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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal IPTEK Conference SENATIK STT Adisutjipto Yogyakarta Sewagati Research Horizon
Fajar Baskoro
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Aerospace Engineering Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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Multivariate Time Series Forecasting Using Recurrent Neural Networks for Meteorological Data Hariadi, Victor; Saikhu, Ahmad; Zakiya, Nurotuz; Wijaya, Arya Yudhi; Baskoro, Fajar
SENATIK STT Adisutjipto Vol 5 (2019): Peran Teknologi untuk Revitalisasi Bandara dan Transportasi Udara [ISBN 978-602-52742-
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/senatik.v5i0.365

Abstract

Rainfall is related to a number of factors that are interdependent and influenced by dynamic global time, region and climate factors. Determination of relevant predictors is important for the efficiency of the rainfall estimator model. Although some climate modeling studies in one region/country have high accuracy, this model is not necessarily suitable for other regions. Determination of predictor variables by considering spatio-temporal factors and local / global features results in a very large number of inputs. Feature selection produces minimal input so that it gets relevant predictor variables and minimizes variable redundancy. Recurrent Neural Networks is one of the artificial neural networks that can be used to predict time series data. This study aims to predict rainfall by combining the SVM classification method and the RNN method. Tests on the Perak 1 daily and monthly weather data (WMO ID: 96933) and Perak 2 Station daily and monthly data(WMO ID: 96937), showed high accuracy results with an R2 are 92.1%; 94.1%; 90.9% and 89.6%.
Designing an Optimal Education and Training Model for Relationship Manager BRIguna BRI West Indonesia Region Baskoro, Fajar; Pancakusuma, Muhammad Bayu; Raharjo, Susilo Toto
Research Horizon Vol. 5 No. 2 (2025): Research Horizon - April 2025
Publisher : LifeSciFi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54518/rh.5.2.2025.473

Abstract

The rapid advancement of digital technology and shifts in consumer behavior have driven the banking industry, including Bank Rakyat Indonesia (BRI), to adapt by enhancing the capabilities of its human resources (HR). In this context, it is crucial to develop an effective education and training framework for BRI employees, particularly RM Briguna, to remain competitive in the digital era. This study focuses on assessing the education and training requirements of RM Briguna employees in the digital age and creating a tailored model that meets the specific needs of workers, especially those in western Indonesia. Using a qualitative methodology, the research incorporates a literature review analyzing various training models utilized in the banking sector and examines best practices in HR development. The findings highlight that effective training approaches include competency-based programs, on-the-job training, e-learning, blended learning, and gamification. Each model presents distinct benefits and limitations, which must be carefully evaluated during the design process. An analysis of existing training initiatives reveals a need for updates in curricula and methods to align with industry demands and emerging technological trends. The ideal education and training framework should prioritize flexibility, innovation, and alignment with employees' unique needs. Strategic investment in HR development and collaboration with educational institutions will play a vital role in equipping BRI to tackle digital-era challenges. By implementing an optimized training model, BRI can cultivate a skilled and adaptable workforce—particularly RM Briguna in western Indonesia—capable of driving the company’s growth and success.
UAV LAND COVER CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK FEATURE MAP WITH A COMBINATION OF MACHINE LEARNING Maulidiya, Erika; Fatichah, Chastine; Suciati, Nanik; Baskoro, Fajar
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 1, January 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i1.a1214

Abstract

In geographic analysis, land cover plays an important role in everything from environmental analysis to sustainable planning methods and physical geography studies. The Indonesian National Standard (SNI 7645:2014) classifies vegetation analysis based on density. There are four categories of vegetation density index: non-vegetation, bare, medium, and high. Technically, vegetation data can be obtained through remote sensing. Satellite and UAV data are two types of data used in remote sensing to collect information. This research will analyze land cover based on vegetation density information that can be collected through remote sensing. Based on vegetation density information from remote sensing, the information can help in land processing, Land Cover Classification is carried out based on vegetation density. Convolutional neural networks (CNN) have been trained extensively to apply their properties to land cover classification. This research will evaluate features extracted from Convolutional Neural Networks (ResNet 50, Inception-V3, DenseNet 121) which have previously been trained and continued with Decision Tree algorithms, Random Forest, Support Vector Machine and eXtreme Gradient Boosting to perform classification. From the comparison results of classification tests between machine learning methods, Support Vector Machine is superior to other machine learning methods. This is proven by the accuracy results obtained at 85% with feature extraction using ResNet-50 where the processing time is 8 minutes. Followed by the second-best model, namely ResNet-50 with XGBoost which obtained accuracy results of 82% with a processing time of 55 minutes. Meanwhile, the use of feature extraction using the DenseNet-121 method was obtained using a combination of the Support Vector Machine method and the XGBoost method with the accuracy obtained being 81%.
Co-Authors Abd. Rasyid Syamsuri Agus Budi Raharjo Ahmad Hoirul Basori Ahmad Saikhu Airlangga Eka W. Arya Yudhi Wijaya Chastine Fatichah Daniel Oranova Siahaan Darlis Heru Murti Diana Purwitasari Dwi Sunaryono Keke Mulia Rahmawati Khakim Ghozali Maheswari, Clarissa Luna Maulidiya, Erika Nanik Suciati Pancakusuma, Muhammad Bayu Rahdian Seto Hananto Susilo Toto Raharjo Victor Hariadi Waskitho Wibisono Wisda Rubikartin Zakiya, Nurotuz