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All Journal BRITech : Jurnal Ilmiah Ilmu Komputer, Sains dan Teknologi Terapan Jurnal Teknik Informatika C.I.T. Medicom International Journal of Enterprise Modelling Innotech
Rifka Widyastuti, Rifka
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Transportation

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EKSTRAKSI CIRI CITRA KAYU MERBAU DAN KAYU JATI BOJONEGORO MENGGUNAKAN DISCRETE WAVELET TRANSFORM Hasan Basri; Widyastuti, Rifka
BRITech, Jurnal Ilmiah Ilmu Komputer, Sains dan Teknologi Terapan Vol 1 No 2 (2020): Periode Januari
Publisher : Institute Teknologi dan Bisnis Bank Rakyat Indonesia

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Abstract

Keberagaman hasil kayu di indonesia mempunyai keunggulan masing-masing. Untuk mendukung terjaminnya kualitas suatu jenis kayu, maka diperlukan suatu sistem yang dapat mengidentifikasi jenis kayu. Salah satu tahapan identifikasi menggunakan citra digital yaitu proses ekstraksi ciri citra. Ekstraksi ciri citra menggunakan Discrete Wavelet Transform dengan objek penelitian kayu merbau dan kayu jati bojonegoro menghasilkan nilai ciri yang baik pada koefisien LL.
Smart City Weather and Disaster Monitoring Architecture: LoRaWAN Integration with COBIT 2019 Governance Yulistiawan, Bambang Saras; A, Galih Prakoso Rizky; Widyastuti, Rifka; Mulianingtyas, RR Octanty
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 2 (2025): May: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol17.2025.1267.pp59-74

Abstract

Climate change, urbanization, and the increasing frequency of natural disasters such as floods and forest fires demand that Indonesian cities adopt real-time, integrated, and reliable environmental monitoring systems. Within the context of smart cities, LoRaWAN technology offers wide coverage, low power consumption, and cost-efficient operations, making it highly relevant for city-scale multi-sensor monitoring systems. This study proposes the design of a LoRaWAN-based weather and disaster monitoring system architecture integrated into the smart city framework, while simultaneously adopting the IT governance principles of COBIT 2019. The methodology includes a literature review and the mapping of five COBIT domains (EDM03, APO03, BAI03, DSS02, MEA01) to LoRaWAN’s technical components, ranging from sensors, gateways, and network servers to application servers, dashboards, and public notification modules. The analysis demonstrates that the proposed design enhances data standardization, end-to-end security, monitoring, scalability, and device governance. The integration of COBIT 2019 further enables the optimization of risk management, monitoring effectiveness, incident response, and regulatory compliance. In conclusion, the proposed architecture provides a comprehensive framework to support resilient, adaptive, and sustainable smart cities. However, this architecture has not yet been implemented in practice, thus necessitating further implementation and evaluation to ensure the system’s effectiveness and sustainability in operational environment.
A New Framework for IT Governance Excellence Yulistiawan, Bambang Saras; Mulianingtyas, Rr Octanty; Widyastuti, Rifka; A , Galih Prakoso Rizky
International Journal of Enterprise Modelling Vol. 19 No. 3 (2025): September: Enterprise Modelling
Publisher : International Enterprise Integration Association

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Abstract

The rapid digital transformation requires an organization to have adaptive, integrated governance and management of information technology services (IT). However, two popular frameworks, COBIT and ITIL, have weaknesses when implemented separately. COBIT seems too normative and strategic, while ITIL is too operational and procedural; therefore, both of them fail to align the requirements between the strategic direction and information technology service execution. This study proposes the CITIGOV Model, an integrative framework that aligns the strengths of COBIT and ITIL in a model of modular and adaptive governance and information technology service. With three main domains, Strategic Governance, Service-Oriented Management, and Continuous Value Optimization, and seven elements of sustainable operations in IT governance. This study employs the Design Science Research method and has been validated through a literature review, theoretical analysis, and the mapping of modern digital organisation needs. The result of this study not only delivers theoretical contributions to IT framework integration, but also the practical implications as well as relevant guidance implementation and applicability in the context of public or private organisations. Keywords:
PREDIKSI BIAYA LOGISTIK MENGGUNAKAN METODE K-MEANS Widodo, Abung; Widyastuti, Rifka; Arisena Hendrawan, Satya
Innotech: Jurnal Ilmu Komputer, Sistem Informasi dan Teknologi Informasi Vol 1 No 1 (2024): Innotech Issue Januari 2024
Publisher : Universitas Siber Indonesia

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Abstract

K-Means Clustering is a non-hierarchical data clustering method that groups data in the form of one or more clusters/groups. The role of costs is very important and cannot be separated from the activities of a company. The GAP between operational costs and work unit business exceeds the breakdown costs, causing an increase in operational costs every year. Financing logistics needs can be done with this method because this method can select and group clusters to be made which are considered suitable for operational costs which have a number of different groupings. It also aims to optimize the use of costs and optimize company profits. In the first experiment using 3 centroid cluster centers obtained randomly. The 3 centroid centers used in financing the company's operations are office maintenance costs, machine maintenance costs, and stationery costs which are grouped in the 2020 and 2021 periods for each operational cost date. This grouping aims to increase operational cost efficiency and increase the company's profits more optimally.
Real Time Pill Counting on Low Power Device: A YOLOv5 Pipeline with Confidence Thresholding and NMS A, Galih Prakoso Rizky; Widyastuti, Rifka
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 5 (2025): Intelligent Decision Support System (IDSS)- INPRESS
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol17.2025.1286.pp225-241

Abstract

Manual pill counting is still commonly performed in healthcare facilities and pharmacies, but this method is vulnerable to human error and requires significant processing time. This study develops an automatic pill counting pipeline using the YOLOv5 deep learning model, optimized for low-power devices such as Raspberry Pi, Orange Pi, and Jetson Nano. Unlike earlier techniques that depend on conventional retrieval or machine-learning approaches, this pipeline integrates real-time object detection with customized confidence thresholding and Non-Maximum Suppression (NMS), enabling high accuracy and fast performance on edge hardware with limited resources. The development process includes collecting and annotating a dataset of pill images with variations in shape, color, and orientation, followed by training YOLOv5 using optimized parameters. A simple webcam is used as the input device, and system performance is evaluated under different lighting and background conditions. Experimental results show that the model achieves 98% precision, 88% recall, 95% mAP@0.5, and 67% mAP@0.5:0.95, with an average inference speed of around 15 milliseconds per image. Tests on ten pill-counting scenarios under optimal lighting demonstrate strong performance, with only minor discrepancies in dense cases involving 50 and 127 pills, producing accuracies of 98% and 99.21%. These results indicate that the optimized YOLOv5 pipeline provides fast and accurate real-time pill counting on low-power devices. Future work will enhance robustness to lighting variations, validate using external datasets, and incorporate color and shape feature analysis to improve performance in challenging scenarios.
Co-Authors A, Galih Prakoso Rizky Bambang Saras Yulistiawan Hasan Basri Mulianingtyas, RR Octanty Rizky A, Galih Prakoso Satya Arisena Hendrawan Widodo, Abung