Klasifikasi Trombosit Pada Citra Hapusan Darah Tepi Berdasarkan Gray Level Co-Occurrence Matrix Menggunakan Backpropagation

Penelitian ini bertujuan untuk melakukan klasifikasi sel trombosit pada citra hapusan darah berdasarkan tekstur fitur yaitu menggunakan Gray Level Co-occurrence Matrix (GLCM). Fitur yang digunakan adalah Angular Second Moment (ASM), Invers Different Moment (IDM), dan Entropi. Fitur-fitur tersebut menjadi masukan pada proses klasifikasi. Backpropagation digunakan untuk mengklasifikasi antara sel leukosit, sel trombosit normal dan sel trombosit raksasa. Hasil pengujian, backpropagation mampu mengklasifikasikan jenis sel dengan akurasi pada leukosit 90.31%, trombosit normal, 93.88% dan trombosit raksasa 86.22%. Berdasarkan jenis citra AB 85.71%, citra AL, 87.75%, citra BG 84.69% dan citra RG 83.67%. Jadi sistem klasifikasi ini mampu digunakan sebagai alat bantu bagi dokter atau analis medis untuk mempercepat proses diagnosis trombosit pada bidang kesehatan. ===================================================================== This study aims to classify platelet cells in blood smear image based on feature texture using Gray Level Co-occurrence Matrix (GLCM). The features used are Angular Second Moment (ASM), Inverse Different Moment (IDM), and Entropy. These features become input to the classification process. Backpropagation is used to classify between leucocyte cells, normal platelet cells and giant platelet cells. Test results, backpropagation able to classify cell types with accuracy on leukocyte 90.31%, normal platelet 93.88% and giant platelet 86.22%. Based on AB image type 85.71%, AL image, 87.75%, BG image 84.69% and image RG 83.67%. So this classification system can be used as a tool for doctors or medical analysts to accelerate the process of platelet diagnosis in the field of health

Pemanfaatan Power Sprayer Guna Mengendalikan Hama Kopi di Desa Klungkung Jember

Coffee is one of the plantation commodities that are in great demand in Indonesia. Coffee production in East Java is the largest in Indonesia, one of the coffee-producing areas in East Java, namely Jember Regency. Some of the factors causing it, one of them from cultivation techniques and inadequate care and maintenance. In particular, many coffee pests are not handled properly. In addition, there is a factor in the level of technology absorption and the application of farm management as well as a less efficient and effective marketing system which has an impact on the income level of farmers. Therefore, it is necessary to innovate cultivation techniques and maintain coffee plants in order to maintain optimal coffee growth and produce better fruit, so as to increase farmers' income. The microcontroller-based sprayer battery is an innovative sprayer to increase coffee production in Klungkung village. The stages of this service activity start from the stage of preparation and coordination with partners, digging information (literature studies) in compiling counseling and training materials from controlling plant pest organisms, especially coffee from spraying techniques according to SOPs, coffee production management, to the coffee marketing system. The results of this dedication is the farmer of Klungkung village get benefits in good coffee cultivation techniques and in spraying pests using Power Sprayer technology

PEMBUATAN MEDIA PROMOSI DENGAN TEKNOLOGI DIGITAL MARKETING UNTUK MENINGKATKAN NILAI EKONOMI

Digital marketing merupakan aktivitas promosi baik itu untuk sebuah brand ataupun produk menggunakan media elektronik (digital). Keberhasilan Marketing atau penjualan merupakan satu paket kesatuan yang tidak hanya aktivitas promosi secara digital semata tetapi juga ada beberapa hal yang perlu diperhatikan yaitu kualitas dari produk yang menarik, inovatif serta cara SDM dalam hal ini pegawai memperlakukan pelanggan dengan baik misalnya dalam hal pelayanan dan ketepatan waktu pembuatan produk. Permasalahan yang terdapat pada mitra yaitu peralatan pendukung yang masih kurang standart dan minim sehingga menghambat proses pembuatan. Kurangnya ilmu pengetahuan SDM tentang kualitas produk “desain†yang baik, pemasaran dari mulut ke mulut perlu adanya dukungan terkait pemasaran dengan “digital marketing†dan komunikasi dengan pelanggan terkait proses pemesanan dan pembayaran belum ada manajemen usaha (SOP) yang jelas. Terkait dengan permasalahan mitra diatas maka perlu adanya dukungan terhadap mitra yaitu Pemberian pelatihan terkait kualitas produk terkait desain yang menarik dan menerapkannya pada katalog produk, Pendampingan dalam pembuatan digital marketing pada social media seperti IG dan Facebook, Pendampingan terkait manajemen usaha, pembuatan SOP kaitannya prosedur pembelian dan pembayaran serta komunikasi dengan pelanggan dan Pemberian bantuan teknologi yang mampu memberikan dukungan terhadap usaha mitra. Kata Kunci — Digital Marketing, Manajemen Usaha, Media Promos

