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All Journal Infotech Journal Blockchain Frontier Technology (BFRONT)
Sigit Anggoro
Universitas Jenderal Achmad Yani
Computer Science & IT Control & Systems Engineering Education Library & Information Science

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SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN GURU TERBAIK PADA SMK NEGERI 1 MAJA MENGGUNAKAN METODE ANALYTICAL HIERARCHY PROCESS (AHP) Hary Rizqi Ramadhani; Gunawan Abdillah; Sigit Anggoro
INFOTECH journal Vol. 10 No. 2 (2024)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v10i2.10097

Abstract

A decision support system is a system using a model that is built to help solve semi-structured problems. The Analytical Hierarchy Process (AHP) method is a method for solving a complex, unstructured situation into several components in a hierarchical arrangement, by giving a subjective value about the relative importance of each variable, and determining which variable has the highest priority in order to influence the outcome. this situation. The results of accuracy testing can be concluded that testing data on civil servant teachers which was carried out by comparing the data of civil servant teachers selected by the principal and using the system obtained an accuracy of 83.3%. In previous research, the Teacher Performance Assessment Team (PKG) was carried out by assuming the importance of each criterion without being given a weight, while the results obtained from system calculations, there was a weight given to each criterion. Testing was carried out on 47 civil servant teacher data with six planned civil servant teachers by the Principal in order to obtain the title of best teacher. Based on the test results, there were two civil servant teachers who were different from the data planned by the Principal, so the level of accuracy of the decision support system for selecting the best teacher using the AHP method was 83.3%.
Blockchain Integration to Enhance Federated Learning Model Integrity Yane Devi Anna; Sherli Triandari; Sigit Anggoro; Ardirra Yolandita; Adele Valerry
Blockchain Frontier Technology Vol. 5 No. 2 (2026): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/bfront.v5i2.929

Abstract

Federated Learning is a distributed machine learning approach that enables model training without transferring raw data, thereby preserving user privacy. To improve conciseness, overlapping explanations of FL’s privacy benefits across the Abstract, Introduction, and Literature Review have been consolidated, highlighting its importance in sensitive domains while removing redundancy. This allows greater emphasis on the study’s novelty, particularly the Smart Contract design featuring multi-layer verification and reputation checking mechanisms. Despite its advantages, FL faces significant challenges related to model integrity, including parameter manipulation, model poisoning attacks, and limited trust among participating nodes. This study explores the integration of blockchain technology to address these issues. Leveraging decentralization, immutability, and transparency, blockchain is used to validate model updates, record contributions, and manage node reputation. The study employs a literature review and technical architecture design for a blockchain-integrated FL system. The results indicate that blockchain implementation enhances the reliability and security of FL training, especially in low-trust environments, with strong relevance for healthcare, finance, and IoT applications.
Co-Authors Adele Valerry Ardirra Yolandita Gunawan Abdillah Hary Rizqi Ramadhani Sherli Triandari Yane Devi Anna