Article Per Year (5 Year)
p-Index From 2021 - 2026
4.607
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) JOIN (Jurnal Online Informatika) Jurnal CoreIT Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Artificial Intelligence and Data Mining JMM (Jurnal Masyarakat Mandiri) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JISKa (Jurnal Informatika Sunan Kalijaga) Abdimasku : Jurnal Pengabdian Masyarakat Dinamika Informatika: Jurnal Ilmiah Teknologi Informasi Innovation in Research of Informatics (INNOVATICS) Journal of Soft Computing Exploration Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Information System Exploration and Research Journal of Electronics Technology Exploration (JOeTEx) Scientific Journal of Informatics Journal of Soft Computing Exploration
Jumanto Unjung
Department Of Computer Science, Universitas Negeri Semarang, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Social Sciences Other

Published : 31 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Journal of Electronics Technology Exploration (JOeTEx)

 1 
Malaria Disease Detection System in Humans Using Convolutional Neural Network (CNN) Yana, Natasya Siska Fitri; Shabaha, Achmad Rozin; Unjung, Jumanto
Journal of Electronics Technology Exploration Vol. 3 No. 2 (2025): December 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joetex.v3i2.646

Abstract

Malaria is a deadly disease transmitted by the Plasmodium parasite. Detection is performed by trained microscopists who analyze microscopic images of blood smears. This analysis can be done automatically using modern deep learning techniques. The need for skilled labor can be significantly reduced by developing accurate and efficient automated models. In this article, we propose a fully automated convolutional neural network (CNN)-based model for diagnosing malaria from microscopic images of blood smears. Various techniques including knowledge distillation, data augmentation, autoencoder, feature extraction with CNN model to optimize and improve model accuracy and reasoning performance. Our deep learning model can detect malaria parasites from microscopic images with 95% accuracy requiring more than 27,600 images. This shows that the mode is able to provide more accurate predictions compared to malaria disease detection models using other algorithms such as in previous studies with an accuracy of 90%. By using CNN algorithm, this article can contribute novelty in the development of effective malaria detection methods for malaria disease.
Classification of Pancreatic Cancer Diagnosis with CatBoost Using Urine Biomarker Combination Tanga, Yulizchia Malica Pinkan; Utami, Putri; Darmawan, Aditya Yoga; Unjung, Jumanto
Journal of Electronics Technology Exploration Vol. 4 No. 1 (2026): June 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joetex.v4i1.651

Abstract

Uncontrolled cell growth in the pancreatic gland, is one of the most aggressive types of cancer with a high mortality rate, called pancreatic cancer. This research focuses on improving early diagnosis methods for pancreatic cancer by using CatBoost. Urine biomarker datasets were collected and subjected to pre-processing, including label coding, standardized scaling, and balancing via the Synthetic Minority Oversampling Technique (SMOTE). The CatBoost model achieved an accuracy of 98.89%, specificity of 99.35%, sensitivity of 98.71%, and Area Under the Curve (AUC) of 0.9951. These results show that the CatBoost model significantly outperforms the diagnosis models in previous studies, overcoming the challenges of early detection and classification of pancreatic cancer. This study shows that CatBoost is effective for diagnosing pancreatic cancer and suggests that future research explore other models on larger and more diverse datasets.
Co-Authors Abas Setiawan Ahmad, Kamilah Al Qohar, Bagus Alamsyah - Anisa, Devi Nurul Budi Prasetiyo Budi Prasetiyo, Budi Ceorido Ghalib Wibowo DAA Pertiwi Darmawan, Aditya Yoga Devi Ajeng Efrilianda Dimas Fanny Hebrasianto Permadi Dullah, Ahmad Ubai Dwika Ananda Agustina Pertiwi Endang Sugiharti, Endang Farih, Habib al Franki SW Hakim, M. Faris Al Heri Ardiyanto Khalidah, Saidatul M. Faris Al Hakim M. Faris Al Hakim Maylinna Rahayu Ningsih Much Aziz Muslim Much Aziz Muslim Much Aziz Muslim Much. Aziz Muslim Nina Fitriani Ningsih, Maylinna Rahayu Nurriski, Yopi Julia Nurul Hidayat Pertiwi, Dwika Ananda Agustina Prabowo Yudho Jayanto Purwoko, Agus Putri Utami, Putri Qohar, Bagus Al Rianto, Nur Azis Kurnia Riza Arifudin Riza Arifudin Rofik Rofik Rofik Rofik, Rofik Roshan Aland Hakim Sanjani, Fathimah Az Zahra Shabaha, Achmad Rozin Syamsudin, Hilmi Tanga, Yulizchia Malica Pinkan Tiara Lailatul Nikmah Ulum, Muhammad Nurul Ihyaul Utami, Annisaa Wibowo, Kevyn Alifian Hernanda Yana, Natasya Siska Fitri Zaenal Abidin