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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 253 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.
Evaluation of E-Government Governance with the Implementation of the COBIT 2019 Method at the West Pasaman Civil Registration Office Septiawan, Edo; Veri, Jhon; Sumijan, S
Jurnal KomtekInfo Vol. 12 No. 4 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i4.651

Abstract

The rapid development of information technology has encouraged government agencies to adopt Electronic Government (E-Government) to improve the efficiency, transparency, and accountability of public services. However, implementation challenges remain, especially in regional agencies such as the Population and Civil Registration Office (Dukcapil) of West Pasaman Regency, where inconsistencies and lack of system integration are still widespread. This study aims to analyze and evaluate E-Government governance. The method used in this study is the COBIT 2019 framework to assess the level of capability and propose strategic improvements in IT service management. This study adopted a qualitative case study approach, guided by the COBIT 2019 Design and Implementation framework. Data were collected through direct observation and in-depth interviews with Dukcapil personnel, supported by 145 structured questions and documentary analysis. This process focuses on five COBIT processes: EDM04 (Ensure Resource Optimization), APO07 (Manage Human Resources), BAI09 (Manage Assets), DSS01 (Manage Operations), and MEA01 (Monitor, Evaluate, and Assess Performance and Conformance). The results show that the capability level of the DSS01 process is at level 2 (Managed Process), while the capability target is level 3 (Established Process). This gap reflects the need for improvement in operational process management, including strengthening documentation and standardizing practices. Based on these results, strategic recommendations are developed that are contextually oriented to local bureaucratic conditions. This research can be a reference for the implementation of COBIT 2019 to effectively identify governance weaknesses and provide actionable strategies in increasing E-Government maturity at the regional level, supporting better service delivery and organizational performance.
Optimization of LPG Gas Distribution Routes with a Combination of the Saving Matrix Method and Nearest Neighbor Amin Amirul Mukminin, Andi; Hendrik, Billy; Sovia, Rini
Jurnal KomtekInfo Vol. 12 No. 4 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i4.656

Abstract

Distribution is an important process in economic activities, which involves the delivery of goods or products from producers to end consumers. Efficiency in the distribution system highly depends on the selection of optimal routes, which can affect costs, time, and the quality of service provided. PT Amartha Anugrah Mandiri, which operates in the distribution of 3 kg LPG, faces significant challenges in terms of inefficient distribution route selection, limited fleet capacity, and unstructured variations in LPG demand. The distribution routes currently used do not consider the aspects of distance, time, and cost efficiency, resulting in the wastage of resources such as fuel and time. This research aims to optimize LPG distribution routes. The methods used in this study are the Saving Matrix and Nearest Neighbor. The Saving Matrix method is used to reduce distribution distance and costs by combining existing delivery routes, while the Nearest Neighbor is applied to determine the order of visits to the nearest bases gradually. Both methods are designed to produce distribution routes that are efficient in terms of time, distance, and cost, as well as to maximize the use of the existing fleet. The data in this study were obtained thru direct observation at PT. Amartha Anugrah Mandiri. The data collected included base locations, LPG demand, vehicle capacity, and operational costs. There are 22 bases served with a total delivery reaching 1120 LPG 3 kg cylinders spread across various sub-districts of Batam City. Deliveries are carried out using trucks with a maximum capacity of 560 cylinders, so in one day, distribution requires more than one trip. Using this data, the distance matrix and savings matrix were calculated to design a more efficient distribution system. The research results show that the application of these two methods successfully reduced the total distance traveled, delivery time, and operational costs significantly, as well as improved the efficiency of LPG distribution. This research is expected to contribute to the company so that the 3 kg LPG delivery process can run optimally.
Convolutional Neural Network Method in Detecting Digital Image Based Physical Violence Elpina, Elpina Sari Dewi Hasibuan; Yuhandri, Y; Sumijan, S
Jurnal KomtekInfo Vol. 12 No. 4 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i4.657

Abstract

Physical violence in the educational environment has a serious impact on mental health, safety, and student achievement, in addition to causing physical injury, violence can cause psychological trauma that interferes with the learning process, due to the limited supervision system, lack of officers, and the absence of automatic detection technology. This research aims to design and develop an automatic detection system of physical violence using digital image processing technology. This study uses the Convolutional Neural Network (CNN) method with the stages of digital image collection and labeling, preprocessing, model training, and evaluation using accuracy, precision, recall, and F1-score metrics. The CNN architecture was chosen because it is efficient and accurate, and it supports data augmentation to improve generalization. The dataset was taken from kaggle and primary data at the al-falah huraba Islamic boarding school which consisted of 2000 images which included: 800 images of violence on CCTV of the dormitory room, 500 images of violence simulation of training videos and 500 non-violent images. The results showed that the developed CNN model was able to detect physical violence with an accuracy of above 88%, making it feasible to apply in surveillance camera-based school surveillance systems (CCTV). The system is able to classify images in real-time into two categories: safe and hard. This research contributes to the use of artificial intelligence to support efficient and affordable technology-based education security.
Sentiment Analysis in Platform X with the Support Vector Machine Method for Generation Z Sri Dewi, Apriandini; Defit, Sarjon; Nurcahyo, Gunadi Widi
Jurnal KomtekInfo Vol. 12 No. 4 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i4.659

