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All Journal Scientific Journal of Informatics ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Elektrika International Journal of New Media Technology MIND (Multimedia Artificial Intelligent Networking Database) Journal J-Dinamika: Jurnal Pengabdian Kepada Masyarakat JISA (Jurnal Informatika dan Sains) Journal Zetroem Indonesian Applied Physics Letters Journal of Community Development Algoritme Jurnal Mahasiswa Teknik Informatika Jurnal Pengabdian Masyarakat Bangsa International Journal of Health and Information System (IJHIS) Journal of Educational Engineering and Environment NERO (Networking Engineering Research Operation) International Journal of Healthcare and Information Technology Science and Technology: Jurnal Pengabdian Masyarakat International Journal of Technology, Food and Agriculture
Fitri, Zilvanhisna Emka
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering 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 Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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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.
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. 
Web Platform for Automated Detection of Abnormal Red Blood Cells Using Computer Vision Hasanah, Qonitatul; Fitri, Zilvanhisna Emka; Phoa, Victor; Sari, Dian Kartika
International Journal of Healthcare and Information Technology Vol. 3 No. 2 (2026): January
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/ijhitech.v3i2.6718

Abstract

Accurate identification of red blood cell (RBC) morphological abnormalities is essential for anemia screening and hematological assessment; however, manual microscopic examination remains time-consuming, subjective, and highly dependent on expert availability. While recent deep learning studies have demonstrated promising accuracy in RBC classification, many focus primarily on model performance without addressing practical deployment constraints or system-level integration for routine laboratory use. In this study, a web-based prototype system for automated RBC abnormality classification is proposed using a lightweight MobileNetV2 architecture. The dataset consisted of 1,320 microscopic blood smear images collected from Klinik & Laboratorium Parahita in Jember and Surabaya, covering six RBC categories with balanced class distribution. All images were anonymized and verified by a certified clinical pathologist prior to use. The model was trained using transfer learning and evaluated on a held-out test set to assess generalization performance. The proposed model achieved a test accuracy of 89.77%, with consistent precision, recall, and F1-score across classes, indicating reliable multi-class classification performance. Analysis of misclassified samples revealed uncertainty primarily between morphologically similar RBC types, reflected by lower confidence scores. These results demonstrate that lightweight deep learning models can provide effective and efficient support for RBC morphology analysis when integrated into an accessible web-based system. The proposed approach contributes a deployment-oriented diagnostic support tool that has the potential to assist laboratory professionals by improving screening efficiency and consistency while preserving clinical oversight.
Co-Authors Abdul Madjid Arif, Praditya Zainal Arizal Mujibtamala Nanda Imron Atmadji, Ery Setiyawan Jullev ATTHOILLAH, EDY Avisenna Harkat Budi Prasetyo Dian Kartika Sari, Dian Kartika FAUZI, HILMIY AHMAD Firmansyah, Achmad Dinofaldi Hanip, Muhammad Harkat, Avisenna Hasan, Baharuddin Hasanah, Niswatin Hikmah, Lailatul Holili, Rexy Solehudin Abdi I Putu Dody Lesmana Imron, Arizal Mujibtamana Nanda Intan Permatasari Irawan, Ahmad Dandi Johan, Anju Alicia Kahar, Saliyah Binti Kholiq, M. Avan Dwi Adi Nur Kholiq, Muhammad Avan Dwi Adi Nur KOMARIAH, SITI INGEFATUL maya weka santi, maya weka Mubarok, Sulton Muhammad Hanip Muhammad Yunus NAJAMUDDIN, AHMAD NANDA Nining Haryuni Ni’mah, Khomsatun Nugroho Setyo Wibowo Nurkholis Nurkholis Oryza Ardhiarisca Parahita, Syavina Octavia Phoa, Victor Prasetya, Bahtiar Adi Prasetyo, Dafit Ari Prihatiana, D Puspitasari, Trismayanti Dwi PUTRI, DESTI FITRI AISYAH Qonitatul Hasanah Rachmawati, Ervina Rahma Rina Wijayanti Ramadania, Elsa Manora Reski Yulina Widiastuti, Reski Yulina Riansyah, S Rukmi, Dyah Laksito Safitri, Nadia Ayu Sahenda, Lalitya Nindita Sono, S. H Sumardi . Syahputri, Lindri Nalentine Yolanda Ubaidillah, Ahmad Aris Ubaidillah, Yanuar Ardhika Rahmadhani Wahyono, Nanang Dwi Wahyu Muldayani Widjonarko . Zamzami, Muhammad Viqih