni Yunis Simamora, Siska Simbolon, Agata Putri Handayani Simbolon, Roma Sinta Simbolon, Romasinta Siskawati Amri Sonya Enjelina Gorat Vinsensia, Desi