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GROWTH RESPONSE AND YIELD OF ONION (Allium ascalonicum L) AGAINST CHICKEN MANURE FERTILIZER AND DIFFERENT OF CROPPING DISTANCE APPLICATION Andre Wahyu Pratama; Ansoruddin Ansoruddin; Sri Susanti Ningsih
Bernas : Jurnal Penelitian Pertanian Vol 14, No 3 (2018): Bernas October 2018
Publisher : Universitas Asahan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1229.303 KB)

Abstract

This research was conducted in Dusun Benteng Jaya Village, Sei Balai Subdistrict, Batu Bara District, Province North Sumatera with altitude of place 5 m asl, flat topography and climate type C. The research was conducted in May 2017 to 2017. This research was arranged based on Factorial Randomized Block Design with 2 treatment factors and 3 replications. The first factor is the dosage of chicken manure (A) fertilizer with 4 levels: A0 0 kg / plot, A1 = 0.5 kg / plot, A2 = 1.0 kg / plot and A3 = 1.5 kg / plot. The second factor was treatment of crop distance (J), with 3 levels ie J0 = 20 cm x 20 cm (25 plants per plot), J1 = 20 cm x 25 cm (20 plants per plot), and J3 = 20 cm x 50 cm (10 plants per plot). The results of the treatment of chicken manure have a significant effect on the growth and production of onion, with the best dose at 1.5 kg / plot (A3)treatment. Different plant distance treatment significantly affected on growth and production of onion, with the best cropping distance 20 cm x 50 cm (J3). The interaction between chicken manure application and different cropping distance treatment on growth and production of onion showed no significant effect on all parameters observed.
DETEKSI TINGKAT KEJERNIHAN AIR MENGGUNAKAN FUZZY LOGIC BERBASIS PENGOLAHAN CITRA SEBAGAI INFORMASI MANAJEMEN PERUSAHAAN AIR MINUM ANDRE WAHYU PRATAMA; RADEN RORO HAPSARI PENI AGUSTI
JURNAL TEKNIK ELEKTRO Vol 8 No 3 (2019): SEPTEMBER 2019
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jte.v8n3.p%p

Abstract

Abstrak Klasifikasi citra merupakan kegiatan mengkaji citra dengan maksud untuk mengidentifikasi objek dan menilai arti pentingnya objek tersebut. Dalam mengidentifikasi objek tersebut ditemukan faktor-faktor ketidakpastian, karena itu perlu adanya klasifikasi dengan menggunakan fuzzy logic yang dapat digunakan untuk menentukan jenis objek tersebut. Penelitian ini bertujuan untuk melakukan pengecekan tingkat kejernihan air pada citra air perusahaan air minum, banyak informasi yang bisa didapat pada citra air, dan informasi tersebut dapat digunakan untuk menyederhanakan analisis citra, misalkan ekstraksi warna. Citra memiliki citra warna, salah satunya adalah citra warna model RGB. Selain itu sebuah citra juga memiliki citra skala keabuan (grayscale) yang dapat di identifikasi dengan ekstraksi fitur orde satu. Unsur-unsur citra RGB dan skala keabuan tersebut dapat dijadikan masukan ke sistem fuzzy logic, sehingga sistem fuzzy logic dapat mengenali atau membedakan suatu objek berbentuk citra berdasarkan warnanya. Hasil dari pengujian penelitian ini diperoleh tingkat akurasi pelatihan citra air jernih, air sedang dan air keruh dengan akurasi 91,66%. Penelitian ini bertujuan untuk menjadikan perusahaan air minum memberikan pelayanan yang lebih baik dalam melakukan pengecekan tingkat kejernihan air. Pada saat ini informasi memegang peranan yang sangat penting dan menjadi sangat diperlukan, baik itu informasi dalam bentuk teks maupun dalam bentuk citra. Kata Kunci : citra, warna, keabuan, fuzzy logic, web, internetAbstractImage classification is an activity of studying images with the intention of identifying objects and assessing the importance of the object. In identifying the object found uncertainty factors, because of that the need for classification using fuzzy logic can be used to determine the type of object. This study aims to check the water clarity level in the water image of drinking water companies, a lot of information can be obtained on water imagery, and that information can be used to simplify image analysis, for example color extraction. The image has a color image, one of which is the RGB image color image. In addition, an image also has a grayscale image which can be identified by first-order feature extraction. The elements of RGB and grayscale images can be used as input to fuzzy logic systems, so that fuzzy logic systems can recognize or distinguish an object in the form of an image based on its color.The results of this research test showed that the accuracy of training for images of clear water, medium water and turbid water training with an accuracy of 91.66%. This study aims to make drinking water companies provide better service in checking water clarity. At this time information plays a very important role and becomes very necessary, both information in the form of text and in the form of images. Keywords : image, color, grayscale, fuzzy logic, web, internet