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All Journal Jurnal Buana Informatika JURNAL NASIONAL TEKNIK ELEKTRO Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer MIND (Multimedia Artificial Intelligent Networking Database) Journal Applied Technology and Computing Science Journal Jurnal Teknologi Informasi dan Terapan (J-TIT) JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Djtechno: Jurnal Teknologi Informasi Journal of Community Development Jurnal Ilmu Komputer dan Informatika Jurnal Akuntansi AKUNESA Jurnal Rekayasa elektrika Jurnal ilmiah teknologi informasi Asia

Zilvanhisna Emka Fitri

Politeknik Negeri Jember

Author-ID : 2490560

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Nursing Public Health Social Sciences

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Penentuan Tingkat Kematangan Cabe Rawit (Capsicum frutescens L.) Berdasarkan Gray Level Co-Occurrence Matrix Zilvanhisna Emka Fitri; Ully Nuhanatika; Abdul Madjid; Arizal Mujibtamala Nanda Imron
Jurnal Teknologi Informasi dan Terapan Vol 7 No 1 (2020)
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v7i1.121

The demand for cayenne pepper in Indonesia tends to increase annually, but the productivity of cayenne pepper continues to decline and depends on the changing seasons. One of the factors that must be considered in the harvest of cayenne pepper is the level of maturity. This research aims to classify the maturity level of cayenne pepper using the extraction of color and texture features. The extraction of features based on the color is taken from the mean saturation value, while the extraction of feature-based textures uses the value of the Gray Level Co-Occurrence Matrix (GLCM) feature ASM (Angular Second Moment), contrast, IDM (Inverse Difference (Entropy) and correlation (Correlation) then using angles of 0 ° and 45 °. These features become input in the classification process using the Backpropagation method. The results of the system training are able to classify the level of maturity of cayenne pepper with an accuracy of 81.4% and an accuracy of the testing process of 74.2%. Permintaan cabai rawit di Indonesia cenderung meningkat setiap tahunnya, namun produktivitas cabai rawit terus menurun dan bergantung pada pergantian musim. Salah satu faktor yang harus diperhatikan dalam panen cabai rawit adalah tingkat kematangan. Penelitian ini bertujuan untuk melakukan klasifikasi tingkat kematangan cabai rawit menggunakan ekstraksi fitur warna dan tekstur. Ekstraksi fitur berdasarkan warna diambil dari nilai mean saturasi, sedangkan ekstraksi fitur berdasarkan tekstur menggunakan nilai fitur Gray Level Co-occurrence Matrix (GLCM) yaitu ASM (Angular Second Moment), Kontras (Contrast), IDM (Inverse Difference Momentum), Entropi (Entropy) dan Korelasi (Correlation) dan menggunakan sudut 0° dan 45°. Fitur-fitur tersebut menjadi masukan pada proses klasifikasi menggunakan metode Backpropagation. Hasil pelatihan sistem mampu mengklasifikasi tingkat kematangan cabai rawit dengan akurasi sebesar 81,4% dan akurasi proses pengujian cabai rawit sebesar 74,2%.

Comparison of Classification for Grading Red Dragon Fruit (Hylocereus Costaricensis) Zilvanhisna Emka Fitri; Ari Baskara; Abdul Madjid; Arizal Mujibtamala Nanda Imron
JURNAL NASIONAL TEKNIK ELEKTRO Vol 11, No 1: March 2022
Publisher : Jurusan Teknik Elektro Universitas Andalas

Pitaya is another name for dragon fruit which is currently a popular fruit, especially in Indonesia. One of the problems related to determining the quality of dragon fruit is the postharvest sorting and grading process. In general, farmers determine the grading system by measuring the weight or just looking at the size of the fruit, of course, this raises differences in grading perceptions so that it is not by SNI. This research is a development of previous research, but we changed the type of dragon fruit from white dragon fruit (Hylocereus undatus) to red dragon fruit (Hylocereus costaricensis). We also adapted the image processing and classification methods in previous studies and then compared them with other classification methods. The number of images in the training data is 216, and the number of images in the testing data is 75. The comparison of the accuracy of the three classification methods is 84% for the KNN method, 85.33% for the Naive Bayes method, and 86.67% for the Backpropagation method. So that the backpropagation method is the best classification method in classifying the quality grading of red dragon fruit. The network architecture used is 4, 8, 3 with a learning rate of 0.3 so that the training accuracy is 98.61% and the testing accuracy is 86.67%.

