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INTRODUCING INTERNET OF THINGS (IOT) FOR A COMMUNITY VILLAGE BASED ON INDUSTRIAL REVOLUTION 4.0 Sulistiyanti, Sri Ratna; Setyawan, F.X. Arinto; Komarudin, M.; Warsono, Warsono
Journal of Community Research and Service Vol 2, No 2: July 2018
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jcrs.v2i2.13148

Abstract

AbstractA house is a gathering place for a family, where each family member has a different passion and busyness. If at any time a family member is traveling and wants to monitor and control conditions from a distance, an electronic device is used. One solution to determine the condition of home security at all times, an internet connection is needed by using the concept of the Internet of Things (IoT). IoT is a concept of using an internet network to transfer data or share information with certain applications. The IoT concept is one of the IR 4.0 based systems. In Indonesia, the system has not been widely implemented, especially for village areas. The purpose of this activity is to introduce the concept of IoT in a region as a system of monitoring and controlling homes. The method used is socialization and training of IoT-based smart home models. The system that is introduced is setting lights, monitoring gas (kitchen security), and monitoring using a camera. The results obtained were an increase in knowledge from 45.4% to 79%, and increased skills from 33% to 63%.Keywords: IoT, Village, Industrial Revolution 4.0.
STUDY OF XCEPTION MACHINE LEARNING ARCHITECTURE IN WASTE CLASSIFICATION SYSTEM Mulyani, Yessi; Budi Wintoro, Puput; Komarudin, M.; Kurniawan, Rian
Journal of Applied Science, Engineering and Technology Vol. 2 No. 2 (2022): December 2022
Publisher : INSTEP Network

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Abstract

Garbage generated every day can be a problem because some types of waste are difficult to decompose so they can pollute the environment. Waste that can potentially be recycled and has a selling value is inorganic waste, especially cardboard, metal, paper, glass, plastic, rubber and other waste such as product packaging. Various types of waste can be classified using machine learning models. The machine learning model used for classification of waste systems is a model with the Convolutional Neural Network (CNN) method. The selection of the CNN architecture takes into account the required accuracy and computational costs. This study aims to determine the best architecture, optimizer, and learning rate in the waste classification system. The model designed using the Xception architecture with the Adam optimizer and a learning rate of 0.001 has an accuracy of 87.81%.
Analisis Tingkat Pemahaman Mahasiswa Teknik Elektro Terhadap Materi Semikonduktor untuk Memenuhi Kebutuhan Industri: Studi Kasus di Universitas Lampung Purwiyanti, Sri; Fitriawan, Helmy; Komarudin, M.; Alam, Syaiful
PARAMETER: Jurnal Pendidikan Universitas Negeri Jakarta Vol. 37 No. 2 (2025): Parameter
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/parameter.372.05

Abstract

The semiconductor industry is a strategic sector that plays a crucial role in supporting digital transformation and national technological independence. To build a competitive semiconductor ecosystem, it is essential to develop human resources (HR) with a strong understanding of semiconductor concepts and applications. This study aims to analyze the level of understanding among electrical engineering students regarding semiconductor materials as a foundation for strengthening HR capacity in this field. A qualitative approach was applied through a survey involving 35 students of the Electrical Engineering Department, University of Lampung, specializing in Electronics and Control. The survey instrument consisted of a questionnaire with three answer categories: “do not know,” “know but cannot explain,” and “know and can explain,” covering four main themes—semiconductor physics, semiconductor technology, IC design, and semiconductor applications. The results revealed that most students only mastered basic concepts, while understanding of more advanced materials remained limited. These findings indicate the need to improve learning effectiveness through more interactive and contextual methods, along with the enrichment of materials relevant to industry needs. The results of this study are expected to provide valuable insights for strengthening semiconductor education in higher institutions to support the growth of Indonesia’s semiconductor industry.