cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mesran
Contact Email
mesran.skom.mkom@gmail.com
Phone
+6282161108110
Journal Mail Official
jurnal.josyc@gmail.com
Editorial Address
Jalan Sisingamangaraja No. 338, Medan, Sumatera Utara
Location
Kota medan,
Sumatera utara
INDONESIA
Journal of Computer System and Informatics (JoSYC)
Published by Forum Kerjasama Pendidikan TInggi
ISSN : 27147150     EISSN : 27148912     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering
Journal of Computer System and Informatics (JoSYC) covers the whole spectrum of Artificial Inteligent, Computer System, Informatics Technique which includes, but is not limited to: Soft Computing, Distributed Intelligent Systems, Database Management and Information Retrieval, Evolutionary computation and DNA/cellular/molecular computing, Fault detection, Green and Renewable Energy Systems, Human Interface, Human-Computer Interaction, Human Information Processing Hybrid and Distributed Algorithms, High Performance Computing, Information storage, Security, integrity, privacy and trust, Image and Speech Signal Processing, Knowledge Based Systems, Knowledge Networks, Multimedia and Applications, Networked Control Systems, Natural Language Processing Pattern Classification, Speech recognition and synthesis, Robotic Intelligence, Robustness Analysis, Social Intelligence, Ubiquitous, Grid and high performance computing, Virtual Reality in Engineering Applications Web and mobile Intelligence, Big Data
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 7 Documents
 1 
Search results for , issue "Vol 6 No 2 (2025): February 2025" : 7 Documents clear
Deteksi Dini Kanker Payudara Menggunakan Citra Ultrasonografi Berbasis Convolutional Neural Networks dan Particle Swarm Optimization Azahra, Serina; Kusumaningtyas, Pramesti; Rofi'i, Mohammad
Journal of Computer System and Informatics (JoSYC) Vol 6 No 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i2.6637

Abstract

The increasing prevalence of breast cancer, one of the cancers with the highest incidence rate in the world, demands the development of effective early detection methods to increase patients' chances of recovery and reduce treatment costs. The main challenge in early detection of breast cancer is the identification of early symptoms that are often not felt, so many cases are only detected at an advanced stage. This study aims to develop a breast cancer early detection model using ultrasound images based on the convolutional neural networks (CNN) artificial neural network method optimized with the particle swarm optimization (PSO) algorithm. CNN is used to extract complex features from medical images, while PSO optimizes model parameters to improve accuracy. The dataset consists of 1,607 images classified into normal, benign, and malignant categories, through the process of model training and validation. The results showed that the integration of particle swarm optimization and convolutional neural networks resulted in an accuracy of 90.67%, higher than the convolutional neural networks method alone at 87.33%. In addition, the CNN-PSO model also excels in precision, sensitivity, and F1-score value. This research provides an effective diagnostic technology solution for primary healthcare facilities, with implications for improved early detection and reduced delayed diagnosis. This technology can be applied more widely through training of health workers and equitable distribution of diagnostic devices to improve service accessibility.
IndoBERT-Based Sentiment Analysis for Understanding Hotel Guests’ Preferences Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 6 No 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i2.6864

Abstract

The rapid growth of the hospitality industry and the increasing reliance on online reviews emphasize the need for advanced sentiment analysis tools to understand customer preferences effectively. This study explores the application of IndoBERT, a pre-trained language model tailored for the Indonesian language, in classifying sentiments from hotel guest reviews. Utilizing a dataset of 715 reviews, the study employed the Knowledge Discovery in Databases (KDD) framework for systematic data preprocessing, feature extraction, and machine learning analysis. IndoBERT demonstrated exceptional performance, achieving perfect precision, recall, and F1-scores of 1.00 for both positive (657 reviews) and negative (53 reviews) sentiment classes. The ROC curve analysis also yielded a mean AUC score of 0.86, validating the model's robustness and reliability. The results highlight IndoBERT's capability to accurately capture linguistic nuances and contextual meaning, offering actionable insights into factors influencing guest satisfaction, such as cleanliness, staff behavior, and service quality. This research contributes to advancing natural language processing applications in regional contexts and provides practical implications for enhancing service strategies in the hospitality sector. Future research should expand the model's application to other industries and explore multimodal approaches for a more comprehensive understanding of customer behavior.
Improved Sentiment Classification Using Multilingual BERT with Enhanced Performance Evaluation for Hotel Guest Review Analysis Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 6 No 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i2.6870

