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Journal : Sebatik

Analysis of Information Technology Governance Implementation in Consulting Firms Using the COBIT Framework Approach: A Literature Review Mirza, Arvin Muhammad; Wirani, Yekti; Sucahyo, Yudho Giri
Sebatik Vol. 29 No. 1 (2025): June 2025
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v29i1.2610

Abstract

Information Technology (IT) governance is a critical element in supporting the achievement of an organization’s or company’s strategic objectives and ensuring the effective and efficient utilization of IT resources. Consulting firms, as the entities that prioritize delivering optimal services to their clients or partners, require a structured and well-implemented IT governance to support improvements in their business service quality. The effectiveness of IT governance implementation can be more accurately measured through the use of internationally recognized standard frameworks, such as the COBIT framework. This study aims to analyze existing literature that discusses the implementation of IT governance in consulting firms using the COBIT framework approach. This study is conducted through a literature review of relevant academic publications. The findings of this study are expected to contribute to a deeper understanding of IT governance practices in the consulting service sector and serve as a reference for the development of more effective strategies to strengthen IT governance implementation.
Smart Waste Management Design for Higher Education: Case Study of Al-Azhar University of Indonesia Ananta, Aditya; Wirani, Yekti; Sucahyo, Yudho Giri
Sebatik Vol. 29 No. 2 (2025): December 2025
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v29i2.2622

Abstract

Al-Azhar University of Indonesia (UAI) has faced significant challenges in waste management. This has caused the UI GreenMetric ranking to be lower. After conducting observations and interviews with the Technical Implementation Unit for Facilities and Infrastructure (SarPras), several major problems found included the absence of waste sorting, excessive waste accumulation, and large waste generation at temporary disposal sites. To improve UAI's ranking in UI Greenmetric and achieve the Sustainable Development Goals target, this study produced a smart waste management system based on the Internet of Things (IoT), Embedded Systems, and Machine Learning (ML). Some of the main components of the design results include a smart bin that uses ultrasonic sensors to reduce accumulation; a web application for monitoring and reward systems; and automatic waste sorting for recycling using Raspberry Pi, infrared sensors, and the ML FOMO (Faster Objects, More Objects) algorithm secured through a CCTV system. In addition, this system is supported by digital education for waste reuse and collection route optimization using ML predictions of the Linear Regression model. With the cooperation of third party sorted waste transportation, this design improves waste management and UAI GreenMetric UI ranking with validation and approval by SarPras and Information Technology Center (PTI).