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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Scientific Journal of Informatics ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JURNAL NASIONAL TEKNIK ELEKTRO Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Ilmiah Teknologi Informasi Asia Jurnal Penelitian Pendidikan IPA (JPPIPA) Elektrika Matrik : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer MIND (Multimedia Artificial Intelligent Networking Database) Journal KOMPUTIKA - Jurnal Sistem Komputer Applied Technology and Computing Science Journal Jurnal Teknologi Informasi dan Terapan (J-TIT) Dielektrika : Jurnal Ilmiah Kajian Teori dan Aplikasi Teknik Elektro JISA (Jurnal Informatika dan Sains) JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Djtechno: Jurnal Teknologi Informasi Jurnal Ilmu Komputer dan Informatika Jurnal Rekayasa elektrika Journal of Educational Engineering and Environment
Imron, Arizal Mujibtamala Nanda
Teknik Elektro, Fakultas Teknik, Universitas Jember, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Social Sciences

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Classification of White Blood Cell Abnormalities for Early Detection of Myeloproliferative Neoplasms Syndrome Based on K-Nearest Neighborr Fitri, Zilvanhisna Emka; Syahputri, Lindri Nalentine Yolanda; Imron, Arizal Mujibtamala Nanda
Scientific Journal of Informatics Vol 7, No 1 (2020): May 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v7i1.24372

Abstract

The myeloproliferative neoplasms (MPNs) are clonal hematopoietic stem cell disorders characterized by dysregulated proliferation and expansion of one or more of the myeloid lineages. The initial symptoms of MPN is a bone marrow abnormalities when producing red blood cells, white blood cells and platelets in large numbers and uncontrolled. An automatic and accurate white blood cell abnormality classification system is needed. This research uses digital image processing techniques such as conversion to the modified CIELab color space, segmentation techniques based on threshold values and feature extraction processes that produce four morphological features consisting of area, perimeter, metric and compactness. then the four features become input to the K-Nearest Neighborr (KNN) method. The testing process is based on variations in the value of K to get the best accuracy percentage of 94.3% tested on 159 test data.
Classification of White Blood Cell Abnormalities for Early Detection of Myeloproliferative Neoplasms Syndrome Based on K-Nearest Neighborr Fitri, Zilvanhisna Emka; Syahputri, Lindri Nalentine Yolanda; Imron, Arizal Mujibtamala Nanda
Scientific Journal of Informatics Vol 7, No 1 (2020): May 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v7i1.24372

Abstract

The myeloproliferative neoplasms (MPNs) are clonal hematopoietic stem cell disorders characterized by dysregulated proliferation and expansion of one or more of the myeloid lineages. The initial symptoms of MPN is a bone marrow abnormalities when producing red blood cells, white blood cells and platelets in large numbers and uncontrolled. An automatic and accurate white blood cell abnormality classification system is needed. This research uses digital image processing techniques such as conversion to the modified CIELab color space, segmentation techniques based on threshold values and feature extraction processes that produce four morphological features consisting of area, perimeter, metric and compactness. then the four features become input to the K-Nearest Neighborr (KNN) method. The testing process is based on variations in the value of K to get the best accuracy percentage of 94.3% tested on 159 test data.
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

Abstract

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%.
The UNJUK KERJA MOTOR BRUSHLESS DIRECT CURRENT AXIAL FLUX 3 FASA STATOR GANDA TERHADAP PERBEDAAN JENIS KAWAT ENAMEL PADA KUMPARAN STATOR Faisal Alif Hidayat; Widyono Hadi; Arizal Mujibtamala Nanda Imron
DIELEKTRIKA Vol 7 No 2 (2020): DIELEKTRIKA
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/dielektrika.v7i2.244

