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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 Seek original and unpublished research papers based on theoretical or experimental works for the publication globally.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 Solicit original and unpublished research papers, based on theoretical or experimental works.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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Optimization Of A 20 Kv Capacitor Bank Energy Management System For Electrical Usage In Indihome Network Devices Mendrofa, Dicky Efelin Teoly; Dalimunthe, Muhammad Erpandi; Erivianto, Dino
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 6, No 2 (2025)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v6i2.26390

Abstract

The increasing demand for stable and efficient electrical energy in telecommunication systems, particularly in IndiHome network infrastructure, requires a reliable energy management strategy. One of the key challenges faced is the presence of reactive power, which can reduce power factor efficiency and lead to higher energy losses. This study focuses on optimizing the energy management system using a 20 kV capacitor bank to improve power factor and reduce electrical losses in IndiHome network devices. The research involves the design, implementation, and analysis of a capacitor bank control system that responds dynamically to load variations. Through simulation and real-time testing, the system is evaluated based on its ability to maintain a high power factor, reduce energy consumption, and stabilize voltage levels. The results show that the optimized capacitor bank system can improve power factor to above 0.95 and reduce reactive power demand significantly, resulting in improved overall energy efficiency. This optimization not only reduces operational costs but also enhances the reliability and longevity of network equipment by minimizing voltage drops and harmonic disturbances. The study concludes that capacitor bank-based energy management systems are highly effective and essential for modern telecommunication infrastructure. Keyword : Capacitor Bank, Power Factor, Energy Management, Telecommunication, IndiHome, 20 kV, and Reactive Power
Arabic NLP: A Survey of Pre-Processing and Representation Techniques Alkaabi, Hussein Ala'a; Jasim, Ali kadhim; Darroudi, Ali
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 6, No 2 (2025)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v6i2.25562

Abstract

The rapid growth of Arabic Natural Language Processing (NLP) has underscored the vital role of upstream tasks that prepare raw text for modeling. This review systematically examines the key steps in Arabic text pre-processing and representation learning, highlighting their impact on downstream NLP performance. We discuss the unique linguistic challenges posed by Arabic, such as rich morphology, orthographic ambiguity, dialectal diversity, and code-switching phenomena. The survey covers traditional rule-based and statistical methods and modern deep learning approaches, including subword tokenization and contextual embeddings. Special attention is given to how pre-trained language models like AraBERT and MARBERT interact with pre-processing pipelines, often redefining the balance between explicit text normalization and implicit representation learning. Furthermore, we analyze existing tools, benchmarks, and evaluation metrics, and identify persistent gaps such as dialect adaptation and Romanized Arabic (Arabizi) processing. By mapping current practices and open issues, this review aims to guide researchers and practitioners towards more robust, adaptive, and linguistically-aware Arabic NLP pipelines, ensuring that the data fed into models is as clean, consistent, and semantically meaningful as possible.
Identifying Dominant Factors of Divorce in Marbau Selatan Village Using K-Means Clustering Anggraini, Sindi; Hasugian, Abdul Halim
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 6, No 2 (2025)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v6i2.26447

Abstract

The increasing rate of divorce in Marbau Selatan Village reflects a broader trend in Indonesia and highlights an urgent social issue that threatens family resilience. This study applied the K-Means Clustering algorithm to analyze and classify divorce cases based on demographic and social characteristics. Data were collected from 85 divorce records registered between 2021 and 2025, focusing on key variables such as age, gender, case type, and cause of divorce. The clustering process generated three distinct groups, namely: conflicts and repeated disputes, abandonment by one party, and economic hardship. The results demonstrated that persistent conflicts represented the most dominant factor, followed by abandonment and financial problems. These findings suggest that K-Means is effective for revealing hidden patterns in divorce data, providing valuable insights for local stakeholders. The study contributes to data-driven policy recommendations, such as premarital counseling, family economic empowerment, and community-based mediation, to reduce divorce rates and improve household harmony in rural areas.
Integration of Artificial Intelligence in Management Information Systems to Improve the Effectiveness of Strategic Decision-Making in the Digital Era Wasesa, Istikha Ruchitra Hayudirga; Permatasari, Dhyta; Angkat, Fhatiya Alzahra; Al-Khowarizmi, Al-Khowarizmi
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 6, No 2 (2025)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v6i2.26051

