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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Computer Networks, Architecture and High Performance Computing International Journal Software Engineering and Computer Science (IJSECS) Abdi Implementasi Pancasila: Jurnal Pengabdian kepada Masyarakat
Hindarto, Djarot
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Public Health Social Sciences

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Tourism Destination Recommendation Using Blockchain Technology and MCDM Approach Sanjaya, Irfan; Azimah, Ariana; Hindarto, Djarot; Sani, Asrul
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15482

Abstract

The rapid advancement of digital tourism services has revolutionized how travelers search and select destinations, yet privacy and trust issues remain major challenges in centralized recommendation systems. User data such as preferences, location history, and feedback are often stored on centralized servers, making them vulnerable to data breaches and manipulation. This research proposes a Blockchain-Driven Multi-Criteria Decision Making (MCDM) Approach to develop a privacy-preserving and trustworthy tourist recommendation system. The proposed framework integrates blockchain technology to ensure secure, transparent, and immutable data management, while MCDM techniques such as the Analytic Hierarchy Process (AHP) and TOPSIS are employed to evaluate and rank tourist destinations based on multiple criteria, including popularity, cost, safety, accessibility, and sustainability. The blockchain layer enforces decentralized data verification through smart contracts and cryptographic consensus, ensuring that user privacy is protected without sacrificing system transparency. The experimental results indicate improved recommendation accuracy, reduced privacy risks, and enhanced user trust compared to conventional systems. The proposed model achieved 12.5% higher recommendation accuracy and 30% lower privacy risk compared to centralized models. This study demonstrates that combining blockchain and MCDM can effectively support transparent and fair decision-making in digital tourism, offering a scalable and secure foundation for next-generation recommendation systems.
Blockchain and SVM Integration for Distributed DDoS Attack Detection Hia, Septua Ginta Putra; Hayati, Nur; Hindarto, Djarot; Sani, Asrul
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15483

Abstract

Rapid developments in information technology have increased dependence on network services, but have also triggered an increase in cyber threats such as Distributed Denial of Service (DDoS). These attacks can paralyze systems by flooding servers with simultaneous fake traffic. Conventional rule-based detection methods are now less effective in dealing with dynamic attack patterns, requiring an adaptive approach based on machine learning. This research develops a Support Vector Machine (SVM) model enhanced with Blockchain technology to improve accuracy and data security in detecting DDoS attacks. The dataset used is CICDDoS2023 from the Canadian Institute for Cybersecurity, which contains various variants of modern DDoS attacks. The research stages include data pre-processing, training the SVM model using the RBF kernel, and integrating Blockchain with training data hash recording through a smart contract using Remix Ethereum to ensure data integrity. Performance evaluation was carried out using accuracy, precision, recall, and F1-score metrics based on the confusion matrix results. The integration of SVM and Blockchain showed an increase in security and detection accuracy compared to conventional SVM models. This approach not only improves the reliability of the DDoS attack detection system, but also creates a transparent and tamper-proof data validation mechanism. The research results are expected to contribute to the development of adaptive, decentralized network security systems with a high level of confidence in attack detection results.
A Blockchain-Assisted Neural Network Model for Flood Detection and Data Integrity Assurance Melanza, Fattan Rezky; Hindarto, Djarot; Wedha, Bayu Yasa; Sani, Asrul
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15487

Abstract

Flooding is one of the most frequent natural disasters and has substantial impacts on social, economic, and environmental conditions. Therefore, early detection plays a critical role in minimizing potential damage and supporting effective disaster response. This study proposes a Flood Detection System Using an Artificial Neural Network (ANN) with Blockchain-Based Data Integrity, which integrates predictive analytics and secure data management in a unified framework. The ANN model processes multisource environmental data such as satellite imagery, rainfall intensity, water level fluctuations, and soil moisture obtained from Google Earth Engine (GEE). Training is conducted using a sigmoid activation function and backpropagation algorithm to identify spatial and temporal patterns associated with flood-prone areas. The resulting classification outputs are stored in a blockchain ledger to ensure immutability, transparency, and protection against unauthorized data modification. Experimental evaluations demonstrate that the proposed hybrid approach achieves an accuracy of 95.82%, supported by precision, recall, and F1-score values that indicate consistent model performance across varying environmental conditions. The integration of blockchain provides verifiable and tamper-proof documentation of ANN predictions and related metadata. Overall, this research contributes a reliable, secure, and technically robust method for early flood detection, offering valuable support for data-driven decision-making in disaster mitigation and environmental risk management.
Co-Authors Afarah, Sabrina Fajrul Afarini, Nihayah Alim, Syariful Alwi, Muchamad Nurdin Amalia, Nadia Amanda, Djaja Andrianingsih Anna Marina Ariana Azimah Arie Gunawan Arko Djajadi Asrul Sani Cahyo, Fajar Yusuf Nur Dazki, Erick Desmana, Satriawan Djunarto Eko Hadianto, Eko Endah Tri Esti Handayani Farahdinna, Frenda Fauziah Fauziah Fauziah Gustian, Yonathan Wijaya Handayani, Endah Tri Esti Haryono Haryono Hendrata, Ferial Hia, Septua Ginta Putra Hidayat, Muhammad Rizky Amirullah Indrajit, Eko Indriawan, Rizal ISWAHYUDI ISWAHYUDI Judijanto, Loso Melanza, Fattan Rezky Mochamad Hariadi Nur Hayati Nurfaizi, Kelfin Paays, Emmanuel Abet Rossi Pamungkasari, Panca Dewi Prawira, Khairul Thamrin Putra, Tri Dharma R. Eko Indrajit Rahman, Ben Ramadhan, Duta Pramudya Ramadhani, Nursetiaji Ratih Titi Komalasari Ririn Ikana Desanti Sanjaya, Irfan Santoso, Handri Saputra, Adi Dwifana Saputra, Adi Dwifana Saputra Sari Ningsih Sari Ningsih Sentot Imam Wahjono Setiawan, Adrian Tri Sholihati, Ira Diana Sjah, William Surya Suherman, Endang Sze, Edward Usman, Sabrina Fajrul Ula Wahyuddin, Mohammad Iwan Wedha, Alessandro Benito Putra Bayu Wedha, bayu Yasa WIJANARKO, SIGIT Wijaya, Yunan Fauzi Wirayasa, I Ketut Adi Yudhakusuma, Dudi Yudistira, Hernan Yulianto, Lili Dwi