发布求助
文献互助 智能选刊 最新文献

JISKA (Jurnal Informatika Sunan Kalijaga)最新文献

筛选
英文 中文
Implementasi Load Balancing dengan HAProxy di Sistem Informasi Akademik UIN Sunan Kalijaga 在 UIN Sunan Kalijaga 学术信息系统中使用 HAProxy 实现负载平衡
JISKA (Jurnal Informatika Sunan Kalijaga) Pub Date : 2024-01-25 DOI: 10.14421/jiska.2024.9.1.39-49
A. Wirawan, Rahmadhan Gatra, H. Hidayat, Daru Prasetyawan
{"title":"Implementasi Load Balancing dengan HAProxy di Sistem Informasi Akademik UIN Sunan Kalijaga","authors":"A. Wirawan, Rahmadhan Gatra, H. Hidayat, Daru Prasetyawan","doi":"10.14421/jiska.2024.9.1.39-49","DOIUrl":"https://doi.org/10.14421/jiska.2024.9.1.39-49","url":null,"abstract":"Efficiently managing academic information systems (AIS) is essential for educational institutions to provide reliable services to students and faculty. This research explores the integration of HAProxy load balancing and file synchronization techniques to optimize the performance of AIS. HAProxy is employed to distribute incoming requests across multiple backend servers, and the backend will call web service to access the data saved in the database to facilitate seamless data sharing and access. Additionally, file synchronization mechanisms are implemented to maintain consistency across scripts used in the backend system. The study conducts performance evaluations and benchmarks to assess the impact of HAProxy load balancing and file synchronization on AIS responsiveness and reliability. The results reveal significant system scalability and fault tolerance improvements, reducing downtime and enhancing user experience. This research contributes to optimizing academic information systems, enhancing their ability to handle increased loads, and ensuring the efficient delivery of educational services.","PeriodicalId":518302,"journal":{"name":"JISKA (Jurnal Informatika Sunan Kalijaga)","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Improving Stock Price Prediction Accuracy with StacBi LSTM 利用 StacBi LSTM 提高股价预测精度
JISKA (Jurnal Informatika Sunan Kalijaga) Pub Date : 2024-01-25 DOI: 10.14421/jiska.2024.9.1.10-26
Mohammad Diqi, Hamzah Hamzah
{"title":"Improving Stock Price Prediction Accuracy with StacBi LSTM","authors":"Mohammad Diqi, Hamzah Hamzah","doi":"10.14421/jiska.2024.9.1.10-26","DOIUrl":"https://doi.org/10.14421/jiska.2024.9.1.10-26","url":null,"abstract":"This research aimed to enhance stock price prediction accuracy using the Stacked Bidirectional Long Short-Term Memory (StacBi LSTM) model. The study addressed the challenge of capturing long-term dependencies and temporal patterns inherent in stock price data. The research objectives were to evaluate the model's performance across different input sequence lengths and identify the optimal length for prediction. Leveraging a dataset from the Indonesian Stock Exchange, the model's predictions were evaluated using key metrics such as RMSE, MAE, MAPE, and R2. Results indicated that the StacBi LSTM model excelled in capturing stock price trends and demonstrated strengths over traditional methods. The optimal input sequence length was identified, balancing computational efficiency and prediction accuracy. This research contributes valuable insights into improving stock price prediction techniques and offers practical implications for traders and investors. Future research directions encompass hybrid models and integrating external factors to enhance predictive capabilities further.","PeriodicalId":518302,"journal":{"name":"JISKA (Jurnal Informatika Sunan Kalijaga)","volume":"337 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Prediksi Deteksi Penyakit Kanker Payudara dengan Menggunakan Algoritma Decision Tree 使用决策树算法进行乳腺癌疾病检测预测
JISKA (Jurnal Informatika Sunan Kalijaga) Pub Date : 2024-01-25 DOI: 10.14421/jiska.2024.9.1.70-78
Ayunie Mellina, S. Suhartono, M. A. Yaqin
{"title":"Prediksi Deteksi Penyakit Kanker Payudara dengan Menggunakan Algoritma Decision Tree","authors":"Ayunie Mellina, S. Suhartono, M. A. Yaqin","doi":"10.14421/jiska.2024.9.1.70-78","DOIUrl":"https://doi.org/10.14421/jiska.2024.9.1.70-78","url":null,"abstract":"Cancer is a deadly disease that is difficult to cure. Early cancer detection can be done through laboratory tests to identify the cancer type. Breast cancer is a type of cancer with initial symptoms in the form of a lump. Data mining and classification methods, such as decision trees with ID3 and C5.0 algorithms, are used to categorize breast cancer. The dataset used is Breast Cancer Coimbra, which was downloaded from UCI Machine Learning in 2018. ID3 has limitations in handling unstructured data and continuous attributes, while C5.0 is better. Both algorithms produce tree models with different levels of accuracy. This study shows that the C5.0 algorithm has the best classification results with 80% accuracy, 84.2% precision, 80% recall, and 80% F1 score. 80% accuracy shows the system's classification ability, so the C5.0 model can be used to predict breast cancer.","PeriodicalId":518302,"journal":{"name":"JISKA (Jurnal Informatika Sunan Kalijaga)","volume":"114 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Klasifikasi Buah dan Sayuran Segar atau Busuk Menggunakan Convolutional Neural Network 使用卷积神经网络对新鲜或腐烂水果和蔬菜进行分类
JISKA (Jurnal Informatika Sunan Kalijaga) Pub Date : 2024-01-25 DOI: 10.14421/jiska.2024.9.1.27-38
Eka Aenun Nisa Munfaati, Arita Witanti
{"title":"Klasifikasi Buah dan Sayuran Segar atau Busuk Menggunakan Convolutional Neural Network","authors":"Eka Aenun Nisa Munfaati, Arita Witanti","doi":"10.14421/jiska.2024.9.1.27-38","DOIUrl":"https://doi.org/10.14421/jiska.2024.9.1.27-38","url":null,"abstract":"Fresh fruits and vegetables contain many nutrients, such as minerals, vitamins, antioxidants, and beneficial fiber, superior to those found in rotten or almost rotten produce. On the other hand, fruits and vegetables that are nearly spoiled or already rotten have significantly lost their nutritional value. Rotten produce also harbors bacteria and fungi that can lead to infections and food poisoning when consumed. Convolutional Neural Network (CNN) offers a programmable solution for classifying fresh and rotten fruits and vegetables. Image processing using the TensorFlow library is employed in this classification process. During testing on the training data, the CNN achieved an accuracy of 90.42%. In comparison, the validation accuracy reached 94.21% when using the SGD optimizer, 20 epochs, a batch size 16, and a learning rate of 0.01. For the testing data, the accuracy obtained was 80.83%.","PeriodicalId":518302,"journal":{"name":"JISKA (Jurnal Informatika Sunan Kalijaga)","volume":"97 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信