Abstract

Sentiment analysis in hotel guest reviews has become essential for evaluating customer satisfaction and service quality. This study improves sentiment classification accuracy by utilizing the Multilingual BERT model with an improved performance evaluation framework. Using the Knowledge Discovery in Databases (KDD) methodology, this research involves data selection, preprocessing, transformation, sentiment classification, and performance evaluation. A dataset of 715 hotel reviews from Qubika Boutique Hotel, sourced from Agoda, was used to assess the model's effectiveness. The classification results showed high accuracy in identifying positive sentiment, with 98% precision, 97% memory, and 98% F1 score, as observed in 432 correctly classified reviews. However, challenges were identified in the classification of neutral sentiment, which achieved a precision of 87% with 127 correctly classified cases, and negative sentiment, where the accuracy was 92%, with 104 correctly identified reviews. The overlap in confidence scores, especially in the range of 0.4-0.6 between neutral and negative sentiment, highlights the need for improved contextual embedding and hybrid modeling techniques. The sentiment distribution analysis revealed that 60-70% of reviews were positive, 20-30% neutral, and 10-15% indicated dissatisfaction, underscoring the need for targeted service improvement. These findings provide valuable insights for data-driven decision-making in hospitality management, enabling businesses to strengthen service power and address critical areas of concern. Future research should focus on refining model interpretability, expanding multilingual datasets, and integrating real-time sentiment analysis to improve classification performance. Strengthening these aspects will contribute to a more robust and scalable sentiment analysis framework, ensuring greater precision in capturing the guest experience and optimizing service strategies in the hospitality industry.
Optimasi Penjadwalan Pengangkutan Sampah Secara Adaftif Menggunakan Algoritma Tabu Search Ahmad, Suprian; Ikhsan, Muhammad
Journal of Computer System and Informatics (JoSYC) Vol 6 No 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i2.6935

Abstract

This research aims to optimize waste transportation scheduling adaptively using the Tabu Search algorithm. Inefficient waste management can lead to increased operational costs and accumulation of waste, so an adaptive optimization approach is needed. Data collection techniques in this research include direct observation, interviews with related parties, and literature study to obtain relevant data. The data collected includes the initial schedule for transporting waste, a list of villages in the three sub-districts, and the distance traveled by the waste transport fleet. The implementation of the Tabu Search algorithm was carried out with the parameters tabu_tenure = 5 and max_iterations = 100. The research results showed that this algorithm was successful in optimizing the waste transportation route, with a total optimal cost of 35.1 in Natal District, 17.5 in Panyabungan Kota District, and 11.5 in Kotanopan District. This optimization process increases the operational efficiency of waste transportation by reducing costs and travel time. In addition, the Tabu Search algorithm is able to overcome complex route problems by finding better solutions than conventional methods. Thus, this research proves that the Tabu Search algorithm can be an adaptive solution for more effective and efficient waste management in Mandailing Natal, and has the potential to be applied in other areas with similar conditions.
Machine Learning-Based GPS Spoofing Detection in UAV Networks: A Comparative Analysis of Anomaly Detection Models Airlangga, Gregorius
Journal of Computer System and Informatics (JoSYC) Vol 6 No 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i2.7033

Abstract

The increasing reliance on Global Positioning System (GPS) technology in Unmanned Aerial Vehicles (UAVs) has exposed them to cybersecurity threats, particularly GPS spoofing attacks that manipulate location data. This study explores the effectiveness of various machine learning-based approaches in detecting GPS spoofing in UAV communication networks. Supervised classification models, unsupervised anomaly detection techniques, and deep learning-based autoencoders are evaluated to determine their capability in identifying spoofed signals. The dataset used for training and testing contains multi-dimensional UAV network parameters with labeled GPS spoofing instances. Experimental results indicate that traditional anomaly detection models, such as Isolation Forest, One-Class SVM, and Local Outlier Factor, struggle with detection accuracy and exhibit high false-positive rates. The autoencoder-based approach achieves the highest accuracy (91.20%) but has poor precision (3.97%) and recall (4.73%), highlighting limitations in threshold selection and anomaly classification. Computational complexity analysis reveals that deep learning models, despite their accuracy advantages, require significant computational resources, making them less feasible for real-time UAV applications. This study identifies critical challenges in GPS spoofing detection, including dataset bias, environmental variability, and model hyperparameter sensitivity.
Deep Learning-Based Fetal Health Classification: A Comparative Analysis of Convolutional and Recurrent Neural Networks Airlangga, Gregorius
Journal of Computer System and Informatics (JoSYC) Vol 6 No 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i2.7056