Abstract

Pada fenomena dewasa kini, krisis energi merupakan suatu bentuk masalah yang paling menjadi perbincangan khalayak, termasuk di Indonesia. Berdasarkan masalah tersebut, perlu pengembangan dan perbaikan terhadap teknologi yang sudah maupun belum ada untuk masyarakat. Pengujian unjuk kerja dilakukan menggunakan motor Brushless Direct Current Axial Flux 3 fasa stator ganda. Pengujian dilakukan dengan dua kawat enamel yang berbeda jenis (Hellenic dan Supreme) 0,3 mm. Setiap kumparan pada kawat enamel memiliki panjang 13,5 m yang terpasang pada stator. Dari hasil pengujian dengan menggunakan perbedaan jenis kawat enamel Hellenic dan Supreme, didapat perbedaan pada kecepatan maupun torsi yang dihasilkan. Namun untuk motor Brushless Direct Current kawat enamel Hellenic lebih baik karena memiliki kuat torsi yang lebih tinggi (0,336 Nm) dengan perubahan panas yang rendah jika dibandingkan dengan kawat enamel Supreme (0,24 Nm) dengan perubahan panas yang tinggi. Diketahui perubahan resistansi pada kawat enamel sangat signifikan berubah saat pengujian berulang yang dipengaruhi perubahan suhu.
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

Abstract

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.
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

Abstract

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 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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (517.489 KB) | DOI: 10.25077/jnte.v11n1.899.2022

Abstract

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%.
Ensiklopedia Digital Berdasarkan Klasifikasi Varietas Buah Mangga (Mangifera spp.) Menggunakan Algoritma Backpropagation Zilvanhisna Emka Fitri; Riska Aprilia; Abdul Madjid; Arizal Mujibtamala Nanda Imron
Komputika : Jurnal Sistem Komputer Vol 11 No 2 (2022): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v11i2.5513

Abstract

Mango is a leading fruit commodity that is able to increase industrial development and exports in Indonesia. There are 33 species of mangoes spread throughout the territory of the Republic of Indonesia and have many variations of fruit shapes in each type. However, the problem that occurs is the difficulty of information related to data on mango varieties, so to help these problems, the researchers created a digital encyclopedia system that is able to provide information related to the diversity of mango varieties. This encyclopedia can classify and identify 5 types of mangoes, namely Apple Mango, Gedong Gincu Mango, Golek Mango, Manalagi Mango and Gadung Mango. Parameters used to distinguish mango varieties are area, perimeter, eccentricity, major axis length and diameter. The classification method used, namely backpropagation, is able to classify the five mango varieties with a training accuracy of 99.6% and a testing accuracy of 96%. Keywords - digital encyclopedia; mango varieties; computer vision; shape parameters; backpropagation.
Ensiklopedia Digital Varietas Ubi Jalar Berdasarkan Klasifikasi Citra Daun Menggunakan KNearest Neighbor Bahtiar Adi Prasetya; Zilvanhisna Emka Fitri; Abdul Madjid; Arizal Mujibtamala Nanda Imron
Elektrika Vol 14, No 1 (2022): April 2022
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/elektrika.v14i1.4329

Abstract

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.
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)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (544.388 KB) | DOI: 10.29207/resti.v6i1.3571

Abstract

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.
Co-Authors Abdul Madjid Abdul Madjid Abdul Madjid Abdul Madjid ABDUL MADJID Abdul Madjid Ahmad Aris Ubaidillah Ainun Nurkharima Noviana Ali Rizal Chaidir Anam, Khairul Ari Baskara Bahtiar Adi Prasetya Brilyan Andi Syahbana Dyah Laksito Rukmi Faisal Alif Hidayat Fitri, Zilvanhisna Emka Gramandha Wega Intyanto Guido Dias Kalandro I Putu Dody Lesmana Immawan Wicaksono Lalitya Nindita Sahenda Mega Silvia NISKE ELMY PAULINA Nugroho Setyo Wibowo Panji Eka Prasetya Pramuditha Shinta Dewi Puspitasari Prawidya Destarianto Ramadania, Elsa Manora Rifqi Hakim Ariesdianto Riska Aprilia Rizkiyah Rizkiyah Slamet Riyadi Sudarti Sudarti Sulton Mubarok Sulton Mubarok Sumardi Sumardi Syahputri, Lindri Nalentine Yolanda Ulandari Susika Ully Nuhanatika Wahyu Muldayani Widjonarko Widyono Hadi Wildan Bakti Nugroho Yanuar Ridwan Hisyam Zilvanhisna Emka Fitri Zilvanhisna Emka Fitri Zilvanhisna Emka Fitri Zilvanhisna Emka Fitri ZILVANHISNA EMKA FITRI Zilvanhisna Emka Fitri