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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Blend Sains Jurnal Teknik Teknologi Informasi dan Digital (TRIDI)
Fazar Sidik
Politeknik Piksi Input Serang
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Other

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Design of the HealthyQue Mobile Application for Hospital Registration Based on Android Using the Design Thinking Approach and UML Modeling Fajar Mahardika; Rizki Ripai; Sony Veri Shandy; Fazar Sidik; Riki Aldi Pari; Kukuh Muhammad
Blend Sains Jurnal Teknik Vol. 4 No. 2 (2025): Edisi Oktober
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/blendsains.v4i2.1287

Abstract

The increasing number of patient visits to hospitals each year poses challenges in providing fast and efficient services, especially during the registration process. Conventional registration systems often cause long queues and patient discomfort. This study aims to design the HealthyQue mobile application as a digital solution for hospital registration based on the Android platform. The application development follows the Design Thinking approach through five stages: empathize, define, ideate, prototype, and test, to deeply explore user needs and behaviors. The results of this process are realized in the form of system modeling using Unified Modeling Language (UML), including use case diagrams, activity diagrams, sequence diagrams, and class diagrams to structurally depict the system's workflow and architecture. This application offers features such as online patient registration, doctor schedule checking, and real-time queue notifications. The implementation of HealthyQue is expected to improve hospital administrative efficiency, reduce patient waiting times, and enhance service quality. The design results demonstrate that the Design Thinking approach effectively produces solutions aligned with user needs, while UML modeling supports the system documentation in a technical and systematic manner.
IMPLEMENTASI LOCK MAC ADDRESS PADA RT/RW NET Zaenal Mutaqin Subekti; Kikim Mukiman; Rahmadi; Verawati; Iqbal Firmansyah; Riki Aldi Pari; Fazar Sidik
Jurnal Teknologi Informasi dan Digital Vol. 1 No. 1 (2023): Teknologi Informasi dan Digital
Publisher : LPPM Universitas Bani Saleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65624/tridi.v1i1.2

Abstract

Jaringan internet pada penyedia jasa insternet memiliki koneksi yang kurang stabil sehingga para client RT/RW Net ini mengalami gangguan saat menggunakan internet, Pada jaringan RT/RW Net ini juga belum dilengkapi dengan keamanan, yang mengakibatkan client yang tidak berlangganan dapat tetap mengakses internet RT/RW Net melalui wifi yang dipancarkan oleh provider. Untuk menanggulangi permasalahan ini dapat menerapkan lock mac address pada router, supaya dapat mengamankan jaringan wifi, sehingga client yang tidak berlangganan tidak dapat terhubung ke jaringan internet RW/RT Net, pada penelitian menggunakan metode ndlc (Network Development Life Cycle) dengan 6 tahapan yaitu analysis, design, simulation prototyping, implementation, monitoring dan management. Dengan melakukan penerapan lock mac address pada router, menghasilkan client yang ingin menggunakan akses internet maka mac address harus di daftarkan terlebih dahulu pada router, supaya client dapat akses internet dan juga client tanpa harus memasukan username dan password karena sudah di daftarkan, ini menjadi memudahkan client dan menjadi pengamanan pada jaringan internet RW/RT Net.
Deep Learning-Based Classification of Cikadu Batik Motifs Using ResNet50 and MobileNetV2 Rizki Ripai; Fajar Mahardika; Fazar Sidik; Nurul Badriah; Angga Maulana Purba
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16368

Abstract

Batik motif recognition is essential for cultural heritage preservation and the digitization of traditional Indonesian textile knowledge. This study proposes a deep learning-based framework for the automatic classification of Cikadu Batik motifs from Tanjung Lesung, Banten — a regionally distinct batik pattern that has not been systematically studied in prior computational literature. Two convolutional neural network (CNN) architectures were implemented and comparatively evaluated under identical experimental conditions: ResNet50, a high-capacity model employing residual skip connections, and MobileNetV2, a lightweight model utilizing depthwise separable convolutions and inverted residual blocks. A curated dataset of 2,500 images spanning five motif classes was constructed through collaboration with local batik artisans, preprocessed via resizing (224×224), pixel normalization, and augmentation (rotation, zoom, horizontal flip, brightness adjustment), and partitioned using a stratified 70:15:15 split. Both models were trained with transfer learning from ImageNet weights, using the Adam optimizer (lr=0.0001), categorical cross-entropy loss, batch size of 32, and early stopping over 30 epochs. Model evaluation employed accuracy, precision, recall, F1-score, AUC-ROC, inference time, and parameter count. ResNet50 achieved 95.51% accuracy, 95.67% precision, 95.34% recall, 95.50% F1-score, and 99.56% AUC-ROC, with an inference time of 18.2 ms and 25.64 million parameters. MobileNetV2 achieved 92.13% accuracy, 92.28% precision, 91.98% recall, 92.13% F1-score, and 98.89% AUC-ROC, with an inference time of 8.7 ms and only 3.54 million parameters — approximately 7× lighter and 2× faster. These results empirically establish a clear accuracy-efficiency trade-off, with ResNet50 favored for accuracy-critical server-based systems and MobileNetV2 better suited for real-time mobile deployment. This study constitutes the first published benchmark for deep learning-based Cikadu Batik classification and provides a principled basis for architecture selection in regional batik recognition applications
Co-Authors Angga Maulana Purba Fajar Mahardika Iqbal Firmansyah Kikim Mukiman Kukuh Muhammad Mahardika, Fajar Nurul Badriah Rahmadi Riki Aldi Pari Riki Aldi Pari Rizki Ripai Rizki Ripai Sony Veri Shandy Subekti, Zaenal Mutaqin Verawati