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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Mantik Penusa JIPN (Journal of Informatics Pelita Nusantara) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer ComTech: Computer, Mathematics and Engineering Applications Jurnal Mantik Jurnal Sains dan Teknologi TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Jurnal Teknik Informatika C.I.T. Medicom Journal of Intelligent Decision Support System (IDSS) INFOKUM Journal of Computer Networks, Architecture and High Performance Computing International Journal of Basic and Applied Science Jurnal Media Informatika Journal Basic Science and Technology Jurnal Mandiri IT CEBONG Journal INTERNATIONAL JOURNAL OF MECHANICAL COMPUTATIONAL AND MANUFACTURING RESEARCH LEBAH Indonesia Accounting Research Journal Journal of Computer Science and Research International Journal of Enterprise Modelling Journal of Defense Technology and Engineering
Hengki Tamando Sihotang
Universitas Putra Abadi Langkat
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts 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 Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Forecasting the Number of Students in Multiple Linear Regressions Fristi Riandari; Hengki Tamando Sihotang; Husain Husain
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 2 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i2.1348

Abstract

The most important element of higher education was students, therefore every university must continue to improve services in the future, and one of them was by using decision support. This case could be done by utilizing the University of Big Data. Predicting the number of prospective students in higher education was done by utilizing data mining and multiple linear regression approaches. By using 2 independent variables, namely administration costs (X1), accreditation score (X2), and the number of students who was registered each year as dependent variable (Y). For the test data, it used database for the last 13 years. By using multiple linear regression, the intercept value was sought and the coefficient of determination until the regression coefficient was obtained with the equation Y = 45.28 + -0.02.X1 + 121.58.X2, noted that if X2 was constant, the increasing of one unit was in X1 would have the effect of increasing -0.02 units on Y. Secondly, if X1 was constant, the increasing of one unit was in X2, would have the effect of increasing 121.58 units in Y. Thirdly, if X1 and X2 were equal to zero, the magnitude of Y was 45.28 units. Therefore, the proposed approach could be provided the acceptable predictive results.
Sistem Pakar untuk Identifikasi Kandungan Formalin dan Boraks pada Makanan dengan Menggunakan Metode Certainty Factor Hengki Tamando Sihotang; Fristi Riandari; Pilisman Buulolo; Husain Husain
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 1 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i1.1364

Abstract

Tujuan dari penelitian ini adalah untuk mengetahui identifikasi kandungan zat pengawet berbahaya boraks dan formalin pada makanan. Metode yang digunakan untuk mengidentifikasi kandungan zat berbahaya pada makanan dengan menggunakan Certainty Factor dengan teknik pemberian bobot pada setiap premis (gejala) hingga memperoleh persentase keyakinan untuk mengidentifikasi makanan yang mengandung formalin dan boraks. Hasil penelitian ini adalah Kandungan boraks pada makanan, dari 4 sampel makanan (100%) yaitu 4 sampel atau seluruh sampel tidak mengandung boraks dengan persentase sebesar 100%. Kandungan formalin pada makanan, dari 4 sampel makanan (100%) yaitu ada 2 sampel makanan positif mengandung formalin dengan persentase sebesar 50% dan ada 2 makanan negative mengandung formalin dengan persentase sebesar 50%. Dari hasil pemeriksaan menggunakan spektrofoto meter UV-VIS kadar formalin yang terendah terdapat pada sampel (Ikan Segar) dengan nilai 0,6631 mg/l. Kadar formalin yang tertinggi terdapat pada sampel C (Mi Bakso) dengan nilai 1,7140 mg/l.
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.
Recurrent neural network for adaptive cyber attack prediction on critical defense systems Jonson Manurung; Hengki Tamando Sihotang
Journal of Defense Technology and Engineering Vol. 1 No. 1 (2025): July, Journal of Defense Technology and Engineering
Publisher : Fakultas Teknik dan Teknologi Pertahanan, Universitas Pertahanan Republik Indonesia

