Article Per Year (5 Year)
p-Index From 2020 - 2025
0.562
P-Index
This Author published in this journals
All Journal Jurnal Sains dan Teknologi Jurnal Ekonomika Dan Bisnis
Fudholi, Muhammad Fahmi
Unknown Affiliation
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
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.
Co-Authors ., Bagastian Eko Putro, Dimas Hermana, BP Putra Juarsa, Doris Kevinda Sari Muhammad Fadli Ryan Randy Suryono Susanto, Erliyan Redy