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APLIKASI MONITORING DATA KETERSEDIAAN SOAL BERBASIS WEB PADA SITUS TANYA JAWAB BRAINLY Prasetyo, Moch. Adji; Bayu Saputra, Andika; Priadana, Adri; Syahruddin, Fajar
Jurnal Teknomatika Vol 14 No 1 (2021): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v14i1.1134

Brainly is a website that allows users to ask each other and answer questions related to school lessons openly to other users. On the site, you must first create an account as a questioner or answerer. Brainly harness the power Freelance to answer questions on the Brainly website. In working on the questions carried out by the Brainly Freelancer, there are often delays in updating questions at the Uniform Resource address Locator (URL) assigned to Freelancers. This results in Freelancers often being hampered in their work meet the target of working on the questions because the questions that have been answered are still not replaced with a new question. Therefore, the researcher designed and built an Application for Monitoring Data Availability of Web-Based Questions on the Tanya Site Answer on the Brainly site which aims to make it easier for Brainly Freelancers to meet their target for working on questions. This application is built using the Python programming language by utilizing the Flask framework. The results of this study state that the process contained in the application has been running smoothly as evidenced by the results of black box testing. User testing is done with Brainly Freelancers opening the application and viewing the availability of unanswered questions on the Brainly URL with a table view.

Implementation of Web Scraping to Build a Web-Based Instagram Account Data Downloader Application Himawan, Arif; Priadana, Adri; Murdiyanto, Aris
IJID (International Journal on Informatics for Development) Vol. 9 No. 2 (2020): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2020.09201

Instagram has been used by many groups, such as business people, academics, to politicians, to take advantage of the insights gained by processing and analyzing Instagram data for various purposes. However, before processing and analyzing data, users must first pass data collection or downloading from Instagram. The problem faced is that most data collection methods are still done manually as for many parties that offer Instagram account data download services with various price options. This research applied a web scraping method to automatically build a web-based Instagram account data download application so that several parties can use it. The web scraping method was chosen because by using this method, researchers do not need to use Instagram's Application Programming Interface (API), which has access restrictions in retrieving data on Instagram. In this study, application testing was conducted on 15 Instagram accounts with various publications, namely between 100 and 11000. Based on the download data analysis results, the application of the web scraping method to download Instagram account data can successfully download a maximum of 2412 account data. In this application, users can download Instagram account data to Data Collection and then manage it like deleting and exporting data collection in the form of CSV, Excel, or JSON.

Online Integrated Development Environment (IDE) in Supporting Computer Programming Learning Process during COVID-19 Pandemic: A Comparative Analysis Kusumaningtyas, Kartikadyota; Nugroho, Eko Dwi; Priadana, Adri
IJID (International Journal on Informatics for Development) Vol. 9 No. 2 (2020): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2020.09202

COVID-19 has spread to various countries and affected many sectors, including education. New challenges arise in universities with study programs related to computer programming, which require a lot of practice. Difficulties encountered when students should setting up the environment needed to carry out programming practices. Furthermore, they should install a text editor called Integrated Development Environment (IDE) to support it. There is various online IDE that supports computer programming. However, students must have an internet connection to use it. After all, many students cannot afford to buy internet quotas to access online learning material during the COVID-19 pandemic. According to these problems, this study compares several online IDEs based on internet data usage and the necessary supporting libraries' availability. In this study, we only compared eleven online IDEs that support the Python programming language, free to access, and do not require logging in. Based on the comparative analysis, three online IDEs have most libraries supported. They are REPL.IT, CODECHEF, and IDEONE. Based on internet data usage, REPL.IT is an online IDE that requires the least transferred data. Moreover, this online IDE also has a user-friendly interface to place the left and right sides' code and output positions. It prevents the user from scrolling to see the results of the code that has been executed. The absence of advertisements also makes this online IDE a more focused appearance. Therefore, REPL.IT is highly recommended for users who have a limited internet quota, primarily to support the learning phase of computer programming during the COVID-19 pandemic.

