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All Journal JISA (Jurnal Informatika dan Sains) Sistem Pendukung Keputusan dengan Aplikasi
Salih, Azar Abid
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Meningkatkan Administrasi Bisnis Melalui Sistem Pendukung Keputusan: Tinjauan Komprehensif Zangana, Hewa Majeed; Salih, Azar Abid
Sistem Pendukung Keputusan dengan Aplikasi Vol 4 No 2 (2025)
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/spk.v4i2.1138

Abstract

Decision Support Systems (DSS) are critical tools in modern business administration, aiding in data analysis, decision-making, and strategic planning, the evolution of DSS has been driven by advancements in technology, increasing the complexity and volume of data businesses handle, understanding the impact of DSS on business processes and outcomes is essential for leveraging their full potential. To review and synthesize existing research on the impact of Decision Support Systems on business administration, and to identify key benefits, challenges, and best practices associated with the implementation and use of DSS in business settings. Conducted a comprehensive literature review of academic journals, industry reports, and case studies on DSS in business administration, also analyzed data from studies focusing on different aspects of DSS, including implementation strategies, technological advancements, and their effects on decision-making processes. DSS significantly improve decision-making efficiency and accuracy by providing timely and relevant information, successful implementation of DSS is associated with enhanced strategic planning, better resource allocation, and improved overall business performance, common challenges include high implementation costs, complexity of integration with existing systems, and the need for ongoing user training and support. Decision Support Systems play a pivotal role in enhancing business administration by transforming data into actionable insights. Businesses that effectively implement and utilize DSS can achieve competitive advantages through improved decision-making capabilities. Future research should focus on addressing the challenges of DSS implementation and exploring emerging technologies that can further enhance their effectiveness
Improved Lung Disease Classification Using Bagging and Averaged Ensemble Models Tayib, Hana Ali; Salih, Azar Abid
JISA(Jurnal Informatika dan Sains) Vol 8, No 2 (2025): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v8i2.2440

Abstract

One of the essential medical imaging tasks for early diagnosis and treatment planning is categorizing lung diseases from chest X-ray (CXR) images. This work constructs a strong ensemble learning platform on a variety of deep models for boosting diagnosis performance to detect and identify lung disease. Three prtrianed CNN models InceptionV3, ResNet50, and EfficientNetV2M were trained on a CXR dataset, motivated by the complementary architectural features and the success demonstrated in medical imaging problems, such as chest X-rays. These three networks belong to different families of the CNNs and therefore make different contributions for diversity and stability in the ensemble. The models were then ensembled in two methods: averaging (soft voting) and bagging with hard voting (maximum bootstrap aggregation) in the first method. Various sets of pre-trained models were experimented with for the averaged ensemble. According to experimental results, the soft voting (averaged) ensemble between EfficientNetV2M and InceptionV3 performed better than the other models' combinations and achieved the highest accuracy of 93.00% in classification. This was followed by the combination of EfficientNetV2M and ResNet50 with an accuracy of 92.09%, then InceptionV3 and ResNet50 with a value of 91.75%, and the complete ensemble of the three models with an accuracy of 92.14%.The bagging hard voting strategy was somewhat with lower accuracy, but the InceptionV3 based bagging ensemble attained 90.56%, EfficientNetV2M attained 91.00%, and ResNet50 attained 88.00%. It is evident from the results that soft voting strategy, InceptionV3 and EfficientNetV2M ensemble provides the best optimal and stable classification performance among all the configurations that were attempted. The study proves that ensemble learning improves the accuracy of lung disease classification models, and choosing the right architectures is essential, with EfficientNetV2M and InceptionV3 showing improved performance, resulting in early diagnosis and improved patient outcomes.
Co-Authors Tayib, Hana Ali Zangana, Hewa Majeed