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Computer Science (CO-SCIENCE)
Published by Universitas Bina Sarana Informatika
ISSN : -     EISSN : 27749711     DOI : https://doi.org/10.31294/coscience
Core Subject : Science,
Computer Science & IT
Computer Science (CO-SCIENCE) pertama kali publikasi tahun 2021 dengan nomor ISSN (Elektonik): 2774-9711 yang diterbitkan oleh Lembaga Ilmu Pengetahuan Indonesia (LIPI). Computer Science (CO-SCIENCE) adalah jurnal yang diterbitkan oleh Program Studi Ilmu Komputer Universitas Bina Sarana Informatika. Computer Science (CO-SCIENCE) terbit 2 kali setahun (Januari dan Juli) dalam bentuk elektronik. Redaksi menerima naskah berupa artikel ilmiah dan penelitian pada bidang: Networking, Aplication Mobile, Software Engineering, Web Programming, Mobile Computing, Cloud Computing, Data Mining, dan Aplikasi Sains.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 121 Documents
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Analisis Performa Model ResNet-50 Pada Diagnosis Pneumonia Balita Berdasarkan Citra Radiografi Thorax Rahmawati, Ami; Yulianti, Ita; Nurajizah, Siti; Hidayatulloh, Taufik; Sari, Ani Oktarini
Computer Science (CO-SCIENCE) Vol. 5 No. 1 (2025): Januari 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i1.7618

Abstract

One of the most serious complications of ARI is pneumonia, where this disease causes sufferers to experience pain when breathing and limited oxygen intake. According to the World Health Organization (WHO), pneumonia is classified as a life-threatening disease due to the high mortality rate caused. To be able to diagnose this disease, patients usually undergo various medical examination methods, one of which is through chest radiography. However, the challenge in diagnosing pneumonia generally lies in the complexity and uncertainty in interpreting the results of these methods. Therefore, this study was conducted with the aim of building an image classification model based on the Chest radiography dataset from toddler patients using the ResNet-50 architecture, which is a variant of the Convolutional Neural Networks (CNN) algorithm. The combination of the two methods is applied to analyze and process images and obtain pattern recognition with high accuracy. The research methods used include the application of data augmentation, CNN architecture design, model training, and performance evaluation. The evaluation results show that the model has quite good performance with an accuracy of 85%, which indicates the model's ability to classify images with a fairly high level of accuracy, and has the potential to help the pneumonia diagnosis process more efficiently and accurately.
Analisis Sentimen Ulasan Pelanggan Menggunakan Algoritma Naive Bayes pada Aplikasi Gojek Heristian, Sujiliani; Napiah, Musriatun; Erawati, Wati
Computer Science (CO-SCIENCE) Vol. 5 No. 1 (2025): Januari 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i1.7775

Abstract

Transportation is a means that a person uses to move from one place to another. One mode of transportation that is popular among the public is online motorcycle taxis, such as Gojek. Gojek continues to innovate to meet customer needs more effectively, as well as expand the scope of its services. This research aims to identify the number of positive and negative sentiments in the user review dataset, evaluate the performance of the algorithm used, and measure the level of customer satisfaction with Gojek services. Analysis was carried out on 6,485 customer reviews, which resulted in 4,387 positive sentiments and 2,098 negative sentiments. The classification model used, namely Naive Bayes, shows an accuracy of 88.5%, precision of 88.1%, and recall of 89.0%. The results of this research indicate that the Naive Bayes method provides good performance in analyzing the sentiment of user reviews of Gojek services
Customer Churn Prediction Pada Sektor Perbankan Dengan Model Logistic Regression dan Random Forest Mufida, Ely; Andriansyah, Doni; Hertyana, Hylenarti
Computer Science (CO-SCIENCE) Vol. 5 No. 1 (2025): Januari 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i1.7576

Abstract

– Customer churn is a detrimental phenomenon in the banking sector because it can reduce revenue and increase the cost of acquiring new customers. This research aims to compare the performance of two models, Logistic Regression and Random Forest, to predict customer churn using datasets from Kaggle. The research process involves data preprocessing such as z-score normalization and dividing the dataset into training data (70%) and testing data (30%). The model was evaluated using a confusion matrix with Accuracy, precision, recall and F1-Score values. Logistic Regression achieved 76.85% Accuracy, 79% precision, 94% recall, and 86% F1-Score, showing quite good performance but susceptible to false positives. In contrast, Random Forest shows superior performance with 83.12% Accuracy, 84% precision, 96% recall, and 90% F1-Score. Random Forest is suitable for problems with high recall requirements because it is more reliable in detecting potential customer churn. To further improve model performance, it is recommended to perform hyperparameter optimization and feature importance analysis. This churn prediction model is expected to help banks reduce churn and increase customer retention.
Penerapan Integrasi Algoritma K-Means Dan Naïve Bayes Untuk Klasifikasi Wilayah Rawan Banjir Di Jakarta Sinatrya, Irfan Maulana; Pohan, Achmad Baroqah; Yunita, Yunita; Amalia, Hilda; Lestari, Ade Fitria
Computer Science (CO-SCIENCE) Vol. 5 No. 2 (2025): Juli 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i2.6900

