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JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING
Published by Universitas Medan Area
ISSN : 25496247     EISSN : 25496255     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
JURNAL TEKNIK INFORMATIKA, JITE (Journal of Informatics and Telecommunication Engineering) is a journal that contains articles / publications and research results of scientific work related to the field of science of Informatics Engineering such as Software Engineering, Database, Data Mining, Network, Telecommunication and Artificial Intelligence which published and managed by the Faculty of Informatics Engineering at the University of Medan Area .
Arjuna Subject : -
Articles 412 Documents
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Designing a Website-Based Technology Winnicode News Portal Application with Prototyping Method to Enhance User Engagement for Technology News Reader Sekar Wangi, Sari Asih; Winarsih, Nurul Anisa Sri
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 2 (2025): Issues January 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i2.13267

Abstract

A news portal is a platform that usually takes the form of a website that presents various kinds of news according to its type. With the development of technology and the emergence of social media, the audience of these news portal websites has decreased. People prefer to read news through social media pages because they are easier to access. Based on this, it is necessary to develop an efficient and eye-friendly news portal website to attract people to read news through the website. By using the prototype method, the website is designed based on a pre-made design. So that the developers have a benchmark in developing the website to be built. After that, an evaluation is needed from user reviews and testing is carried out gradually to improve the website to make it more efficient and appropriate. Of the 10 test points that have been carried out in the study, a 100 percent success rate has been achieved from the test. This shows that the performance of the website that has been built can meet the criteria to be published and can be used by the wider community. From the development of this application, people can read with the theme of technology that is more comfortable, easily accessible and can send criticism to the publisher if there is a mismatch of news, have additional information about the latest news, or suggestions for the website so that it can be developed
Analysis Role of Digital Marketing and Self-Image Improving High School Students' Self-Presentation in Batam Using Instagram Liegestu, Suryo Pramono; Muhammad, Ardiansyah; Eryc
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 2 (2025): Issues January 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i2.13368

Abstract

With the rapid development of digital technology, Instagram serves as a critical platform for shaping self-image and promoting self-presentation among students. This study explores the influence of digital marketing and self-image on the self-presentation of high school students in Batam through Instagram, with Digital marketing and self-image serves as the independent variable and self-presentation as the dependant variable. Using a quantitative research method, data were collected from 353 respondents across 16 private high schools in Batam. The study finds that both digital marketing and self-image significantly affect students' self-presentation, with digital marketing enabling creative self-promotion and self-image enhancing confidence and personality expression. Key Instagram features like stories, highlights, reels, and posts are instrumental in fostering positive self-image and social interaction. Statistical analysis revealed that 22.8% of self-presentation is influenced by the studied variables, highlighting the role of external factors. This research underscores Instagram's role in inspiring fashion trends, promoting products, and cultivating a polished appearance, contributing to students' confidence and social perception. These findings have implications for understanding digital media's impact on youth behaviour and social identity
Precision and Accuracy of Ultrasonic and Infrared Laser ToF IoT Sensors Rahman, Arif
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 2 (2025): Issues January 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i2.13406

Abstract

Non-contact distance measurement using sensors allows measurements when conventional tools reach their limits. These measurements can be integrated into the Internet of Things (IoT) through IoT-enabled microcontrollers, but sensor accuracy and precision must first be verified. This study compares two sensors—infrared time-of-flight and ultrasonic—in terms of accuracy and precision. An object reflecting sound and electromagnetic waves was placed at set distances, and sensor readings were compared to ruler measurements. The sensors showed good precision, with the infrared sensor outperforming the ultrasonic one. However, both lacked the accuracy compared to actual distances. The infrared sensor was consistently more accurate, requiring less correction to align with true distances. While the ultrasonic sensor performed better at short distances, its accuracy dropped significantly beyond 40 cm. Overall, the infrared sensor proved superior in accuracy. These findings highlight the potential for integrating such sensors into standalone or IoT-connected systems for reliable measurements.
Performance Evaluation of CNN-LSTM and CNN-FNN Combinations for Pneumonia Classification Using Chest X-ray Images Putra, Bernardus Septian Cahya; Tahyudin, Imam
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 2 (2025): Issues January 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i2.13503

