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All Journal Jurnal Teknologi Terapan International Journal of Engineering, Technology and Natural Sciences (IJETS)
Ardhi Wicaksono Santoso
Department Of Electrical Engineering And Informatics, Vocationa College, Universitas Gadjah Mada, Yogyakarta, Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Sistem Keamanan Pintu Laboratorium Berbasis Sensor Fingerprint dan Magnetic Lock Ardhi Wicaksono Santoso
Jurnal Teknologi Terapan Vol 6, No 1 (2020): Jurnal Teknologi Terapan
Publisher : P3M Politeknik Negeri Indramayu

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2125.775 KB) | DOI: 10.31884/jtt.v6i1.236

Abstract

Abstrak-- Sistem pintu keamanan di laboratorium biasanya menggunakan kunci konvensional. Departemen Teknik Listrik dan Informatika, Perguruan Tinggi Kejuruan, Universitas Gadjah Mada memiliki 24 laboratorium dan puluhan ruang kelas. Semakin tinggi jumlah laboratorium dan ruang kelas, semakin banyak kunci yang dibutuhkan. Kendala yang dihadapi oleh asisten laboratorium adalah sulitnya menemukan kunci dan kehilangan kunci.Salah satu cara mengatasi masalah di atas membuat sistem penguncian pintu otomatis menggunakan sensor sidik jari. Sistem ini bertujuan untuk meningkatkan keamanan dan memfasilitasi akses untuk mengunci ruangan. Sistem ini dibuat menggunakan mikrokontroler sebagai prosesor dan sensor sidik jari. Identitas pengakses laboratorium disimpan dalam memori untuk membuka kunci pintu. Di pintu masuk, pintu kunci magnetik dipasang, yang terhubung ke sistem mikrokontroler. Sistem dapat berjalan seperti yang dimaksudkan dan dapat mendeteksi sidik jari yang tersimpan dalam memori. Sistem dapat mengidentifikasi sidik jari pengguna yang disimpan dalam memori dengan persentase keberhasilan 95% dari total 40 percobaan membuka kunci.Kata Kunci: Sidik Jari, Kunci Magnetic, Mikrokontroller, Sistem Kunci Pintu, Keamanan
Identification of Medicine Leaf Images Using Invariant Moment and K-Nearst Neighboor Fredianto; Enny Sela; Suhirman; Ardhi Wicaksono Santoso
Jurnal Internasional Teknik, Teknologi dan Ilmu Pengetahuan Alam Vol 3 No 1 (2021): International Journal of Engineering, Technology and Natural Sciences
Publisher : University of Technology Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (414.351 KB) | DOI: 10.46923/ijets.v3i1.114

Abstract

Medicinal plants have benefits for preventing or curing various diseases. The number of medicinal plants and the lack of knowledge about the types of medicinal plants make it difficult for people to distinguish the types of medicinal plants. This difficultness causes people to prefer to use chemical drugs rather than medicinal plants. This study develops a system of identification of medicinal plants. There are four steps to build the system: input leaf images, pre-processing, invariant moment feature extraction, and K-Nearest Neighbours (K-NN) pattern recognition. A 100 images samples images from 5 types of medicinal plants were involved in this study. The identification process of leaf image begins with the cropping, resizing process, and several morphological operations. Then feature extraction stage uses invariant moments. The final stage of pattern recognition uses K-NN. The result of this research is that the system can identify the types of medicinal plants. Using the Manhattan distance, the study archives the highest average accuracy.
Identification of Medicine Leaf Images Using Invariant Moment and K-Nearst Neighboor Fredianto; Enny Sela; Suhirman; Ardhi Wicaksono Santoso
Jurnal Internasional Teknik, Teknologi dan Ilmu Pengetahuan Alam Vol 3 No 1 (2021): International Journal of Engineering, Technology and Natural Sciences
Publisher : University of Technology Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (414.351 KB) | DOI: 10.46923/ijets.v3i1.114

Abstract

Medicinal plants have benefits for preventing or curing various diseases. The number of medicinal plants and the lack of knowledge about the types of medicinal plants make it difficult for people to distinguish the types of medicinal plants. This difficultness causes people to prefer to use chemical drugs rather than medicinal plants. This study develops a system of identification of medicinal plants. There are four steps to build the system: input leaf images, pre-processing, invariant moment feature extraction, and K-Nearest Neighbours (K-NN) pattern recognition. A 100 images samples images from 5 types of medicinal plants were involved in this study. The identification process of leaf image begins with the cropping, resizing process, and several morphological operations. Then feature extraction stage uses invariant moments. The final stage of pattern recognition uses K-NN. The result of this research is that the system can identify the types of medicinal plants. Using the Manhattan distance, the study archives the highest average accuracy.
Co-Authors Damayanti, Elok Enny Sela Fredianto