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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 Seek original and unpublished research papers based on theoretical or experimental works for the publication globally.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 Solicit original and unpublished research papers, based on theoretical or experimental works.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 128 Documents
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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.
Designing a Female Hero Educational Game using Adobe Animate and the ADDIE Method Sembiring, Asha; Sagala, Alon Jala Tirta; Yahaya, Wan Ahmad Jaafar Wan
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v5i1.18325

Abstract

Many do not know that heroes in Indonesia are not only men but women who also participate in the struggle and act as heroes in Indonesia. Female heroes are also very instrumental for Nusa and the Nation. Technology development is now increasingly advanced. For example, mobile phones are owned by every group, such as children, teenagers to the elderly. Mobile phones can also be used as entertainment media such as games that can increase children’s interest in learning who tend to like animated images and can increase knowledge, so education is not too saturated. This study aims to develop an educational game that can introduce national female heroes using the ADDIE (Analysis, Design, Development, Implementation, Evaluation) method. This development method focuses on iteration and reflection, so continuous improvement can be made that focuses on feedback. Using a questionnaire, software testing techniques focused on functional specifications and usability testing on a Likert scale. Blackbox testing can provide an overview of the program combined state and perform functional testing. Likert scale questionnaire testing can produce as expected. The result of the research is an educational game introducing the heroine, which is expected to be used to introduce the national heroine to children and students.
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
The impact of the project-based learning (PBL) on the motivation of first-year students at Universitas Negeri Medan Hasan, Hanapi; Jalinus, Nizwardi; Abdullah, Rijal; Ambiyar, Ambiyar; Fadhilah, Fadhilah; Putri, Tansa Trisna Astono
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v5i1.17973

Abstract

The goal of this study is to look at the effect of new learning approaches like project based learning (PBL) on the enthusiasm for studying of undergraduates at Universitas Negeri Medan. In the quasi-experimental design, the Times-Series Design with Control Group was used. The one-way ANOVA test was used to evaluate the data. The results demonstrated that the pretest and posttest motivation questionnaires in the control and experimental classes of the PBL paradigm differed. The mean of pretest motivation score for the experiment group was 3.50, by input score that varied from 3.00-3.97 and a standard deviation of 0.58. The mean of posttest motivation score for the experiment group was 3.83, by a score that ranged from 3.03-4.00 and a standard deviation of 0.61.
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.
Long Distance Interpersonal Communication Patterns of Parents and Children Tamsil, Ilma Saakinah; Andary, Ria Wuri; Khairunnisak, Khairunnisak
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v5i1.18665

Abstract

Well - established family communication will give attachment and mutual need to form communication patterns . For children who live far from their parents, communication is relied on media intermediaries and quite a few experience a lack of communication effective . The urgency of this research is seen long- distance interpersonal communication patterns of parents and children especially for Communication Science students at Medan Area University class of 2022 who come from from outside Medan and its obstacles . This qualitative research method uses Focus Group Discussion which involves ten parents and ten child as a participant. The result is the long- distance interpersonal communication patterns of parents and children use secondary communication patterns that is via cellphone. Characteristics of interpersonal communication can be strengthen interpersonal relationships between parents and children especially in context parenting . Ten parent participants applied authoritative communication patterns , meanwhile authoritarian communication patterns , permissive communication patterns No found. However, communication is established tend to only fulfill material needs and focus more on the mother. The role of fathers is still minimal, what can be done impact on fatherless parenting. Obstacle factor long distance communication patterns viz internet connection , busy parents and children making communication limited. It is important for parents and children to maintain effective communication in order to benefit the not lost because distance physique .
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.
Implementation of a Wireless Sensor Network with Mesh Topology with XBee for Centralized Building Room Monitoring Hutabarat, Nicodemus; Siraita, Regina Sirait; Junaidi, Junaidi; Pardede, Morlan
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.v5i2.20002

Abstract

Room monitoring in office buildings or campuses such as POLMED, especially the use of lights and indoor activities after working hours, is very necessary. In this research, a wireless sensor network with a mesh topology was implemented. A network with a mesh topology allows each node to establish communication with surrounding nodes and send information through intermediary nodes. This implementation is able to provide benefits by increasing the network area, number of sensor nodes and network reliability. The monitoring system is designed to consist of sensor nodes and server nodes. Each server node consists of a light sensor, motion sensor and XBee RF module for communication placed in each room, so that each node can communicate and send information to the monitoring center. The server node as a monitoring center receives data from sensor nodes which can be displayed in room mapping. In testing the sensor node network, it can successfully send a message to the server node if there is at least one intermediate node that can forward the message to the destination. From the test results, it was found that sending one frame with 17 bytes was 0.09 s, 0.11 s, and 0.12 s for 1 hop, 2 hops and 3 hops respectively.

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