Article Per Year (5 Year)
p-Index From 2021 - 2026
8.891
P-Index
This Author published in this journals
All Journal Publikasi Pendidikan JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Simantec Jurnal Ilmiah Kursor Scan : Jurnal Teknologi Informasi dan Komunikasi Proceeding International Conference on Information Technology and Business Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics Jurnal Informatika dan Teknik Elektro Terapan Jurnal Sistem Informasi dan Bisnis Cerdas Format : Jurnal Imiah Teknik Informatika Sistemasi: Jurnal Sistem Informasi InComTech: Jurnal Telekomunikasi dan Komputer J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Journal of Information Systems and Informatics bit-Tech Journal of Robotics and Control (JRC) JATI (Jurnal Mahasiswa Teknik Informatika) Jifosi Indonesian Journal of Data and Science Nusantara Science and Technology Proceedings SINTA Journal (Science, Technology, and Agricultural) Jurnal Pengabdian Masyarakat Indonesia Jurnal Ilmiah Teknologi Informasi dan Robotika Jurnal Manajemen Informatika Jayakarta Jurnal Teknologi dan Manajemen International Journal Of Computer, Network Security and Information System (IJCONSIST) Algoritme Jurnal Mahasiswa Teknik Informatika Literasi Nusantara Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Kohesi: Jurnal Sains dan Teknologi Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Router : Jurnal Teknik Informatika dan Terapan Modem : Jurnal Informatika dan Sains Teknologi Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika Router : Jurnal Teknik Informatika dan Terapan
Eva Yulia Puspaningrum
Department Of Informatics, Universitas Pembangunan Nasional Veteran Jawa Timur, Jl. Raya Rungkut Madya, Surabaya, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering 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 Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Veterinary Other

Published : 92 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 92 Documents
Search

 6   7   8   9   10 
IMPLEMENTASI CONTRAST LIMITED ADAPTIVE HISTOGRAM EQUALIZATION DALAM PENGOLAHAN CITRA PADA ALGORITMA GENERATIVE ADVERSARIAL NETWORK Attaqwa, Syukur Iman; Puspaningrum, Eva Yulia; Saputra, Wahyu S.J.
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 3S1 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3S1.5316

Abstract

Pengolahan citra, terutama teknik peningkatan kontras seperti Contrast Limited Adaptive Histogram Equalization (CLAHE), berperan krusial dalam meningkatkan kinerja model Generative Adversarial Networks (GANs). Penelitian ini mengevaluasi dampak CLAHE pada akurasi klasifikasi gambar menggunakan GANs. Hasil penelitian menunjukkan bahwa penerapan CLAHE berhasil meningkatkan akurasi klasifikasi sebesar 20% dibandingkan dengan model yang tidak menggunakan CLAHE, mencapai akurasi sebesar 76,20%. Temuan ini mengindikasikan bahwa CLAHE efektif dalam meningkatkan kualitas data gambar, sehingga model GAN dapat belajar fitur-fitur yang lebih relevan dan menghasilkan output yang lebih akurat.
IMPLEMENTASI PROGRESSIVE WEB APPLICATION (PWA) DALAM PENGEMBANGAN SISTEM PESAN-ANTAR MAKANAN (STUDI KASUS: WIRAWIRI BOJONEGORO) Bimantara, Candra Kusuma Muhammad; Akbar, Fawwaz Ali; Puspaningrum, Eva Yulia
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 2 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i2.6132

Abstract

Layanan pesan-antar makanan daring mengalami pertumbuhan pesat seiring dengan kemajuan teknologi dan perubahan gaya hidup modern. Salah satu layanan lokal, WiraWiri Bojonegoro, menawarkan jasa pesan-antar makanan dengan menggandeng UMKM dan PKL sebagai mitra. Namun, sistem saat ini masih bergantung pada WhatsApp untuk pemrosesan pesanan dan pemilihan driver secara manual, sehingga mengakibatkan antrian panjang dan kurang efisien. Untuk mengatasi masalah ini, penelitian ini mengembangkan sistem pesan-antar makanan mengimplementasikan Progressive Web Application (PWA). teknologi PWA menghadirkan pengalaman pengguna yang responsif, cepat, dan dapat diakses baik online maupun offline. Pada penelitian ini di dapat sistem pesan antar berbasis Progressive Web Application (PWA) dengan menerapkan push notification, serta kemampuan menambahkan aplikasi ke layar utama (home screen). Secara keseluruhan, fitur-fitur pada sistem pesan-antar berfungsi dengan baik berdasarkan hasil pengujian fungsionalitas.
KLASTERISASI MAHASISWA MAGETAN MENGGUNAKAN K-MEANS UNTUK OPTIMASI STRATEGI PROMOSI PERGURUAN TINGGI Aqsa Prima Cahya; Muhammad Asyraf; Yudhistira Nanda Kumala; Eva Yulia Puspaningrum
JIFOSI Vol. 6 No. 1 (2025): Smart Systems and Data-Driven Approaches in Business and Technology
Publisher : UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/jifosi.v6i1.478

