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All Journal Jurnal Mandiri IT Journal Innovations Computer Science
Prihandayani, Tiwuk Wahyuli
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Computer Science & IT Library & Information Science Mathematics

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Design and Development of a Web-Based Toddler Health Card (KSB) Application: A Case Study at Posyandu Kenanga, Depok, Using the Rapid Application Development (RAD) Method Permatasari, Kharina; Nurcahyo, Widyat; Faizah, NM; Prihandayani, Tiwuk Wahyuli
Journal Innovations Computer Science Vol. 4 No. 1 (2025): May
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i1.234

Abstract

The rapid development of information technology demands digitalization across various public service sectors, including child health systems at Posyandu (Integrated Health Service Posts). Posyandu Kenanga Depok conducts monthly child health monitoring activities to ensure nutritional status and child development. However, the manual recording system for Child Health Cards (Kartu Sehat Balita/KSB) creates various operational problems. Posyandu cadres experience difficulties in data recording processes, information storage, and monthly report generation. Additionally, paper-based KSB held by parents are prone to loss and damage, resulting in poorly documented child development data. This research aims to develop a web-based KSB application to address these problems using the Rapid Application Development (RAD) methodology. The RAD method was chosen for its ability to accelerate system development processes by actively involving users in every development stage. The application was developed using PHP programming language, CodeIgniter 4 framework, MySQL database, and Visual Studio Code editor. The system is designed with three user levels: Posyandu cadres for managing child data and generating reports, parents for monitoring child development, and doctors for managing immunization and vitamin data. Development results demonstrate that the application successfully automates child data recording processes, facilitates monthly report generation, and enables parents to access child development information in real-time. The system also provides growth chart visualizations and immunization schedule reminders to support optimal child health monitoring.
Attention-based convolutional neural networks for interpretable classification of maritime equipment fabrianto, luky; Prihandayani, Tiwuk Wahyuli; Rasenda, Rasenda; Faizah, Novianti Madhona
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.426

Abstract

This study introduces a Convolutional Neural Network with an Attention Mechanism (CNN+AM), utilizing the Squeeze-and-Excitation (SE) block, to classify critical ship components: generators, engines, and oil-water separators (OWS). The SE block enhances the model's ability to focus on discriminative features, thereby improving classification performance. To overcome the limitation of the original dataset, which contained only 199 images, extensive data augmentation techniques were applied, expanding the dataset to 2,648 images. The augmented dataset was divided into training (70%), validation (15%), and testing (15%) sets to ensure reliable evaluation. Experimental results show that the CNN-AM achieved an accuracy of 72.39%, surpassing the baseline CNN model with 68.16%. These findings confirm that the attention mechanism significantly improves generalization and the ability to differentiate visually similar classes. Furthermore, the integration of interpretability tools, such as Gradient-weighted Class Activation Mapping (Grad-CAM), provides visual explanations of model predictions, increasing trust and reliability for safety-critical maritime applications. The proposed approach demonstrates strong potential for real-time ship component monitoring, offering meaningful contributions to predictive maintenance and operational safety within the maritime industry.
Co-Authors Fabrianto, Luky Faizah, NM Faizah, Novianti Madhona Nurcahyo, Widyat Permatasari, Kharina Rasenda, Rasenda