Abstract

The integration of Artificial Intelligence (AI) into Management Information Systems (MIS) has emerged as a strategic imperative for enhancing the effectiveness of organizational decision-making in the digital era. This study aims to analyze the factors influencing successful AI adoption in MIS, evaluate its impact on strategic decision-making effectiveness, and explore the mediating role of dynamic capabilities. Grounded in the Technology Acceptance Model (TAM) and Dynamic Capabilities Theory, a conceptual framework was developed and tested using a mixed-methods approach. Quantitative data were collected from 715 respondents across six industry sectors in Indonesia, while qualitative insights were derived from case studies in 25 organizations with varying levels of AI implementation maturity. Results from Structural Equation Modeling revealed that perceived usefulness, ease of use, organizational readiness, and management support significantly influence AI adoption in MIS. The integration of AI was found to improve decision quality (34.7%), speed (42.3%), predictive accuracy (28.6%), strategic alignment (31.2%), and risk assessment capabilities (36.8%). Qualitative findings highlighted key implementation challenges, including data quality, skills gaps, employee resistance, and integration complexity. This study contributes theoretically by enriching TAM with organizational and strategic dimensions, and practically by offering a comprehensive framework to guide AI integration in MIS for sustained competitive advantage.
Risk Management Analysis of Information Technology with Failure Mode and Effect Analysis Method at PT XYZ Virgiawan, Adisty Kharisma; Santi, Rusmala; Alfresi, Aminullah Imal
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 6, No 2 (2025)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v6i2.26392

Abstract

PT Kencana Inti Perkasa is a company engaged in the management of crude palm oil (Crude Palm Oil) which uses information technology for its smooth operations. In the company there are potential information technology risks that can have an impact on the company so that they need to be identified and given an assessment. Risk problems that occur in PT Kencana Inti Perkasa's information technology are damaged hardware, virus attacks, risks to data, and risks to the network. This research aims to produce RPN values, get a list of risk priorities and develop recommendations for control measures using the Failure Mode and Effect Analysis method. This process involves identifying failure modes, assessing the severity, frequency of occurrence, and detectability of each failure mode. The final result of this research found that there are 30 failure modes consisting of 4 risks categorized as High, 2 risks categorized as Medium, 22 risks categorized as Low, and 2 risks categorized as Very Low. The highest Risk Priority Number (RPN) value, 167.83 and in the High category, arises from the incorrect use of printers caused by human error, including technological resources that are routinely utilized by the company. Another risk in the High category is the slow network capacity with an RPN of 152.06. Furthermore, computer damage due to virus attacks obtained an RPN value of 122.26, while illegal access to PC information was also categorized as High with an RPN value of 121.09, which belongs to the company's vital technology resources. These findings indicate that these risks need to be the main focus in handling, considering the high RPN value indicates a significant level of urgency.
Anomaly Detection on CBTC Wayside Units with the Random Forest Algorithm for Condition-Based Maintenance Amali, Rully Burhan; Alamsyah, Ahmad Tossin; Sutiyo, Sutiyo
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 6, No 2 (2025)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v6i2.25625

Abstract

This study proposed an anomaly detection model for wayside units in Communication-Based Train Control (CBTC) systems using the Random Forest algorithm. The primary goal was to identify deviations in technical parameters such as voltage, temperature, humidity, and signal strength (RSL) that may indicate potential failures in the system. Data were collected from IoT-based sensors deployed on MRT Jakarta’s CBTC wayside units and transmitted via HTTP to a cloud database for further processing. The Random Forest model was trained using labeled data and evaluated using unseen test data. The evaluation metrics, accuracy, precision, recall, and F1-score, reached 100%, indicating that the model correctly identified both normal and anomalous conditions without misclassification. Further analysis showed that high humidity, excessive panel temperature, and low RSL values were the most frequent anomaly indicators. Based on this, the system also generated maintenance recommendations, making it not only reactive but also proactive in supporting condition-based maintenance (CBM). The results demonstrated that the Random Forest-based anomaly detection system is highly effective, scalable, and reliable for real-time monitoring of railway infrastructures. This approach can serve as a foundation for future development of smart maintenance systems in other safety-critical domains.
Herbal Plant Image Retrieval Using HSV Color Histogram and Random Forest Algorithm Azmi, Fadhillah; Gibran, M Khalil; Saleh, Amir
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 6, No 2 (2025)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v6i2.26495