Abstract

Semakin bertambahnya tahun, data mahasiswa magetan akan terus bertambah hingga menghasilkan tumpukan data yang berlimpah. Perlu adanya pengolahan data sehingga tumpukan data tersebut dapat dimanfaatkan sebagai ladang informasi. Penelitian ini bertujuan untuk mengkluster data mahasiswa Kabupaten Magetan yang sedang berkuliah di Universitas yang ada di Surabaya melalui proses data mining dengan algoritma K-Means serta metode Elbow dan Silhouette Coefficient dalam pembentukan clusternya. Data atribut yang digunakan pada penelitian ialah nama, asal sekolah, dan juga universitas. Data bersumber dari mahasiswa sendiri melalui pengisian google form oleh Organisasi Ikatan Mahasiswa Magetan di Surabaya, dimana data yang digunakan merupakan data mahasiswa angkatan 2023 dan 2024 dengan total sampel data sebanyak 250 items. Setelah melakukan perhitungan dengan metode Elbow didapatkan jumlah cluster sebanyak 4. Kemudian dilakukan evaluasi menggunakan metode Silhouette Coefficient dan didapatkan rata-rata terdekat dari nilai 1 adalah cluster 2, dengan nilai 0,62. Karena kohesivitas yang lebih baik serta model yang lebih sederhana, hasil cluster yang paling optimal adalah sebanyak 2 cluster pada epoch ke-5 dengan cluster 1 sebanyak 65 items, dan cluster 2 sebanyak 160 items. Adanya penelitian ini diharapakan dapat membantu universitas yang ada di Surabaya untuk menunjang strategi promosi berdasarkan hasil cluster universitas yang banyak diminati dari masing-masing sekolah di Kabupaten Magetan.
Studi Performa TF-IDF dan Word2Vec Pada Analisis Sentimen Cyberbullying Ahmad Hilman Dani; Eva Yulia Puspaningrum; Retno Mumpuni
Router : Jurnal Teknik Informatika dan Terapan Vol. 2 No. 2 (2024): Juni : Router: Jurnal Teknik Informatika dan Terapan
Publisher : Asosiasi Profesi Telekomunikasi dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/router.v2i2.76

Abstract

On August 14, 2023, Indonesia had approximately 228 million social media users, a number that is expected to continue growing to reach 267 million by 2028. Social media can be used to spread both positive and negative information, and one of the various negative effects is cyberbullying. Consequently, much research is conducted in the field of machine learning to develop sentiment analysis. One crucial step in sentiment analysis is word weighting. The two most common word weighting methods are TF-IDF and Word2Vec. These methods can be compared to determine which one produces better classification results, allowing cyberbullying sentiments on social media to be detected more accurately. Based on nine test scenarios, the final results showed that TF-IDF performed better than Word2Vec in this study, with an accuracy of 84%.
Implementasi Algoritma K-Nearest Neighbor (KNN) untuk Identifikasi Penyakit pada Tanaman Jeruk Berdasarkan Citra Daun Abiyan Naufal Hilmi; Eva Yulia Puspaningrum; Henni Endah Wahanani
Router : Jurnal Teknik Informatika dan Terapan Vol. 2 No. 2 (2024): Juni : Router: Jurnal Teknik Informatika dan Terapan
Publisher : Asosiasi Profesi Telekomunikasi dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/router.v2i2.78