Comparison of Neural Network Methods for Classification of Banana Varieties (Musa paradiasaca)

Every region in Indonesia has a very large diversity of banana species, but no system records information about the characteristics of banana varieties. The purpose of this research is to make an encyclopedia of banana types that can be used for learning by classifying banana varieties using banana images. This banana variety classification system uses image processing techniques and artificial neural network methods as classification methods.The varieties of bananas used are pisang merah, pisang pisang mas kirana, pisang klutuk, pisang raja and pisang cavendis. The parameters used are color features (Red, Green, and Blue) and shape features (area, perimeter, diameter, and length of fruit). The intelligent system used is the Backpropagation method and the Radial Basis Function Neural Network. The results showed that both methods were able to classify banana varieties with an accuracy rate of 98% for Backpropagation and 100% for the Radial Basis Function Neural Network

Penerapan Neural Network untuk Klasifkasi Kerusakan Mutu Tomat

The decrease in quality and productivity of tomatoes is caused by high rainfall, bad weather and cultivation so that the tomatoes become rotten, cracked, and spotting occurs. The government is trying to provide training to improve the quality of tomatoes for farmers. However, the training was not effective so the researchers helped create a system that was able to educate farmers in the classification of damage to tomato quality. This system serves to facilitate farmers in recognizing tomato damage thereby reducing the risk of crop failure. In this study, the classification method used is backpropagation with 7 input parameters. The input consists of morphological and texture features. The output of this classification system consists of 3 classes are blossom end rot, fruit cracking and fruit spots caused by bacterial specks. The best accuracy level of the system in classifying tomato quality damage in the training process is 89.04% and testing is 81.11%

Application of Feature Selection for Identification of Cucumber Leaf Diseases (Cucumis sativa L.)

According to data from BPS Kabupaten Jember, the amount of cucumber production fluctuated from 2013 to 2017. Some literature also mentions that one of the causes of the amount of cucumber production is disease attacks on these plants. Most of the cucumber plant diseases found in the leaf area such as downy mildew and powdery mildew which are both caused by fungi (fungal diseases). So far, farmers check cucumber plant diseases manually, so there is a lack of accuracy in determining cucumber plant diseases. To help farmers, a computer vision system that is able to identify cucumber diseases automatically will have an impact on the speed and accuracy of handling cucumber plant diseases. This research used 90 training data consisting of 30 healthy leaf data, 30 powdery mildew leaf data and 30 downy mildew leaf data. while for the test data as many as 30 data consisting of 10 data in each class. To get suitable parameters, a feature selection process is carried out on color features and texture features so that suitable parameters are obtained, namely: red color features, texture features consisting of contrast, Inverse Different Moment (IDM) and correlation. The K-Nearest Neighbor classification method is able to classify diseases on cucumber leaves (Cucumis sativa L.) with a training accuracy of 90% and a test accuracy of 76.67% using a variation of the value of K = 7.

Deteksi Keaslian Uang Kertas Berdasarkan Fitur Gray Level Co-Occurrence Matrix (GLCM) Menggunakan K-Nearest Neighbor

Abstract. Rupiah is the currency of Indonesia. One form is rupiah banknotes. The issuance and circulation of rupiah banknotes are under the authority of Bank Indonesia (BI) as the central bank. Currently, many incidents of counterfeiting are troubling the public. One of the characteristics of the authenticity of money that has not yet been found in counterfeit money is invisible ink, which is an invisible print that can only be seen when the money is exposed to ultraviolet light. Behind it, prolonged exposure to ultraviolet light harms eye and skin health. A system for detecting the authenticity of banknotes was created to overcome these problems using image processing techniques. The research stages are literature study, collecting banknote images illuminated by ultraviolet light, image processing (rotation, cropping, and resizing), RGB color component solving, GLCM feature extraction, and classification using the k-Nearest Neighbor (KNN) method. The KNN method can classify the authenticity of banknotes with an accuracy of 88% using the values of K = 3 and 7.Keywords: Rupiah Banknotes, Authenticity of Money, Gray Level Co-occurrence Matrix, K-Nearest Neighbor Abstrak. Rupiah merupakan mata uang Indonesia. Salah satu bentuknya adalah uang kertas rupiah. Penerbitan dan pengedaran uang kertas rupiah menjadi kewenangan Bank Indonesia (BI) sebagai bank sentral. Meski demikian, saat ini banyak kejadian pemalsuan uang yang meresahkan masyarakat. Salah satu ciri keaslian uang yang sampai saat ini belum ditemukan juga ada pada uang palsu ialah invisible ink, yaitu cetakan tidak kasat mata yang hanya terlihat ketika uang disinari cahaya ultraviolet. Dibalik hal itu, pancaran sinar ultraviolet yang berkepanjangan rupanya berbahaya bagi kesehatan mata dan kulit. Untuk mengatasi permasalahan tersebut, dibuatlah sistem pendeteksi keaslian uang kertas yang memanfaatkan teknik image processing. Tahapan penelitian yaitu studi literatur, pengumpulan citra uang kertas yang disinari sinar ultraviolet, pengolahan citra (rotasi, cropping, dan resize), pemecahan komponen warna RGB, ekstraksi fitur GLCM, dan klasifikasi dengan metode k-Nearest Neighbor (KNN). Metode KNN mampu mengklasifikasi keaslian uang kertas dengan perolehan akurasi 88% menggunakan nilai K = 3 dan 7.Kata Kunci: Uang Kertas Rupiah, Keaslian Uang, Gray Level Co-occurrence Matrix, KNearest Neighbo

