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All Journal INTEGER: Journal of Information Technology Journal Of Artificial Intelligence And Software Engineering
Novitasari, Desy Candra
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Computer Science & IT

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Improving the Accuracy of COCOMO II in Software Projects Using Hybrid GWO-PSO Putri, Rahmi Rizkiana; Novitasari, Desy Candra
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7603

Abstract

Accurate business forecasting provides an important foundation for managing software projects effectively. If the business estimate is not accurate, it can have an impact on the quality of project management to become less efficient. It can be risky such as an excess budget, to failing to meet the set schedule. This research includes the hybrid Grey Wolf Optimization (GWO)-Particle Swarm Optimization (PSO) method to optimize the results of business estimation, thereby resulting in more valid and reliable business estimates of software projects. The implementation of the proposed method showed a Mean Magnitude Relative Error (MMRE) value of 321.16%, which is 1243.23% lower than the results of conventional COCOMO II. The results of the trial prove that the accuracy of business estimates has increased, thus making a significant contribution to improving the effectiveness of software project management. Thus, this study provides a more reliable COCOMO II business estimation framework that can be adopted by practitioners and researchers to improve the planning, control, and evaluation process of software projects.
Analisis Sentimen Cuitan X terhadap Pendaki Asing Gunung Rinjani Menggunakan Algoritma SVM dan Random Forest Novitasari, Desy Candra; Putri, Rahmi Rizkiana
INTEGER: Journal of Information Technology Vol 10, No 2 (2025): September
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.0.v10i2.8119

Abstract

Social media platforms have become significant sources of public opinion regarding various issues, including tourism activities. This study analyzes public sentiment towards foreign climbers on Mount Rinjani through X (Twitter) social media posts using Support Vector Machine (SVM) and Random Forest algorithms. Data collection employed web scraping techniques with six relevant keywords, resulting in 4,777 unique tweets after cleaning and duplicate removal. The lexicon-based labeling approach revealed an imbalanced distribution with 63.58% negative sentiment, 18.8% positive sentiment, and 17.63% neutral sentiment. To address class imbalance, Synthetic Minority Oversampling Technique (SMOTE) was applied, creating a balanced dataset of 9,111 samples. Text preprocessing included case folding, normalization, tokenization, stopword removal, and stemming using Indonesian language tools. Feature extraction utilized TF-IDF vectorization with parameters optimized for Indonesian text analysis. The dataset was split into 70% training, 15% validation, and 15% testing using stratified sampling. Evaluation results demonstrated that SVM achieved superior performance with 95.7% accuracy, 96% precision, 95.7% recall, and 95.7% F1-score, while Random Forest achieved 94.4% accuracy, 94.4% precision, 94.4% recall, and 94.4% F1-score. The dominance of negative sentiment indicates public concerns regarding foreign climbing activities that require stakeholder attention. This research contributes to sentiment analysis methodology for Indonesian social media text and provides practical insights for sustainable tourism management in Mount Rinjani National Park
Co-Authors Putri, Rahmi Rizkiana