Abstract

The development of image processing technology today can create systems that are able to effectively recognize digital images, one of which is in the field of agriculture for plant disease identification. Citrus plants experience a decrease in productivity due to pathogen attacks on leaves such as Black Spot, Cancer, and CVDP so that disease identification is needed. The classification method that can be used to classify images is the K-Nearest Neighbor (K-NN) algorithm because it is simple and has high accuracy in image management. This study aims to implement and determine the performance of the K-NN algorithm in identifying citrus plant diseases based on leaf images. This research uses a dataset from the Kaggle website of 1,096 images. There are 12 research scenarios using the comparison between test data and training data as much as 4, namely (90% training data + 10% test data, 80% training data + 20% test data, 70% training data + 30% test data, 60% training data + 40% test data) and testing with 3 random state values (42, 32, 22). The results showed that the K-NN algorithm is very effective in identifying citrus plant diseases with the highest accuracy value in the 90% training data scenario and 10% test data with a value of K = 2 which is 98.5%.
Klasifikasi Penyakit Diabetes Menggunakan Particle Swarm Optimaze Pada Algoritma Support Vector Machine Zalfa Ibtisamah Arishandy; Daniswara, Sena; Yulia Puspaningrum, Eva
Jurnal Ilmiah Teknologi Informasi dan Robotika Vol. 7 No. 2 (2025): Jurnal Ilmiah Teknologi Informasi dan Robotika
Publisher : Universitas Pembangunan Nasional Veteran Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/jifti.v7i2.163

Abstract

Penyakit diabetes mellitus menjadi salah satu tantangan kesehatan global dengan prevalensi yang semakin meningkat setiap tahunnya. Penelitian ini bertujuan untuk mengklasifikasikan penyakit diabetes menggunakan algoritma Support Vector Machine (SVM) yang dioptimalkan oleh metode Particle Swarm Optimization (PSO) dan teknik Synthetic Minority Over-sampling Technique (SMOTE). Dataset yang digunakan berasal dari National Institute of Diabetes and Digestive and Kidney Diseases dengan total 768 data. Proses penelitian mencakup tahapan pra-pemrosesan data, penanganan ketidakseimbangan data menggunakan SMOTE, optimasi parameter SVM menggunakan PSO, dan evaluasi model menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil pengujian menunjukkan bahwa model SVM-SMOTE-PSO mencapai akurasi sebesar 83,95%, meningkat dibandingkan model SVM-SMOTE tanpa PSO yang hanya mencapai 82,72%. Peningkatan ini terutama terlihat pada prediksi kelas minoritas, di mana PSO membantu mengoptimalkan parameter model SVM. Dengan demikian, metode ini terbukti efektif dalam meningkatkan akurasi dan keseimbangan prediksi klasifikasi penyakit diabetes.
Perbandingan Kinerja Arsitektur Resnet-50 Dan Googlenet Pada Klasifikasi Penyakit Alzheimer Dan Parkinson Berbasis Data MRI Shawn Hafizh Adefrid Pietersz; Basuki Rahmat; Eva Yulia Puspaningrum
Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika Vol. 2 No. 2 (2024): Juni: Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/uranus.v1i2.110

Abstract

Alzheimer's and Parkinson's diseases are neurodegenerative conditions that affect the brain, with Alzheimer's causing cognitive and behavioral decline, while Parkinson's leads to motor and non-motor impairments. Both diseases have significant impacts on the health and quality of life of patients, with prevalence increasing in recent years. Although the exact causes of these diseases are still unknown, MRI (Magnetic Resonance Imaging) is widely used to detect brain activity and serves as one of the diagnostic methods. With technological advancements, intelligent systems in image processing for image classification have been extensively used and have become a popular field due to their ability to replicate human visual capabilities. Image classification is performed using various supervised learning machine learning algorithms based on the shape, texture, and color of the images. This study employs two Convolutional Neural Network (CNN) architectures, ResNet50 and GoogLeNet, to compare the performance of these models in classifying MRI scans of patients with Alzheimer's and Parkinson's diseases. The results show that the ResNet50 model outperforms the GoogLeNet model, with parameters set to 100 epochs, a batch size of 128, a learning rate of 0.0001, and the Adam optimizer, achieving an accuracy rate of 90%.
Convolutional layer exertion on few-shot learning for brain tumor classification Sunarko, Victor Immanuel; Puspaningrum, Eva Yulia; Widiastuty, Riana Retno; Hadi, Surjo; Awang, Mohd Khalid; Mas Diyasa, I Gede Susrama
Jurnal Ilmiah Kursor Vol. 13 No. 2 (2025)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i2.430

