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INDONESIA
Jurnal Buana Informatika
Published by Universitas Atma Jaya Yogyakarta
ISSN : 20872534     EISSN : 20897642     DOI : -
Core Subject : Science,
Computer Science & IT
Arjuna Subject : -
Articles 594 Documents
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Design and Implementation of Load Balancing for Quality of Service Improvement Widiasari, Indrastanti Ratna; Efendi, Rissal
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

At the Information Technology Faculty, Satya Wacana Christian University, load balancing systems are implemented where the web server serves 500 users. This is to prevent server overload or downtime during simultaneous access to the web server. Test results indicate significant differences in CPU usage, request time, and bandwidth between load balancing and single servers. The use of load balancing is more effective than relying on a single server, as evidenced by test results. The CPU usage with load balancing is significantly lower, with a difference of up to 45% compared to a single server. The request time with load balancing is also slightly better, with only 21.5ms compared to 42ms for a single server. However, the difference in bandwidth between load balancing and a single server is not very significant. The highest bandwidth recorded on a single server is 182kb/s, while with load balancing it reaches 165kb/s.
Prediksi Penyakit Batu Ginjal dengan Menerapkan Convolutional Neural Network Waluyo Poetro, Bagus Satrio; Mulyono, Sri; Vani Aulia Pramesti
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

Kidney stones are a health problem that requires intensive treatment. If the disease is not treated quickly, it can lead to impaired kidney function and complications to other organs. Computerized Tomography Scan (CT Scan) with high resolution is used to scan the human body for disease diagnosis. The doctor will explain the diagnosis within a few days or one week. This research aims to create a prediction model for the classification of kidney stone disease through CT Scan images by applying the Convolutional Neural Network (CNN) method of DenseNet-121 architecture and deployment using Streamlit. The results of the model in this study with the application of CNN DenseNet-121 architecture are accuracy 98.18%, precision 96.36%, recall 100%, and F1-score 98.14%.
Sistem Penjadwalan Karyawan dengan Algoritma Genetika Fajarlestari, Maria Karmelia; Hardiyanti, Mawar
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

Employee scheduling is a complex problem in Human Resource Management (HRM) that significantly impacts operational efficiency. This study develops an employee scheduling system using a genetic algorithm. The employee schedules are constructed by considering scheduling rules and various components such as the number of days, shifts, employee quality, and scheduling requests. The genetic algorithm, proven effective in solving various optimization problems, is employed to generate optimal schedules through the processes of selection, crossover, and mutation. The results indicate that the genetic algorithm can effectively produce employee schedules, with fitness values indicating improved schedule quality as iterations increase. The findings of this study are anticipated to be useful in HRM, aiming to improve both employee efficiency and satisfaction.
Sentiment Analysis of DKI Jakarta 2024 Election (Case Study: Anies Baswedan and Ridwan Kamil) Safrudin, Muhammad Safrul; Aini, Syarifah
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

This study analyzes public sentiment toward two potential candidates for the 2024 Jakarta gubernatorial election, Anies Baswedan and Ridwan Kamil, using Twitter data. Applying the TextBlob model for text extraction and Naive Bayes for sentiment classification found that sentiment toward Anies Baswedan is mostly positive, 52.2%, while neutral sentiment dominates for Ridwan Kamil. The accuracy of the Naive Bayes model reached 80% for Anies Baswedan and 72% for Ridwan Kamil, with higher precision, recall, and F1-score for Anies' data. These results indicate that the model is more accurate in classifying sentiment toward Anies compared to Ridwan Kamil. The implications of these findings are important for political campaign strategies, where Anies can leverage the existing positive support, while Ridwan Kamil has an opportunity to strengthen public engagement through a more strategic approach, in line with the sentiment emerging on social media.

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