Abstract

Advances in information technology and the increasing use of social media have significantly influenced the behavior of Generation Z. The generation born between 1997 and 2012 is known to be very familiar with the digital world, but also faces challenges such as lack of in-person social interaction and the risk of mental health disorders. This study aims to identify and classify public sentiment towards Generation Z on social media, especially on platform X (formerly Twitter). The method used is the Support Vector Machine (SVM). This research was carried out through several stages, namely the collection of 1607 data in the form of text using crawling techniques, pre-processing of text (tokenization, case folding, removal of stopwords, stemming, and normalization), and feature extraction using the Term Frequency-Inverse Document Frequency (TF-IDF) method. The processed data is then classified into three sentiment categories: positive, negative, and neutral using SVM. Evaluation was carried out by measuring accuracy, recall value, and F1-score value through a confusion matrix. The results showed that the measurement of an accuracy value of 85%, a precision value of 85%, a value of recall of 95% and an F1-score value of 90% that SVM was able to classify sentiment with high accuracy and stability. In addition, SVM has been shown to be more effective than other methods studied in previous studies. The data analyzed shows that most sentiment towards Generation Z is negative, reflecting public concern about the behavior and mindset of this generation. This research is expected to be a reference for academics, practitioners, and policymakers in understanding public opinion and designing targeted policies for the younger generation. Keywords: Sentiment Analysis, Generation Z, Support Vector Machine, Social Media, Machine Learning.
Combination of Support Vector Machine and Artificial Neural Network Methods in Negative Content Filtering System Wira, M Wira Sanjaya; Yuhandri, Y; Hendrik, Billy
Jurnal KomtekInfo Vol. 12 No. 4 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i4.660

Abstract

Local Wi-Fi network access has become a common necessity in everyday digital activities, but it is vulnerable to misuse to access negative content. This content includes pornographic material, hate speech, and violent content that can adversely affect users, especially in educational settings. For this reason, a system that is able to filter malicious content automatically and efficiently is needed. This research aims to design an artificial intelligence-based negative content filtering system that can be run on local network devices. The methods used include image classification using Convolutional Neural Network (CNN) and Artificial Neural Network (ANN), as well as text classification with DistilBERT and Support Vector Machine (SVM). To maintain user privacy, the model is trained using a federated learning approach that allows for decentralized learning. Knowledge distillation is also applied to produce lightweight models that can be run on edge devices such as routers. The datasets used include NSFW Image Dataset, OpenPornSet, as well as a collection of toxic comments from Reddit and Twitter. The evaluation was carried out in a simulation of a local network with 50 active devices. The test results showed an ANN accuracy rate of 93.4% in recognizing visual content, and SVM accuracy of 91.7% in detecting text-based hate speech. This research can be a reference in the application of AI-based content filtering systems for safe and responsible digital access protection
Convolutional Neural Network Architecture Densenet121 to Identify Tuberculosis Nugraha, Fajri; S, Sumijan; Sovia, Rini
Jurnal KomtekInfo Vol. 12 No. 4 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i4.662

Abstract

Smoking habits and the normalization of smoking activities are often a problem in many developing countries in the world. Cigarette smoke can cause many health problems that increase the risk of developing diseases and worsen the condition of people with the disease, one of which is Tuberculosis (TB). In Indonesia, based on the WHO Global TB Report 2024, Indonesia ranks second in the world in TB cases, it is estimated that there are more than 1,000,000 new cases every year, this disease is a very serious health problem and has obstacles in the identification process. This research aims to develop a TB disease identification system using Deep Learning. The methods used in this study are Convolutional Neural Network (CNN) and Densenet121 architecture. Convolutional Neural Network (CNN) was chosen for its ability to perform X-ray image analysis for visual validation, while Densenet121 was chosen because of its flexible architecture that can be applied to a wide range of computer vision applications, including image classification, object identification, and semantic segmentation. The research stage includes data collection, then preprocessing the image, namely resize, normalization, and conversion to arrays, then building a Convolutional Neural Network model with the selected architecture, then model training, model performance evaluation using accuracy and AUC metrics and ending with testing and validation by experts. The dataset used in this study is X-Ray data of tuberculosis patients taken from Kaggle to build a Deep Learning model that is able to identify TB through 100 chest X-ray image datasets. The results of the study show that the CNN model is able to identify tuberculosis with an accuracy rate of up to 90%, so it can help speed up early diagnosis or screening so that patients can continue to receive treatment and treatment. Therefore, the application of deep learning with the Convolutional Neural Network (CNN) method and DenseNet121 architecture based on X-Ray image data is an effective approach in the early detection of tuberculosis and seeks to make an important contribution to the control of lung diseases related to exposure to cigarette smoke in Indonesia.

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