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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Jurnal Rekayasa Elektrika 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 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 JISA (Jurnal Informatika dan Sains) JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Djtechno: Jurnal Teknologi Informasi Jurnal Ilmu Komputer dan Informatika
Imron, Arizal Mujibtamala Nanda
Teknik Elektro, Fakultas Teknik, Universitas Jember, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Physics Social Sciences

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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

Mangga merupakan komoditas buah unggulan yang mampu meningkatkan perkembangan industri dan ekspor di Indonesia. Terdapat 33 spesies buah mangga yang tersebar di seluruh wilayah Republik indonesia dan memiliki banyak variasi bentuk pada setiap jenisnya. Namun permasalahan yang terjadi adalah sulitnya informasi terkait data pada varietas mangga, sehingga untuk membantu permasalahan tersebut maka peneliti membuat sistem ensiklopedia digital yang mampu memberikan informasi terkait keanekaragaman varietas buah mangga tersebut. Ensiklopedia ini dapat mengklasifikasikan dan mengidentifikasi 5 jenis mangga yaitu Mangga Apel, Mangga Gedong Gincu, Mangga Golek, Mangga Manalagi dan Mangga Gadung. Parameter yang digunakan untuk membedakan varietas mangga yaitu area, perimeter, eccentricity, major axis length dan diameter. Metode klasifikasi yang digunakan yaitu backpropagation mampu mengklasifikasi lima varietas mangga tersebut dengan akurasi pelatihan sebesar 99,6% dan akurasi pengujian sebesar 96%. Kata Kunci – ensiklopedia digital; varietas mangga; computer vision; parameter bentuk; backpropagation.
Perintah Kontrol Gerak Kursi Roda Elektrik Menggunakan Sensor Elektromiograf Arizal Mujibtamala Nanda Imron; Wahyu Muldayani; Sumardi Sumardi
Jurnal Rekayasa Elektrika Vol 15, No 1 (2019)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (660.165 KB) | DOI: 10.17529/jre.v15i1.12744

Abstract

Paralysis is a disease that can limit the mobility of the sufferer. One solution that can help people with paralysis in carrying out their mobility is the use of an electric wheelchair. In this study, an electric wheelchair with specifications where the wheelchair motion control uses muscles on both arms, so that the electric wheelchair is very suitable for patients with paralysis in the legs and weak hand strength in turning the wheels from the wheelchair. The input of motion control commands is carried out through an electromyograph sensor mounted on the flexor muscle in both patients’ arms. The output of each sensor is given a threshold of 2 volts to distinguish control commands or not. When the sensor output is more than the same as the threshold, it is considered logic one and the other is considered logic zero. The method is used to interpret the output as a control command by impulse detection. The electric wheelchair movement that can be done is forward, turn right, and turn left.
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.
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.
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.
Penerapan Fitur Warna dan Tekstur untuk Identifikasi Kerusakan Mutu Biji Kopi Arabika (Coffea Arabica) di Kabupaten Bondowoso Zilvanhisna Emka Fitri; Brilyan Andi Syahbana; Abdul Madjid; Arizal Mujibtamala Nanda Imron
Jurnal Ilmiah Teknologi Informasi Asia Vol 15 No 2 (2021): Volume 15 Nomor 2 (8)
Publisher : LP2M INSTITUT TEKNOLOGI DAN BISNIS ASIA MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v15i2.593

Abstract

Plantation crops are also a source of foreign exchange Indonesia is coffee. There are only two types of coffee that have economic value for cultivation, namely Arabica coffee and Robusta coffee. Bondowoso is a district in East Java that develops Arabica coffee. The problem is that farmers still use direct observation (manual) on each coffee bean to determine the quality of coffee beans so that this research is expected to be able to assist farmers in sorting the damage to the quality of coffee beans based on color and texture. The features used are color features and GLCM texture features at 0̊ and 45̊ angles. The total number of data is 198. The Backpropagation method is able to classify quality damage to Arabica coffee beans with a training accuracy rate of 100% and a testing accuracy rate of 97.5% at a learning rate variation of 0.5.
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.
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

Abstract

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.
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%.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1.311 KB) | DOI: 10.17529/jre.v17i2.20806

Abstract

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.
Co-Authors Abdul Madjid Abdul Madjid Abdul Madjid ABDUL MADJID Abdul Madjid Abdul Madjid Ahmad Aris Ubaidillah Ainun Nurkharima Noviana Anam, Khairul Ari Baskara Bahtiar Adi Prasetya Brilyan Andi Syahbana Dyah Laksito Rukmi Faisal Alif Hidayat Fitri, Zilvanhisna Emka I Putu Dody Lesmana Lalitya Nindita Sahenda Mega Silvia NISKE ELMY PAULINA Nugroho Setyo Wibowo Pramuditha Shinta Dewi Puspitasari Prawidya Destarianto Ramadania, Elsa Manora Rifqi Hakim Ariesdianto Riska Aprilia Rizkiyah Rizkiyah Slamet Riyadi Sudarti Sudarti 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