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Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE)
Published by Universitas Muhammadiyah Sumatera Utara
ISSN : -     EISSN : 27213838     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering
ournal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) is being published in the months of March and September. It is academic, online, open access (abstract), peer reviewed international journal. The aim of the journal is to: Disseminate original, scientific, theoretical or applied research in the field of Engineering and allied fields. Dispense a platform for publishing results and research with a strong empirical component. Aqueduct the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. Seek original and unpublished research papers based on theoretical or experimental works for the publication globally. Publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics, Communication and Telecommunication, Education Science and all interdisciplinary streams of Social Sciences. Impart a platform for publishing results and research with a strong empirical component. Create a bridge for significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. Solicit original and unpublished research papers, based on theoretical or experimental works. Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) covers all topics of all engineering branches. Some of them are Computer Science & Engineering, Information Technology, Electronics & Communication, Electrical and Electronics, Electronics and Telecommunication, Education Science and all interdisciplinary streams of Social Sciences.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 18 Documents
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Search results for , issue "Vol 5, No 2 (2024)" : 18 Documents clear
Message Security Application Using Mobile-Based AES Algorithm Rifki, Mhd Ikhsan; Syamia, Nanda
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v5i2.20834

Abstract

All information in data exchange transactions on communication networks travels through the network infrastructure. The network infrastructure sends various types of data, including text, images, and documents with various extensions. These documents may be private and confidential. Therefore, it is crucial to have a message security option that is not only user-friendly but also has a high level of security system complexity. The purpose of this research is to build a text message security system using the Advanced Encryption Standard (AES) algorithm on mobile devices. We can utilize it as a security solution to safeguard text messages received through mobile devices. The AES security algorithm is known to have reliability in processing data encryption and decryption. The research will describe components such as the design, implementation, and analysis of application requirements. Important features include encryption, decryption, and key management in addition to an easy-to-use UI. The research findings show that this program successfully secures text messages while maintaining the confidentiality and integrity of the data sent. Additionally, we adjusted the application's parameters to align with mobile device standards, ensuring consistent encryption and decryption procedures that users can easily operate. For application testing, we use the black box testing method to ensure that the design and application function as intended, adhere to security regulations, and provide a positive user experience. This study's results suggest that AES-based mobile messaging security applications can serve as a tool to fulfill the secure communication expectations of mobile users.
An IoT Design Effectiveness and Reliability of Electric Power Circuit Breakers Aryza, Solly; Lubis, Zulkarnain; Putra, Khairil
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v5i2.20908

Abstract

Along with the very rapid development of technology in the current era of globalization. It became3The main instrument in the progress of various social and life aspects. Currently, many people use technology to help solve several problems in life. Almost every activity carried out by humans always uses technology. The use of automated security instruments has become an option nowadays. In modern times like now there are many kinds of technology, and therefore I will develop technology with better security instruments.
Neural Network Algorithm for Biometric Analysis of Human Retina Image Nst, Tuti Adi Tama; DEA, Bambang Hidayat; Andini, Nur; Zakaria, Hasballah
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v5i2.20000

Abstract

Identity recognition is an important process because many systems require a valid user identity for security and access control. Identity recognition such as passwords, signatures, id cards have some weaknesses that are they can be duplicated, stolen, forgotten, and even lost. Identity recognition using biometric techniques is known to be more reliable. Biometric technique is a recognition and classification technique that uses human behavior and physical atributes. In this research, a non-realtime simulation system is designed to identify a person by biometric of retina image. The system can identify one's identity through pattern of retinal blood vessels. The processes of this system divided into two stages that are training stages and testing stage. The identification process begins with prepocessing retinal photo. Biometric features extracted by using Discrete Orthonormal S Transform (DOST). Biometric classification by using Adaptive Resonance Theory 2 (ART 2) with unsupervised learning process that can recall previously learned patterns . The results obtained from this study showed 65% of accuracy  for the right retina image and 50% of accuracy for the left retina image. Computing time is about 6 seconds. Further development is needed to improve the accuracy of the system as a security and access control systems.
Forecasting The Number of Health Center Patient Arrivals Using Cheng’s Fuzzy Time Series Method Aisyah, Aisyah; Rakhmawati, Fibri
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v5i2.20943