Abstract

Pneumonia is one of the deadliest infectious diseases worldwide, particularly affecting children under five years old and the elderly, with a significant mortality rate annually. This disease is caused by bacterial, viral, or fungal infections, leading to inflammation in the air sacs (alveoli) of the lungs, which disrupts respiratory function. A major challenge in diagnosing pneumonia lies in the reliance on radiological expertise to interpret chest X-ray images, a process that is time-consuming and prone to errors in interpretation. This study aims to compare the performance of deep learning models, specifically the combination of Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) and CNN with Feedforward Neural Networks (FNN), in classifying pneumonia based on chest X-ray images. The results indicate that the CNN & LSTM model achieved an accuracy of 96.59%, a loss of 9.95%, precision of 96%, recall of 95%, and F1-score of 96%, slightly outperforming the CNN & FNN model, which achieved an accuracy of 96.13%, a loss of 12.16%, precision of 96%, recall of 94%, and F1-score of 95%. The advantage of CNN & LSTM lies in its ability to capture sequential patterns through LSTM, making it more effective in detecting positive pneumonia cases. In conclusion, the CNN & LSTM model outperforms the CNN & FNN model in accuracy, recall, and F1-score, making it a more reliable choice for automatic pneumonia classification. The findings suggest the potential use of deep learning models, particularly CNN & LSTM, to support medical professionals and the public in quickly and accurately detecting pneumonia through chest X-ray images analysis
Implementation Augmented Reality On Banjarnegara Culture As A Learning Media Using Marker-Based Tracking Ilham Fitriyanto, Pratama; Azrino Gustalika, Muhamad
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 2 (2025): Issues January 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i2.13623

Abstract

Local Content is a subject that introduces the local culture of a region. At SD Negeri 1 Paseh, Banjarnegara, the teaching of Local Content faces challenges due to the lack of interactive learning media and conventional teaching methods that rely heavily on textbooks. This makes learning less engaging, lowers students' interest, and affects their understanding of Banjarnegara's culture. Thus, this study aims to develop a learning application based on Augmented Reality using the marker-based tracking method, which utilizes 2D images as markers to display 3D visuals. This approach helps enhance students' understanding of Banjarnegara's local culture in Local Content learning. The functional testing of the MULOK application was conducted using Blackbox Testing, which successfully evaluated its functionality. Distance and angle testing showed that markers were detectable within a range of 5-200 cm and at optimal angles between 20 and 160 degrees. Response time testing revealed an input-to-output response time of 1-2 seconds. Pretest and posttest evaluations were also applied to measure the application's effectiveness and to determine the improvement in students' understanding. The results showed an average score increase of 83.53%, demonstrating that Augmented Reality-based learning is more effective compared to conventional methods. Students do not need to visit the locations where culinary and handicraft products are made directly to observe and learn about them. By simply using the Augmented Reality Mulok application, students can effectively study Banjarnegara culture as part of Mulok learning
Implementation of Transfer Learning on CNN using DenseNet121 and ResNet50 for Brain Tumor Classification Putri, Farah Azhari Pranata Restia; Tanjung, Juliansyah Putra; Dharshini, N P
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 2 (2025): Issues January 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i2.13952

Abstract

Brain tumors are conditions characterized by abnormal cell growth in the brain, which can disrupt brain function. Early detection and accurate classification are crucial to ensuring effective treatment. This study aims to improve the accuracy of brain tumor classification by implementing Convolutional Neural Networks (CNN) using Transfer Learning approaches on DenseNet121 and ResNet50 models. Transfer Learning leverages knowledge from pre-trained models on larger datasets, thereby accelerating the training process and enhancing performance on the brain tumor dataset. The dataset used consists of medical images, including images of brain tumors and images without tumors. The data was divided into two parts, with 80% for training and 20% for validation. This split ensures that the model learns optimally from the training data and is tested on unseen data to objectively evaluate its performance. Experimental results show that the ResNet50 model achieved an accuracy of 98.44% on the validation data, while the DenseNet121 model achieved an accuracy of 96.31%. In conclusion, the ResNet50 model outperformed DenseNet121 in brain tumor classification. The implications of this study demonstrate that the Transfer Learning approach with ResNet50 can serve as an effective tool for automated brain tumor diagnosis, potentially improving patient outcomes through more accurate detection and classification
Tsukamoto Fuzzy In IoT-Based Automatic Control System Of Kitchen Smoke MSME Palembang Crackers Putri, Nadia; Lindawati; Aryanti
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 2 (2025): Issues January 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i2.13975

Abstract

MSMEs, particularly producers of *kerupuk kemplang* from Palembang, often face challenges in managing kitchen smoke generated during production. This smoke not only pollutes the air and poses health risks to workers but also reduces comfort and productivity. Therefore, this study aims to design an Internet of Things (IoT)-based control system using the Fuzzy Tsukamoto algorithm to automatically regulate the exhaust fan speed based on temperature, smoke concentration, and carbon monoxide (CO) levels. This system introduces technological innovation to enhance efficiency and productivity in MSME kitchen management. The method involves using MQ135, MQ7, and DHT11 sensors to detect kitchen environmental conditions in real time. The collected data is processed by the NodeMCU ESP8266 microcontroller using the Fuzzy Tsukamoto algorithm and is then used to adjust the exhaust fan speed via an AC dimmer. The monitoring results are displayed on the Blynk IoT application for easy access. The study results show that the system successfully reduces smoke concentration by up to 30 ppm and CO levels by 40 ppm while maintaining the kitchen temperature within an optimal range of 49°C to 55°C. With a Mean Absolute Percentage Error (MAPE) of 7.66% and an accuracy rate of 92.34%, the system proves to be effective and responsive to changes in kitchen environmental conditions. The implementation of this Fuzzy Tsukamoto and IoT-based system has a positive impact on improving air quality, ensuring worker health, and increasing MSME productivity. Additionally, this system supports a more modern, efficient, and environmentally friendly kitchen management approach, making it an innovative solution for the *kerupuk kemplang* production industry
Comparative Analysis of the Performance of Four Symmetric Algorithms on Digital File Security Manurung, Rodiyah Aini; Sutarman, Sutarman; Efendi, Syahril
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 2 (2025): Issues January 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i2.13978