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Abstract

The threat of cyber attacks against critical defense systems is becoming increasingly complex and dynamic, requiring adaptive and proactive prediction mechanisms. This study aims to develop a Recurrent Neural Network (RNN) model to predict cyber attacks on critical defense systems with high accuracy and generalization capabilities against new attacks. The CICIDS2020 dataset was used to train and test the model, with 70% of the data allocated for training, 15% for validation, and 15% for testing. The RNN architecture was optimized by selecting the number of hidden layers, the number of neurons per layer, the activation function, and the application of dropout and regularization to minimize the risk of overfitting. The model was trained using the Backpropagation Through Time (BPTT) algorithm and evaluated using accuracy, precision, recall, F1-score, and AUC metrics. The results show that RNN outperforms LSTM, Random Forest, and SVM algorithms, with an accuracy of 97.8%, precision of 96.5%, recall of 95.9%, F1-score of 96.2%, and AUC of 0.981, and is capable of detecting rare attacks. These findings confirm the effectiveness of RNN in capturing long-term temporal patterns in cyberattack data and providing adaptive predictions for new attacks. The practical implications of this research include strengthening critical defense systems through early detection and real-time mitigation of cyberattacks, as well as providing a basis for the development of reliable proactive security systems.
Blockchain-enhanced security framework for defense supply chain management: an AI-driven smart contract approach with distributed ledger technology Hondor Saragih; Jonson Manurung; Hengki Tamando Sihotang; I Made Aditya Pradhana Putra
Journal of Defense Technology and Engineering Vol. 1 No. 2 (2026): January, Journal of Defense Technology and Engineering
Publisher : Fakultas Teknik dan Teknologi Pertahanan, Universitas Pertahanan Republik Indonesia

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Abstract

Defense supply chains face critical security challenges including counterfeit components, unauthorized access, data tampering, and supply chain attacks that compromise operational integrity and national security. Existing blockchain implementations suffer from limited scalability, inadequate threat detection mechanisms, and insufficient integration with modern AI technologies for real-time security monitoring. This research develops an AI-Enhanced Blockchain Security Framework combining smart contracts with distributed ledger technology specifically designed for defense supply chain management. The framework employs multi-signature authentication, cryptographic verification, and machine learning-based anomaly detection across a three-layer architecture (blockchain layer, security layer, analytics layer). Validation using the DataCo supply chain dataset (180K operations) and Backstabber's knife collection attack patterns (174 documented attacks) demonstrates 94.7% attack detection accuracy, 87.3% reduction in unauthorized access attempts, and 99.2% data integrity verification rate. The system achieved 850 transactions per second (TPS) throughput with 1.8-second average latency and 40% cost reduction compared to traditional centralized systems. Smart contract execution showed 99.96% reliability across 10,000 test scenarios with automated enforcement of security policies. Statistical validation confirmed significant superiority over conventional approaches (p<0.001). Future work includes quantum-resistant cryptography, federated learning for privacy-preserving analytics, cross-chain interoperability, and integration with IoT sensors for real-time supply chain monitoring.
Artificial Intelligence Based Multilevel Optimization Models for Complex Decision Systems Hengki Tamando Sihotang; Wildan Alrasyid
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 6 (2026): Computer Science
Publisher : Institute of Computer Science (IOCS)

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Abstract

Complex decision systems, such as supply chains, smart cities, and healthcare networks, are characterized by hierarchical structures, dynamic environments, and high levels of uncertainty, making them difficult to optimize using traditional methods. Conventional optimization approaches, which are typically static and single-level, are limited in their ability to handle interdependent decisions and rapidly changing conditions. This study proposes an Artificial Intelligence-based multilevel optimization model to address these challenges by integrating hierarchical optimization with advanced AI techniques. The proposed framework combines multilevel optimization encompassing strategic, tactical, and operational decision layers with Artificial Intelligence methods, including neural networks for prediction, reinforcement learning for adaptive decision-making, and genetic algorithms for global optimization. A simulation-based methodology is employed to model complex environments and evaluate system performance under various scenarios. The results demonstrate that the proposed model significantly outperforms traditional optimization approaches. It achieves higher accuracy, faster convergence, and greater adaptability in dynamic and uncertain environments. Sensitivity analysis confirms the robustness of the model under varying conditions, while scalability tests indicate its effectiveness in handling large-scale systems. These findings highlight the advantages of integrating AI with multilevel optimization for complex decision-making. It offers both theoretical and practical implications for improving decision-making in complex systems. Future research is recommended to enhance computational efficiency, improve model interpretability, and validate the framework through real-world applications across various domains.
A Unified Artificial Intelligence and Stochastic Optimization Framework for Decision-Making in Highly Complex Systems Hengki Tamando Sihotang; Galih Prakoso Rizky A
Jurnal Teknik Informatika C.I.T Medicom Vol 18 No 1 (2026): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Decision-making in highly complex systems is increasingly challenged by uncertainty, dynamic environments, and the availability of large-scale, high-dimensional data. Traditional optimization methods often lack adaptability, while standalone Artificial Intelligence models struggle to explicitly handle uncertainty in a principled manner. To address these limitations, this research proposes a unified framework that integrates Artificial Intelligence with Stochastic Optimization for enhanced decision-making in complex and uncertain environments. The proposed framework combines data-driven learning and probabilistic optimization within a closed-loop architecture consisting of data input, AI-based prediction, stochastic decision-making, and continuous feedback. Advanced AI models, including deep learning and reinforcement learning, are employed to extract patterns and generate predictive insights from real-time and historical data. These outputs are then incorporated into stochastic optimization models, which evaluate decisions under uncertainty using probabilistic constraints and scenario-based analysis. The framework is further strengthened by an adaptive feedback mechanism that continuously updates both learning and optimization components. Experimental evaluation demonstrates that the proposed approach outperforms traditional optimization and pure AI models in terms of decision accuracy, robustness under uncertainty, and adaptability to dynamic environments. The framework also shows improved stability and computational efficiency when applied to large-scale systems. Practical applications in domains such as finance, logistics, and smart city management highlight its real-world relevance. Overall, this research contributes to decision science by bridging the gap between learning and uncertainty modeling, providing a scalable and integrated solution for intelligent decision-making in highly complex systems.
Dynamic portfolio optimization using differential evolution: a Markowitz modern portfolio theory approach Hengki Tamando Sihotang; Jonson Manurung; Bambang Saras Yulistiawan; Galih Prakoso Rizky A.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2449-2458

