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All Journal Smatika Jurnal : STIKI Informatika Jurnal
Dwi Soca Baskara
State University of Malang
Computer Science & IT

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Development of a Virtual Mentor Integrated with Retrieval-Augmented Generation Artificial Intelligence for Project-Based Learning Dwi Soca Baskara; Nabil Muttaqin; Dio Lingga Purwodani
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 01 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i01.2258

Abstract

Project-Based Learning (PjBL) is a learning model that can enhance the quality of higher education, particularly in developing critical thinking, creativity, and collaboration skills. However, implementing PjBL often faces challenges such as limited resources and the need for intensive guidance from lecturers. To overcome these challenges, Artificial Intelligence (AI) technology offers great potential, although traditional AI systems often provide responses that are less relevant to the context of the learning material. The Retrieval-Augmented Generation (RAG) technique in AI can serve as a solution, enabling the system to generate more accurate and contextually relevant responses. By utilizing data sources such as course materials, RAG can enhance the relevance of AI responses in supporting project-based learning. It is expected that developing an AI-based virtual mentor using the RAG approach can optimize students’ PjBL experiences. Specifically, this virtual mentor is designed to provide contextual guidance, help students overcome project-related challenges, and foster independent learning, thereby improving the quality and effectiveness of PjBL in higher education.
Design of an Automated Verification to Improve the Efficiency and Optimization of IPR Management Mei Rismawati; Dwi Soca Baskara
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 01 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i01.2162

Abstract

Copyright is an essential element of innovation in academic and educational environments. However, the current manual verification and management process of Intellectual Property Rights (IPR) is highly inefficient, particularly in institutions such as Universitas Negeri Malang, which face delays and data duplication due to paper-based workflows. To address this issue, this study aims to develop an Automated Copyright Verification System based on Multi-Agent Artificial Intelligence to enhance efficiency and optimize IPR management. The proposed system, developed using a Prototype Model, leverages an agent-based architecture to model IPR verifiers with distinct functions and objectives. These agents are supported by Vision Language Models (VLM) and Natural Language Processing (NLP). Its key features include ID card data compliance checks and automated text recognition using VLM. The implementation of this system is expected to reduce staff workload, accelerate responses, and ensure data accuracy in IPR management, supporting a sustainable innovation ecosystem.
Design and Development of a Multi-Agent Artificial Intelligence-Based Financial Planner for Institutional Financial Management Optimization Intan Dina Kartika; Dwi Soca Baskara; Hardika
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 01 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i01.2167

Abstract

Institutional financial management often faces optimization challenges due to limited understanding of influencing factors, difficulties in data integration, and the lack of human expertise. These constraints hinder the identification of opportunities, risk management, and sustainability. An adaptive and automated financial planning system is required. This study proposes the design of an Artificial Intelligence (AI)-based system for automated financial planning aligned with institutional standards. The system addresses these challenges by integrating financial needs analysis, standardized cost references, and automated budget summary preparation. Using a prototyping approach, the system employs AI agents to conduct in-depth analysis and produce comprehensive budgets. The proposed system leverages RAGflow, an open-source Retrieval-Augmented Generation (RAG) engine that uses deep document understanding to provide truthful question-answering from complex data.
Co-Authors Dio Lingga Purwodani Hardika Intan Dina Kartika Mei Rismawati Nabil Muttaqin