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All Journal 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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Optimisation of Erythrocyte Abnormality Classification using Watershed Segmentation Parahita, Syavina Octavia; Fitri, Zilvanhisna Emka; Imron, Arizal Mujibtamala Nanda
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 1 (2025): Oktober 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v6i1.9580

Abstract

According to the World Health Organization (WHO), Polycythemia vera (PV) belongs to one of the main categories of Myeloproliferative Neoplasm (MPN). The results of laboratory diagnosis of PV are characterized by an increase in the number of erythrocytes, hemoglobin, leukocytes and platelets. Generally, blood examination uses automatic full blood count (FBC), but this method cannot detect abnormalities in the shape of erythrocytes, so further processing is needed from microscopic examination by creating a system that is able to detect and identify red blood cell abnormalities automatically. The system is a combination of digital image processing methods and intelligent systems methods commonly known as computer vision. The watershed segmentation method is used to separate closely packed cells, while the backpropagation method is an intelligent system capable of classifying erythrocyte shape abnormalities. The amount of data used is 340 training data and 50 test data, while the most optimal learning rate is 0.6 with a maximum epoh of 100 so that the system accuracy is 88%, specificity is 0.056 and sensitivity is 0.714.
Ensiklopedia Digital Varietas Ubi Jalar Berdasarkan Klasifikasi Citra Daun Menggunakan KNearest Neighbor Prasetya, Bahtiar Adi; Fitri, Zilvanhisna Emka; Madjid, Abdul; Imron, Arizal Mujibtamala Nanda
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.
Genshin Impact Game Character Selection Using Weighted Product Mubarok, Sulton; Fitri, Zilvanhisna Emka; Imron, Arizal Mujibtamala Nanda
Journal of Educational Engineering and Environment Vol. 3 No. 1 (2024): Journal of Educational Engineering and Environment
Publisher : Fakultas Teknik Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/jeee.v3i1.3816

Abstract

The recurring problem among Genshin Impact players in choosing characters, especially for new players, is a common issue. Therefore, this study aims to develop a decision support system to assist players in selecting characters using the weighted product method. The study was conducted by collecting data on the available characters in Genshin Impact and evaluating relevant criteria such as character rarity, role, weapon, and element. There are 52 characters or alternatives used in this study. The weighted product method was used to determine the relative weights of each criterion and then used to calculate the value of each character. In conclusion, the implementation of the weighted product method has been successfully carried out based on the three main steps, which are determining the weight of criteria, normalizing alternatives, and determining the preference of each alternative. From the results of the system testing that has been conducted by 25 users, a percentage of 84% was obtained based on user satisfaction with the decision support system for character selection in the Genshin Impact game. 9 respondents chose the option "strongly agree" and the remaining 16 chose the option "agree", indicating that the system is satisfactory for what the users want.
Optimalisasi Teknik Image Enhancement untuk Klasifikasi Varietas Apel Menggunakan SVM dan CNN Johan, Anju Alicia; Fitri, Zilvanhisna Emka; Imron, Arizal Mujibtamala Nanda; Arif, Praditya Zainal
ZETROEM Vol 7 No 2 (2025): ZETROEM
Publisher : Prodi Teknik Elektro Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/ztr.v7i2.5513

Abstract

One of the largest export commodities in Indonesia is fruit commodities, one of which is apples. Apples have many varieties that differ in shape, color and size, which can cause identification and highlighting of apples to have limitations by requiring manual inspection from experts. This manual inspection is influenced by the expert's ability and experience in assessing the texture, color pattern, smell and characteristics of apples. In addition, the large diversity of apple varieties does not guarantee the completeness and ease of access related to information and data on apple varieties. The availability of this information is very important in supporting increased fruit production and determining superior apple varieties. So, a system is made that can classify apple varieties such as ana apples, manalagi apples, fuji apples, red delicious apples and rome beauty apples automatically. The apple variety classification methods used are SVM and CNN. The accuracy result of the SVM method is 94% based on texture feature parameters. While the CNN accuracy result is 100% Using learning rate 0.001 and epoh 20.
Implementasi Fuzzy Inference System untuk Pengstabilan Arus pada Baterai Lithium di Electric Vehicle Imron, Arizal Mujibtamala Nanda; Utomo, Satryo Budi; Darmawan, Dimas Aldy; Kaloko, Bambang Sri; Fitri, Zilvanhisna Emka
Faktor Exacta Vol 18, No 3 (2025)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v18i3.26372

Abstract

The application of renewable energy in electric vehicles (EVs) is a crucial aspect that requires careful consideration. It is essential to understand the capacity characteristics of lithium polymer batteries to develop effective testing procedures. These procedures should involve monitoring the battery's voltage, current, and temperature during the discharge process with a lamp loading of 5 watts. The results of research prove that fuzzy control is an effective method for minimising the increase in battery temperature by stabilising the current used by the battery. The fuzzy control system effectively regulated a battery with a capacity of 3300 mAh and a voltage of 11.1 Volts, maintaining a stable current of 0.3 A from the 3rd minute until the battery reached its maximum capacity at the 63rd minute. Furthermore, the implementation of fuzzy control has been observed to delay the temperature rise in the battery. Specifically, the use of fuzzy control enables a delay in the temperature rise time by approximately 14 minutes when compared to the system without control. The temperature rise has a significant impact on the discharge speed of lithium polymer batteries.
Application of Feature Selection for Identification of Cucumber Leaf Diseases (Cucumis sativa L.) Sahenda, Lalitya Nindita; Ubaidillah, Ahmad Aris; Fitri, Zilvanhisna Emka; Madjid, Abdul; Imron, Arizal Mujibtamala Nanda
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

Abstract

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. 
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 : 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.
Co-Authors Abdul Madjid Abdul Madjid ABDUL MADJID Abdul Madjid Abdul Madjid Abdul Madjid 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 Darmawan, Dimas Aldy 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 Kaloko, Bambang Sri 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 Sahenda, Lalitya Nindita Satryo Budi Utomo Slamet Riyadi Sudarti Sudarti Sulton Mubarok Sumardi . Sumardi Sumardi Syahputri, Lindri Nalentine Yolanda Ubaidillah, Ahmad Aris Ulandari Susika Ully Nuhanatika Wahyu Muldayani Widiastuti, Reski Yulina Widjonarko . Widyono Hadi Wildan Bakti Nugroho Yanuar Ridwan Hisyam Zilvanhisna Emka Fitri Zilvanhisna Emka Fitri