PENGENALAN HURUF LATIN PADA ANAK USIA DINI DENGAN PENERAPAN METODE BACKPROPAGATION

Early childhood has difficulty remembering Latin letters or Roman characters than adults. Some of the factors that cause it are cognitive development, motivation, interest in learning, emotions and environmental factors. To overcome this, an innovative media is needed so that children can easily remember Latin letters. One of the innovative media applies digital image processing techniques and artificial intelligence. The fonts used are 10 types of letter models with image processing techniques such as preprocessing, binaryization, pixel mapping and creating vector as feature extraction.  While the artificial intelligence used is the backpropagation method. The total data is 208 letter images with 625 input features with 500 epochs, the best learning rate used by the system is 0.025 so that the best training accuracy is 93.96% and testing accuracy is 92.31%

PENERAPAN ANALYTICAL HIERARCHY PROCESS UNTUK PEMILIHAN PAKET WEDDING ORGANIZER DI KABUPATEN JEMBER

Persiapan pernikahan sering ditangani oleh jasa wedding organizer dan permasalahan yang terjadi adalah ketersediaan dana yang dimiliki oleh client sehingga akan mempengaruhi pemilihan paket pernikahan, lokasi dan tema pernikahan. Selama ini penyesuaian dana dan kebutuhan pernikahan dilakukan secara manual sehingga membuang waktu, tenaga dan kurang efisien bagi penyedia jasa wedding organizer. Untuk menyelesaikan permasalah tersebut maka dibuatlah sebuah sistem pen-dukung keputusan untuk pemilihan paket pernikahan pada Wedding Organizer di Kabupaten Jember dengan metode Analyti-cal Hierarchy Process (AHP). Berdasarkan hasil perhitungan, didapatkan bahwa kriteria dana memiliki bobot prioritas terbesar bila dibandingkan kriteria tamu undangan, lokasi pernikahan, tema pernikahan dan catering pernikahan. Bobot prioritas dari kriteria dana sebesar 0.335, kemudian kriteria dana tersebut dibandingkan dengan kriteria pemilihan paket wedding organizer. Hasil perhitungan dengan metode AHP didapatkan bahwa bobot prioritas terbesar pada kriteria Paket E Menengah yaitu 0.203, maka paket pernikahan yang direkomendasikan adalah Paket E Menengah dengan nilai consistency ratio (CR) sebesar 0.098

Ensiklopedia Digital Varietas Ubi Jalar Berdasarkan Klasifikasi Citra Daun Menggunakan KNearest Neighbor

Sweet potato is a source of carbohydrates which is an alternative food in order to accelerate food diversification. This is due to the high productivity of sweet potato so it is very profitable to cultivate. Sweet potato has many varieties, one of the differences is observed based on leaf shape which has four kinds of leaf shape, namely cordate, lobed, triangular and almost divided. The problem that often occurs is that many varieties have similarities, causing difficulties in distinguishing sweet potato varieties, especially for novice farmers. To overcome this problem, the researchers created a digital encyclopedia of sweet potato varieties based on leaf shape using computer vision. The parameters used are area, perimeter, metric, length, diameter, ASM, IDM, entropy, contrast and correlation at angles of 0°, 45°, 90° and 135°. The amount of data used is 256 training data and 40 testing data. The K-Nearest Neighbor method is able to classify sweet potato leaf images for digital encyclopedias with an accuracy of 95% with variations in the values of K = 23 and K = 25