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All Journal JELTII (Journal of English Language Teaching and Islamic Integration) Jurnal Ilmu Komputer dan Desain Komunikasi Visual Jurnal Teknologi Dan Sistem Informasi Bisnis Informatics, Electrical and Electronics Engineering International Journal Of Computer, Network Security and Information System (IJCONSIST) Jurnal Iqtisaduna Journal Of Informatics And Busisnes Nusantara Journal of Computers and its Applications ITIJ Submit : Jurnal Ilmiah Teknologi Informasi dan Sains Nusantara Computer and Design Review
Angga Lisdiyanto
Universitas Nahdlatul Ulama Sidoarjo
Religion Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Other

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Journal : ITIJ

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3D S-UNET an Efficient Architecture for 3 Dimensional Segmentation of Brain Tumors on MRI Images Wibowo, M Sadewa Wicaksana; Muhammad Shodiq; Bety Qorry Aina; Angga Lisdiyanto
Information Technology International Journal Vol. 3 No. 2 (2025): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v3i2.53

Abstract

One of the deadliest diseases worldwide is brain tumors. In identifying brain tumors, experts perform a subjective analysis that requires considerable time. Previous research has developed automatic 3D brain tumor segmentation using Deep Learning (DL) approaches such as 3D UNet and 3D ResNet. However, these approaches demand significant computational resources. In resource-constrained settings, key criteria for determining the best architecture include memory consumption, inference speed, and accuracy. Therefore, this study introduces the development of the 3D S-UNet architecture, constructed by combining 3D ShuffleNet-V2 as an encoder and 3D UNet as a decoder. The integration of these 3D data processors allows the architecture to be more precise in identifying brain tumor locations and capture richer feature values compared to 2D data processing. The researchers compare 3D S-UNet with another Lightweight Deep Learning architecture, 3D Mobile-UNet. The results show that 3D S-UNet has a smaller memory consumption, using 0.56GB for the highest allocated memory and 1.71GB for reserved memory. In terms of inference speed, 3D S-UNet is faster compared to the other three architectures, achieving a speed of 135.881 milliseconds. 3D S-UNet demonstrates favorable results with a Whole Tumor (WT) dice score, sensitivity, and specificity of 83%, 85%, and 88%, respectively.
GPS-Based Digital Business Technology Innovation in Community Health Centers: Application Development for Health Staff Performance Management Fitrani, Laqma; Angga Lisdiyanto
Information Technology International Journal Vol. 3 No. 2 (2025): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Attendance management in community health centers (Puskesmas) often relies on manual procedures that are prone to inaccuracy, limited traceability, and weak verification mechanisms, particularly for staff performing field-based duties. These limitations hinder the effectiveness of performance monitoring and reduce administrative efficiency. This study aims to develop a GPS-based digital attendance system designed to enhance accuracy, accountability, and transparency in monitoring employee presence at Puskesmas Mantup. The research methodology comprises four stages: Observation to identify operational constraints; Planning and Analysis to formulate functional and non-functional requirements; System Design to model data structures, user interfaces, and workflow diagrams; and Implementation to develop the application using web technologies integrated with geolocation services. System functionality was validated through Blackbox Testing to ensure reliability across key processes, including login authentication, location validation, shift scheduling, and automated recording of attendance events. The results indicate that the system successfully performs real-time GPS verification, prevents false check-ins outside the designated radius, and supports both shift and non-shift attendance schemes. Additionally, the dashboard and reporting features provide comprehensive visibility for administrators in evaluating employee performance. Overall, the GPS-based attendance system substantially improves monitoring accuracy and operational efficiency, offering a scalable solution for adoption in primary healthcare settings.
Co-Authors Achmad Mufliq Aflachul Mu’minin Agus Setia Budi Ahmad Wahyudi Anggay Luri Pramana Angkawidjaja , Steven Arda Surya Editya Ariansyah, Fery Almas Arief Saputro Bety Qorry Aina Catur Lega Wibisono Cholilurrohman Daffa, Muhammad Falikhuddin Fitrani, Laqma Galih Muhamad Rachman Ganal Arief Rahmawan Lailatul Musyaffaah Laqma Dica Fitrani Masti Fatchiyah Maharani Moch. Fahrul Orizky Muhammad Muharrom Al Haromainy Muhammad Shodiq Neny Kurniati Pratama Wirya Atmaja Purnomo, Ryan Riza Akhsani Setyo Prayoga Rizky Aditya Nugroho Syahri Mu’min Tri Septianto Wahyu Eko Pujianto Wibawa, Koerniawan Dwi Wibowo, M Sadewa Wicaksana Winarti Winarti Winarti