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Exploring the Role of Vocational High Schools in Fostering Entrepreneurial Mindset and Skills in Medan City Luqman, Faizal; Sugiarto, Lilik
Development: Studies in Educational Management and Leadership Vol. 1 No. 2 (2022): Development: Studies in Educational Management and Leadership
Publisher : Islamic Educational Management Programme

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47766/development.v1i2.529

Abstract

Transferring vocational entrepreneurship education to various contexts necessitates extensive localization. This paper examines the challenges and considerations associated with transferring vocational entrepreneurship training to diverse regions and emphasizes the significance of adapting the training to meet the specific needs and demands of the target region. It highlights the importance of understanding the local economic, social, and cultural landscapes in order to tailor the training program effectively. The availability and accessibility of resources, such as technology and funding, are also discussed as essential adaptation factors. Engaging local stakeholders, such as governments, educational institutions, and industry associations, is emphasized as a crucial element for assuring the relevance and sustainability of training. In addition, the paper emphasizes the significance of continuous evaluation and feedback mechanisms to monitor the outcomes and impact of transferred training and make adjustments as necessary. By addressing these factors and adapting vocational entrepreneurship training to local conditions, it can contribute to the empowerment of individuals, the promotion of economic development, and the cultivation of thriving entrepreneurial ecosystems in a variety of regions.
Evaluating Steganography Detection in JPEG Images Using Gaussian Mixture Model and Cryptographic Keys Saputro, Indrawan Ady; Nugraha, Febrianta Surya; Sugiarto, Lilik; Prabowo, Iwan Ady
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6084

Abstract

This study introduces a novel approach that integrates Gaussian Mixture Models (GMM) with MD5 hash-based verification to detect hidden messages embedded via Least Significant Bit (LSB) steganography in JPEG images. Unlike previous methods, the proposed dual-layer technique combines probabilistic modeling with data integrity verification. The model was trained and evaluated using a dataset comprising both original and stego-JPEG images. The experimental results achieved an accuracy of 78.67% and a precision of 89.15%, indicating good class separation between stego and non-stego images (AUC-ROC = 0.8659). However, the recall rate of 69.70% suggests that there is room for improvement in detecting all stego instances. Although MD5 is a hash function rather than an encryption algorithm, it effectively aids in identifying data anomalies resulting from message embedding. Overall, this lightweight approach offers a practical solution for steganalysis and can be further enhanced through the integration of hybrid deep learning techniques in future research.
Automated and Efficient Monitoring System for Organic Waste Compost Processing based on The Internet of Things (IoT) Sugiarto, Lilik; ady saputra, indrawan; Wariyanto Abdullah, Robi
BEST Vol 8 No 1 (2026): BEST
Publisher : Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/7madhs96

Abstract

In developed countries, waste has been regarded as an important component of management systems as well as reuse practices. In contrast, developing countries, particularly Indonesia, still face various challenges in waste management. Approximately 60% of the total national waste generation originates from household waste, and about 39.98% of this amount has not been optimally managed. Processing organic waste into compost is an environmentally friendly alternative that can reduce waste volume while increasing the value of household and agricultural waste. However, conventional composting methods often encounter difficulties, especially in maintaining temperature and moisture stability, causing the decomposition process to be less optimal. Based on these issues, this study aims to design and implement an automated efficiency and monitoring system for compost processing based on the Internet of Things (IoT). The developed system utilizes an ESP32 microcontroller, a soil moisture sensor for moisture measurement, a DS18B20 sensor for compost temperature monitoring, as well as an automatically controlled water pump and a 12 V DC fan. Sensor data are transmitted in real time to the Blynk platform for remote monitoring purposes. The experimental results indicate that the system is capable of maintaining moisture levels within the ideal range of 50–60% and compost temperature within the optimal range of 30–40°C, enabling the composting process to operate more stably, efficiently, and in a controlled manner.