JTRISTE

Analisis User Interface Pada Aplikasi Potio Dengan Metode Heuristic Evaluation

Justin Edbert, Arianti, Renny — Tue, 31 Mar 2026 00:00:00 +0800

Potio is a mobile-based reminder application that can help its users to take vitamins or medicines consistently. This study aims to analyze the User Interface of the Potio application, because the application UI design is limited only by the developer, without involving the application users. The analysis was conducted to determine whether improvements are necessary or not. In this case, the need for improvements is determined from the results of the calculation of questionnaire data for each Heuristic Evaluation principle that has been submitted to expert respondents. From the calculation results using the Severity Rating formula, there are two principles that have a fairly high average Severity Rating value, namely the Help Users Recognize and Recover from Errors principle and the Help and Documentation principle. This second principle has a rounded value of 1, where a value of 1 means that improvements are not really necessary because the problems found do not affect user comfort.

Pengujian White Box Basis Path Testing Fitur Menghitung Aplikasi ComfyLearn

Michelle Valene Tanod, Husni Angriani, Afifah — Tue, 31 Mar 2026 00:00:00 +0800

This research aims to test the learning features in the ComfyLearn application using the white box testing method with the basis path technique. ComfyLearn is an Android-based educational application aimed at young children in learning while playing. To ensure the quality and reliability of applications, systematic software testing is required. This research applies the white box testing method with the basis path testing technique to test the program code for the learning counting feature, because this method is able to evaluate the program logic as a whole. This technique includes making flowcharts, flowgraphs, graph matrices, calculating cyclomatic complexity, determining independent paths, and preparing test cases. Based on the calculation results, it shows that the counting feature has a cyclomatic complexity value of 5, including the low category. This means that the complexity of the program logic is not too high so there is relatively little potential for errors to be found. There are 5 independent paths that need to be tested. Testing is carried out through unit testing and manual testing to verify whether the learning process has run as expected. The test results show that the program code in the learning counting feature functions well and meets expectations after making improvements. Thus, it can be concluded that the code structure in this feature section is quite reliable and can be used as a basis for further development.

Sistem Pakar Troubleshooting Kerusakan Hardware Komputer Berbasis Web Dengan Metode Forward Chaining Pada Laboratorium STMIK Kharisma Makassar

Faisal T Supu, Sofyan S. Thayf, Marlina — Tue, 31 Mar 2026 00:00:00 +0800

This research aims to develop a web-based expert system designed to assist in troubleshooting computer hardware issues at the STMIK Kharisma Makassar Laboratory. The system employs a forward chaining method to diagnose problems based on symptoms reported by users. The research methodology utilized is R&D with a descriptive and quantitative approach. Test results indicate that this system can deliver accurate diagnoses and relevant solutions, thereby enhancing efficiency in addressing hardware damage issues.

A Automated Brain Tumor Classification using Deep Convolutional and Transfer Learning

Vinodkumar VIN, Archana S. Vaidya, Manisha S. Patil — Wed, 13 May 2026 00:00:00 +0800

Brain cancers are some of the fastest growing and most deadly types of neurological disease. Early detection with accuracy is very important to improve survival of patients. Manually reading MRI scans is a slow process. It requires special skills and can differ from one observer to another. It is in this context that the automatic computer-aided diagnosis has emerged as a vital research area. In this work we use deep learning based methods for classified various types of brain tumors using MRI. We developed a baseline convolutional neural network and compared it with four transfer-learning models: MobileNetV2, VGG16, VGG19, and ResNet50V2. To ensure data diversity and robustness, we merged two publicly available MRI tumor datasets and normalized, balanced, and pre-processed the data to a constant 224 × 224 pixel size for each image of the four categories: glioma, meningioma, pituitary tumor, and no tumor. The experimental results show that transfer learning performs significantly superior to the CNN baseline. ResNet50V2 became highly effective provided 97.2% accuracy, high precision, and excellent recall. These findings demonstrate that combining pre-trained neural networks with integrated datasets can provide better result, scalable framework for automated brain tumor identification.