Pub Date : 2024-01-25DOI: 10.14421/jiska.2024.9.1.39-49
A. Wirawan, Rahmadhan Gatra, H. Hidayat, Daru Prasetyawan
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.
{"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}
Pub Date : 2024-01-25DOI: 10.14421/jiska.2024.9.1.10-26
Mohammad Diqi, Hamzah Hamzah
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.
{"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}
Pub Date : 2024-01-25DOI: 10.14421/jiska.2024.9.1.70-78
Ayunie Mellina, S. Suhartono, M. A. Yaqin
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.
{"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}
Pub Date : 2024-01-25DOI: 10.14421/jiska.2024.9.1.27-38
Eka Aenun Nisa Munfaati, Arita Witanti
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%.
{"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}
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