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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Jurnal Rekayasa Elektrika Jurnal Buana Informatika ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JURNAL NASIONAL TEKNIK ELEKTRO Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Applied Technology and Computing Science Journal JISA (Jurnal Informatika dan Sains) JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Djtechno: Jurnal Teknologi Informasi Journal of Community Development

Zilvanhisna Emka Fitri

Politeknik Negeri Jember

Author-ID : 2490560

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

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The The Classification of Acute Respiratory Infection (ARI) Bacteria Based on K-Nearest Neighbor Zilvanhisna Emka Fitri; Lalitya Nindita Sahenda; Pramuditha Shinta Dewi Puspitasari; Prawidya Destarianto; Dyah Laksito Rukmi; Arizal Mujibtamala Nanda Imron
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 12 No 2 (2021): Vol. 12, No. 02 August 2021
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2021.v12.i02.p03

Acute Respiratory Infection (ARI) is an infectious disease. One of the performance indicators of infectious disease control and handling programs is disease discovery. However, the problem that often occurs is the limited number of medical analysts, the number of patients, and the experience of medical analysts in identifying bacterial processes so that the examination is relatively longer. Based on these problems, an automatic and accurate classification system of bacteria that causes Acute Respiratory Infection (ARI) was created. The research process is preprocessing images (color conversion and contrast stretching), segmentation, feature extraction, and KNN classification. The parameters used are bacterial count, area, perimeter, and shape factor. The best training data and test data comparison is 90%: 10% of 480 data. The KNN classification method is very good for classifying bacteria. The highest level of accuracy is 91.67%, precision is 92.4%, and recall is 91.7% with three variations of K values, namely K = 3, K = 5, and K = 7.

Comparison of Neural Network Methods for Classification of Banana Varieties (Musa paradiasaca) Zilvanhisna Emka Fitri; Wildan Bakti Nugroho; Abdul Madjid; Arizal Mujibtamala Nanda Imron
Jurnal Rekayasa Elektrika Vol 17, No 2 (2021)
Publisher : Universitas Syiah Kuala

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 Zilvanhisna Emka Fitri; Rizkiyah Rizkiyah; Abdul Madjid; Arizal Mujibtamala Nanda Imron
Jurnal Rekayasa Elektrika Vol 16, No 1 (2020)
Publisher : Universitas Syiah Kuala

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%.

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.

Application of Feature Selection for Identification of Cucumber Leaf Diseases (Cucumis sativa L.) Lalitya Nindita Sahenda; Ahmad Aris Ubaidillah; Zilvanhisna Emka Fitri; Abdul Madjid; Arizal Mujibtamala Nanda Imron
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.1046

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.

Pengenalan Pola Sinyal Electromyography (EMG) pada Gerakan Jari Tangan Kanan WAHYU MULDAYANI; ARIZAL MUJIBTAMALA NANDA IMRON; KHAIRUL ANAM; SUMARDI SUMARDI; WIDJONARKO WIDJONARKO; ZILVANHISNA EMKA FITRI
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 8, No 3 (2020): ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektro
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v8i3.591

ABSTRAKSinyal EMG merupakan salah satu sinyal yang dapat digunakan untuk memberikan perintah pada kursi roda listrik. Sinyal EMG yang digunakan diambil dari sinyal otot fleksor dan ekstensor yang berada di tangan kanan. Sinyal tersebut diambil menggunakan sensor Myo Armband. Klasifikasi sinyal EMG diambil dari pergerakan jari yang mewakili perintah gerak yaitu jari kelingking untuk bergerak maju, jari manis untuk berhenti, jari tengah untuk belok kanan dan jari telunjuk untuk belok kiri. Setiap sinyal EMG diekstraksi fitur untuk menentukan karakteristik sinyal sehingga fitur yang diperoleh adalah Average Absolute Value, Root Mean Square, Simple Integral Square, EMG Simple Variant and Integrated EMG. Kemudian fitur tersebut digunakan sebagai input dari metode klasifikasi Artificial Neural Network Backpropagation. Jumlah data latih yang digunakan adalah 800 data sedangkan data uji yang digunakan adalah 200 data. Tingkat keberhasilan proses klasifikasi ini sebesar 93%.Kata kunci: electromyogram, artificial neural network, klasifikasi sinyal, tangan kanan, Myo Armband. ABSTRACTEMG signal is one of the signals that can be used to give orders to electric wheelchairs. The EMG signal used is taken from the flexor and extensor muscle signals in the right hand. The signal is taken using the Myo Armband sensor. The EMG signal classification is taken from the movement of the finger which represents the command of motion ie the little finger to move forward, ring finger to stop, middle finger to turn right and index finger to turn left. Each EMG signal is extracted features to determine the signal characteristics so that the features obtained are Average Absolute Value, Root Mean Square, Simple Integral Square, EMG Simple Variant and Integrated EMG. Then the feature is used as input from the Backpropagation classification method. The amount of training data used is 800 data while the test data used is 200 data. The success rate of this classification process is 93%.Keywords: electromyogram, artificial neural network, signal classification, right hand, Myo Armband.

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.

Co-Authors Abdul Madjid Abdurrahman Salim Ahmad Aris Ubaidillah Ainun Nurkharima Noviana Aji Seto Arifianto Anam, Khairul Anni Nur Aisyah Ardan Venora Falahudin Ari Baskara Defi Tamara Dyah Laksito Rukmi Imron, Arizal Mujibtamala Nanda Lalitya Nindita Sahenda M. Haerul Anam Mega Silvia Pramuditha Shinta Dewi Puspitasari Prawidya Destarianto Rizkiyah Rizkiyah Slamet Riyadi Sumardi Sumardi Ulandari Susika Wahyu Muldayani Widjonarko Widya Yuristika Oktavia Wike Sri Widari Wildan Bakti Nugroho Yanuar Ridwan Hisyam

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