Implementation of Web Scraping to Build a Web-Based Instagram Account Data Downloader Application Himawan, Arif; Priadana, Adri; Murdiyanto, Aris
IJID (International Journal on Informatics for Development) Vol. 9 No. 2 (2020): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2020.09201

Instagram has been used by many groups, such as business people, academics, to politicians, to take advantage of the insights gained by processing and analyzing Instagram data for various purposes. However, before processing and analyzing data, users must first pass data collection or downloading from Instagram. The problem faced is that most data collection methods are still done manually as for many parties that offer Instagram account data download services with various price options. This research applied a web scraping method to automatically build a web-based Instagram account data download application so that several parties can use it. The web scraping method was chosen because by using this method, researchers do not need to use Instagram's Application Programming Interface (API), which has access restrictions in retrieving data on Instagram. In this study, application testing was conducted on 15 Instagram accounts with various publications, namely between 100 and 11000. Based on the download data analysis results, the application of the web scraping method to download Instagram account data can successfully download a maximum of 2412 account data. In this application, users can download Instagram account data to Data Collection and then manage it like deleting and exporting data collection in the form of CSV, Excel, or JSON.

Online Integrated Development Environment (IDE) in Supporting Computer Programming Learning Process during COVID-19 Pandemic: A Comparative Analysis Kusumaningtyas, Kartikadyota; Nugroho, Eko Dwi; Priadana, Adri
IJID (International Journal on Informatics for Development) Vol. 9 No. 2 (2020): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2020.09202

COVID-19 has spread to various countries and affected many sectors, including education. New challenges arise in universities with study programs related to computer programming, which require a lot of practice. Difficulties encountered when students should setting up the environment needed to carry out programming practices. Furthermore, they should install a text editor called Integrated Development Environment (IDE) to support it. There is various online IDE that supports computer programming. However, students must have an internet connection to use it. After all, many students cannot afford to buy internet quotas to access online learning material during the COVID-19 pandemic. According to these problems, this study compares several online IDEs based on internet data usage and the necessary supporting libraries' availability. In this study, we only compared eleven online IDEs that support the Python programming language, free to access, and do not require logging in. Based on the comparative analysis, three online IDEs have most libraries supported. They are REPL.IT, CODECHEF, and IDEONE. Based on internet data usage, REPL.IT is an online IDE that requires the least transferred data. Moreover, this online IDE also has a user-friendly interface to place the left and right sides' code and output positions. It prevents the user from scrolling to see the results of the code that has been executed. The absence of advertisements also makes this online IDE a more focused appearance. Therefore, REPL.IT is highly recommended for users who have a limited internet quota, primarily to support the learning phase of computer programming during the COVID-19 pandemic.

Gender-Aware Prediction of Liver Disease Using Machine Learning and Clinical Laboratory Data Umar Zaky; Muhammad Habibi; Adri Priadana; Thomas Edyson Tarigan
International Journal of Artificial Intelligence in Medical Issues Vol. 4 No. 1 (2026): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/wtsdw234

Liver disease is a major health problem that may progress silently and lead to severe clinical complications if not detected early. Machine learning offers a promising approach for supporting early screening by identifying predictive patterns from clinical and biochemical patient data. This study developed an explainable gender-aware machine learning framework for liver disease prediction using demographic information and clinical biomarkers. The dataset consisted of 570 patient records after duplicate removal, including age, gender, total bilirubin, direct bilirubin, alkaline phosphatase, SGPT, SGOT, total protein, albumin, albumin/globulin ratio, and liver disease status. Several machine learning algorithms were evaluated under three experimental scenarios: original data, class-weighted learning, and SMOTENC-based oversampling. Model performance was assessed using accuracy, precision, recall, specificity, F1-score, and ROC-AUC. The experimental results showed that Gradient Boosting combined with SMOTENC achieved the best F1-score, with an accuracy of 0.7632, precision of 0.7935, recall of 0.9012, specificity of 0.4242, F1-score of 0.8439, and ROC-AUC of 0.7759. The model correctly identified 73 of 81 liver disease cases in the testing set, indicating strong sensitivity for early screening. Gender-based evaluation showed comparable F1-scores for male and female patients, with values of 0.8430 and 0.8462, respectively. Feature importance analysis identified SGOT, alkaline phosphatase, age, and direct bilirubin as the most influential predictors. These findings suggest that an explainable and gender-aware machine learning approach can support liver disease risk prediction using routinely available clinical biomarkers, although further validation using larger and more balanced datasets is required

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