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All Journal Jurnal Edukasi Elektro Transmisi: Jurnal Ilmiah Teknik Elektro ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Academia Open
MARTINI, NI PUTU DEVIRA AYU
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Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Physics Social Sciences Other

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Simultaneous Localization and Mapping pada Smart Automated Guided Vehicle menggunakan Iterative Closest Point berbasis K-Means Clustering MARTINI, NI PUTU DEVIRA AYU; SUMANTRI, BAMBANG; DEWANTARA, BIMA SENA BAYU
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 10, No 4: Published October 2022
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v10i4.742

Abstract

ABSTRAKAutomated Guided Vehicle (AGV) merupakan salah satu jenis mobile robot yang digunakan untuk mengangkut barang menuju tempat tujuan. AGV mampu bekerja pada lingkungan yang dinamis tanpa menggunakan garis pemandu. Namun sebelumnya harus mempunyai informasi yang cukup terhadap lingkungan kerjanya. Teknik ini dikenal dengan Simulataneous Localization and Mapping (SLAM) yang digunakan robot untuk menggambar peta sekaligus mengetahui posisi robot di dalam peta. Pada penelitian ini, metode yang digunakan yaitu SLAM berbasis Iterative Closest Point (ICP) dengan algoritma K-Means yang menggunakan kumpulan titik dari sensor laser range finder (LRF) untuk membangun peta lingkungan. Pemetaan SLAM menggunakan algoritma K-Means memiliki error hasil scan jarak 77,69% lebih kecil dan waktu eksekusi 0,18% lebih cepat dibandingkan dengan KD-Tree. Peta yang dihasilkan dengan algoritma KMeans pada ICP-SLAM memberikan hasil yang lebih baik & mendekati keadaan ruangan sebenarnya dibandingkan menggunakan algoritma KD-Tree.Kata kunci: ICP-SLAM, K-Means, Laser Range Finder. ABSTRACTAutomated Guided Vehicle (AGV) is a type of mobile robot that is used to transport goods to destination. AGV is able to work in a dynamic environment without guidelines. However, it must have sufficient information about its working environment beforehand. This technique is known as Simultaneous Localization and Mapping (SLAM) which is used by a robot to be able to draw a map as well as to determine its position on the map. In this research, the method used is SLAM based on Iterative Closest Point (ICP) with the K-Means algorithm that uses a collection of points from the Laser Range Finder (LRF) sensor to build an environmental map. SLAM using the K-Means algorithm has 77,69% smaller distance error and 0,18% faster execution time than KD-Tree. The map generated by the K-Means algorithm on an ICP-SLAM gives better results & closer to the actual state than using the KD-Tree. Keywords: ICP-SLAM, K-Means, Laser Range Finder.
SISTEM PEMANTAUAN DAN KENDALI KONSUMSI LISTRIK RUMAH TANGGA DENGAN LOGIKA FUZZY BERBASIS INTERNET OF THINGS Kamila, Raisha Kintan; Martini, Ni Putu Devira Ayu; Krisnawati, Luh
Transmisi: Jurnal Ilmiah Teknik Elektro Vol 26, No 4 Oktober (2024): TRANSMISI: Jurnal Ilmiah Teknik Elektro
Publisher : Departemen Teknik Elektro, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/transmisi.26.4.214-223

Abstract

Energi listrik merupakan kebutuhan esensial dalam kehidupan sehari-hari, terutama dengan semakin kompleksnya penggunaan listrik di rumah tangga. Konsumsi listrik yang tidak terkendali dapat meningkatkan biaya serta berdampak negatif terhadap lingkungan. Oleh karena itu, pengelolaan penggunaan listrik menjadi sangat penting. Dengan alasan ini, kami mengembangkan sebuah alat untuk memantau dan mengendalikan konsumsi listrik rumah tangga menggunakan teknologi Internet of Things (IoT) berbasis Fuzzy Logic. Sistem ini memungkinkan pengguna untuk memantau penggunaan listrik secara real-time melalui perangkat smartphone dan memberikan rekomendasi terkait efisiensi energi. Penelitian ini menggunakan metode yang melibatkan pengembangan prototipe yang mampu membuat keputusan terkait tingkat konsumsi listrik dengan menggunakan Fuzzy Logic, serta modul IoT untuk memantau dan mengendalikan aliran listrik. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan dapat memberikan informasi konsumsi listrik secara akurat dan membantu pengguna dalam mengurangi pemborosan energi. Dari beberapa percobaan yang telah dilakukan, hasil sensor tegangan dan arus dibandingkan dengan Clamp meter masing-masing memiliki akurasi sebesar 99.94% dan 94.37%. Melalui sistem pemantauan tingkat konsumsi listrik dan notifikasi, tingkat keberhasilan yang dicapai oleh algoritma Fuzzy Logic sebesar 100%. Dengan demikian, sistem ini berpotensi memberikan solusi efektif untuk pemantauan tingkat konsumsi listrik.
Implementing IoT-Based Smart Garden System at SMP Al Izhar Rahmawati, Yosy; Zuchriadi, Achmad; Martini, Ni Putu Devira Ayu; Sherila, Ayu Mika
Academia Open Vol 10 No 2 (2025): December (in progress)
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/acopen.10.2025.11246