Abstract

Information security is crucial to prevent misuse that could harm others. Information can be accessed through various electronic devices such as mobile phones, computers, and tablets in the form of text, images, audio, and video, whether public or confidential. In the digital era, image files are highly susceptible to authenticity risks as they can be easily shared through various communication media. This facilitates unrestricted digital file exchange, raising concerns about authenticity and the risk of modifications before reaching the recipient. Therefore, digital file exchanges require a security system to ensure that transmitted data remains original and intact. Cryptography is a field of study that protects data security in communication. It consists of algorithms and keys, where algorithms perform encryption and decryption, while keys enhance security levels. This study examines image encryption by using different key lengths with the same image, as well as encrypting images of varying sizes using the same key length, employing AES, DES, 3DES, and RC6 algorithms. The results show that the DES algorithm is the fastest in encryption and decryption compared to the other three algorithms. DES is 13.3% faster than 3DES and 10.2% faster than RC6. Additionally, the key length used does not significantly impact processing time, but image size greatly affects encryption and decryption speed. These findings indicate that in cryptographic implementations for digital images, file size is a critical factor to consider to maintain efficiency without compromising encryption and decryption speed
Analysis of Combined Contrast Limited Adaptive Histogram Equalization (CLAHE) and Median Filter Methods for Enhancement of CCTV Screenshot Image Quality Noor, Fredy; Muhathir, Muhathir; Fadlisyah, Fadlisyah; Syahputra, Dinur
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 2 (2025): Issues January 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i2.14016

Abstract

The quality of CCTV images often deteriorates due to poor lighting, low-quality cameras, and noise, hindering effective security analysis. This study aims to assess the combined effect of Contrast Limited Adaptive Histogram Equalization (CLAHE) and median filtering on improving the quality of CCTV screenshot images by enhancing contrast and reducing noise. Using a quantitative approach, four low-quality CCTV images were processed with CLAHE to improve contrast, followed by median filtering to reduce noise. Image quality was evaluated using two metrics: Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). Results showed that CLAHE significantly improved image contrast, with MSE values ranging from 17.7513 to 159.092 and PSNR from 39.4809 to 47.1987. After applying the median filter, MSE values decreased to 12.1238–22.1747, and PSNR increased to 34.7288–37.3442, indicating noise reduction. The combination of CLAHE and median filter showed even better results, with MSE values ranging from 0.000993935 to 0.00508972, and PSNR ranging from 71.1032 to 78.1966. This combination significantly improved the quality of the CCTV screenshots, making them more suitable for security and forensic analysis. The findings suggest that CLAHE and median filtering can effectively enhance image clarity. Future studies should focus on optimizing these techniques for various lighting conditions and exploring other methods to address extreme noise levels in CCTV images
Development of a Prototype System for Monitoring and Controlling Cat Care Based on the Internet of Things Rahmawati, Yosy; Zuchriadi, Achmad; Wahyudyan, Dzidan Wiladnurrizqi
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 2 (2025): Issues January 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i2.14131

Abstract

In today's fast-paced and stressful daily life, many people experience fatigue and stress, necessitating solutions to alleviate these feelings. Taking care of pets, especially cats, can have a positive impact on human mental health. However, caring for cats requires time and effort, particularly in monitoring their health and the environmental conditions of their living space. This study aims to design and develop a prototype of a cat care monitoring and control system based on the Internet of Things (IoT), enabling real-time monitoring and automated control of various aspects, including cat health, body temperature, and feeding. The system integrates multiple components, including ESP-32, MLX90614 GY-906 Sensor, Ultrasonic Sensor, DHT11 Sensor, RTC DS1302, Servo Motor, Relay, LCD 16x2, 5V Water Pump, Exhaust Fan, and the Telegram application as a monitoring interface. The study's findings indicate that air-conditioned rooms provide the most stable and comfortable temperature and humidity conditions for both pets and humans. Additionally, the DHT11 sensor is more suitable for indoor humidity measurement, whereas the DTH sensor is more optimal for outdoor environments. The ultrasonic sensor proves to be practical for distance measurement, while the load cell delivers precise results for weight measurement requiring high accuracy and stability. This research offers an innovative solution to enhance cat monitoring and care, contributing to an overall improvement in pet quality of life.

Page 37 of 42 | Total Record : 412

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