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Collaborative E-Learning Model Development for Increase Quality Learning in Vocational School Kurnaedi, Didi; Widyarto, Setyawan
bit-Tech Vol. 7 No. 2 (2024): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i2.1846

Abstract

The creation of a collaborative e-learning model is an urgent need, especially in Tangerang, to improve the quality of education in Vocational High Schools (SMK). Vocational learning requires integration between theory and practice, but traditional e-learning systems have not been able to meet this need. This study aims to develop a personal collaborative e-learning model that combines interactive support-based learning, context-based collaboration, and practice-oriented learning. The research methodology uses the ADDIE model  with a Research and Development  approach. The subjects of the study involved 22 teachers and 375 students from various vocational schools in Tangerang. Data collection was carried out through questionnaires, observations, interviews, and test results, with three trial classes for one semester. The results showed that the collaborative e-learning paradigm significantly increased student engagement, learning quality, and learning outcomes. As many as 82% of students felt their learning experience was more meaningful than the previous e-learning method, while 85% of students actively participated in discussion forums and collaboration. The test results showed an average increase in scores of 15%. Teachers also assessed that this strategy supports the development of students' practical skills and facilitates project-based learning. Satisfaction evaluations showed that 80% of students felt more satisfied with the learning experience using this collaborative model. The collaborative e-learning model developed is not only relevant for learning in SMK Tangerang, but also has the potential to improve the standard of vocational education in Indonesia. Training and infrastructure support are needed to ensure the sustainability of the implementation of this model.
Pengaruh Latihan Continuous Running Terhadap Peningkatan Kebugaran Jasmani Amni, Hazrina; sumaryanti, Sumaryanti; Wulandari, Indri; Widyarto, Setyawan; Agus, Apri; Sukarmin, Yustinus
Jurnal Sporta Saintika Vol 8 No 1 (2023): Jurnal Sporta Saintika Edisi Maret 2023
Publisher : Departemen Kesehatan Dan Rekreasi Fakultas Ilmu Keolahragaan Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/sporta.v8i1.282

Abstract

This study aims to determine the effect of Continuous Running Exercise on Increasing Physical Fitness for S1 students of the Sports Science Study Program, Faculty of Sports Science, Padang State University class of 2021/2022. The research method used in this research is a quasi-experimental method. The population in this study were 215 undergraduate students of the FIK UNP Sports Science Study Program, class of 2021/2022. The sampling technique was carried out by purposive sampling, with a total sample of 20 people. The data collection technique uses a physical fitness test research instrument with a 1600 m rockport run. The results of the study were analyzed statistically using a comparison test (t-test) at a significance level of 5%. The results of this study indicate that the average level of physical fitness of the respondents before being given treatment was (45.30), whereas after being given treatment it was (50.35). There is a significant effect of Continuous Running Exercise on Increasing Physical Fitness of Students of the Sports Science Study Program, Faculty of Sports Science, Padang State Universitywith the results showing the tcount value (4.25) > ttable value (1.73).
Dampak Model Mental Pengguna terhadap Implementasi Multi-Factor Authentication untuk Mitigasi Risiko Password Guessing di Konteks Organisasi Triantoro, Ery; Widyarto, Setyawan
Dinamik Vol 31 No 1 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i1.10290

Abstract

This study conducts a Systematic Literature Review (SLR) to explore the impact of users’ mental models on the implementation of Multi-Factor Authentication (MFA) as a strategy for mitigating password guessing risks in organizational environments. Amid the growing landscape of cyber threats, single-factor authentication has proven to be vulnerable, making MFA an essential layered security solution. However, the adoption of MFA remains slow. Existing studies indicate that expert users perceive MFA as a useful additional layer of verification, whereas non-expert users often view it as a burdensome task (a chore) and may even struggle to distinguish between different types of MFA. Dependence on mobile devices emerges as a common source of frustration for both groups. These findings emphasize that understanding users’ mental models is crucial for improving the implementation and usability of MFA. Innovations such as adaptive MFA or Single Input Multi-Factor Authentication (SIMFA) show potential as solutions to balance security requirements and user experience.
Deep Reinforcement-Driven Clustering and Routing Protocol for Smart Vehicular Networks Riki, Riki; Widyarto, Setyawan
International Journal of Artificial Intelligence Research Vol 9, No 2 (2025): December
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i2.1576

Abstract

This study proposes a Deep Reinforcement-Driven Clustering and Routing Protocol (DRCRP) to enhance energy efficiency and routing stability in smart vehicular networks. The protocol integrates an Actor–Critic deep reinforcement learning framework with Proximal Policy Optimization (PPO) to enable adaptive decision-making in dynamic Internet of Vehicles (IoV) environments. Through continuous learning, DRCRP adjusts cluster head selection and routing paths according to real-time vehicular mobility, residual energy, and link quality. Simulation experiments conducted using NS-2 and VanetMobiSim show that DRCRP achieves superior performance compared to benchmark algorithms such as AI-EECR, GWO-CH, and DMCNF. Quantitatively, the proposed model improved the Packet Delivery Ratio (PDR) by up to 4.3%, reduced End-to-End Delay by 18–22%, and lowered Energy Consumption by 12–16%. Moreover, DRCRP effectively minimized communication overhead and extended cluster head and member lifetimes, confirming its ability to balance reliability and energy efficiency. These results demonstrate the capability of reinforcement learning-based architectures to support intelligent, sustainable, and scalable vehicular communication systems under complex mobility conditions