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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi e-Jurnal Arus Elektro Indonesia 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 Penelitian Pendidikan IPA (JPPIPA) Faktor Exacta Techne : Jurnal Ilmiah Elektroteknika Elektrika International Journal of New Media Technology 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) Journal Zetroem Djtechno: Jurnal Teknologi Informasi Jurnal Ilmu Komputer dan Informatika Algoritme Jurnal Mahasiswa Teknik Informatika Jurnal Pengabdian Masyarakat Bangsa Jurnal Rekayasa elektrika Journal of Educational Engineering and Environment Jurnal ilmiah teknologi informasi Asia
Arizal Mujibtamala Nanda Imron
Teknik Elektro, Fakultas Teknik, Universitas Jember, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities 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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences

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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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (812.104 KB) | DOI: 10.17529/jre.v16i1.15535

Abstract

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%.
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 | 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.
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 | 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.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v16i1.15535

Abstract

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%.
Implementing K-Nearest Neighbor to Classify Wild Plant Leaf as a Medicinal Plants Zilvanhisna Emka Fitri; Lalitya Nindita Sahenda; Sulton Mubarok; Abdul Madjid; Arizal Mujibtamala Nanda Imron
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 1 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.2220

Abstract

in leaf shape. Therefore, this study aimed to create a system to help increase public knowledge about wild plant leaves that also function as medicinal plants by the KNN method. Leaves of wild plants, namely Rumput Minjangan, Sambung Rambat, Rambusa, Brotowali, and Zehneria japonica, are also medicinal plants in comparison. Image processing techniques used were preprocessing, image segmentation, and morphological feature extraction. Preprocessing consists of scaling and splitting the RGB components and using an RGB component decomposition process to find the color component that best describes the leaf shape and generate the blue component image. The segmentation process used a thresholding technique with a gray threshold value (T) of less than 150, which best separates objects and backgrounds. Some morphological feature extraction used are area, perimeter, metric, eccentricity, and aspect ratio. Based on the results of this research, the KNN method with variations in K values, namely 13, 15, and 17, obtained a system accuracy of 94.44% with a total of 90% training data and 10% test data. This comparison also affected the increase in system accuracy.
Media Pembelajaran Pengenalan Buah (Fruits Zone) untuk Anak KB Menggunakan Deep Learning KOMARIAH, SITI INGEFATUL; PUTRI, DESTI FITRI AISYAH; PERMATASARI, INTAN; FITRI, ZILVANHISNA EMKA; ATMADJI, ERY SETIYAWAN JULLEV; WIDIASTUTI, RESKI YULINA; IMRON, ARIZAL MUJIBTAMALA NANDA
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 9, No 1 (2024): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v9i1.13-24

Abstract

ABSTRAK Keterbatasan media pembelajaran dan metode pembelajaran yang masih terpusat pada kemampuan guru menjadi kendala bagi Pos Alamanda 105 Jumerto, Jember. Dibutuhkan sebuah media pembelajaran yang interaktif dan dapat diakses dimanapun untuk meningkatkan kemampuan siswa khususnya dalam pengenalan buah. Solusinya, peneliti mengembangkan media pembelajaran interaktif pengenalan buah pada anak usia dini. Metode yang digunakan adalah Deep Learning (CNN) dengan arsitektur yaitu Resnet18. Arsitektur Resnet-18 dipilih karena tidak menghilangkan gradien dan fitur citra meski layer yang digunakan semakin dalam, sehingga connected layer dapat mengenali objek dengan akurat. Penelitian ini menggunakan 21 jenis buah populer dan buah unik yang dilengkapi fitur suara berbahasa Indonesia dan Bahasa Inggris. Jumlah data sebanyak 2100 citra buah dengan learning rate sebesar 0.0002 dan maksimal epoch sebesar 100 mampu mengklasifikasikan buah dengan tingkat akurasi sebesar 96% (pelatihan sistem) dan 95% (pengujian sistem). Kata Kunci: Media Pembelajaran, Fruits Zone , Deep Learning, ResNet18 ABSTRACT Limitations in learning media and teaching methods that are still centered on teachers' abilities pose challenges for Pos Alamanda 105 in Jumerto, Jember. An interactive learning media accessible anywhere is needed to enhance students' abilities, especially in fruit recognition. The solution is researchers developing an interactive early childhood fruit recognition learning media. The method used is Deep Learning (CNN) with the Resnet18 architecture. Resnet-18 architecture is chosen because it preserves gradients and image features even as the layers go deeper, allowing the connected layer to accurately recognize objects. This study covers 21 popular and unique fruits with voice features in Indonesian and English. With 2100 fruit images, a learning rate of 0.0002, and a maximum epoch of 100, the system achieves a classification accuracy of 96% (training) and 95% (testing).Keywords: Learning Media, Fruits Zone , Deep Learning, ResNet18
Fruit Zone : Media Pembelajaran Interaktif Pengenalan Buah Anak Kelompok Belajar Menggunakan ResNet18 Komariah, Siti Ingefatul; Putri, Desti Fitri Aisyah; Rahmawati, Siska Yulia; Fitri, Zilvanhisna Emka; Atmadji, Ery Setiyawan Jullev; Widiastuti, Reski Yulina; Imron, Arizal Mujibtamala Nanda
Faktor Exacta Vol 17, No 1 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i1.21101