Abstract

Forecasting is the science and art of estimating future events. Forecasting is useful in predicting events covering the short, medium and long term.  In the historical data that will be taken and project it into the future so that it involves a forecast, the forecasting involvement uses the amount of data that has been taken. The number of patient arrivals at the health center has increased and decreased every month, this increase and decrease can affect the facilities provided by the health center, so this study intends to do forecasting on the number of health center patient arrivals to meet the service facilities that must be provided. This study was carried out from January 2022 to December 2023 at the Medan Amplas Health center by utilizing Cheng's Fuzzy Times Series method, therefore the results of this calculation were 70,464 patients by getting a MAPE value of 8%.
Herbal Plant Classification Using Multi-Feature Extraction and Multilayer Perceptron Simanjuntak, Englis Franata; Sipahutar, Yohannes Saputra; Pasaribu, Martin Josua; Saleh, Amir
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v5i2.20835

Abstract

Herbal plants used for medicine have prompted many researchers in the field of computer science to develop an efficient way to identify these plants through their leaves. This study will propose artificial neural networks, such as Multilayer Perceptron (MLP), to classify herbal plants. This method is used with feature extraction methods like the Gray Level Co-occurrence Matrix (GLCM), Hue Saturation Value (HSV), and Histogram of Oriented Gradients (HOG) to find out about the leaves' texture, color, and histogram. The dataset used was taken directly with a digital camera from various types of herbal plants that people usually see in everyday life. The dataset, which consisted of 450 images, was classified into nine classes. The entire dataset will be processed using a combined feature extraction method before the MLP method is used for clustering. This method is used to better understand the diversity of herbal plants and improve classification accuracy. The experimental results show that the combination of the feature extraction method and the MLP algorithm can achieve the highest accuracy of 95.56% in identifying various types of plants. This research provides significant benefits and contributes to the development of an herbal plant recognition system capable of accurate classification.
Survival Analysis of Recovery Rates in Diabetes Mellitus Patients Using the Kaplan-Meier Method (Case Study: Malahayati Islamic Hospital) Anggreini, Yopie Puspita; Husein, Ismail
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v5i2.20910

Abstract

Diabetes mellitus (DM) is a major global health issue characterized by insulin hormone deficiencies, leading to rising incidence rates and significant risks for society. This study analyzes survival times of DM patients at Malahayati Islamic Hospital Medan using the Kaplan-Meier method for time-to-recovery estimates and the Log-Rank test to evaluate differences in survival functions. Results indicate that survival estimates range from 0.99238 at day 0 to 0.00000 at day 13. No significant differences were found based on gender, age, disease diagnosis, or support type. However, females and older patients showed slightly longer recovery times, and patients with Type II DM recovered faster than those with Pneumonia and Type II DM. Patients receiving Nebulizer and Oxygen support showed quicker recovery compared to those with Thorax Photo, EKG, and Lab support. 
An Analysis 5G Network Latency for Upload and Download: Case Study of 5G Network in Medan City Hulu, Fitria Nova; Rusdi, Muhammad; Yadi, Indra
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v5i2.20001

Abstract

This research analyzes 5G network latency for upload and download activities in Medan City. Using a quantitative approach, data is collected through latency measurements at various test points throughout the city. Measurements are carried out with special network test tools that are able to detect latency accurately. The research results show that the average data speed for download activities is 14.62 Mbps while uploading is 22.72 Mbps, with an average latency value of 46.6 ms from the combined upload and download processes. Latency variability was also analyzed based on location and time factors, showing that the city center area had lower latency than the suburbs. This study indicates that the implementation of edge computing technology can further optimize 5G network performance in Medan City. These findings are important for telecommunications service providers to improve service quality and user experience
Image Segmentation Using Hybrid Clustering Algorithms for Machine Learning-Based Skin Cancer Identification Maulana, Riza; Interiesta, Diva Cahaya; Sofy, Annisa Kurnia; Maulana, Ilham Habib; Saleh, Amir
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v5i2.21016