Abstract

Brain tumors, though relatively rare, pose a significant threat due to their critical location within the brain, impacting essential bodily functions. Accurate and timely diagnosis is vital, but traditional diagnostic methods are time-intensive and rely heavily on large labeled datasets. This study addresses these challenges by proposing a Few-Shot Learning (FSL) framework enhanced with Convolutional Neural Networks (CNNs) to classify brain tumors using MRI images. By employing the Matching Network architecture, the model leverages limited training data through an N-way-K-shot setup. Training results demonstrated accuracy levels of 71.58% (1-shot) and 82.89% (5-shot) for 1-layer CNNs, 66.65% (1-shot) and 84.03% (5-shot) for 3-layer CNNs, and 63.43% (1-shot) and 84.94% (5-shot) for 5-layer CNNs. However, validation accuracy revealed overfitting concerns, with the highest performance at 51.56% (1-layer, 1-shot). These results underscore the potential of FSL in medical imaging while highlighting the need for advanced augmentation and feature representation techniques to improve generalization.
Implementation of Web-Based Regional Innovation Selection Process Automation: A Case Study of Pasuruan Regency Firza Prima Aditiawan; Agung Mustika Rizki; Eva Yulia Puspaningrum
SINTA Journal (Science, Technology, and Agricultural) Vol. 6 No. 2 (2025)
Publisher : Perkumpulan Dosen Muda (PDM) Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37638/sinta.6.2.373-386