Abstract

An optimal investment portfolio is one of the main focuses in the financial world to minimize risk while maximizing returns. However, the challenge that arises is how to choose the right asset allocation amidst dynamic market uncertainty. This study aims to optimize portfolios based on Markowitz modern portfolio theory (MPT) by using the differential evolution (DE) algorithm as an optimization technique. The data used includes stocks, bonds, and other financial instruments taken from trusted data sources, such as Bloomberg and Yahoo finance, with an observation period of the last five years. The results show that this approach succeeds in finding optimal portfolios with the right asset weights, higher expected returns, and minimized risks compared to conventional approaches. The implication of this research is that the DE algorithm can be effectively used to address portfolio optimization problems in complex and volatile market environments, offering a more adaptive solution for investors to maximize their returns.
Edukasi Sustainable Development Goals (SDGs) sebagai Upaya Meningkatkan Kesadaran Mahasiswa terhadap Pembangunan Berkelanjutan Hengki Tamando Sihotang; Fristi Riandari; Wildan Alrasyid; Jonson Manurung; Kharisma Wiati Gusti; Ezra Natasya.S; Yudistira Alif Raditya; Dahayu Annisa Nathania
Lebah Vol. 19 No. 6 (2026): July: Pengabdian
Publisher : IHSA Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/lebah.v19i6.594

Abstract

Sustainable Development Goals (SDGs) merupakan agenda pembangunan global yang bertujuan mewujudkan pembangunan berkelanjutan melalui 17 tujuan utama yang mencakup aspek sosial, ekonomi, dan lingkungan. Perguruan tinggi memiliki peran strategis dalam meningkatkan literasi SDGs di kalangan mahasiswa sebagai agen perubahan. Kegiatan Pengabdian kepada Masyarakat (PKM) ini bertujuan meningkatkan pemahaman mahasiswa mengenai konsep dan implementasi SDGs melalui kegiatan sosialisasi daring. Kegiatan dilaksanakan pada 05 Juni 2026 melalui Zoom Meeting dengan melibatkan 300 mahasiswa Fakultas Ilmu Komputer Universitas Pembangunan Nasional Veteran Jakarta. Metode yang digunakan meliputi tahap persiapan, sosialisasi, diskusi, dan evaluasi menggunakan instrumen pre-test dan post-test. Hasil kegiatan menunjukkan adanya peningkatan pemahaman pada seluruh indikator yang diukur. Rata-rata pemahaman peserta meningkat dari 47,0% pada saat pre-test menjadi 90,3% pada saat post-test dengan peningkatan sebesar 43,3%. Hasil ini menunjukkan bahwa sosialisasi SDGs efektif dalam meningkatkan literasi pembangunan berkelanjutan dan kesadaran mahasiswa terhadap Agenda 2030
Evaluation of the Performance of an Ultrasonic-IoT Based Rice Field Rat Repellent System in Reducing Attack Intensity and Crop Losses Hengki Tamando Sihotang; Galih Prakoso Rizky A
International Journal of Mechanical Computational and Manufacturing Research Vol. 14 No. 3 (2025): Nov-Feb 2026: INPRESS
Publisher : Trigin Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/computational.v14i3.287