Abstract

Learning media is very important in supporting learning activities in early childhood. Limited learning media and learning methods that are still centered on the ability and experience of teachers are an obstacle to improving learning at Pos Alamanda 105 Jumerto, Jember. An interactive, cheap, easy and accessible learning media is needed to improve students' abilities, especially in fruit recognition using both Indonesian and English. The solution, researchers used Deep Learning method for interactive learning media of fruit introduction in early childhood. The method used is Convolutional Neural Network with Resnet18 architecture. This research uses 21 types of popular fruits and unique fruits equipped with voice features in Indonesian and English. The total data of 2100 fruit images with a learning rate of 0.0002 and a maximum epoch of 100 wereable to classify the fruit with an accuracy rate of 96% (system training) and 95% (system testing).
Peningkatan Kemampuan Berbahasa Inggris pada Anak Usia Dini Melalui Media Pembelajaran Fruits Zone di Pos Paud Alamanda 105 Kabupaten Jember Putri, Desti Fitri Aisyah; Komariah, Siti Ingefatul; Permatasari, Intan; Fitri, Zilvanhisna Emka; Imron, Arizal Mujibtamala Nanda
Jurnal Pengabdian Masyarakat Bangsa Vol. 1 No. 10 (2023): Desember
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v1i10.572

Abstract

Pentingnya pemanfaatan media pembelajaran untuk mendukung aktivitas belajar pada anak usia dini merupakan aspek yang tidak dapat diabaikan. Pemasalahan yang terjadi adalah terbatasnya staf pengajar, kemampuan berbahasa asing pada anak serta kurangnya pemanfaatan teknologi tepat guna pada media pembelajaran yang belum diterapkan di Pos PAUD Alamanda 105, Kelurahan Jumerto, Kecamatan Patrang, Kabupaten Jember. Umumnya media pembelajaran yang digunakan menggunakan media buku, flashcard, serta benda-benda yang ditemukan di sekitar sekolah sebagai media pembelajaran utamanya pada materi pengenalan buah, sayuran, hewan dan menghitung benda. Sebagai inovasi pada media pembelajaran kami memanfaatkan teknologi berupa computer vision berbasis website bernama Fruit Zone. Fruit Zone sendiri merupakan media pembelajaran pengenalan 21 jenis buah baik dalam bahasa Indonesia maupun bahasa Inggris yang juga merupakan produk dari Program Kreatifitas Mahasiswa (PKM) Karya Inovasi pada tahun 2023. Tahapan kegiatan ini terdiri dari observasi dan survey mitra, analisis kebutuhan mitra, perancangan dan pembuatan  media pembelajaran Fruit Zone, pengujian media pembelajaran di mitra dan analisa hasil pembelajaran. Pada proses pengujian aplikasi ini, kami melibatkan 11 orang siswa dan 2 orang guru. Berdasarkan hasil observasi yang telah dilakukan terjadi peningkatan kemampuan siswa dalam mengenali nama buah baik bahasa inggris dan bahasa indonesia sebesar 80.82% hingga 90.91%. Hal ini menunjukkan bahwa media pembelajaran Fruit Zone efektif dalam meningkatkan kemampuan siswa Pos PAUD Alamanda 105.
Pengenalan Pola Sinyal Electromyography (EMG) pada Gerakan Jari Tangan Kanan MULDAYANI, WAHYU; IMRON, ARIZAL MUJIBTAMALA NANDA; ANAM, KHAIRUL; SUMARDI, SUMARDI; WIDJONARKO, WIDJONARKO; FITRI, ZILVANHISNA EMKA
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 8, No 3: Published September 2020
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.
Co-Authors ABDUL MADJID Abdul Madjid Abdul Madjid Abdul Madjid Abdul Madjid Abdul Madjid Ahmad Aris Ubaidillah Ainun Nurkharima Noviana Ali Rizal Chaidir Anam, M. Khairul Ari Baskara Arif, Praditya Zainal Atmadji, Ery Setiyawan Jullev Atmadji, Ery Setiyawan Jullev Bambang Sri Kaloko Bambang Sujanarko Brilyan Andi Syahbana Brilyan Andi Syahbana Candra Putri Rizkiyah Ramadhani Dananjaya Endi Pratama Dyah Laksito Rukmi Erwinda, Yudha Teja Faisal Alif Hidayat Fitri, Zilvanhisna Emka Fitri, Zilvanhisna Emka Guido Dias Kalandro I Putu Dody Lesmana Immawan Wicaksono Intan Permatasari Irawan, Ahmad Dandi Johan, Anju Alicia KOMARIAH, SITI INGEFATUL Lalitya Nindita Sahenda Lia Santoso Mega Silvia Mubarok, Sulton NISKE ELMY PAULINA Nugroho Setyo Wibowo Panji Eka Prasetya Parahita, Syavina Octavia Pramuditha Shinta Dewi Puspitasari Prasetya, Bahtiar Adi Prawidya Destarianto Putri, Desti Fitri Aisyah PUTRI, DESTI FITRI AISYAH Rahmawati, Siska Yulia Ramadania, Elsa Manora Reski Yulina Widiastuti, Reski Yulina Rifqi Hakim Ariesdianto Riska Aprilia Rizkiyah Rizkiyah Slamet Riyadi Sudarti Sudarti Sulton Mubarok Sumardi . Sumardi Sumardi Syahputri, Lindri Nalentine Yolanda Ulandari Susika Ully Nuhanatika Wahyu Muldayani Widiastuti, Reski Yulina Widjonarko . Widyono Hadi Wildan Bakti Nugroho Yanuar Ridwan Hisyam Zilvanhisna Emka Fitri Zilvanhisna Emka Fitri