Abstract

Early identification of skin cancer is crucial to increasing the chances of a cure and reducing mortality rates. This research aims to develop a method for identifying skin cancer using image processing techniques, specifically the hybrid clustering method. This method integrates machine learning with fuzzy c-means clustering (FCM) and hierarchical clustering (HC) segmentation techniques to segment skin cancer more accurately. Hybrid clustering is used to separate suspicious areas in skin images, resulting in more precise segmentation compared to conventional methods. The segmentation results are then used as input for various machine learning methods that are trained to recognize patterns in identifying types of skin cancer. Tests were carried out using data obtained from the Kaggle Dataset, and the results showed that the proposed method was able to achieve a high level of accuracy in identifying skin cancer. After segmentation, the ensemble learning method yielded the best identification results. The Random Forest algorithm, which is applied to process and analyze features from skin images, shows higher performance compared to other machine learning methods. Tests show that the Random Forest method with the proposed segmentation achieves an accuracy level of up to 89%, while other machine learning methods such as K-Nearest Neighbor only achieve an accuracy level of around 86%. This research makes an important contribution to the development of efficient and reliable diagnostic tools for skin cancer identification, with appropriate segmentation methods proven to increase accuracy.
Integration of Probabilistic Multi-Class Labeling and Adaptive K-Means Clustering with KNN Classification: Application to Weather Data Lubis, Husni; Lubis, Ihsan; Harahap, Herlina; Tommy, Tommy; Siregar, Rosyidah
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v5i2.20905

Abstract

Clustering and classification technologies are pivotal in data analysis, helping to uncover hidden patterns in complex datasets. Despite their broad applications across fields such as pattern recognition, market segmentation, anomaly detection, and weather prediction, these techniques face significant limitations. Clustering methods like K-Means assume known cluster numbers and data distributions, while classification approaches such as K-Nearest Neighbors (KNN) rely heavily on the quality of labeled data. These challenges are particularly pronounced in the context of dynamic weather data, which exhibits high variability and complexity. This research addresses these limitations by integrating probabilistic multi-class labeling with an adaptive K-Means clustering approach. Probabilistic labeling allows data points to belong to multiple classes, reflecting the nuanced nature of overlapping weather conditions. Adaptive K-Means dynamically determines the optimal number of clusters, overcoming traditional constraints. By combining these methods with KNN classification, the proposed approach enhances the accuracy of weather classification. KNN leverages cluster centroids and class probabilities to provide more precise predictions. This approach provides a robust foundation for further research and optimization of adaptive methods applicable to other complex data types. Ultimately, the proposed model contributes significantly to advancing data analysis methods, particularly for dynamic and multi-class datasets like weather data.
Modeling The Proportion Of Measles Cases In North Sumatra By The Jack-Knife Partial Least Squares Method Pasaribu, Sarif Muda; Husein, Ismail
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v5i2.20928

Abstract

Campak masih menjadi masalah kesehatan di berbagai belahan dunia, terutama di negara berkembang, salah satunya Indonesia. Pada tahun 2022, Sumatera Utara akan menjadi salah satu provinsi yang masuk dalam daftar daerah dengan status kejadian luar biasa (KLB). Menurut data yang diperoleh dari Dinas Kesehatan Provinsi Sumatera Utara, pada tahun 2022 terdapat 127 kasus campak yang tersebar di 33 kabupaten/kota di Sumatera Utara. PLSR dapat menangani masalah multikolinearitas yang dapat menghasilkan estimasi titik. Untuk mengukur akurasi estimasi pada PLSR, diperlukan teknik analisis atau teknik empiris seperti Jackknife, yaitu teknik untuk menaksir standar error suatu estimator melalui proses resampling. Maka penggabungan estimasi interval Jackknife dan PLSR menjadi metode yang tepat untuk menangani data yang bersifat multikonferensi. Berdasarkan pemaparan hasil dan pembahasan di atas, maka dihasilkan pemodelan proporsi kasus campak di Sumatera Utara tahun 2022 dengan menggunakan metode Jack-Knife Partial Least Squares. Jack-Knife Partial Least Squares dimaksudkan untuk membentuk model yang lebih kuat dan sederhana, sehingga dapat memberikan prediksi secara efisien dengan melibatkan penjelasan yang minimal. Model Jack-Knife Partial Least Squares berhasil menunjukkan nilai tertinggi dari masing-masing variabel, variabel y = 0,9744, variabel y = 0,9472, variabel y = 0,9933, variabel y = 0,9962, variabel y = 0,9957, variabel y = 0,8912.

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