Abstract

General Background: The integration of Internet of Things (IoT) technology in agriculture presents new opportunities for sustainable practices and hands-on education. Specific Background: However, its implementation in junior high school curricula, particularly as a practical environmental learning tool, remains limited. Knowledge Gap: Most schools lack access to real-world IoT-based applications that foster student engagement with digital literacy and environmental awareness simultaneously. Aims: This study aimed to design and implement an IoT-based Smart Garden system for automatic irrigation and lighting at SMP Al Izhar Pondok Labu, while evaluating its educational and technological impacts. Results: The program involved 50 participants who built functional Smart Garden prototypes using sensors and microcontrollers, resulting in significant gains in STEM motivation (+1.1), understanding of IoT (+1.2), and its application in agriculture (+1.2). Novelty: The innovation lies in integrating project-based learning with low-cost IoT hardware to create a replicable educational model that combines STEM skills, environmental education, and digital literacy. Implications: The success of this initiative highlights the potential of IoT-enhanced environmental projects to enrich school-based learning, strengthen 21st-century competencies, and inspire broader adoption of smart, sustainable practices in education.Highlight : The program introduced IoT-based Smart Garden systems to junior high school students through hands-on workshops, fostering practical STEM skills. Students showed a 25% increase in understanding and motivation, based on pre- and post-test assessments, highlighting educational impact. The initiative provides a replicable model for integrating environmental and digital literacy in school curricula, promoting sustainability. Keywords : Automatic Irrigation, Environmental Sustainability, Internet of Things (IoT), Smart Garden, STEM Education
Implementation of Low-Cost Smart Waste Bin with Fuzzy Logic Control Using MQ-2 and HY-SRF05 Sherila, Ayu Mika; Martini, Ni Putu Devira Ayu
Jurnal Edukasi Elektro Vol. 9 No. 2 (2025): Jurnal Edukasi Elektro Volume 9, No. 2, November 2025
Publisher : DPTE FT UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jee.v9i2.88875

Abstract

Urban waste management systems often fail to address hygiene and efficiency when relying on conventional bins or rigid threshold-based controllers. This paper presents a low-cost smart trash bin that integrates dual-sensor fuzzy logic control to adaptively regulate lid positions in real time. Unlike prior designs dependent on a single sensing modality, the proposed system fuses odor intensity (via MQ-2 gas sensor) and fill-level data (via HY-SRF05 ultrasonic sensor) within a Sugeno-type fuzzy inference framework. The controller supports five graded lid positions, ranging from fully closed to fully open, ensuring nuanced and hygienic responses to varying environmental conditions. Experimental validation across 25 scenarios yielded robust performance, with maximum errors below 9% and an average deviation of only 1.91%. The most accurate results were observed in the fully closed state, while the highest deviations occurred in the almost closed position due to mechanical limitations and nonlinear actuator behavior. Despite these drawbacks, the system consistently demonstrated adaptive decision-making and stable operation, confirming its suitability for real-world applications. Furthermore, comparative analysis against threshold-based and AI-based controllers reveals that the fuzzy approach achieves superior adaptability and near-ANN accuracy while maintaining computational efficiency compatible with Arduino-based platforms. These findings establish fuzzy sensor fusion as a practical pathway toward sustainable, IoT-enabled, and educationally relevant waste management solutions, paving the way for integration into future smart city infrastructures
Co-Authors Achmad Zuchriadi Bambang Sumantri Bima Sena Bayu Dewantara Kamila, Raisha Kintan Krisnawati, Luh Yosy Rahmawati