Abstract

Herbal plants have significant importance in traditional medicine and are often useful in various natural health products. Visual identification of these plants is usually carried out based on the shape of the leaves and often encounters difficulties in distinguishing species due to similarities in shape and color. Therefore, a system capable of automatically and efficiently recognizing and searching for herbal plant images is needed. This study aims to implement an image search engine for herbal plants based on leaf color similarity. The method used includes color feature extraction using an HSV (Hue, Saturation, Value) histogram with an 8×8×8 bin configuration, resulting in a 512-dimensional feature vector. This histogram feature is then used as input for the Random Forest classification algorithm to group images based on the type of herbal plant. The dataset used consists of 450 herbal leaf images from 9 different classes, obtained through direct image capture using a digital camera. The test results indicates that the developed system is able to classify types of herbal plants with an accuracy of 95.56%. In addition, the computation time and system response during both training and testing processes are relatively fast and efficient. The advantage of this system lies in the simplicity of feature extraction while still being able to provide high classification performance. This system has great potential to be used as an educational tool as well as an initial component in the development of mobile applications for automatic herbal plant identification.
Twitter Sentiment Analysis on the Iran-Israel Conflict Using the Naïve Bayes Classification Algorithm Karima, Annisa; Ulya, Athiyatul; Achriadi, Teuku Sukma; Zufia, Anni
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 6, No 2 (2025)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v6i2.26093

Abstract

The armed conflict between Iran and Israel, which has attracted global attention, has sparked various public reactions, including from the Indonesian community. Given its potential impact on global social and economic stability, it is important to systematically analyze public perceptions using a sentiment analysis approach. A total of 310 tweets were collected through a crawling process and processed using several preprocessing stages, such as text cleaning, normalization, stopword removal, tokenization, stemming, and translation. Labeling was performed directly using the Naive Bayes algorithm, by comparing three algorithms: Gaussian Naive Bayes, Multinomial Naive Bayes, and Bernoulli Naive Bayes. Performance evaluation was conducted using metrics such as accuracy, precision, recall, and F1-score. The classification results showed that Multinomial Naive Bayes achieved an accuracy of 75.81%, Gaussian Naive Bayes achieved 77.42%, while Bernoulli Naive Bayes achieved 87.1%. Bernoulli Naive Bayes demonstrated superior performance in handling textual data with word frequency representation. This study contributes to strengthening the use of machine learning methods for public opinion analysis on social media, particularly in the context of geopolitical issues. The findings indicate that Bernoulli Naive Bayes is more suitable for classifying public opinion texts compared to the Gaussian and Multinomial variants.
Bird Sound Quality Analysis for Chirping Masters Using Mel Frequency Cepstrum Coefficiens (MFCC) and Svm Classification Algorithm Zahron, Almeranda Haryaveda Nurul; Kurniawan, Rakhmat
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 6, No 2 (2025)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v6i2.26410

Abstract

Birds play an important role in ecosystems as indicators of environmental health and biodiversity. In Indonesia, there are approximately 1,531 bird species, including songbirds that are popular for their melodious chirps. Bird sounds are used for communication, territorial marking, and are a key attraction in bird song competitions. However, obtaining a bird with high-quality vocalization requires specific training, one of which is the mastering method using recordings of champion bird songs. Additionally, the Support Vector Machine (SVM) algorithm has proven effective in classifying bird species based on sound, achieving 77% accuracy after noise reduction. The combination of MFCC and SVM allows for more systematic and accurate analysis of bird vocalizations. This research is expected to contribute to the field of ornithology, the development of songbird husbandry techniques, and serve as a guide for bird enthusiasts in selecting high-quality master bird sounds.
SMART WASTE BIN : IOT-BASED SMART TRASH BIN MONITORING SYSTEM Dari, Ulan; Ikhwani, Muhammad; Saptari, Mochamad Ari
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 6, No 2 (2025)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v6i2.25986

Abstract

Unmonitored waste accumulation can cause environmental pollution and health risks, especially in campus environments with dense daily activities. This research developed the “Smart Waste Bin,” an IoT-based system that monitors trash bin conditions in real time. The system uses an ultrasonic sensor to measure the height of the waste and a NodeMCU ESP32 as the main controller. Data is displayed on an LCD and sent to a monitoring website and Telegram application for notifications. The system classifies waste levels into three statuses: Empty (1–11 cm, green), Nearly Full (12–20 cm, yellow), and Full (21–30 cm, red). It also includes an automatic lid operated by a servo motor. When the bin is full, the lid remains closed to prevent overflow and maintain cleanliness. Testing showed the prototype successfully detected bin status and sent notifications with 90–93% accuracy. However, the system heavily depends on stable internet connectivity. Overall, it effectively enhances waste monitoring using IoT integration

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