Abstract

Fetal health monitoring plays a crucial role in prenatal care, enabling early detection of complications that may impact pregnancy outcomes. Traditional methods, including cardiotocography (CTG), rely on expert interpretation, which can introduce variability and potential misdiagnoses. In this study, deep learning techniques are employed to classify fetal health conditions based on CTG data. A comparative analysis is conducted on six architectures: Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (BiLSTM), and Attention-based LSTM. The models are evaluated using accuracy, precision, recall, and F1-score under a 10-fold cross-validation framework. Results indicate that CNN outperforms all other models, achieving an accuracy of 97.18% due to its hierarchical feature extraction capabilities. GRU demonstrates competitive performance with an F1-score of 95.50% while maintaining computational efficiency. The study further includes a complexity analysis, revealing that recurrent models, particularly BiLSTM and Attention-LSTM, introduce significant computational overhead without yielding substantial performance gains. Potential threats to validity, including dataset bias and overfitting, are analyzed to ensure robust findings. The insights gained from this research highlight the advantages of CNN-based architectures in automated fetal health assessment and suggest future work integrating hybrid models and explainable AI techniques. These findings contribute to advancing AI-driven fetal monitoring systems, aiding clinical decision-making, and improving perinatal care.
Prediksi Cuaca Menggunakan Data Historis dengan Algoritma Regresi Linear untuk Analisis Perubahan Suhu Pratama, Egi; Fatchan, Muhammad; Aguswin, Ahmad
Journal of Computer System and Informatics (JoSYC) Vol 6 No 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i2.6950

Abstract

Tokyo, the capital of Japan located on the state of Honshu, is facing with subtropical climate complexity, combining extreme temperature variations reached in hot summer season (>35 degrees) and cold winter season temperatures below 0 degrees. Current research explored the regression linear algorithm potential to predict daily maximum temperature within the context of complex urban weather dynamics. Based on the meteorology dataset collected in total of 639 days including key variables of temperature, humidity, rainfall, and air pressure, study developed weather prediction model. The outcomes demonstrated exceptional performance with Root Mean Squared Error at 0.80 and R-squared of 0.99, showing the near full coverage of model’s ability to capture all possible weather variability patterns. As a result, the research findings not only confirmed the effectiveness of linear regression for urban weather prediction but also open the possibility of similar model integration within more sophisticated weather forecast systems. Data-centered approach made significant contribution to the modern weather prediction technology responsive to urban society requirement.

Page 1 of 1 | Total Record : 7

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 7 No 2 (2026): February 2026 Vol 7 No 1 (2025): November 2025 Vol 6 No 4 (2025): August 2025 Vol 6 No 3 (2025): May 2025 Vol 6 No 2 (2025): February 2025 Vol 6 No 1 (2024): November 2024 Vol 5 No 4 (2024): August 2024 Vol 5 No 3 (2024): May 2024 Vol 5 No 2 (2024): February 2024 Vol 5 No 1 (2023): November 2023 Vol 4 No 4 (2023): August 2023 Vol 4 No 3 (2023): May 2023 Vol 4 No 2 (2023): Februari 2023 Vol 4 No 1 (2022): November 2022 Vol 3 No 4 (2022): August 2022 Vol 3 No 3 (2022): May 2022 Vol 3 No 2 (2022): Februari 2022 Vol 3 No 1 (2021): November 2021 Vol 2 No 4 (2021): Agustus 2021 Vol 2 No 3 (2021): Mei 2021 Vol 2 No 2 (2021): February 2021 Vol 2 No 1 (2020): November 2020 Vol 1 No 4 (2020): August 2020 Vol 1 No 3 (2020): Journal of Computer System and Informatics (JoSYC) - May 2020 Vol 1 No 2 (2020): Journal of Computer System and Informatics (JoSYC) - Februari 2020 Vol 1 No 1 (2019): Journal of Computer System and Informatics (JoSYC) - November 2019 More Issue