Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Jurnal Teknik Mesin, Industri, Elektro dan Informatika Jurnal Penelitian Teknologi Informasi dan Sains
Ferat Kristanto
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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

Found 2 Documents
Search

 1 
Mengintegrasikan Teknologi IoT dan Smart Destinations dalam Pengelolaan Pariwisata Berkelanjutan Agung Yuliyanto Nugroho; Sri Yulianto Fajar Pradapa; Ferat Kristanto; Syah Riza Octavy Sandy
Jurnal Teknik Mesin, Industri, Elektro dan Informatika Vol. 3 No. 3 (2024): September : JURNAL TEKNIK MESIN, INDUSTRI, ELEKTRO DAN INFORMATIKA
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jtmei.v3i3.4270

Abstract

The integration of Internet of Things (IoT) technology with the concept of Smart Destinations offers an innovative solution in tourism management with the aim of improving operational efficiency and tourist experience. IoT technology, which includes interconnected sensor devices and communication systems, provides the ability to unify and manage various aspects of a destination in real-time. Meanwhile, Smart Destinations use digital technology to optimize the tourist experience through personalization, resource efficiency, and desire. This article discusses the background and key benefits of IoT integration in Smart Destinations, including improving the visitor experience through data and analytics, better crisis management, and more efficient resource management. Application examples such as smart parking systems, automated energy management, and providing contextual information to tourists through sensors are described to illustrate the practical application of this technology. However, challenges such as data privacy and security, technology compatibility issues, and significant investment requirements are also discussed. In conclusion, while this integration faces several obstacles, its potential benefits in improving the quality and efficiency of tourist destinations make it a promising approach for the future of the tourism industry.
Mengejar Kinerja Maksimal: Teknik Pengoptimalan Terkini dalam Pembelajaran Mesin Agung Yuliyanto Nugroho; Ferat Kristanto
Jurnal Penelitian Teknologi Informasi dan Sains Vol. 2 No. 3 (2024): SEPTEMBER : JURNAL PENELITIAN TEKNOLOGI INFORMASI DAN SAINS
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jptis.v2i3.2358

Abstract

In the era of data revolution and artificial intelligence, machine learning model optimization has become one of the most dynamic and crucial research areas. This article reviews the latest techniques in machine learning model optimization with a focus on pursuing maximum performance. We discuss various methods applied to improve model accuracy and efficiency, ranging from hyperparameter tuning techniques, advanced optimization algorithms such as Bayesian optimization, to innovative approaches such as meta-learning and transfer learning. These optimization techniques not only aim to improve model performance but also to overcome challenges related to big data, model complexity, and computational limitations. We investigate how these methods can be integrated in machine learning pipelines to achieve better results with more efficient resources. Through a review of recent literature and case studies of applications in various domains, this article provides in-depth insights into the trends and developments in model optimization, as well as practical recommendations for researchers and practitioners in pursuing maximum performance from their machine learning systems. A better understanding of these cutting-edge techniques is expected to facilitate the achievement of better and more innovative results in future machine learning applications.
Co-Authors Agung Yuliyanto Nugroho Octavy Sandy, Syah Riza Sri Yulianto Fajar Pradapa