Detection of Essential Thrombocythemia based on Platelet Count using Channel Area Thresholding Prawidya Destarianto; Ainun Nurkharima Noviana; Zilvanhisna Emka Fitri; Arizal Mujibtamala Nanda Imron
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 1 (2022): Februari 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Essential Thrombocythemia is one of the Myeloproliferative Neoplasms Syndrome where the mutation of the JAK2V617F gene causes the bone marrow to produce excessive platelets. For early detection of Essential Thrombocythemia disease using a full blood count and peripheral blood smear examination. The main characteristic is that giant platelets are found as large as young lymphocytes with a number of more than 21 cells in one field of view. The purpose of this research is to detect Essential Thrombocythemia by counting the number of platelets in the peripheral blood smear image. This research utilizes computer vision technique where the research stages consist of peripheral blood smear image, color conversion, image enhancement, segmentation, labeling process, feature extraction and K-Nearest Neighbor classification. There are three features used, namely the number of platelet cells, area and perimeter. The K-Nearest Neighbor method is able to classify 215 training data with an accuracy of 98.13% and classify 40 testing data with an accuracy of 100% based on the value of K = 3.

PENGENALAN HURUF LATIN PADA ANAK USIA DINI DENGAN PENERAPAN METODE BACKPROPAGATION Slamet Riyadi; Zilvanhisna Emka Fitri; Arizal Mujibtamala Nanda Imron
Djtechno: Jurnal Teknologi Informasi Vol 2, No 2 (2021): Desember
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v2i2.1480

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%.

Pemanfaatan Power Sprayer Guna Mengendalikan Hama Kopi di Desa Klungkung Jember Abdul Madjid; Abdurrahman Salim; Anni Nur Aisyah; Zilvanhisna Emka Fitri
Journal of Community Development Vol. 3 No. 1 (2022): August
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/comdev.v3i1.70

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.

Identifikasi Penyakit Daun Jeruk Siam Menggunakan K-Nearest Neighbor Rifqi Hakim Ariesdianto; Zilvanhisna Emka Fitri; Abdul Madjid; Arizal Mujibtamala Nanda Imron
Jurnal Ilmu Komputer dan Informatika Vol 1 No 2 (2021): JIKI - Desember 2021
Publisher : CV Firmos

Jeruk siam adalah salah satu jeruk local yang mempunyai nilai jual yang tinggi di Indonesia. Tahun 2020, tingkat produksi jeruk siam mengalami penurunan menjadi 712.585 ton di Jawa Timur. Salah satu faktor utama yang menyebabkan menurunnya tingkat produksi jeruk siam yaitu serangan penyakit pada daun jeruk siam. Dua penyakit yang sering menyerang daun jeruk siam adalah penyakit kanker yang disebabkan oleh patogen Xanthomonas axonopodis pv.citri dan penyakit ulat peliang. Selama ini, pengamatan pada penyakit daun jeruk siam dilakukan secara manual menggunakan mata sehingga penentuan penyakit tersebut bersifat subyektif. Untuk mengatasi masalah tersebut dibuatlah sistem otomatis identifikasi daun jeruk siam sehat dan daun jeruk siam terserang penyakit dengan bantuan teknik computer vision. Tahapan penelitian yaitu pengumpulan citra daun jeruk, konversi warna, ekstraksi fitur warna dan tekstur serta klasifikasi K-Nearest Neighbor (KNN). Parameter fitur yang digunakan yaitu fitur warna GB, fitur tekstur (ASM, entropi dan kontras). Metode KNN mampu mengklasifikasi dan mengidentifikasi penyakit daun jeruk siam dengan akurasi sebesar 70% dengan variasi nilai K = 21.

Klasifikasi Kerusakan Mutu Tomat Berdasarkan Seleksi Fitur Menggunakan K-Nearest Neighbor NISKE ELMY PAULINA; ZILVANHISNA EMKA FITRI; ABDUL MADJID; ARIZAL MUJIBTAMALA NANDA IMRON
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 6, No 2 (2021): MIND Journal
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v6i2.144-154

