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All Journal The Indonesian Journal of Computer Science Jurnal Komtika (Komputasi dan Informatika)
Adriani, Zahrina Aulia
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Computer Science & IT Electrical & Electronics Engineering Engineering

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A Comprehensive Examination of Risk Management Practices Throughout the Software Development Life Cycle (SDLC): A Systematic Literature Review Adriani, Zahrina Aulia; Teguh Raharjo; Ni Wayan Trisnawaty
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4016

Abstract

Risk management in the software development lifecycle (SDLC) is a continuous process that addresses risks throughout a system's lifecycle, including acquisition, development, maintenance, or operation. Despite its importance, ineffective risk management practices can lead to project failures, impacting organizations financially and reputationally. Therefore, there is a need for a systematic understanding of risk management practices in SDLC. This study conducts a Systematic Literature Review (SLR) related to risk management activities performed by previous research during the SDLC. The SLR method combines Kitchenham with the toll-gate method to select literature for use. This SLR aims to investigate activities in traditional waterfall and agile development processes, which will be mapped into risk management activities in SDLC according to ISO 16085:202. Additionally, the review highlights the challenges encountered in implementing risk management in the SDLC process, including project complexity, adherence to policies and standards, lack of communication, lack of resources, and organizational culture.
Prediction of University Student Performance Based on Tracer Study Dataset Using Artificial Neural Network Adriani, Zahrina Aulia; Palupi, Irma
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 2 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i2.5901

Abstract

In order to increase student performance, several universities use machine learning to analyze and evaluate their data so that it enables to improve the quality of education in the university. To get a new insight from the tracer study dataset as the relevance between university performance and student capability with business and industries work, the author will develop a model to predict student performance based on the tracer study dataset using Artificial Neural Network (ANN). For obtaining attributes that correspond to labels, Phi Coefficient Correlation will be used to select the attributes with high correlation as Feature Selection. The author is also performing the oversampling method using Synthetic Minority Oversampling Technique (SMOTE) because this dataset is imbalanced and evaluates the model using K-Fold Cross-Validation. According to K-Fold Cross Validation, the result shows that K = 3 has a low standard deviation of evaluation score and it's the best candidate of K to split the dataset. The average standard deviation is 0.038 for all score evaluations (Accuracy, Precision, Recall, and F-1 Score). After applied SMOTE to treating the imbalanced dataset with the data splitting 65 training data and 35 testing data, the accuracy value increases by 10% from 0.77 to 0.87.
Co-Authors Irma Palupi Ni Wayan Trisnawaty Teguh Raharjo