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All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal Pengabdian pada Masyarakat Ilmu Pengetahuan dan Teknologi Terintegrasi
Agustin, Sarah
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering

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Time Consumption and Productivity of Sandat Felling Technique in Agroforestry Private Forests in Probolinggo, Indonesia Budiaman, Ahmad; Hardjanto; Agustin, Sarah; Lawrensia; Rahayaan, Yohana Natalia; Maharani, Chandra Puspita; Limbong, Zest
Jurnal Manajemen Hutan Tropika Vol. 30 No. 1 (2024)
Publisher : Institut Pertanian Bogor (IPB University)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.7226/jtfm.30.1.1

Abstract

Agroforestry is a cropping pattern that is commonly applied to private forest management in Indonesia. Agroforestry based private forest is a land-based silviculture that incorporates forestry plants with agricultural crops, plantation crops, and multi-purpose plants. One of the felling techniques used in agroforestry based private forests is the sandat-felling technique (SFT), which is a rope-assist felling technique. The felling technique was used to protect the remaining stand of the agroforestry based private forest. This technique is an innovation in the harvesting of agroforestry based private forests in Indonesia. The time consumption and productivity of this technique are not yet known. This study aims to assess the working time and productivity of SFT in agroforestry based private forests in Probolinggo, East Java, Indonesia. The observed tree-felling technique included rope installation and tree-felling operations. The performance of the SFT was evaluated by analyzing its working time and productivity. The results of the study showed that the total working time of the SFT was 8.65 minutes tree-1, which consisted of 33.34% for rope installation and 66.66% for felling operation. The productivity of the SFT was 2.02 m3 hour-1.
Simulasi Pengenalan Evakuasi Dini terhadap Bencana Kebakaran Guna Meningkatkan Edukasi Siswa SD Labschool UPI Purwakarta Berbasis Internet of Things Agustin, Sarah; Novelia, Eka; Fadhlullah, Wafiq Nur; Kusuma, Surya; Salmadiina, Ashrida; Fauzan, Muhammad Iqbal; Putrinismara, Moktika; Aziz, Shofwan Abdul; Setyowati, Endah
Jurnal Pengabdian pada Masyarakat Ilmu Pengetahuan dan Teknologi Terintegrasi Vol. 8 No. 1 (2023): J-INDEKS
Publisher : Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jindeks.v8i1.4753

Abstract

Perkembangan teknologi saat ini berkembaang di berbagai bidang dan membawa berbagai dampak dalam kehidupan. Dalam bidang pendidikan salah satunya, dengan memnafaatkan teknologi dapat membantu perkembangan pengetahuan wawasan siswa dalam kegiatan belajar mengajar. Sistem Pendeteksi Kebakaran atau SISPEK berbasis Internet of Things (IoT) merupakan sistem yang dirancang untuk mendeteksi kebakaran maupun asap yang diimplementasikan dengan media edukasi sebagai pembelajaran untuk siswa agar mengetahui dan mengenai teknologi IoT yang dapat digunakan dan dimanfaatkan dalam kegiatan sehari-hari. Selain itu, tujuan dalam penelitian ini adalah sebagai pendeteksi kebakaran yang mampu membantu mengidentifikasi apabila terjadi kebakaran didalam ruangan maupun di kelas, sehingga dengan adanya SISPEK dapat membantu pengevakuasian murid dan meminimalisir terjadinya kerugian baik untuk anak sekolah maupun pihak sekolah itu sendiri. Serta hal ini dapat mengurangi kerusakan properti, perlengkapan, dan aset berharga, serta menghindari korban luka bagi anak-anak sekolah. Penelitian ini, menggunakan metode pendekatan User-Centered Design (UCD) dimana prototipe alarm kebakaran yang dirancang merupakan sistem interaktif terhadap pengguna berbasis IoT sebagai bentuk media edukasi keselamatan didalam ruangan atau di kelas. Berdasarkan kuisioner didapatkan hasil pre-test menunjukkan kesadaran tinggi bagi seluruh siswa kelas 5B di SD Lab School UPI Purwakarta, terkait akan pentingnya keselamatan dari kebakaran di sekolah dasar. Antusiasme siswa menjadi faktor kunci keberhasilan simulasi, dengan tingkat kesadaran terhadap kebakaran yang tinggi. Hasil post-test menunjukkan efektivitas kegiatan, dengan 96,3% siswa menyatakan materi bermanfaat. Tingkat pemahaman siswa tentang IoT meningkat, dan 27 siswa menganggap alat pendeteksi kebakaran berbasis IoT (SISPEK) diperlukan oleh sekolah. Oleh karena itu, kegiatan ini sukses meningkatkan kesadaran dan pengetahuan siswa kelas 5B terkait evakuasi dini dan penerapan IoT dalam kehidupan sehari-hari yang dapat memberikan nyamanan dan kemudahan.
Design of A Cataract Detection System based on The Convolutional Neural Network Agustin, Sarah; Putri, Eka Novelia; Ichsan, Ichwan Nul
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 8 No. 1 (2024)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v8i1.1019

Abstract

Cataract, a condition characterized by clouding of the eye's lens, leads to decreased vision and potentially blindness. In Indonesia, it is the predominant cause of blindness, accounting for 81.2% of cases. Given the rising life expectancy, the incidence of degenerative diseases like cataracts is expected to increase. This research aims to develop a cataract detection system capable of classifying eye images as either indicative of cataracts or normal. Utilizing Convolutional Neural Networks (CNN) and RGB-based image processing—including edge detection techniques such as Canny and Prewitt—the system identifies eye contours. This facilitates image segmentation to ascertain the eye's condition. Therefore, image collection and processing models play a crucial role in this study. The research findings indicate that the system functions effectively, with a 98% success rate in accurately processing normal eye images through the CNN model without detecting cataracts. When tested using grayscale imaging, cataract-afflicted eyes—characterized by red spots in the images—were also successfully identified by the CNN model. These test results demonstrate that the designed cataract detection system can accurately classify images into normal or cataract-afflicted eyes with high precision. This system shows promise for use in early cataract detection, potentially helping to reduce the prevalence of cataract-related blindness in Indonesia.
Co-Authors Ahmad Budiaman Aziz, Shofwan Abdul Endah Setyowati Fadhlullah, Wafiq Nur Fauzan, Muhammad Iqbal Hardjanto - Ichwan Nul Ichsan Kusuma, Surya Lawrensia Limbong, Zest Maharani, Chandra Puspita Novelia, Eka Putri, Eka Novelia Putrinismara, Moktika Rahayaan, Yohana Natalia Salmadiina, Ashrida