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All Journal Jurnal Sains dan Teknologi Jurnal Ekonomika Dan Bisnis Jurnal Informatika dan Rekayasa Perangkat Lunak
Fudholi, Muhammad Fahmi
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary

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Pengaruh Keterampilan Teknologi dan Inovasi terhadap Keberhasilan Teknopreneurship di Sektor Digital Eko Putro, Dimas; ., Bagastian; Fudholi, Muhammad Fahmi; Hermana, BP Putra; Juarsa, Doris; Suryono, Ryan Randy
Jurnal Ekonomika Dan Bisnis (JEBS) Vol. 5 No. 1 (2025): Januari - Februari
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jebs.v5i1.2595

Abstract

Penelitian ini mengkaji pengaruh keterampilan teknologi dan inovasi terhadap keberhasilan teknopreneurship di sektor digital. Dengan menggunakan metode kaji literatur, penelitian ini menganalisis berbagai studi relevan untuk mengeksplorasi hubungan antara keterampilan teknologi, inovasi, dan daya saing teknopreneur. Hasil penelitian menunjukkan bahwa keterampilan teknologi, seperti penguasaan perangkat lunak, big data, dan teknologi canggih, memberikan keunggulan kompetitif bagi teknopreneur dalam menciptakan solusi efisien dan adaptif terhadap perubahan pasar. Inovasi dalam produk, layanan, dan model bisnis, seperti subscription dan teknologi berbasis cloud computing, memainkan peran signifikan dalam meningkatkan nilai tambah, loyalitas pelanggan, dan keberhasilan usaha. Sinergi antara keterampilan teknologi dan inovasi menghasilkan produk dan layanan yang relevan dengan kebutuhan pasar sekaligus mendorong keberhasilan teknopreneur. Penelitian ini juga menyoroti pentingnya faktor pendukung, seperti akses ke sumber daya keuangan dan kemitraan strategis, dalam mempercepat pengembangan produk dan memperluas pasar. Studi ini memberikan wawasan bagi pengembangan teori dan praktik teknopreneurship di era digital.
Deteksi Dini Stroke Menggunakan Machine Learning Kevinda Sari; Muhammad Fadli; Fudholi, Muhammad Fahmi; Susanto, Erliyan Redy
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 4 (2025): Agustus 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i4.5590

Abstract

Stroke is one of the leading causes of death and disability worldwide. Early detection of stroke risk is crucial to prevent more severe complications. This study aims to develop a stroke prediction model based on machine learning using an open dataset from Kaggle containing patients' medical and demographic information. Four machine learning algorithms were utilized and compared: AdaBoost, Gradient Boosting, LightGBM, and XGBoost. Data preprocessing steps included missing value imputation, categorical variable encoding, numerical feature normalization, and class balancing using the SMOTEENN method. Additionally, feature selection was performed using the Extra Trees algorithm to enhance model performance. The results showed that the XGBoost model delivered the best performance, achieving an accuracy of 97.16%, an F1-score of 97.49%, and an AUC of 99.75%. This model proved to be effective in detecting stroke cases and holds potential for integration into clinical decision support systems. The study concludes that a combination of modern boosting algorithms and optimal preprocessing techniques can yield a reliable stroke prediction system suitable for implementation in digital healthcare contexts.
Transforming the Data Ecosystem through Machine Learning and Artificial Intelligence: A Systematic Review of Innovative Big Data Frameworks Bagastian, Bagastian; Putro, Dimas Eko; Fudholi, Muhammad Fahmi; Suryono, Ryan Randy
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2026): Volume 7 Number 1 March 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v7i1.1437

Abstract

The digital revolution era has created fundamental transformation in data management and utilization, where machine learning and artificial intelligence integration becomes the primary catalyst in optimizing contemporary data ecosystems. Global data volume predicted to reach 181 zettabytes by 2025 demands innovative approaches in big data management, yet 80% of organizations still experience difficulties integrating AI technology with their existing data infrastructure. This research aims to identify and analyze characteristics of innovative frameworks that integrate machine learning and artificial intelligence in data ecosystem transformation, and formulate comprehensive framework recommendations for the future. The research method employs a qualitative approach with Systematic Literature Review (SLR) on 2021-2022 publications via Google Scholar, with thematic analysis using Critical Appraisal Skills Program (CASP) checklist. Research results identify eight major innovative frameworks including AI for Smart Society 5.0, Big Data-AI-IoT Integration, to Digital Responsibility Accounting, with main characteristics of process automation capabilities, service personalization, edge computing for real-time decision making, and blockchain implementation for data security. Implementation challenges include digital infrastructure limitations, human resource skill gaps, data security, and organizational resistance. Transformation impact proves significant in education, governance, and business intelligence sectors. The conclusion shows that comprehensive future frameworks must be adaptive, ethical, and sustainable by integrating technology, human, and environmental dimensions in a balanced manner. A phased implementation approach is recommended with priority on strengthening digital infrastructure and developing human resource competencies through cross-sector collaboration.
Co-Authors ., Bagastian Bagastian, Bagastian Eko Putro, Dimas Fadli, Muhammad Hermana, BP Putra Juarsa, Doris Kevinda Sari Putro, Dimas Eko Ryan Randy Suryono Susanto, Erliyan Redy