Garuda - Garba Rujukan Digital

MODEL AUDIO RECORDING SYSTEM (ARS) SITE ON SITE Andi Sofyan Anas; Muhammad Tajuddin; Akbar Juliansyah
Prosiding Sains Nasional dan Teknologi Vol 13, No 1 (2023): PROSIDING SEMINAR NASIONAL SAINS DAN TEKNOLOGI 2023
Publisher : Fakultas Teknik Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/psnst.v13i1.9635

Audio Recording System merupakan solusi yang penting dalam merekam percakapan penting secara efektif di lingkungan operasi antar operator site. Tujuannya adalah untuk menyediakan alat yang dapat merekam, menyimpan, dan mengakses rekaman audio dengan baik, sehingga memastikan transparansi, efisiensi, dan keamanan operasional yang lebih baik. Penelitian ini menggunakan metode Plan, Do, Check, Act (PDCA) dalam pengembangan Audio Recording System. Dalam fase Plan, kami menyoroti pentingnya perancangan sistem yang memenuhi kebutuhan komunikasi, menyediakan perangkat keras dan perangkat lunak yang tepat, serta mengembangkan kebijakan dan prosedur yang sesuai. Fase Do melibatkan pemasangan sistem perekaman audio di lokasi yang relevan, pelatihan personel terkait, integrasi sistem dengan infrastruktur yang sudah ada. Fase Check, kami membahas pemantauan kinerja sistem dan pengumpulan data rekaman audio. Kemudian, dalam fase Act, kami menggarisbawahi pentingnya menganalisis data rekaman audio untuk mengidentifikasi kesalahan komunikasi dan mengambil tindakan korektif yang sesuai. Audio Recording System ini membantu meningkatkan transparansi, efisiensi, dan keamanan dalam operasi antar operator site. Kesalahan komunikasi dapat diidentifikasi dan dikelola dengan lebih baik, memastikan bahwa informasi penting dapat disampaikan dengan jelas dan tepat waktu. Selain itu, perekaman audio juga memungkinkan pelacakan riwayat percakapan yang dapat membantu dalam investigasi atau keperluan dokumentasi yang mungkin muncul di masa depan.

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

Pendampingan Pengenalan IoT dalam Pertanian Pintar : Strategi Meningkatkan Produktivitas Tani di Desa Jurit Kabupaten Lombok Timur Akbar Juliansyah; Diman Ade Mulada; Ary Purmadi
Jurnal Pengabdian UNDIKMA Vol. 6 No. 1 (2025): February
Publisher : LPPM Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jpu.v6i1.13067

This community service activity aims to empower farmer groups in Jurit Village, East Lombok Regency, through the introduction of smart farming technology based on the Internet of Things (IoT). The initiative seeks to enhance agricultural productivity and economic empowerment by integrating advanced technology with robust business legalities. The participatory method employed in this program is based on the four-dimensional approach: Purpose, Process, Partnership, and Product. The outcomes of this community service demonstrate an improvement in the understanding and capabilities of the Lombok Organik farmer group in implementing IoT-based smart farming technology. Furthermore, the farmer group successfully established a legally recognized business, enabling them to expand market access and improve the competitiveness of their agricultural products.

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