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The International Journal of Informatics, Data, and Emerging Applications (IDEA International Journal)
Published by Universitas Sam Ratulangi
ISSN : 31645089     EISSN : 31646921     DOI : -
Core Subject :
The International Journal of Informatics, Data, and Emerging Applications (IDEA International Journal) is a scientific journal published by the Faculty of Engineering, Sam Ratulangi University, through the Informatics Engineering Study Program. It provides an international forum for researchers, practitioners, and academics to disseminate scholarly work in informatics and closely related computing disciplines. The journal emphasizes sound computational methods and contributions that advance the design, development, evaluation, or application of digital systems.
Arjuna Subject : -
Articles 6 Documents
 1 
Human Detection using YOLOv8 with Squeeze Excitation Angelita C. Sumera; Vecky Canisius Poekoel; Hebron Prasetya
International Journal of Informatics, Data, and Emerging Applications Vol. 1 No. 1 (2026): International Journal of Informatics, Data, and Emerging Applications
Publisher : Faculty of Engineering, Sam Ratulangi University

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Abstract

Human detection based on vision systems has become a crucial field in the advancement of information technology. With computer vision systems, we can detect human movements in real-time, which is a significant aspect in security and surveillance applications. One effective architecture for object detection, including humans, is YOLO (You Only Look Once). YOLO has the advantage of fast and accurate detection with a single process, enabling real-time object detection. In this research, we developed the latest YOLOv8 architecture optimized for human detection in various situations and conditions. We also utilized the squeeze-and excitation (SE) attention module to enhance human detection accuracy without significantly increasing parameters. This study aims to create a human detection system capable of achieving high accuracy and can be implemented on Jetson Nano with webcam input. The modified architecture has 4.76 million parameters, mAP 0.548, and GFLOPS 12.
Implementation of YOLOv5 Architecture for Clothing Detection Systems Naula Qisty Modjo; Jane Litouw; Febriyanti Ludja
International Journal of Informatics, Data, and Emerging Applications Vol. 1 No. 1 (2026): International Journal of Informatics, Data, and Emerging Applications
Publisher : Faculty of Engineering, Sam Ratulangi University

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Abstract

Object detection is one of the key areas in computer vision that plays a crucial role in processing and analyzing visual data. In various applications such as clothing recognition, object detection is instrumental in identifying and localizing objects in image or videos. This research untilizes one of the Convolutional Neural Network (CNN) architectures, YOLOv5n, to debelop an effective framework for clothing detection. The objective is to enchance the performance of YOLOv5n in terms of accuracy while ensuring applicability to low-cost devices. Additionally, a new clothing dataset is curated for this purpose. The study leverages CPU-based cameras for real-time object detection. By modifying the SPPF module within the YOLOv5n architecture, high accuracy is achieved with parameters totaling 7,086,779, mAP of 0,685, and GFLOPS of 8,3.
Design of Bridging System between Hospital Information Service with National Insurance Erlangga Wahyudi Malli; Ricky Stefanus Phandeirot; Rangga Jufri Serang; Salaki Reynaldo Joshua; Mariyam Nadhira; Sumenge T. G. Kaunang
International Journal of Informatics, Data, and Emerging Applications Vol. 1 No. 1 (2026): International Journal of Informatics, Data, and Emerging Applications
Publisher : Faculty of Engineering, Sam Ratulangi University

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Abstract

Indonesian hospitals, as primary healthcare service providers, are mandated to deliver fast, accurate, and integrated healthcare services that comply with the standards set by Badan Penyelenggara Jaminan Sosial (BPJS). However, in practice, most Hospital Information Systems (HIS) currently in operation remain fragmented and lack full interoperability with BPJS-compliant digital platforms. This fragmentation often results in redundant administrative workflows, repetitive data entry, delayed patient registration, and inconsistencies in medical record synchronization across departments. Consequently, such inefficiencies hinder the hospitals’ ability to meet the service performance benchmarks established by BPJS. To overcome these challenges, this study implements an Application Programming Interface (API)-based integration framework designed to connect existing hospital subsystems with BPJS applications in real time. A comparative research methodology is employed to evaluate the system’s time efficiency before and after the API integration process. Through this integration, data exchange between modules becomes automated and standardized, significantly reducing manual intervention and improving workflow coordination. The experimental results demonstrate a notable enhancement in efficiency, with average processing time reduced from 3 minutes and 12 seconds prior to integration to only 1 minute and 17 seconds afterward. These findings indicate that API-based interoperability not only accelerates data transmission and minimizes administrative delays but also ensures greater system compliance with BPJS operational standards, thereby contributing to a more efficient and reliable Hospital Information System.
Corn Plant Disease Detection Using Deep Learning Priscilla Momongan; Nikita Mamonto; Alya Johanis
International Journal of Informatics, Data, and Emerging Applications Vol. 1 No. 1 (2026): International Journal of Informatics, Data, and Emerging Applications
Publisher : Faculty of Engineering, Sam Ratulangi University

