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All Journal Jurnal Ilmiah Kursor Scientific Journal of Informatics
Subroto, Eka Mira Novita
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Computer Science & IT Decision Sciences, Operations Research & Management

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Pengujian Black Box pada Aplikasi E-Commerce OpenCart dengan Metode Equivalence Partitioning dan Boundary Value Analysis Pratama, Fairus In'am; Subroto, Eka Mira Novita; Haira, Radina Mutia; Yaqin, Muhammad Ainul
Jurnal Ilmiah Informatika Vol. 8 No. 1 (2023): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v8i1.54-64

Abstract

E-commerce applications have become a significant trend in modern trade. One popular open-source e-commerce application is OpenCart. Testing the application is paramount to ensure that OpenCart functions appropriately and meets the required quality and performance. This study aims to conduct Black Box testing on the OpenCart e-commerce platform using the Equivalence Partitioning (EP) and Boundary Value Analysis (BVA) methods. The testing is carried out to ensure the application's functionality directly relates to user satisfaction and trust. EP divides input data into partitions based on valid and invalid values, while BVA tests input values based on specific boundaries. The focus of the testing is on the checkout feature, which is an essential component of any e-commerce application. The testing flow follows the Software Testing Life Cycle (STLC) approach. The test results show a 57.14% level of conformity using EP and 100% using BVA. Based on these two results, the overall conformity level is 78.57%. The testing results indicate that the system performs well within the tested boundary values but may require improvements in the combination of partition usage.STLC (Software Testing Life Cycle). Hasil pengujian menunjukkan tingkat kesesuaian sebanyak 57,14% pada EP dan 100% pada BVA. Berdasarkan dua hasil tersebut, didapatkan tingkat kesesuaian sebesar 78,57%. Hasil pengujian menunjukkan jika sistem dapat berjalan dengan baik pada nilai-nilai batasan yang diuji, tetapi masih perlu perbaikan pada kombinasi penggunaan antara partisi.
A Multi-label book genre classification: Comparison of machine learning techniques and problem transformation methods Subroto, Eka Mira Novita; Faisal, Muhammad
Jurnal Ilmiah Kursor Vol. 13 No. 2 (2025)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i2.389

Abstract

Books play an essential role in life as a source of knowledge and information. The increasing number of books published makes classification more complex, especially in a multi-label context where a book may belong to more than one genre. Furthermore, automatic classification of book genres is required due to the transition of books to e-book and audiobook formats. This research analyzes the application of machine learning techniques using Support Vector Machine (SVM), Logistic Regression (LR), and Multinomial Naive Bayes (MNB) for multi-label book genre classification by comparing their performance through stemming and unstemming process in text preprocessing with TF-IDF and K-Fold cross-validation (k = 10). In addition, two problem transformation methods, Binary Relevance (BR) and Label Powerset (LP), are evaluated. The results show that SVM combined with stemming outperforms other models across all metrics of accuracy, precision, recall, and F1-score. SVM is effective in handling complex and imbalanced data distributions, resulting in more accurate and consistent predictions. The stemming process positively contributes by reducing word variation and allowing the model to focus on word meanings. Among problem transformation methods, LP yields better results because it can capture relationships between labels more effectively than BR.
Co-Authors Haira, Radina Mutia Ida Ayu Putu Sri Widnyani Muhammad Faisal Pratama, Fairus In'am