Abstract

Digital transformation in the regional innovation selection process is key to improving transparency, efficiency, and accountability. To encourage an innovation ecosystem in Pasuruan Regency, Bappelitbangda held the Pasuruan Maslahat Technology Innovation Competition with three categories: (1) Regional Innovation (governance/public services), (2) Technological and Non-Technological Innovation, and (3) Learning Innovation. The main challenges of the competition are the high volume of proposals, process traceability, and consistency of assessment. This article presents the design and implementation of the Maslahat Innovation and Technology Selection Website system to automate the end-to-end flow: registration, proposal upload, administrative verification, multi-reviewer assessment, weighted score aggregation, nomination determination, and publication of results. The three-layer web-based architecture is designed with role control (admin, secretariat, reviewer, participant, public), audit trail, and proportional information disclosure policy. The assessment method uses Simple Additive Weighting (SAW) with min–max normalization and weighting per category. The expected outcome is improved operational efficiency, accountability, and transparency of the selection process so that the sustainability of the competition can be ensured.
Pencarian Jalur Terpendek Jakarta ke Jawa Barat Berbasis Algoritma Genetika Firyal Wishal Nabili; Eva Yulia Puspaningrum; Afina Lina Nurlaili
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The Travelling Salesman Problem (TSP) is a well-known combinatorial optimization problem aimed at finding the shortest route that visits each location exactly once and returns to the starting point. This study aims to determine the shortest travel route from Jakarta to all cities in West Java Province using a Genetic Algorithm (GA). Distance data between cities were obtained from the Central Bureau of Statistics (BPS) of Bekasi Regency and used to construct a distance matrix for distance calculation. The optimization process employed a population size of 100 individuals, a crossover rate of 0.7, a mutation rate of 0.05, and 500 generations. The algorithm used Roulette Wheel Selection for parent selection, PMX (Partially Mapped Crossover) for crossover, swap mutation for mutation, and elitism to preserve the best individuals across generations. Experimental results show that the initial route distance of 2918 km was reduced to 1314 km at generation 110 and remained stable until generation 500. The optimal route found was: Jakarta ? Bekasi ? Karawang ? Tangerang ? Serang ? Pandeglang ? Lebak ? Bogor ? Sukabumi ? Cianjur ? Subang ? Indramayu ? Kuningan ? Cirebon ? Tasikmalaya ? Ciamis ? Majalengka ? Sumedang ? Garut ? Bandung ? Purwakarta ? Jakarta. These results demonstrate that the Genetic Algorithm effectively provides optimal route solutions with fast convergence and high efficiency in solving the TSP.
Co-Authors Abiyan Naufal Hilmi Achmad Junaidi Adityawan, Firza Prima Adyani, Adelia Putri Afina Lina Nurlaili Agung Mujiono, Alfinas Agung Mustika Rizki Agung Mustika Rizki, Agung Mustika Ahmad Fahry Hamidy Ahmad Hilman Dani Akbar, Fawwaz Ali Al Danny Rian Wibisono Ali Muhhamad Saleh Baaboud Andhika Ahnaf Daniswara Andreas Nugroho Sihananto Annisaa Sri Indrawanti annisaa sri indrawanti annisaa sri indrawanti Anny Yuniarti Aqsa Prima Cahya Ariani, Dian Dwi Ariyono Setiawan Aryananda, Rangga Laksana Aswan Aswan Attaqwa, Syukur Iman Awang, Mohd Khalid Az-Zahro', Syaikhhanun Nabila Azizah, Nabila Wafiqotul Bagus Sutikno Putra Basuki Rahmat Basuki Rahmat Basuki Rahmat Masdi Siduppa Bimantara, Candra Kusuma Muhammad Budi Nugroho Budi Nugroho Budi Nugroho Budi Nugroho Chafid, M Putih Daniswara, Sena Devan Cakra Mudra Wijaya Dewi, Deshinta Arrova Dhian Satria Yudha K. Dimas Saputra Diyasa, I Gede Susrama Mas Dwi Anggraeni, Shinta Dwiki Aditama Supangkat Eka Prakarsa Mandyartha Eka Prakarsa Mandyartha, Eka Elzandy, Imeldha Etniko Siagian, Pangestu Sandya Fahmi Al Hafidz, Achmad Fara Disa Durry Faris Syaifulloh Farkhan, Farkhan Fetty Tri Anggraeny Firyal Wishal Nabili Firza Prima Aditiawan Firza Prima Adityawan Firza Prima Adityawan Fitri Rahmawati Hadi, Surjo Hapsari Wiji Utami Hasby Bik, Ahmad Henni Endah Wahanani Humairah, Sayyidah Humam Maulana Tsubasanofa Ramadhan I Gede Susrama Mas Diyasa I Gede Susrama Mas Diyasa I Nyoman Sujana I Wayan Alston Argodi Idhana, Ilham Ainur indrawanti, annisaa sri Karim, Mohammad Daniel Sulthonul Kartini Kartini Lestari, Kusmiyati Lina Nurlaili, Afina M. Syahrul Munir, M. Syahrul Mada Lazuardi Nazilly Made Hanindia Prami Swari Mandyartha, Eka Prakarsa Manggala, Herwantoro Arya Marchel Adias Pradana Maulana, Hendra Merdin Risalul Abrori Moch. Hatta Mohammad Idhom Muhammad Asyraf Muhammad Fernanda Naufal Fathoni Muhammad Misbachuddin Muhammad Muharrom Al Haromainy Muhammad Syafril Hidayat Nabilah, Qonitah Jihan Nanik Suciati Noor Fitria Azzahra Nugroho, Budi Nugroho, Budi Nugroho, Budi Nurcahyo, Syai'in Bayu Nurul Taukid, Mochamad Pallawabonang, Mahabintang Pratama Wirya Atmaja Pratama, Gede Ardi Prisheila Dharmawan, Diaz Putra, Chrystia Aji Putra, Riza Satria Putri, Desya Ristya Retno Mumpuni Rizki, Agung Mustika Rizqi Mar'atus Sholiihah, Eka Royan Fajar Sultoni S J Saputra, Wahyu Safira, Dwi Putri Salsabilah, Andini Fitriyah Samuel Krispama Lumbantoruan Saputra, Raka Aji Saputra, Wahyu S J Saputra, Wahyu S J Saputra, Wahyu S. J. Saputra, Wahyu S.J. Satria Yudha Kartika , Dhian Shawn Hafizh Adefrid Pietersz Shofiya Syidada Sukendah, Sukendah Sunarko, Victor Immanuel Surjohadi, Surjohadi Susrama Mas Diyasa, I Gede Syahrul Hidayat Syaifullah JS, Wahyu Taruna Ardianto Tataq Distasianto Utami, Hapsari Wiji Vita Via, Yisti Wafiqotul Azizah, Nabila Wahyu Caesarendra Wahyu Dwi Lestari Wahyu S.J. Saputra Wahyu Syaifullah Jauharis Saputra Wan Awang, Wan Suryani Wan Suryani Wan Awang Widiastuty, Riana Retno Wiji Utami, Hapsari Yisti Vita Via Yisti Vita Via Yogie Wilvren Saragih Yudha K., Dhian Satria Yudhistira Nanda Kumala YUSMI NUR AINI Zacky Yaser Malik Gumiwang Zalfa Ibtisamah Arishandy ZAMAZANI, ZAIN MUZADID Zuhriyah, Sitti