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All Journal Jurnal Pendidikan Agama Islam Journal of Data Science and Informatics Engineering
Muhammad Hasanuddin
Universitas Pembangunan Panca Budi Medan
Religion Humanities Computer Science & IT Education Languange, Linguistic, Communication & Media Other

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Journal : Journal of Data Science and Informatics Engineering

 1 
Enhancing Data Processing Efficiency through Machine Learning Algorithms: A Comprehensive Study Muhammad Hasanuddin
Journal of Data Science and Informatics Engineering Vol. 1 No. 1 (2025): Desember 2025
Publisher : CV. Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jodsie.v1i1.12

Abstract

This study explores the transformative potential of machine learning algorithms to optimize data processing efficiency across diverse applications and address the growing challenges posed by big data. Specifically, machine learning can significantly enhance railway operations by optimizing maintenance schedules, reducing service interruptions, and improving overall network velocity. By applying advanced analytical techniques to railway data, it is possible to predict potential failures and proactively schedule maintenance, thereby minimizing costly downtime and enhancing the reliability of rail transportation infrastructure. This approach enables the transition from reactive to predictive maintenance strategies, leading to more efficient resource allocation and improved operational safety. This shift towards predictive maintenance, driven by machine learning, is crucial for mitigating risks and extending the lifespan of critical railway assets. This is particularly evident in power systems, where continuous monitoring and fault detection are paramount for maintaining stability and preventing disruptive outages, highlighting the broad applicability of these methodologies.
Applications of Artificial Intelligence in Healthcare: From Diagnosis to Treatment Optimization Muhammad Hasanuddin; Siti Khodijah
Journal of Data Science and Informatics Engineering Vol. 1 No. 1 (2025): Desember 2025
Publisher : CV. Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jodsie.v1i1.13

Abstract

The development of Artificial Intelligence (AI) has brought significant changes to the healthcare sector, particularly in improving diagnostic accuracy, optimizing treatment, and operational efficiency of medical services. Integrating intelligent algorithms with large-scale medical data allows healthcare systems to shift from conventional approaches toward more predictive, personalized, and data-driven services. This research aims to comprehensively examine the application of AI in healthcare, ranging from diagnostic support systems to optimizing patient care and treatment. The method used is a Systematic Literature Review (SLR) with a qualitative-descriptive approach to scientific articles published between 2020 and 2025, sourced from reputable databases such as Scopus, Web of Science, PubMed, and IEEE Xplore. The study results indicate that AI has been widely applied in medical image-based diagnosis, predictive analytics, personalized treatment planning, clinical workflow optimization, and robot-assisted surgery. Although AI has proven to improve the accuracy, speed, and adaptability of healthcare services, the main challenges still lie in ethical aspects, data privacy, algorithmic transparency, and infrastructure gaps between healthcare systems. This research concludes that the successful integration of AI in healthcare services is highly dependent on the implementation of a strong ethical framework, cross-disciplinary collaboration, and the development of transparent and reliable AI models. This finding is expected to serve as a reference for researchers and practitioners in developing sustainable and patient safety-oriented AI solutions.
Peran Kecerdasan Buatan dalam Meningkatkan Efisiensi Pemrosesan Data Cindy Atika Rizki; Siti Khodijah; Muhammad Hasanuddin
Journal of Data Science and Informatics Engineering Vol. 1 No. 2 (2026): April 2026
Publisher : CV. Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jodsie.v1i2.27

Abstract

Perkembangan teknologi informasi telah menyebabkan peningkatan jumlah data yang dihasilkan dalam berbagai sistem digital. Kondisi ini menuntut adanya metode pemrosesan data yang lebih efisien dan adaptif untuk mengelola data dalam skala besar. Kecerdasan buatan (Artificial Intelligence) muncul sebagai salah satu solusi yang mampu meningkatkan efisiensi pemrosesan data melalui kemampuan analisis otomatis, pengenalan pola, serta pembelajaran dari data. Penelitian ini bertujuan untuk menganalisis peran kecerdasan buatan dalam meningkatkan efisiensi pemrosesan data pada sistem informasi modern. Metode penelitian yang digunakan adalah studi literatur dengan pendekatan deskriptif-analitis melalui pengumpulan dan analisis berbagai sumber ilmiah yang relevan dengan topik penelitian. Hasil kajian menunjukkan bahwa penerapan teknik kecerdasan buatan seperti machine learning, deep learning, dan analitik prediktif mampu meningkatkan kecepatan, akurasi, serta efektivitas proses pengolahan data dibandingkan dengan metode pemrosesan data konvensional. Selain itu, integrasi kecerdasan buatan dengan teknologi komputasi modern seperti big data dan cloud computing juga memberikan kontribusi yang signifikan dalam pengelolaan data dalam jumlah besar secara lebih efisien. Meskipun demikian, implementasi kecerdasan buatan masih menghadapi beberapa tantangan, seperti kebutuhan akan kualitas data yang baik, kompleksitas pengembangan model, serta kebutuhan sumber daya komputasi yang memadai. Oleh karena itu, pemanfaatan kecerdasan buatan perlu dikembangkan secara optimal agar dapat mendukung peningkatan efisiensi pemrosesan data di berbagai bidang pada era digital.
Co-Authors Abil Alwi Prayoga Cindy Atika Rizki Siti Khodijah Supiyandi Supiyandi