Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Jurnal technoscientia JURNAL TEKNOLOGI TECHNOSCIENTIA
Muhammad Heidyr Hadiningrat
Unknown Affiliation
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Earth & Planetary Sciences Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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

Found 2 Documents
Search

 1 
PENGOPTIMALAN INTEROPERABILITAS SISTEM NAIVE BAYES DALAM PENINGKATAN DATA MINING DI EKOSISTEM MULTI-LAYANAN GOJEK Muhammad Heidyr Hadiningrat; Yessy Asri
JURNAL TEKNOLOGI TECHNOSCIENTIA Technoscientia Vol 17 No 1 September 2024
Publisher : Lembaga Penelitian & Pengabdian Kepada Masyarakat (LPPM), Universitas AKPRIND Indonesia Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34151/technoscientia.v17i1.4624

Abstract

This research aims to optimize the performance of Data Mining in Gojek's multi-service ecosystem through improving the interoperability of the Naive Bayes system. As a leading multi-service platform, Gojek faces complexity in extracting value from data involving different types of services. The Naive Bayes data classification method, which has been proven effective, is integrated with a focus on optimizing system interoperability. The research involves developing technical solutions that facilitate better integration between services, enabling efficient and accurate data exchange. The literature study investigated the concept of interoperability, Naive Bayes, and similar implementations, while Gojek's needs analysis established the basis of the solution design. The prototype was implemented and evaluated in a test environment replicating Gojek's ecosystem conditions. Results show improved performance with classification accuracy metrics and data exchange efficiency. With trials in the production environment, the optimized solution is widely implemented. The findings of this research are expected to make a positive contribution to the effectiveness of the Gojek system in utilizing data, opening new opportunities for service development, and improving user experience.
PENGOPTIMALAN INTEROPERABILITAS SISTEM NAIVE BAYES DALAM PENINGKATAN DATA MINING DI EKOSISTEM MULTI-LAYANAN GOJEK Muhammad Heidyr Hadiningrat; Yessy Asri
JURNAL TEKNOLOGI TECHNOSCIENTIA Technoscientia Vol 17 No 1 September 2024
Publisher : Lembaga Penelitian & Pengabdian Kepada Masyarakat (LPPM), Universitas AKPRIND Indonesia Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34151/technoscientia.v17i1.4624

Abstract

This research aims to optimize the performance of Data Mining in Gojek's multi-service ecosystem through improving the interoperability of the Naive Bayes system. As a leading multi-service platform, Gojek faces complexity in extracting value from data involving different types of services. The Naive Bayes data classification method, which has been proven effective, is integrated with a focus on optimizing system interoperability. The research involves developing technical solutions that facilitate better integration between services, enabling efficient and accurate data exchange. The literature study investigated the concept of interoperability, Naive Bayes, and similar implementations, while Gojek's needs analysis established the basis of the solution design. The prototype was implemented and evaluated in a test environment replicating Gojek's ecosystem conditions. Results show improved performance with classification accuracy metrics and data exchange efficiency. With trials in the production environment, the optimized solution is widely implemented. The findings of this research are expected to make a positive contribution to the effectiveness of the Gojek system in utilizing data, opening new opportunities for service development, and improving user experience.
Co-Authors Yessy Asri