Abstract

Jakarta, as a metropolitan city in Indonesia, often experiences flooding caused by high rainfall, poor drainage systems, and rapid urbanization. This research aims to classify flood-prone areas in Jakarta using a combination of K-Means Clustering and Naïve Bayes Classifier algorithms. The research phase begins with data collection from the Satu Data Jakarta website, including attributes such as region, sub-district, village, average water level, number of affected RWs, number of affected families, number of affected people, and number of flood events. The collected data is then processed through cleaning and normalization stages before being analyzed using the K-Means algorithm to group areas based on their flooding characteristics. Furthermore, the Naïve Bayes algorithm was used to build a classification model that predicts flood-prone areas. The results showed that the combination of these two algorithms resulted in higher average accuracy compared to the use of conventional Naïve Bayes, having an accuracy of 98.18%% at training and testing data split ratios of 70:30, 80;20 and 90:10. The findings provide valuable insights for flood risk mitigation in Jakarta, assisting the government in taking more effective preventive measures.
Optimalisasi Presensi Sekolah Berbasis QR Code dengan Metode Rapid Application Development Rahmawati, Eva; Brawijaya, Herlambang; Andriansyah, Doni; Mufida, Elly
Computer Science (CO-SCIENCE) Vol. 5 No. 2 (2025): Juli 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i2.8505

Abstract

High School attendance systems play an important role in monitoring student attendance and enforcing discipline in the academic environment. However, many schools still use manual methods such as written attendance lists or teacher name calling, which are inefficient, time-consuming, and prone to manipulation and fraud. These methods present challenges for teachers and administrative staff, leading to inaccurate recording, data loss, and falsification of attendance. To address these issues, this study proposes the development of a QR Code-based school attendance system using the Rapid Application Development (RAD) methodology. RAD was chosen because of its ability to produce prototypes quickly and allow for iterative system improvements according to user needs. The proposed system allows students to scan a unique QR Code to automatically record their attendance, thereby reducing human intervention and minimizing errors. The expected outcomes of this study include increased accuracy, efficiency, and security in recording student attendance. The RAD approach is predicted to accelerate the development process without sacrificing ease of use and system reliability. In addition, this system is expected to be able to prevent fraud in attendance, because QR Code-based authentication provides a more secure validation mechanism. Through a series of trials and evaluations, this study aims to prove that the integration of RAD with QR Code technology can improve the effectiveness of attendance recording compared to conventional methods. Based on the results of the trials and evaluations, it can be concluded that the QR Code-based attendance system with the RAD approach has been proven to improve the efficiency, accuracy, and security of the attendance system in schools.
Sistem Manajemen Proyek Berbasis Website Dengan Metode Kanban Widjaja, Florence Aurelia; Hakim, Bhustomy
Computer Science (CO-SCIENCE) Vol. 5 No. 2 (2025): Juli 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i2.8864

Abstract

The advancement of digital technology has created a growing demand for systems that support work effectiveness, particularly in project management. The Digital Learning Division at PT. Kurnia Ciptamoda Gemilang faces challenges in monitoring projects in a structured and timely manner, necessitating a web-based project management system tailored to the company’s specific needs. This study aims to design a standalone web-based project management system that is not yet integrated with existing systems but is structured to allow future integration. The system was developed using the Next.js framework, applying the Kanban method and the SDLC Waterfall development model. The research involved problem identification, literature review, and interviews with project managers to gather both functional and non-functional requirements. The results show that the developed system includes key features such as workspace management, member management, project and task management, task prioritization, deadline setting, and visualizations in the form of Kanban boards and calendars. Work efficiency is measured through the availability of a more structured workflow and a reduction in miscommunication during task assignment. The system was evaluated using the Black Box Testing method, and the results confirm that all features function according to specifications and meet the users' actual needs in the work environment. The system has proven effective in improving work efficiency, facilitating team coordination, and ensuring that projects proceed as planned.
Implementasi Firewall Mikrotik dalam Pembatasan Akses Situs Terlarang di RT/RW Net Sastra, Ricki; Al Kautsar, Hanggoro Aji
Computer Science (CO-SCIENCE) Vol. 5 No. 2 (2025): Juli 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i2.8897

