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All Journal Sebatik IJISTECH (International Journal Of Information System & Technology) Building of Informatics, Technology and Science IJISTECH Bulletin of Information Technology (BIT) Jurnal Teknik Informatika
Ekawati, Hanifah
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Social Sciences

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Journal : Bulletin of Information Technology (BIT)

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Evaluation Of COCOMO Model Accuracy In Software Effort Estimation Jeklin, Umar; Ibnu Saad, Muhammad; ekawati, Hanifah
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2027

Abstract

Accurate effort estimation underpins on-time,on-budget software delivery. This study empirically assesses the baseline Constructive cost Model (COCOMO) by applying standard organic-mode parameters (a = 2.4, b = 1.05) to the COCOMONASA dataset, which contains 63 NASA projects ranging from 2 KLOC to 100 KLOC. Model ourputs are benchmarked against recorded person-month effort using Mean Absolute Error (MAE), Mean Magnitude of Relative Error (MMRE), and Predcitions at 25 percent error (PRED 0.25). Results show MAE values 295-661 person-months and an MMRE near 1.0, indicating average relative error of ~100 percent. PRED (0.25) equals 0.0, meaning no project is estimated within the industry-accepted 25% band. Sensitivity tests on 5- and 20-project subsets reveal similar patterns, confiriming that the inaccuracy is systemic rather than dataset-specific. Using uncalibrated COCOMO in present-day projects poses a high risk of severe under- or over allocation of resources, potentially trigerring budget overruns and schedule slips. By quantitatively exposing where and how the baseline model fails, this work provides a benchmark for and a roadmap toward-targeted parameter calibration and hybrid approaches that incorporate additional cost drivers or machine-learning techniques. Future research should explore automatic parameter tuning and context-aware hybrid models to achieve dependable effort estimation in contemporary software engineering.
Penerapan Algoritma XGBoost Dalam Prediksi Harga Sewa Kos Di Kota Samarinda Rahman, Amalia; Yusnita, Amelia; Ekawati, Hanifah
Bulletin of Information Technology (BIT) Vol 6 No 4 (2025): Desember 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i1.2304

Abstract

The population growth and increasing economic activity in Samarinda City have led to a rising demand for temporary housing such as boarding houses. However, rental price determination is still largely based on the owner’s intuition rather than objective factors such as available facilities, room specifications, transportation accessibility, and proximity to public amenities. This study aims to develop a rental price prediction model for boarding houses using the Extreme Gradient Boosting (XGBoost) algorithm with a Knowledge Discovery in Database (KDD) approach. The research data were collected through a web scraping process from the Mamikos platform, yielding 231 initial records, which were then cleaned and filtered for outliers, resulting in 225 valid data points. Five main features derived from feature engineering were utilized in the model, namely Facility Score, Combined Specification Score, Nearest Place Score, Transportation Score, and Rental System Score. The evaluation results show that the XGBoost model achieved a Mean Absolute Error (MAE) of Rp348,822, a Root Mean Squared Error (RMSE) of Rp416,139, and a coefficient of determination (R²) of 0.612. These values indicate that the model can explain 61.2% of the variation in rental prices with reasonably good predictive performance. The feature importance analysis reveals that Facility Score and Combined Specification Score are the most influential factors affecting rental prices, while transportation and rental system factors contribute less significantly. This study is expected to serve as a reference for boarding house owners, tenants, and policymakers in determining more objective and competitive rental prices based on a data mining approach.
Co-Authors Amelia Yusnita Azahari, Azahari Cembes, Yosefina Harianto, Kusno Hendri Setiawan M. Irwan Ukkas Irwan Ukkas Ukkas Muhammad Ibnu Saad Saad Pratiwi, Heny Rahman, Amalia Riandi, Tendi Salmon Salmon, Salmon Siti Lailiyah, Siti Yunita Yunita