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All Journal Jurnal Buana Informatika Journal of Artificial Intelligence and Digital Business
Eko Subiyantoro
Program Studi Teknik Informatika STTAR Malang Teknik Informatika
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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Autonomous Cognitive Leveling Game Pada Serious Game Menggunakan Particle Swarm Optimization Subiyantoro, Eko; Azhari, Azhari
Jurnal Buana Informatika Vol 8, No 2 (2017): Jurnal Buana Informatika Volume 8 Nomor 2 April 2017
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v8i2.1080

Abstract

Abstract. Serious games containing the pedagogical aspects and as part of the device/media e-learning support the learning process. Besides, the learning method uses the game are better than the conventional learning, because learning materials that involve animation in the game will enable long-term memory of students. Particle swarm optimization (PSO) method offers a search procedure based on a population consisting of individuals called particles that change their position with respect to time. PSO, by way of initializing the position and velocity of a particle, calculates the fitness function of the solution and updates the position and velocity of a particle to a stop condition are found. The design of PSO on the problem of autonomous cognitive levels of the game on a serious game with a permutation is proposed by using the fitness function the distance between xi+1 (cognitive level game) with xi (cognitive pre-test). The expected outcome of this research is the sequence of levels completed in accordance with the needs of the learner.Keywords: Serious game, cognitive, pso Abstrak. Serious game sangat mendukung proses pembelajaran melalui permainan yang mengandung aspek pedagogis dan merupakan bagian dari alat/media e-learning. Selain itu metode pembelajaran menggunakan permainan lebih baik dibandingkan dengan pembelajaran konvensional, karena animasi materi pembelajaran dalam permainan akan mengaktifkan ingatan jangka panjang siswa.Metode particle swarm optimization (PSO) menawarkan suatu prose­dur pen­­ca­rian berdasar pada populasi yang terdiri atas individu-individu yang di­se­but par­­tikel, mengubah posisi mereka terhadap waktu. PSO dengan cara melakukan inisialisasi posisi dan kecepatan particle, menghitung fungsi fitness dari solusi dan mengupdate posisi dan kecepatan particle sampai kondisi berhenti ditemukan.Perancanagan PSO pada permasalahan autonomus cognitive level game pada serious game diusulkan menggunakan permutasi dengan fungsi fitness jarak antara xi+1(cognitive level game) dengan xi (cognitive pre-test).Hasil yang diharapkan dari penelitian ini adalah adanya urutan level game yang sesuai dengan kebutuhan pembelajar.Kata Kunci: Serious game, cognitive, pso 
A Systems Engineering Approach for Credit Risk Assessment in Agricultural AIoT Data Syaukani, Muhmmad; Subiyantoro, Eko
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 1 (2026): Februari - April
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i1.6389

Abstract

Evaluation of credit risk is an essential element in the process of granting credit at the Rural Credit Bank (RCB), particularly for the Micro, Small, and Medium Enterprises (MSMEs) sector in rural agriculture. The conventional approach based on historical data in finance is often unable to reflect the real conditions of agricultural business, which are influenced by environmental and productivity factors. Research: This aim: To design a methodology for evaluating risk more comprehensively by utilizing agricultural data based on Agricultural Artificial Intelligence of Things (AIoT) through the Systems Engineering Process (SEP) approach. The SEP methodology serves as a framework for systematic work, encompassing Requirements Analysis, System Design, Implementation, Testing, Deployment, and Maintenance stages to ensure the developed system fulfils the RCB technical and operational data needs. Data from agricultural sensors integrated into an in-system computer support risk analysis and credit decisions in a more objective, data-driven way. Approach: This allows the use of real-time, contextual non-financial data as a supplement to conventional financial data. Design results show that SEP implementation can produce a system evaluation risk structure that is structured, adaptive, and aligned with the RCB business processes. This potential increase in accuracy evaluation risk reduces subjectivity in officer credit and supports improvements in inclusion in finance for the MSME sector and rural agriculture.
Co-Authors Azhari Azhari Syaukani, Muhmmad