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All Journal Jurnal Pengabdian Masyarakat Tapis Berseri Bulletin of Intelligent Machines and Algorithms
Anggi Dewi Nurcahyani
Universitas Informatika dan Bisnis Indonesia
Computer Science & IT Education

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Kolaborasi Kreatif Manusia dan Ai Untuk Generasi Masa Depan R. Yadi Rakhman Alamsyah; Reni Nursyanti; Anggi Dewi Nurcahyani
Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) Vol. 5 No. 1 (2026): Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) (Edition April)
Publisher : Pusat Studi Teknologi Informasi Fakultas Ilmu Komputer Universitas Bandar Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jpmtb.v5i1.181

Abstract

Digital transformation is currently dominated by developments in Artificial Intelligence (AI), which are bringing fundamental changes to the creative industry. For today’s youth, particularly vocational high school (SMK) students, mastering AI is no longer just an option but a necessity to maintain relevance and competence in the future.This Community Service Program (PkM) aims to equip students of SMK Bakti Nusantara 666 with a deep understanding of the creative collaboration between humans and AI. Through methods including counseling, practical mentoring in prompt engineering techniques, and evaluations via pre-tests and post-tests, this program targets an 80% increase in participants' digital literacy.The primary focus of this activity is to position AI as an 'intelligent assistant' that expands the imagination without eliminating the originality of human ideas, while consistently upholding ethics and copyright. The results of this program are expected to produce digital talents who are innovative, responsible, and ready to contribute to the creative economy.
Explainable Machine Learning For Early HIV Detection Using Extra Trees and SHAP Algorithms Anggi Dewi Nurcahyani; Ratu Dika Ratu Anisa; Nayla Nurul Azkiya
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 2 (2026): BIMA January 2026 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i2.8

Abstract

Human Immunodeficiency Virus (HIV) remains a global health challenge that requires accurate and reliable early detection approaches. The use of machine learning offers potential in classifying HIV status based on clinical, demographic, and behavioral data. However, the limitations of interpretability in black-box models are an obstacle to clinical application. This study proposes an Explainable Machine Learning approach for early HIV detection by integrating the Extra Trees algorithm and the Shapley Additive exPlanations (SHAP) method. The model was developed using an HIV dataset obtained from the Kaggle platform and processed through standard data preprocessing stages without class balancing. Performance evaluation was conducted using classification metrics, confusion matrices, and learning curves to assess accuracy and learning stability. The results of the experiment show that the Extra Trees model achieved 88% accuracy with strong generalization. SHAP and mean absolute SHAP analyses revealed the dominant features that contributed to the prediction of HIV status consistently at the global and local levels. These findings show that integrating Extra Trees and SHAP produces an HIV early-detection model that is not only competitive in performance but also transparent and clinically relevant, potentially supporting the development of reliable artificial intelligence-based medical decision support systems.
Co-Authors Nayla Nurul Azkiya Nursyanti, Reni R. Yadi Rakhman Alamsyah Ratu Dika Ratu Anisa