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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 International Journal of New Media Technology MIND (Multimedia Artificial Intelligent Networking Database) Journal J-Dinamika: Jurnal Pengabdian Kepada Masyarakat 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) 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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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.
A Combination of Forward Chaining and Certainty Factor Methods for Early Detection of Fever : Dengue Hemorrhagic Fever, Malaria and Typhoid Fitri, Zilvanhisna Emka; Ramadania, Elsa Manora; Wibowo, Nugroho Setyo; Lesmana, I Putu Dody; Imron, Arizal Mujibtamala Nanda
Scientific Journal of Informatics Vol 9, No 1 (2022): May 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i1.33007

Abstract

Abstract. Purpose: Dengue Hemorrhagic and Malaria fevers are the most common arthropod-borne diseases caused by mosquito bites and they also have similar signs and symptoms. Based on the problems, the researcher makes an expert system that aims to help people early detect fever diseases. This system is expected to help and support the infectious disease prevention and control program by the Ministry of Health of the Republic of Indonesia.Methods: This study uses an expert system with a combination of Forward Chaining and Certainty Factor to detect the symptoms of fever. Forward Chaining is a technique that begins with gathering information related to known facts, then combining rules to produce conclusions. The certainty Factor method is used to define a measure of certainty against a fact or rule and to describe the level of expert confidence in dealing with problems. There are 32 symptoms of the disease consisting of dengue fever, malaria and typhoid, it was obtained based on the literature and interviews with internal medicine specialist with 20 case datasets.Result: Based on 20 test data, obtained one data that does not match the test results and the desired target so that the system accuracy obtained is 95%. In addition, the combination of Forward Chaining and Certainty factor has better accuracy when compared to expert systems in previous studies.Novelty: Forward Chaining to find three rules and assigning weights to the Certainty Factor that has been set by the expert makes the combination of the two methods produce better 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
The Implementation of Channel Area Thresholding in Early Detection System of Acute Respiratory Infection (ARI) Fitri, Zilvanhisna Emka; Imron, Arizal Mujibtamana Nanda
Indonesian Applied Physics Letters Vol. 5 No. 1 (2024): June 2024
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v5i1.55626

Abstract

Acute respiratory infections (ARI) are infectious diseases that affect both children and adults, particularly in the context of climate change. Bacteria are one of the causes of ARI. According to the government, the discovery of the bacteria that cause ARI is an indicator of successful management of infectious diseases. The current obstacle is the limited number of medical analysts, which results in longer microscopic examination times and requires a high level of objectivity. Therefore, a system for the early detection of ARI-causing bacteria was developed using digital image processing techniques, specifically channel area thresholding as one of the segmentation methods. This research employs four shape features for bacterial classification: the number of bacterial colonies, area, perimeter, and shape. The Naí¯ve Bayes intelligent system method is used for the classification process. The system had an accuracy rate of 86.84% in the classification of four types of bacteria: S. aureus, S. pneumoniae, C. diphteriae and M. tuberculosis
PERHITUNGAN KOLONI BAKTERI SUSU SEGAR PADA RUANG WARNA YCBCR Fitri, Zilvanhisna Emka; Sahenda, Lalitya Nindita; Holili, Rexy Solehudin Abdi; Rukmi, Dyah Laksito
Networking Engineering Research Operation Vol 8, No 2 (2023): Nero - November 2023
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v8i2.19094

Abstract

The problem with fresh milk on the SPR farm is the manual milking process, which causes the milk to be less hygienic and becomes an ideal growing medium for microbes. Therefore, it is necessary to carry out a procedure for checking the microbiological status as an indicator of food safety. To test for microbial contamination in fresh milk, namely the Total Plate Count (TPC) test, but in this study the focus is on the calculation of bacterial colonies using digital image processing techniques. The stages of the research carried out are the preprocessing process (cropping and color conversion to YCbCr space), image enhancement (addition of brightness and inverse image), the segmentation combination process (gray degree and channel area thresholding) and colony calculation using labeling based on the proximity of 8 neighbors to the feature area. From the results of the study, it was found that bacterial colonies had a wide area range of 150 ≤ area ≤ 8000. A comparison of manual TPC calculations with the system has been carried out on 5 test samples and obtained an average error difference of 0.176.Keywords : channel area thresholding, bacterial colonies, fresh milk, TPC, YCbCr
Diabetic Retinopathy Severity Level Detection Using Convolution Neural Network Firmansyah, Achmad Dinofaldi; Kahar, Saliyah Binti; Fitri, Zilvanhisna Emka
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 8 No. 1 (2024)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v8i1.1112

Abstract

Diabetic retinopathy is a common complication of diabetes mellitus, leading to damage and blockage of retinal blood vessels. Early and accurate detection of diabetic retinopathy severity levels is crucial for timely treatment and prevention of blindness. Diagnostic methods rely on manual examination and human interpretation, resulting in slower and less efficient treatment processes. As a branch of artificial intelligence, computer vision offers a potential solution to analyze retinal images quickly and accurately. The developed system employs image processing techniques and a CNN-based classification model to detect and classify the severity levels of diabetic retinopathy. By providing an automated and efficient approach, the system aims to assist doctors and optometrists in making informed decisions and reducing subjectivity in diagnosis. Early detection through this system can facilitate prompt treatment and improve patient outcomes. The developed system achieves promising results through experimentation and testing with various datasets, with accuracy ranging from 80% to 97%. This project's integration of artificial intelligence, machine learning, and image processing technologies demonstrates their potential in healthcare applications, particularly in diabetic retinopathy diagnosis.
PELATIHAN PENGENALAN HURUF MENGGUNAKAN WEBSITE ALPHABET DI POS PAUD ALAMANDA 105 JEMBER Fitri, Zilvanhisna Emka; Hasan, Baharuddin; Madjid, Abdul; Imron, Arizal Mujibtamana Nanda
Science and Technology: Jurnal Pengabdian Masyarakat Vol. 1 No. 2 (2024): Juni
Publisher : CV. Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/scitech.v1i2.32

