Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Infotronik : Jurnal Teknologi Informasi dan Elektronika International Journal of Advances in Data and Information Systems
Zahra, Nabila Athifah
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
EVALUASI KESIAPAN IMPLEMENTASI E-LEARNING UPN VETERAN JAWA TIMUR : METODE E-LEARNING READINESS Zahra, Nabila Athifah; Safitri, Eristya Maya; Al Arsya, Fadiyah Dhara; Barmin, Aidah Maryam; Amanda, Ardina Sagita
Jurnal Teknologi Informasi dan Elektronika (INFOTRONIK) Vol 8 No 2 (2023): Vol 8 No 2 Tahun 2023
Publisher : Universitas Sangga Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32897/infotronik.2023.8.2.2268

Abstract

Electronic Learning atau E-learning merupakan salah satu infrastruktur elektronik utama di perguruan tinggi. Electronic Learning ini memiliki kegunaan sebagai media pembelajaran bagi mahasiswa dan dosen. Dengan ini penelitian bertujuan untuk menganalisis implementasi infrastruktur e-bisnis pada E-Learning UPN Veteran Jawa Timur dengan tujuan untuk mengukur sejauh mana kebermanfaatan dari penerapan infrastruktur e-bisnis. Metode penelitian yang digunakan dalam penelitian ini adalah menggunakan metode kuantitatif dengan penyebaran kuesioner yang disebarkan ke-30 responden yang merupakan mahasiswa UPN Veteran Jatim. Dari hasil kuesioner tersebut berupa skala likert yang akan dihitung menggunakan metode E-learning Readiness. Berdasarkan hasil analisis tersebut diperoleh skor sebesar 3.69. Skor terendah berada pada faktor technology yaitu sebesar 3.39 dan skor tertinggi berada pada factor innovation yaitu sebesar 4.02. Skor tersebut menginterpretasikan bahwa E-learning sudah siap digunakan tetapi masih memerlukan perubahan. Dengan mengetahui skor E-Learning Readiness, maka dapat digunakan sebagai pertimbangan perbaikan infrastruktur E-Learning
Predicting Software Defects at Package Level in Java Project Using Stacking of Ensemble Learning Approach Zahra, Nabila Athifah; Arifiyanti, Amalia Anjani; Kartika, Dhian Satria Yudha
International Journal of Advances in Data and Information Systems Vol. 6 No. 1 (2025): April 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i1.1368

Abstract

Compared to manual and automated testing, AI-driven testing provides a more intelligent approach by enabling earlier prediction of software defects and improving testing efficiency. This research focuses on predicting software defects by analyzing CK software metrics using classification algorithms. A total of 8924 data points were collected from five open-source Java projects on GitHub. Due to class imbalance, undersampling was applied during preprocessing along with data cleaning and normalization. The final dataset consists of 1314 instances (746 clean and 568 buggy). The predictive model is developed in two stages: base learner (level-0) using AdaBoost, Random Forest (RF), Extra Trees (ET), Gradient Boosting (GB), Histogram-based Gradient Boosting (HGB), XGBoost (XGB), and CatBoost (CAT) algorithms, and meta-learner (level-1) that optimizes the results using ensemble stacking techniques. The stacking model achieved an ROC-AUC score of 0.8575, outperforming all individual classifiers and effectively distinguishing defective from non-defective software components. The comparison of performance improvements between the base model (tree-based ensemble) and stacking was statistically validated using paired t-tests. All p-values were below 0.05, confirming the significance of Stacking’s superior performance, with the largest gain observed against Gradient Boosting (+0.0411, p = 0.0030). The confusion matrix of stacking model is the most optimal model because it has high of True Positive and True Negative, while  False Positive and False Negative values are relatively low. These findings affirm that ensemble stacking yields a more robust and balanced classification system, enhancing defect prediction accuracy and enabling earlier issue detection in the Software Development Life Cycle (SDLC). 
Co-Authors Al Arsya, Fadiyah Dhara Amalia Anjani Arifiyanti Amanda, Ardina Sagita Barmin, Aidah Maryam Dhian Satria Yudha Kartika Safitri, Eristya Maya