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All Journal International Journal Software Engineering and Computer Science (IJSECS) Journal of Tourism Sciences, Technology and Industry
Niarman, Abdurrahman
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Arts Computer Science & IT Decision Sciences, Operations Research & Management Environmental Science Languange, Linguistic, Communication & Media Social Sciences

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Comparative Analysis of PHP Frameworks for Development of Academic Information System Using Load and Stress Testing Niarman, Abdurrahman; Iswandi; Candri, Argi Kartika
International Journal Software Engineering and Computer Science (IJSECS) Vol. 3 No. 3 (2023): DECEMBER 2023
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v3i3.1850

Abstract

There are many programming languages available for the developers to pick when they want to make a web services project such as Java JSP, Python, ASP.Net, PHP and many others. The reason that writer chose PHP as its research topic is that PHP is still being one of the most promising languages especially for developing web services. Several PHP Frameworks are now available on the internet promising that their framework can do better than others. Some popular PHP frameworks are Laravel, Symfony, CakePHP, Yii, and CodeIgniter. Performance is the most aspect to be concerned because of the market’s demands entailing them to do so. The next concern that developer would likely need to decide is whether to go with Pure PHP or to go with PHP Framework. To answer those concerns, this research has been conducted in which a module is taken from an academic information system and will be developed into three similar and equivalent web applications by using Laravel, CodeIgniter, and Pure PHP. The author's aim in conducting this research is to obtain more in-depth information regarding the use of the pure PHP programming language and several frameworks such as CodeIgniter and Laravel. There are many statements stating that using the PHP programming language is no longer feasible, even though in reality using PHP for developing web-based applications is still the choice of many programmers based on statistics from w3tech. The results of research conducted by the author show that the PHP programming language is still very reliable in handling load and stress tests. The pure PHP programming language provides slightly better performance results than the CodeIgniter and Laravel frameworks. Hence, the use of pure PHP or frameworks can be determined from the level of needs of the application development to be created.
Classification of Chronic Kidney Disease based on health care records using machine learning with Support Vector Machine Niarman, Abdurrahman; iswandi, iswandi; Amuharnis, Amuharnis
Journal of Tourism Sciences, Technology and Industry Vol 2, No 2 (2023): JTSTI-Journal Of Tourism Science, Technology and Industry
Publisher : Institut Seni Indonesia Padangpanjang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26887/jtsti.v2i2.4080

Abstract

Chronic Kidney Disease (CKD) is a global health concern with a rising prevalence that necessitates early and accurate diagnosis for effective management. This study proposes the application of Machine Learning (ML), specifically Support Vector Machine (SVM), to classify CKD based on health care records. Leveraging a comprehensive dataset of patient health records, including clinical and demographic information, the research aims to develop a predictive model that can assist in the timely identification of individuals at risk of CKD. The methodology involves preprocessing the health care records, extracting relevant features, and implementing the SVM algorithm for classification. The dataset is divided into training and testing sets to evaluate the model's performance.  The SVM classification model that was developed after going through the data preprocessing process produced results that were good enough to be able to classify whether a patient was diagnosed with CKD or not with an accuracy level of 98% and a total of 400 lines of data and 25 features.
Co-Authors Amuharnis Candri, Argi Kartika Iswandi Iswandi Iswandi