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Analisis Faktor Yang Mempengaruhi Penerimaan Mahasiswa Terhadap QRIS Menggunakan Technology Acceptance Model (TAM) sri, Sri Indriani; Adam Bachtiar; Indriaturrahmi; Akbar Juliansyah
Infotek: Jurnal Informatika dan Teknologi Vol. 7 No. 2 (2024): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v7i2.26489

This study aims to analyze what factors influence student acceptance of QRIS at Mandalika Education University using the Technology Acceptance Model (TAM) as the basis for the research model. The factors analyzed include perceived ease of use, perceived usefulness, attitude toward using, behavioral intention to use, and actual system use. The research method used is quantitative method by distributing questionnaires to Mandalika Education University students. Respondents who were sampled in this study amounted to 120 respondents with criteria that have been determined by the author with technical random sampling Data collection using a 5 Likert scale questionnaire method The data collected were analyzed using Structural Equation Modeling (SEM) with the help of AMOS software. The results showed that perceived ease of use has a significant effect on perceived usefulness, attitude towards using has a significant effect on behavioral intention to use, behavioral intention to use has a significant effect on actual system use

Glaucoma Detection Based on Texture Feature of Neuro Retinal Rim Area in Retinal Fundus Image Nugraha, Gibran Satya; Juliansyah, Akbar; Tajuddin, Muhammad
International Journal of Health and Information System Vol. 1 No. 3 (2024): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ijhis.v1i3.21

One method for detecting glaucoma is by comparing ratios in the area of neuroretinal rim. Comparing area ratios in the neuroretinal rim is difficult for ophthalmologists since it requires high accuracy and is highly dependent on the patient's retinal condition. In this study, we sought to perform neuro retinal rim feature extraction based on histogram and gray level co-occurrence matrix (GLCM) of normal retinal images and glaucoma, automatically distinguish between normal eyes and eyes with glaucoma, and evaluate the method's validity using the measures of accuracy, sensitivity, and specificity We adopted a machine learning approach in conducting automatic feature extraction of the retinal rim through three main stages: 1) image acquisition, 2) pre-processing, and 3) classification. We used a dataset from RIM-ONE for normal eyes images and DRISTHI-GS for glaucoma images.Classification was carried out on 154 images (80 images for glaucoma images and 74 images for normal images). Regarding true positive, false negative, false positive, and true negative, we examined the sensitivity, specificity, and accuracy of automatic extraction and classification. The highest findings are 96.10%, 98.75%, and 93.24%, respectively. This study showed that automatic texture features and classification are possible, accurate and important in detecting glaucoma.

Glaucoma Detection Based on Texture Feature of Neuro Retinal Rim Area in Retinal Fundus Image Nugraha, Gibran Satya; Juliansyah, Akbar; Tajuddin, Muhammad
International Journal of Health and Information System Vol. 1 No. 3 (2024): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ijhis.v1i3.21

One method for detecting glaucoma is by comparing ratios in the area of neuroretinal rim. Comparing area ratios in the neuroretinal rim is difficult for ophthalmologists since it requires high accuracy and is highly dependent on the patient's retinal condition. In this study, we sought to perform neuro retinal rim feature extraction based on histogram and gray level co-occurrence matrix (GLCM) of normal retinal images and glaucoma, automatically distinguish between normal eyes and eyes with glaucoma, and evaluate the method's validity using the measures of accuracy, sensitivity, and specificity We adopted a machine learning approach in conducting automatic feature extraction of the retinal rim through three main stages: 1) image acquisition, 2) pre-processing, and 3) classification. We used a dataset from RIM-ONE for normal eyes images and DRISTHI-GS for glaucoma images.Classification was carried out on 154 images (80 images for glaucoma images and 74 images for normal images). Regarding true positive, false negative, false positive, and true negative, we examined the sensitivity, specificity, and accuracy of automatic extraction and classification. The highest findings are 96.10%, 98.75%, and 93.24%, respectively. This study showed that automatic texture features and classification are possible, accurate and important in detecting glaucoma.