AbstrakTomat (Lycopersicum esculentum Mill.) merupakan satu komoditas unggulan pertanian karena penjualan jangka panjangnya baik. Menurunnya jumlah produktivitas dan mutu tomat disebabkan oleh curah hujan yang tinggi, cuaca dan budidaya yang tidak baik sehingga buah tomat menjadi busuk, retak, dan timbul bercak. Penyuluhan terkait peningkatan mutu tomat dinilai kurang efektif sehingga dibutuhkan sebuah sistem identifikasi kerusakan mutu buah tomat yang mampu memberikan edukasi kepada petani. Penelitian ini adalah pengembangan penelitian sebelumnya, untuk mendapatkan citra segmentasi dan ekstraksi fitur digunakan penggunaan contrast stretching dan deteksi tepi sobel. Namun kedua teknik tersebut diganti penggunaan operasi citra negatif. Didapatkan fitur yang optimal adalah gabungan fitur morfologi dan pada masing-masing sudut berdasarkan seleksi fitur. Persentasi akurasi metode KNN pada pelatihan sebesar 86.6% sedangkan akurasi pengujiannya sebesar 70%.Kata kunci: kerusakan mutu, tomat, seleksi fitur, K-Nearest NeighborAbstractTomato (Lycopersicum esculentum Mill.) is one of the leading agricultural commodities because of its good long-term sales. The decrease in the amount of productivity and quality of tomatoes is caused by high rainfall, bad weather and cultivation so that the tomatoes become rotten, cracked, and have spots. Counseling related to improving the quality of tomatoes is considered ineffective so that a system for identifying damage to the quality of tomatoes is needed that is able to provide education to farmers. This study is a development of previous research, to obtain segmented images and feature extraction using contrast stretching and sobel edge detection. However, both techniques were replaced by using negative image operations. The optimal feature is a combination of morphological features and correlations at each angle based on feature selection. The percentage of accuracy of the KNN method in training is 87%, while the accuracy in the testing is 70%.Keywords: quality damage, tomato, feature selection, K-Nearest Neighbo

PENERAPAN ANALYTICAL HIERARCHY PROCESS UNTUK PEMILIHAN PAKET WEDDING ORGANIZER DI KABUPATEN JEMBER Zilvanhisna Emka Fitri; Arizal Mujibtamala Nanda Imron; Ulandari Susika; Yanuar Ridwan Hisyam
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 6 No. 2 (2021)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v6i2.81

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.

Red Dragon Fruit (Hylocereus costaricensis) Ripeness Color Classification by Naïve Bayes Algorithm Zilvanhisna Emka Fitri; Mega Silvia; Abdul Madjid; Arizal Mujibtamala Nanda Imron; Lalitya Nindita Sahenda
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 5 No 1 (2022): June
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v5i1.3690

Dragon fruit is a unique fruit that is popular in Indonesia. besides having a sweet taste, this fruit also contains fiber, vitamins and minerals that are good for health. Dinas Pertanian Kabupaten Banyuwangi noted that the total dragon fruit production was 906,511.61 tons and the total productivity was 261.14 Kw/Ha in 2018. This shows that Kabupaten Banyuwangi is one of the largest producers of red dragon fruit in East Java Province. One of the problems in determining the quality of dragon fruit is choosing the harvest time, considering that dragon fruit is a non-climatic fruit. Non-climateric fruit is when we harvest fruit in its raw state, the fruit will never become ripe, so determining the harvest time for dragon fruit is very important. The determination made by paying discoloration and sizes of dragon fruit that is considered less effective. To overcome this, a system was created that was able to determine the level of dragon fruit maturity automatically by utilizing digital image processing techniques and intelligent systems. The parameters used are color features and GLCM texture features using angles 0°, 45°, 90° and 135° These features are parameters in the classification process using the Naïve Bayes method. Naïve bayes is able to classify the level of maturity of red dragon fruit (Hylocereus costaricensis) with an accuracy rate of 87.37%.

Deteksi Keaslian Uang Kertas Berdasarkan Fitur Gray Level Co-Occurrence Matrix (GLCM) Menggunakan K-Nearest Neighbor Defi Tamara; M. Haerul Anam; Wike Sri Widari; Ardan Venora Falahudin; Widya Yuristika Oktavia; Zilvanhisna Emka Fitri; Aji Seto Arifianto
Jurnal Buana Informatika Vol. 13 No. 02 (2022): Jurnal Buana Informatika, Volume 13, Nomor 2, Oktober 2022
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v13i02.5716

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 Neighbor

Co-Authors Abdul Madjid Abdul Madjid ABDUL MADJID Abdul Madjid Abdurrahman Salim Ainun Nurkharima Noviana Aji Seto Arifianto Anni Nur Aisyah Ardan Venora Falahudin Ari Baskara Arizal Mujibtamala Nanda Imron Brilyan Andi Syahbana Cherry Triwidiarto Datik Lestari Defi Tamara Dyah Laksito Rukmi Lalitya Nindita Sahenda M. Haerul Anam Mega Silvia NISKE ELMY PAULINA Oryza Ardhiarisca Pramuditha Shinta Dewi Puspitasari Prawidya Destarianto Rahma Rina Wijayanti Rifqi Hakim Ariesdianto Rizkiyah Rizkiyah Slamet Riyadi Sulton Mubarok Supriyadi Supriyadi Ulandari Susika Ully Nuhanatika Widya Yuristika Oktavia Wike Sri Widari Wildan Bakti Nugroho Yanuar Ridwan Hisyam

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