Abstract

Rodent infestation remains a major constraint in rice production, causing significant yield losses and threatening agricultural sustainability. Conventional rodent control methods, such as chemical rodenticides and manual trapping, often exhibit limited effectiveness and pose environmental and health risks. This study aims to evaluate the performance of an ultrasonic–Internet of Things (IoT)-based rice field rodent repellent system in reducing attack intensity and crop yield losses under real field conditions. The research employed a comparative field experiment conducted over one planting season, involving treated plots equipped with the ultrasonic–IoT system and untreated control plots managed using conventional practices. Rodent attack intensity was assessed through indicators including the percentage of damaged rice clumps, active burrow counts, and observable rodent activity, while yield loss was measured based on harvested grain output (kg/ha). System performance was further evaluated through the consistency of ultrasonic signal emission and the reliability of IoT-based data transmission. The results demonstrate a clear reduction in rodent attack intensity in treated fields compared to control fields, accompanied by a significant decrease in yield loss. The ultrasonic–IoT system operated reliably throughout the observation period, maintaining stable signal emission and continuous data logging despite variable field conditions. However, environmental factors such as weather variability and rodent migration patterns influenced system effectiveness to some extent. Overall, the findings indicate that the ultrasonic–IoT-based rodent repellent system is an effective, environmentally friendly, and data-driven approach that supports smart and sustainable agriculture. The system is best implemented as part of an integrated pest management strategy to enhance long-term effectiveness and scalability.
Co-Authors A, Galih Prakoso Rizky Achiriani, Tri Wahyuningtiyas Afrisawati Afrisawati Agustina Simangunsong Aisyah Alesha Alrasyid, Wildan Anthoni Anggrawan Anthony Anggrawan Bambang Saras Yulistiawan Bambang Saras Yulistiawan Bosker Sinaga Budi Arif Dermawan Calvin Berkat Iman Hulu Chandra, Suherman Dadang Pyanto Dahayu Annisa Nathania Dara Ramadhani Aresti Delano, Aldrich Desi Vinsensia Dini Anggraini Dwiki Rivaldo Naidu Efendi, Syahril Elpridawati Purba Endang Mistaorina Laia Erwin Panggabean Ezra Natasya.S Fadiel Rahmad Hidayat Firmansyah Firmansyah Fitri Andhika Fransisco alexander Simbolon Fristi Riandari Fristi Riandari Fristi Riandari Fristi Riandari Galih Prakoso Rizky A Galih Prakoso Rizky A Galih Prakoso Rizky A Galih Prakoso Rizky A Galih Prakoso Rizky A Galih Prakoso Rizky A. Guntur Syahputra Harapan Lumbantoruan Harapan Lumbantoruan Harpingka Fitria Br. Sibarani Harpingka Fitriai Br. Sibaran Hasugian , Paska Marto Henry Eko Hapsanto Herlina Zebua Herman Mawengkang Hikmah Adwin Adam Hondor Saragih Husain Husain Hutahaean, Harvei Desmon I Made Aditya Pradhana Putra Jacob, Halburt Jane Irma Sari Jelita Sari Simanungkalit Jijon Raphita Sagala Joan De Mathew Jonhariono Sihotang Jonhariono Sihotang Jonson Manurung Jonson Manurung Jonson Manurung Judijanto, Loso Kharisma Wiati Gusti Kouvelis Geovany Ortizan Laia, Endang Mistaorina Lemos, Sgarbossa Carlo Manurung, Jonson Maria Santauli Siboro Martinus Ndruru Melda Agustina Nababan Michaud, Patrisius Mochamad Wahyudi Muhammad Rafli Muhammad Zarlis Murni Marbun Normi Verawati Marbun Nurul Afifah Arifuddin Panjaitan, Firta Sari Patricius Michaud Felix Patrisia Teresa Marsoit Pilisman Buulolo Praffi Ramadhani Pujiastuti, Lise R. Fanry Siahaan R. Mahdalena Simanjorang Rasenda Rasenda Rasenda Rasenda Rifka Widyastuti Rifka Widyastuti Ririn Pebrina Br. Marpaung Rizky, Galih Prakoso Rohit Gautama Roma Sinta Simbolon Rosulastri Purba RR Octanty Mulianingtyas Rr Octanty Mulianingtyas Santiwati Sihotang Santoso, Heroe Satriyo Wibowo Sethu Ramen Sim, Lee Choi Simbolon, Agata Putri Handayani Simbolon, Roma Sinta Simbolon, Romasinta Siringoringo, Rimmar Siskawati Amri Sitio, Arjon Samuel Song , Jiang Lou Sri Devi Sulindawaty, Sulindawaty Tarisa Tarigan Teresa, Patrys Vinsensia, Desi Wildan Alrasyid Wildan Alrasyid Wildan Alrasyid Yudistira Alif Raditya