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All Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) Journal of Computer Networks, Architecture and High Performance Computing J-SAKTI (Jurnal Sains Komputer dan Informatika)
Turmudi, Ahmad
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Energy

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Pengujian Dan Penerapan Manajemen Kas RW Dengan Metode Black Box Hardianto, H; Hardianti, Fazrin Putri; Irawan, Reza Riyaldi; Harits, Dalhats Abiyyu Idzal; Turmudi, Ahmad
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 8, No 1 (2024): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v8i1.774

Abstract

Software testing is an important part of application development to ensure its quality and reliability. The study uses the Black Box method to perform partitioning equivalence testing on RW cash management applications. The aim of this study is to find errors and bugs in the application so that they can be fixed before used by the user. Test results show that applications that meet the specifications in the expected output table do not experience error gaps.
Pengujian Dan Penerapan Manajemen Kas RW Dengan Metode Black Box Hardianto, H; Hardianti, Fazrin Putri; Irawan, Reza Riyaldi; Harits, Dalhats Abiyyu Idzal; Turmudi, Ahmad
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 8, No 1 (2024): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v8i1.774

Abstract

Software testing is an important part of application development to ensure its quality and reliability. The study uses the Black Box method to perform partitioning equivalence testing on RW cash management applications. The aim of this study is to find errors and bugs in the application so that they can be fixed before used by the user. Test results show that applications that meet the specifications in the expected output table do not experience error gaps.
Design of Mask Detection Application Using Tensorflow Lite based on Android Mobile Effendi, M Makmun; Turmudi, Ahmad; Arwan, Asep
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4329

Abstract

A mask is a type of personal protective equipment (PPE) that is essential for protecting the nose and mouth from contamination by droplets or airborne particles. The use of masks became highly popular during the Covid-19 pandemic, which began in December 2019 in China and peaked in Indonesia in 2020. Despite the pandemic subsiding and vaccinations increasing immunity, some companies still require masks to prevent the spread of illnesses such as colds and flu, especially in work processes that produce smoke, such as soldering and welding. To ensure employees comply with mask usage, effective supervision is necessary. Manual supervision is less efficient, thus a digital detection method is needed. This study developed a mask detection application using deep learning algorithms and the TensorFlow Lite framework on an Android platform. The application can detect mask usage with 100% accuracy at a distance of 1 to 5 meters. The system was tested under various lighting conditions and environments to ensure reliability. Additionally, the implementation of this technology can be extended to other public areas to ensure compliance with health protocols. This tool helps companies easily monitor and enforce mask-wearing discipline among employees, thereby enhancing workplace safety and health. Future work could explore the integration of this system with other health monitoring tools to create a comprehensive safety solution.
Design of Mask Detection Application Using Tensorflow Lite based on Android Mobile Effendi, M Makmun; Turmudi, Ahmad; Arwan, Asep
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4329

Abstract

A mask is a type of personal protective equipment (PPE) that is essential for protecting the nose and mouth from contamination by droplets or airborne particles. The use of masks became highly popular during the Covid-19 pandemic, which began in December 2019 in China and peaked in Indonesia in 2020. Despite the pandemic subsiding and vaccinations increasing immunity, some companies still require masks to prevent the spread of illnesses such as colds and flu, especially in work processes that produce smoke, such as soldering and welding. To ensure employees comply with mask usage, effective supervision is necessary. Manual supervision is less efficient, thus a digital detection method is needed. This study developed a mask detection application using deep learning algorithms and the TensorFlow Lite framework on an Android platform. The application can detect mask usage with 100% accuracy at a distance of 1 to 5 meters. The system was tested under various lighting conditions and environments to ensure reliability. Additionally, the implementation of this technology can be extended to other public areas to ensure compliance with health protocols. This tool helps companies easily monitor and enforce mask-wearing discipline among employees, thereby enhancing workplace safety and health. Future work could explore the integration of this system with other health monitoring tools to create a comprehensive safety solution.
Co-Authors Arwan, Asep Effendi, M Makmun Hardianti, Fazrin Putri Hardianto, H Harits, Dalhats Abiyyu Idzal Irawan, Reza Riyaldi