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Abstract

Detection of diseases on corn leaves based on images requires a Convolutional Neural Network (CNN) model capable of accurately recognizing visual patterns because the symptoms often appear similar across disease classes. A CNN with a transfer learning approach was used due to its ability to automatically extract visual features. In its implementation, we compared three CNN architectures namely VGG16, INCEPTION-V3, and DenseNet to identify the most effective architecture. This comparison is necessary because each model differs in layer depth, feature extraction strategy, and complexity, which can influence model performance on the corn leaf dataset. The training process utilized the Adam optimizer with a learning rate of 0.0001. The results indicate that VGG16 achieved a training accuracy of 90% and validation accuracy of 91%, INCEPTION-V3 achieved a training accuracy of 92.10% and validation accuracy of 93.02%, while DenseNet delivered the highest performance with a training accuracy of 95.41% and validation accuracy of 97.05%. Therefore, DenseNet is considered the most effective and has the potential to serve as the basis for developing an image based automatic detection system for corn leaf diseases.
Rainfall Prediction in Manado City Using Machine Learning Based on BMKG Meteorological Parameters Yves Vincent De Paul Muaya; Tri Sandy Tjakra; Yuben Tabuni; Md. Niaz Morshedul Haque
International Journal of Informatics, Data, and Emerging Applications Vol. 1 No. 1 (2026): International Journal of Informatics, Data, and Emerging Applications
Publisher : Faculty of Engineering, Sam Ratulangi University

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Abstract

Rainfall is a crucial meteorological factor that profoundly impacts various aspects of life, particularly in tropical regions such as Manado City, where fluctuations in precipitation have significant consequences across sectors ranging from agriculture and water resource management to the potential for hydrometeorological disasters like floods and landslides. Accurate rainfall prediction is therefore essential, yet the inherent complexity of tropical atmospheric systems often poses considerable challenges for traditional forecasting methods. This research explores the potential of machine learning to enhance the precision of daily rainfall predictions in Manado City. We implement two distinct machine learning algorithms, namely Support Vector Machine (SVM) and Gated Recurrent Unit (GRU), to forecast rainfall quantities based on historical meteorological parameters obtained from the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG). The dataset utilized encompasses vital variables such as air temperature, relative humidity, wind speed, and atmospheric pressure. The primary objective of this study is to develop, evaluate, and compare the performance of these two models in predicting rainfall, with a specific focus on their capacity to capture complex patterns and dependencies within time-series data. The models will be trained using historical data and rigorously assessed based on standard performance metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R2). It is anticipated that the findings from this research will offer valuable insights into the effectiveness of machine learning algorithms for local rainfall forecasting, thereby contributing to the development of more reliable early warning systems and improved climate adaptation strategies for the community of Manado
An Efficient Deep Learning Model for Whale Shark Detection Imanuel Kutika; Stephan A. Hulukati; Jinsu An; Vicky Nolant Setyanto Lahimade
International Journal of Informatics, Data, and Emerging Applications Vol. 1 No. 1 (2026): International Journal of Informatics, Data, and Emerging Applications
Publisher : Faculty of Engineering, Sam Ratulangi University

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Abstract

Whale sharks (Rhincodon typus) play an essential ecological role as plankton feeders and serve as valuable assets for marine biodiversity and ecotourism. Effective monitoring of their presence and behavior is crucial for conservation and sustainable management; however, conventional observation techniques are often expensive, invasive, and limited in scalability. With the advancement of deep learning-based vision systems, real-time and automated detection has become increasingly feasible. This study employs the lightweight YOLOv10 architecture to develop an efficient whale shark detection system capable of accurate and rapid inference. The model was trained on a curated dataset of underwater images under diverse illumination and visibility conditions. Experimental results show that the proposed YOLOv10-based model achieved a mAP@50 of 97.2% and a mAP@50–95 of 85.5%, while maintaining computational efficiency with only 2,707,430 parameters and 8.4 GFLOPs. These findings highlight the strong balance between accuracy and model compactness, demonstrating that YOLOv10 offers a promising solution for real-time, resource-efficient whale shark detection in marine monitoring applications.

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