cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Naety
Contact Email
jurnalmedicom@iocscience.org
Phone
+6281381251442
Journal Mail Official
jurnalmedicom@iocscience.org
Editorial Address
Perumahan Romeby Lestari Blok C, No C14 Deliserdang, Sumatera Utara, Indonesia
Location
Unknown,
Unknown
INDONESIA
Jurnal Teknik Informatika C.I.T. Medicom
Published by Institute of Computer Science
ISSN : 23378646     EISSN : 2721561X     DOI : -
Core Subject : Science,
Computer Science & IT
The Jurnal Teknik Informatika C.I.T a scientific journal of Decision support sistem , expert system and artificial inteligens which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 3 Documents
 1 
Search results for , issue "Vol 17 No 5 (2025): Intelligent Decision Support System (IDSS)- INPRESS" : 3 Documents clear
Determining Initial Centroid in K-Means using Global Average and Data Dimension Variance Bu'ulolo, Efori
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 5 (2025): Intelligent Decision Support System (IDSS)- INPRESS
Publisher : Institute of Computer Science (IOCS)

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

Abstract

The selection of the right initial centroid greatly affects the quality of clustering results in the K-Means algorithm. This study proposes a new approach in determining the initial centroid by utilizing the global average and variance of data dimensions. The global average is used to represent the overall center position of the data, while the variance of dimensions provides information on the distribution of each feature. This method is tested using three-dimensional synthetic data (X, Y, Z) with 121 data, and compared with the random initialization approach. The results show that the global average and variance-based method produces more balanced clusters, lower Sum of Squared Error (SSE) values, and the highest Silhouette Score value (0.65), as well as faster convergence. Compared to two random initialization scenarios, this method is proven to be more stable in separating clusters based on the distribution of low, medium, and high values. This approach makes an important contribution to the development of a more consistent and effective K-Means initialization strategy, especially for low to medium-dimensional numerical datasets.
OPTIMIZATION OF CONVOLUTIONAL NEURAL NETWORK ALGORITHMIC ACCURACY FOR THE IDENTIFICATION OF DIFFERENT FONT TYPES Kanata, Bulkis; Misbahuddin; Akhdan, M. Rafif
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 5 (2025): Intelligent Decision Support System (IDSS)- INPRESS
Publisher : Institute of Computer Science (IOCS)

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

Abstract

Text not only conveys the message through the words used, but also through its visual aspects. One of the most influential visual elements is the type of font. Recognising and determining font types appropriately is essential, whether in the academic sector, the printing industry, graphic design, or digital systems. However, in practice, manually recognising font types takes time, skill, and high precision. With the advancement of digital technology, the variety of font types is increasing, making the process of identifying fonts more complicated. This requires the development of methods that are able to distinguish different types of fonts precisely and accurately. This study reveals the potential of Convolutional Neural Network (CNN) algorithms as an optimisation in facing font identification challenges, as well as to prove that deep learning can provide more efficient and precise solutions, by comparing three different CNN architectures, namely DenseNet121, ResNet50, and VGG16. The implementation of the method is carried out by applying data augmentation techniques and setting CNN parameters such as the number of epochs, learning rate, batch size, Adam optimiser, and image size. The results showed that the DenseNet121 model achieved an accuracy of up to 96.8%, ResNet50 92.9%, and VGG16 96.4%. The convolutional neural network algorithm proves that it can identify various font types with optimal accuracy.
Real Time Pill Counting on Low Power Device: A YOLOv5 Pipeline with Confidence Thresholding and NMS A, Galih Prakoso Rizky; Widyastuti, Rifka
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 5 (2025): Intelligent Decision Support System (IDSS)- INPRESS
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol17.2025.1286.pp225-241

Abstract

Manual pill counting is still commonly performed in healthcare facilities and pharmacies, but this method is vulnerable to human error and requires significant processing time. This study develops an automatic pill counting pipeline using the YOLOv5 deep learning model, optimized for low-power devices such as Raspberry Pi, Orange Pi, and Jetson Nano. Unlike earlier techniques that depend on conventional retrieval or machine-learning approaches, this pipeline integrates real-time object detection with customized confidence thresholding and Non-Maximum Suppression (NMS), enabling high accuracy and fast performance on edge hardware with limited resources. The development process includes collecting and annotating a dataset of pill images with variations in shape, color, and orientation, followed by training YOLOv5 using optimized parameters. A simple webcam is used as the input device, and system performance is evaluated under different lighting and background conditions. Experimental results show that the model achieves 98% precision, 88% recall, 95% mAP@0.5, and 67% mAP@0.5:0.95, with an average inference speed of around 15 milliseconds per image. Tests on ten pill-counting scenarios under optimal lighting demonstrate strong performance, with only minor discrepancies in dense cases involving 50 and 127 pills, producing accuracies of 98% and 99.21%. These results indicate that the optimized YOLOv5 pipeline provides fast and accurate real-time pill counting on low-power devices. Future work will enhance robustness to lighting variations, validate using external datasets, and incorporate color and shape feature analysis to improve performance in challenging scenarios.

Page 1 of 1 | Total Record : 3

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 17 No 6 (2026): Computer Science Vol 17 No 5 (2025): Intelligent Decision Support System (IDSS)- INPRESS Vol 17 No 4 (2025): Intelligent Decision Support System (IDSS) Vol 17 No 3 (2025): July: Intelligent Decision Support System (IDSS) Vol 17 No 2 (2025): May: Intelligent Decision Support System (IDSS) Vol 17 No 1 (2025): March: Intelligent Decision Support System (IDSS) Vol 16 No 6 (2025): January : Intelligent Decision Support System (IDSS) Vol 16 No 5 (2024): November : Intelligent Decision Support System (IDSS) Vol 16 No 4 (2024): September: Intelligent Decision Support System (IDSS) Vol 16 No 3 (2024): July: Intelligent Decision Support System (IDSS) Vol 16 No 2 (2024): May: Intelligent Decision Support System (IDSS) Vol 16 No 1 (2024): March: Intelligent Decision Support System (IDSS) Vol 15 No 6 (2024): January : Intelligent Decision Support System (IDSS) Vol 15 No 5 (2023): November : Intelligent Decision Support System (IDSS) Vol 15 No 4 (2023): September : Intelligent Decision Support System (IDSS) Vol 15 No 3 (2023): July: Intelligent Decision Support System (IDSS) Vol 15 No 2 (2023): May: Intelligent Decision Support System (IDSS) Vol 15 No 1 (2023): March: Intelligent Decision Support System (IDSS) Vol 14 No 2 (2022): September: Intelligent Decision Support System (IDSS) Vol 14 No 1 (2022): March: Intelligent Decision Support System (IDSS) Vol 13 No 2 (2021): September: Intelligent Decision Support System (IDSS) Vol 13 No 1 (2021): March: Intelligent Decision Support System (IDSS) Vol 12 No 2 (2020): September: Intelligent Decision Support System (IDSS) Vol 12 No 1 (2020): March: Intelligent Decision Support System (IDSS) Vol 11 No 2 (2019): Informatik Vol 11 No 1 (2019): Informatika Vol 10 No 2 (2018): Informatika More Issue