Abstract

Perkembangan anak usia dini dapat dilihat dari proses pengembangan bahasa baik bahasa lisan maupun bahasa tulis. Aspek pengembangan bahasa anak meliputi pengenalan huruf, kata serta merangkai kata menjadi kalimat sederhana untuk menambah kosakata. Indikator dalam menganalisis kesulitan membaca anak terdiri dari kemampuan anak dalam membaca huruf vocal dan huruf konsonan, serta kelancaran anak dalam menirukan bunyi huruf. Website ALPHABET menjadi media pembelajaran alternatif pengenalan huruf yang menyenangkan serta membantu sekolah paud yang memiliki keterbatasan staf pengajar dan media pembelajaran. Terjadi peningkatan kemampuan siswa setelah pelatihan tersebut namun perlu adanya kegiatan pelatihan keberlanjutan untuk meningkatkan daya ingat anak mengingat konsentrasi anak mudah terganggu oleh kondisi di lingkungannya.
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.
Sistem Evaluasi Lahan Penentuan Tanaman menggunakan Metode Forward Chaining SAFITRI, NADIA AYU; ATTHOILLAH, EDY; NAJAMUDDIN, AHMAD NANDA; FAUZI, HILMIY AHMAD; HIKMAH, LAILATUL; PRASETYO, DAFIT ARI; FITRI, ZILVANHISNA EMKA
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 9, No 2 (2024): MIND Journal
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v9i2.153-165

Abstract

ABSTRAKSistem Evaluasi Lahan merupakan sistem untuk menilai kesesuaian suatu lahan bagi berbagai jenis tanaman. Kurangnya pengetahuan petani tentang tingkat kesuburan tanah untuk jenis tanaman tertentu menyebabkan kegagalan dalam usaha pertanian mereka. Kandungan unsur NPK (Nitrogen, Phospor, dan Kalium) dalam tanah menentukan tingkat kesuburan lahan. Penelitian ini bertujuan mengembangkan sistem evaluasi lahan menggunakan teknologi pengolahan citra tanah dan metode forward chaining untuk menentukan kesesuaian lahan dan memberikan rekomendasi tanaman. Hasil penelitian menghasilkan tingkat akurasi dalam prediksi nilai kandungan Nitrogen sebesar 96,6%, Phospor sebesar 91,03%, dan Kalium sebesar 94,69% serta memberikan rekomendasi tanaman yang tepat. Sistem evaluasi lahan ini diharapkan dapat membantu petani meningkatkan produktivitas pertanian mereka.Kata kunci: pertanian, unsur hara NPK, pengolahan citra, sistem pakar, forward chaining ABSTRACTA land evaluation system is a system for assessing the suitability of land for different types of crops. The lack of farmers' knowledge about the soil fertility level for certain crops led to failure in their agricultural endeavors. The soil's content of NPK elements (Nitrogen, Phosphorus, and Potassium) determines soil fertility. The research aims to develop an evaluation system of soil using soil image processing technology and forward chaining methods to determine soil suitability and give plant recommendations. The results of the research yield accuracy in the prediction of nitrogen content values of 96.6%, phosphorus of 91.03%, and potassium of 94.69% and give the correct plant recommendation. This land evaluation system is expected to help farmers increase their agricultural productivity.Keywords: agriculture, NPK hara element, image processing, expert system, forward chaining
Co-Authors Abdul Madjid Anam, M. Khairul Arizal Mujibtamala Nanda Imron Atmadji, Ery Setiyawan Jullev ATTHOILLAH, EDY Avisenna Harkat Budi Prasetyo FAUZI, HILMIY AHMAD Firmansyah, Achmad Dinofaldi Hanip, Muhammad Harkat, Avisenna Haryuni, N Hasan, Baharuddin Hasanah, Niswatin Hikmah, Lailatul Holili, Rexy Solehudin Abdi I Putu Dody Lesmana Imron, Arizal Mujibtamana Nanda Intan Permatasari Irawan, Ahmad Dandi Kahar, Saliyah Binti Kholiq, M. Avan Dwi Adi Nur Kholiq, Muhammad Avan Dwi Adi Nur KOMARIAH, SITI INGEFATUL maya weka santi, maya weka Muhammad Hanip Muhammad Yunus NAJAMUDDIN, AHMAD NANDA Ni’mah, Khomsatun Nugroho Setyo Wibowo Nurkholis Nurkholis Oryza Ardhiarisca Parahita, Syavina Octavia Prasetyo, Dafit Ari Prihatiana, D Puspitasari, Trismayanti Dwi PUTRI, DESTI FITRI AISYAH 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, Yanuar Ardhika Rahmadhani Wahyono, Nanang Dwi Wahyu Muldayani Widjonarko . Zamzami, Muhammad Viqih