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INDONESIA
Jurnal Komtekinfo
Published by Universitas Putra Indonesia YPTK Padang
ISSN : 23560010     EISSN : 25028758     DOI : DOI: 10.35134/komtekinfo.v9i2.1
Core Subject : Science,
Computer Science & IT
Software Engineering, Multimedia, Artificial intelligence, Data Mining, Knowledge Database System, Computer network, Information Systems, Robotic, Cloud Computing, Computer Technology
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 244 Documents
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Utilization of Convolutional Neural Network Method in Customer Identification Based on Facial Images Ade, Ade Puspita Sari; Sarjon Defit; Sumijan
Jurnal KomtekInfo Vol. 12 No. 3 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i3.664

Abstract

Artificial intelligence-based facial recognition technology, especially using the Convolutional Neural Network (CNN) method, is increasingly widespread in various business applications, such as customer data management. This technology allows the system to recognize and identify individuals automatically through facial images, so it is very potential to be applied in customer management. This study aims to implement CNN technology in automatically identifying old customers in a case study in JAVApace Studio. CNN method for facial recognition, optimizing the accuracy of old customer identification, designing CNN system integration in computer vision-based applications, and measuring CNN performance in real-time facial identification. The research method was carried out using a quantitative approach through data collection stages in the form of 875 customer facial images taken in JAVapace Studio, data preprocessing (cropping, resizing, and data augmentation), dataset division for training, validation, and testing. The CNN model used is the ResNet-50 architecture with fine-tuning techniques and freezing layers to improve training efficiency. Model performance evaluation uses a confusion matrix with accuracy, recall, and precision metrics. The results show that the CNN-based facial recognition system achieved 95.7% accuracy in distinguishing existing customers from the test data used. The recall rate was 94.5%, while the precision rate reached 96.2%. The discussion of the results also indicates that the fine-tuning approach is effective in optimizing model performance with an inference time suitable for real-time implementation needs. This study confirms that the implementation of CNN with ResNet-50 architecture is effectively able to recognize the faces of old customers with high levels of accuracy, recall, and precision, making it the right solution in managing customer data automatically and efficiently.
Identification of Skin Diseases in Toddlers Using Convolutional Neural Networks Maharani, Dian; Yuhandri; Very, Jhon
Jurnal KomtekInfo Vol. 12 No. 3 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i3.665

Abstract

The development of Artificial Intelligence (AI) technology, particularly in the field of computer vision, has made a significant contribution to medical image analysis. Skin disease in toddlers is a common health problem, especially in developing countries. Toddlers' skin is highly susceptible to various infections and dermatological conditions, ranging from bacterial and viral infections to allergies. Some skin diseases frequently found in toddlers include eczema, dermatitis, impetigo, and fungal infections. This study aims to develop a skin disease classification system in toddlers using the Convolutional Neural Network (CNN) method that can be implemented in applications. The Convolutional Neural Network (CNN) method and the U-Net architecture are used to identify skin diseases in toddlers, requiring a fast and accurate diagnosis, but limited medical personnel and examination time are challenges. A deep learning-based system is proposed to assist the automatic identification process. The research dataset consists of 100 toddler skin images obtained from Siti Rahmah Islamic Hospital, covering various types of common skin diseases. The preprocessing process includes cropping, resizing to 128x128 pixels, normalization, and data augmentation to increase the diversity of the dataset. The CNN architecture is used in the feature extraction stage through convolution and pooling layers, while the U-Net is applied in the segmentation stage to separate the wound area from healthy skin with high precision through the encoder-decoder mechanism and skip connection. The model is trained using the Adam optimization algorithm with the Binary Cross-Entropy loss function and the accuracy evaluation metric and Mean Intersection over Union (IoU). The results show that the system is able to segment the wound area with 95.7% accuracy on the test data, and produces fast and efficient detection. The application of the CNN and U-Net methods in this study proves its effectiveness in supporting the medical diagnosis process, especially in cases of toddler skin diseases, as well as can be a reference in contributing to improving the quality of health services, especially in the diagnosis of skin diseases in toddlers and the development of computer vision-based decision support systems in the health sector.
Sistem Deteksi Kerumunan Fasilitas Pelayanan Publik dengan Crowd Counting P, Prihandoko; Yuhandri, Muhammad Habib; Pratama , Abdul Hanif
Jurnal KomtekInfo Vol. 11 No. 4 (2024): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v10i4.410

Abstract

Improvement of system management in public services needs special attention in an era of increasing population growth. Crowd Counting is proposed to ensure that the detection system for crowd objects in public facilities can run optimally. This study aims to develop Crowd Counting in a crowd object detection system in public facilities. This development is carried out to improve the performance of the You Only Look Once (YOLO) algorithm based on the Streamlit Framework. The performance of the YOLO algorithm can provide maximum results by combining the streamlit framework based on the image of the captured object at the train station. The test results of the development of Crowd Counting presented provide output with an mAP value of 90%, Recall 95%, and Precision 93.6%. Blackbox testing has also shown that the performance of Crowd Counting has provided quite significant detection accuracy. This research can contribute to the renewal of the detection system and be used as a form of solution in handling crowd problems in public facilities
Tweet Sentiment Classification Towards Mobile Services Using Naive Bayes and Support Vector Machine Muharram, Izza Syahri; Muhammad Faisal
Jurnal KomtekInfo Vol. 12 No. 2 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i2.642

Abstract

This research focuses on sentiment classification of Indonesian-language tweets related to mobile service providers by integrating Support Vector Machine (SVM) and Term Frequency-Inverse Document Frequency (TF-IDF) as the main text representation method. The dataset was sourced from Twitter API and public collections, then went through preprocessing, feature extraction, model training, and performance evaluation phases. The SVM model utilizing TF-IDF exhibited perfect evaluation metrics—100% in accuracy, precision, recall, and F1-score—on the test set, indicating excellent proficiency in detecting both positive and negative sentiments. Nevertheless, such flawless results should be interpreted carefully, as they may suggest limited data diversity. This study contributes to the advancement of sentiment analysis techniques for short and informal Indonesian-language texts on social media platforms.

Page 25 of 25 | Total Record : 244

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