Enhancing the Quality of Learning Through Training in PBL and TPACK-Based Teaching Module Muhmmad Asy’ari; Taufik Samsuri; Laras Firdaus; Saiful Prayogi; Irham Azmi; Mujriah Mujriah; Hunaepi Hunaepi; Akbar Juliansyah; Nova Kurnia; Istin Fitriana Aziza; Helmi Rahmawati
Sasambo: Jurnal Abdimas (Journal of Community Service) Vol. 5 No. 4 (2023): November
Publisher : Lembaga Penelitian dan Pemberdayaan Masyarakat (LITPAM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/sasambo.v5i4.1723

The goal of the PKM activity is to enhance the quality of learning through intensive training in the creation of teaching modules that integrate the concepts of PBL with the application of TPACK. This reflects the urgency of the teacher's role in adapting the learning approach to the current developments and preparing students to face future challenges. Focusing on the improvement of learning quality provides insights into the importance of integrating PBL and TPACK in innovative and relevant modern learning. The implementation method involves knowledge transfer and the Community Development Model through synchronous and asynchronous online activities. The partners in this activity are students of the PPG Daljab Biology Class of 2023 from the Mandalika University of Education. The activity is carried out for a total of 3 meetings. The results of the activity show that the partners' participation is very active, and there is a significant impact of the training on the partners' understanding of the PBL model and the TPACK approach, as well as their integration into teaching modules

Multimodal Learning Interaction in Multimedia Laboratory Environments: An Empirical Framework for Infrastructure Development Jarir; Akbar Juliansyah; Fitri Ramdani; Wirawan Putrayadi; Edy Haryanto
Edu Komputika Journal Vol. 13 No. 1 (2026): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v13i1.50379

Multimedia-supported laboratory environments play an important role in enhancing learning engagement, conceptual understanding, and practical competence in higher education. However, limited empirical evidence exists regarding the relative contributions of the visual, auditory, and kinesthetic interaction dimensions to laboratory learning effectiveness. This study aimed to develop an empirical multimodal interaction framework for multimedia laboratory infrastructure development by examining the influence of these three dimensions on perceived learning effectiveness. A mixed-methods sequential explanatory design was employed. Quantitative data were collected from 101 university students enrolled in PTI Practicum, PTI Non-Practicum, and BK courses. Multiple regression analysis was conducted using responses from 58 students with direct laboratory practicum experience. Qualitative support was obtained through expert validation involving 14 lecturers. The data were analyzed using descriptive statistics, one-way ANOVA, Tukey’s post hoc test, multiple linear regression, and expert triangulation. The descriptive analysis revealed that the kinesthetic dimension had the highest mean score, indicating a stronger preference for hands-on and experiential learning activities. However, the regression analysis demonstrated that the auditory dimension made the strongest contribution to perceived laboratory learning effectiveness, followed by the visual and kinesthetic dimensions. The regression model explained 64.8% of the variance in perceived learning effectiveness, indicating substantial explanatory power. The standardized regression coefficients were subsequently normalized and translated into infrastructure development priorities, forming the basis of the proposed empirical framework. Lecturer validation further emphasized the importance of experiential learning and practical engagement in laboratory environments, while highlighting the complementary roles of instructional communication and multimedia support in enhancing learning effectiveness. These findings suggest that effective multimedia laboratory environments should integrate instructional communication, multimedia visualization, and experiential learning facilities in a balanced manner, recognizing their complementary roles within a multimodal learning ecosystem. This study contributes an empirical framework to support adaptive multimedia laboratory infrastructure development and instructional design in higher education. In practice, the proposed framework may assist laboratory managers and higher education institutions in prioritizing instructional communication, multimedia visualization, and experiential learning facilities according to empirically identified learning interaction needs.

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