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Journal of Software Engineering Ampera
Published by Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer
ISSN : -     EISSN : 27752488     DOI : https://doi.org/10.51519
Core Subject : Science,
Computer Science & IT
Journal of Software Engineering Ampera (Journal-SEA) is an online journal that organized and managed independently by the consortium of informatics lecturers. Journal-SEA is an open-access journal that is provided for researchers, lecturers, and students who will publish research results in the field of all thing about software engineering and its process.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 52 Documents
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Analysis of the Effectiveness of User Manual Book in Supporting Application Usage at PT KPI RU 5 - Balikpapan Ahmad Rifa'i; Ahmad Luthfi
Journal of Software Engineering Ampera Vol. 5 No. 1 (2025): Journal of Software Engineering Ampera
Publisher : APTIKOM SUMSEL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalsea.v5i1.565

Abstract

In the rapid digitalization era, the User Manual Book (UMB) becomes an essential tool in supporting the use of applications in the industrial environment. This study aims to analyze the effectiveness of the UMB developed during the internship at PT Kilang Pertamina International Refinery Unit V Balikpapan, focusing on enhancing user application usage. The research methods include data collection through user surveys and quantitative and qualitative analysis to measure the UMB's effectiveness. The results indicate that a well-developed UMB significantly improves user efficiency and satisfaction in daily operations. Additionally, the study identifies several areas requiring improvement, including clarity of instructions and information relevance. Recommendations for further improvement of the UMB are presented to support more effective information technology implementation in the future. This study contributes to a better understanding of effective UMB design in the oil and gas industry context.
Risk Factors to Diagnosis A Data-Driven Model for Lung Cancer Prediction using BES-DT Firza Septian; Muhammad Sulkhan Nurfatih
Journal of Software Engineering Ampera Vol. 7 No. 2 (2026): Vol. 7 No. 2 (2026): Journal of Software Engineering Ampera
Publisher : APTIKOM SUMSEL

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Lung cancer remains one of the most critical health challenges worldwide, requiring accurate and interpretable predictive models to support early diagnosis. This study proposes a novel framework by integrating the Bald Eagle Search (BES) optimization algorithm with the Decision Tree (DT) classifier, forming the BES-DT model. The dataset, consisting of demographic, behavioral, and clinical risk factors, was preprocessed and optimized using BES to select the most informative features while reducing redundancy. BES, inspired by the hunting behavior of bald eagles, balances exploration and exploitation to identify optimal solutions, enabling DT to construct transparent decision rules for clinical interpretation. Experimental evaluation demonstrated that BES-DT achieved superior performance compared to the baseline DT, with accuracy of 92.6%, precision of 98.8%, recall of 93.0%, F1-score of 95.8%, and ROC-AUC of 0.90, confirming strong discriminative ability. These findings highlight that BES-DT not only improves predictive accuracy but also maintains interpretability, offering a balanced solution for medical decision support. In conclusion, BES-DT shows significant potential in advancing early detection strategies for lung cancer.

Page 6 of 6 | Total Record : 52

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