Abstract

The increasing use of the Internet in RT/RW Net environments has brought both positive and negative impacts. One issue is the unrestricted access to inappropriate content, especially among students, such as online games, pornography, and gambling websites. This study aims to restrict internet user access rights in RT/RW Net networks by implementing firewall filters on MikroTik routers. The method used is firewall filtering configuration based on IP, port, and domain restrictions. The results show that the implementation of the firewall filter on MikroTik routers effectively blocks access to restricted sites and improves network security. Tests were carried out before and after configuration, showing the successful blocking of unwanted content. standards.
Model Prediksi Risiko Kesehatan Perkotaan Berbasis Lingkungan dengan XGBoost Aulia, Muhammad Kahfi; Utaminingsih, Eka; Prihatin, Nanang
Computer Science (CO-SCIENCE) Vol. 5 No. 2 (2025): Juli 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i2.9109

Abstract

Poor urban air quality is a major public health concern, especially in highly urbanized areas. This study aims to predict health risks associated with air pollution using machine learning techniques based on environmental variables. The dataset used, Urban Air Quality and Health Impact, contains 1,000 rows and 46 columns, including temperature, humidity, wind speed, dew point, ultraviolet (UV) index, and health risk scores from major U.S. cities. As an improvement over previous studies using linear regression and Random Forest (R-squared 0.89; Mean Squared Error/MSE 0.65), this research implements an optimized Extreme Gradient Boosting (XGBoost) model. The model was fine-tuned using Randomized Search on key hyperparameters and evaluated with an 80:20 data split. It achieved an R-squared of 0.9692 and MSE of 0.0122. Dew point and wind speed were identified as the most influential features. Although synthetic, the dataset reflects environmental patterns similar to Indonesian urban areas. This study does not adopt a text mining framework but instead uses a supervised regression approach based on environmental features. Its main novelty lies in the first application of an optimized XGBoost model using complex variables such as feels-like temperature to estimate urban health risk. Limitations include the absence of real-world validation with Indonesian data and the lack of analysis on interactions between variables
Penerapan Model Scrum Pada Perancangan Aplikasi Penerimaan Siswa Baru MTS Nu 03 Suradadi Diharja, Prawira; Rosmiati, Mia; Rahmayu, Mulia
Computer Science (CO-SCIENCE) Vol. 5 No. 2 (2025): Juli 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i2.9166

Abstract

Digital transformation in the education sector demands innovation in service delivery, including the student admission process. This study presents an innovative approach through the application of the Scrum software development model to build a new student admission application at MTs NU 03 Suradadi. Unlike conventional methods, Scrum offers advantages in rapid iteration, adaptability to changing requirements, and close collaboration between the development team and stakeholders. The development process was carried out in stages through Scrum phases such as product backlog, sprint planning, development sprints, and sprint reviews and retrospectives. The result is a web-based application featuring key functionalities such as online registration, document uploads, registration proof printing, and user account management. The implementation results show a significant improvement in administrative efficiency and service accessibility, indicated by a 35% increase in student applicants, a reduction in data verification time from 3 days to 1 day, and a user satisfaction rate of 87%. These findings demonstrate that the application of Scrum not only improves product quality but also has a tangible impact in effectively and sustainably supporting the digitalization of educational services.
Studi Komparatif Algoritma K-Means dan K-Medoids untuk Segmentasi Informasi Kesehatan Ananda, Muhammad Dwi; Malik, Karenina Nurmelita; Masruriyah, Anis Fitri Nur; Mardiah, Mardiah
Computer Science (CO-SCIENCE) Vol. 5 No. 2 (2025): Juli 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i2.9207

Abstract

In analyzing medical data to support clinical decisions, segmentation of health information plays a crucial role. This study presents a comparative analysis of K-Means and K-Medoids algorithms in clustering Medical Examination data. This evaluation is conducted using two main internal approaches, namely Silhouette Score and Davies-Bouldin Index in measuring the quality of separation as well as cohesion between clusters. The experiment involved varying the number of clusters to determine the optimal configuration of each algorithm. The results show that K-Means provides representative performance and is more stable against data complexity, compared to the K-Medoids algorithm which is only optimal in a small number of clusters. Statistical analysis using one-way ANOVA was applied to test the significance of performance differences between algorithms based on the average Silhouette Score value, yielding an F-value of 4.8594 with a P-value of 0.0447. This indicates that the performance difference between the two algorithms is statistically significant at 5% significance rate. This research confirms the K-Means algorithm for segmenting health data with diverse distributions and is expected to serve as a foundation for the development of more efficient health data classification systems in the future.

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