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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmiah Informatika Komputer Jurnal Teknologi Informasi dan Ilmu Komputer METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi ILKOM Jurnal Ilmiah Jurnal ULTIMATICS MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURTEKSI Jurnal Teknologi Terpadu Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Journal Of World Science Makara Human Behavior Studies in Asia Journal of Applied Business and Technology Jurnal Media Pengabdian Komunikasi Scientific Journal of Informatics
Prihandoko Prihandoko
Universitas Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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In Vivo Diagnostic Automation: Identification of Malaria Parasites from Red Blood Cells Using Image Segmentation and Convolutional Neural Network Methods Huda, Nurul; Prihandoko, Prihandoko; Dewi, Alfa Yuliana
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.23502

Abstract

Purpose: This study aims to address the limitations of conventional malaria diagnosis—namely, its reliance on manual microscopy, which is time-consuming, labor-intensive, and prone to human error—by developing an automated diagnostic system using the Inception V3 convolutional neural network. The focus is on accurately identifying the four main Plasmodium species responsible for malaria (P. falciparum, P. malariae, P. ovale, and P. vivax) through image-based analysis of red blood cells. The study’s significance lies in its contribution to scalable, AI-assisted diagnostic solutions that support national and global malaria elimination goals, particularly in high-burden countries such as Indonesia. Methods: This study utilized an experimental approach based on a dataset of 194 microscopic images of red blood cells, each labeled according to one of four Plasmodium species. The process involved image enhancement through pre-processing techniques—illumination correction, contrast adjustment, and noise filtering—followed by segmentation using the Otsu thresholding method to isolate parasite-infected cells. Two classification models were applied: Inception V3, a deep learning convolutional neural network, and a traditional Support Vector Machine (SVM), with both evaluated for their accuracy in species identification. Result: The findings revealed that the Inception V3 model significantly outperformed the Support Vector Machine (SVM), achieving highest accuracy of 100%, at select epochs and an average accuracy of 97.93%, with 98.32% validation accuracy compared to 82% for SVM. The high performance of Inception V3 is attributed to its deep architecture, consisting of over 23 million parameters, which enables superior feature extraction and classification of Plasmodium species. These results confirm that CNN-based models, particularly Inception V3, are more effective than traditional machine learning approaches for automated malaria diagnosis. Novelty: In identifying four species of Plasmodium, this study presents a very simple yet highly accurate technique using an Inception V3 model. The method represents 100% accuracy in its multi-class detection as opposed to earlier works concentrating on binary classifications. It therefore adds real usefulness in high-burden, low-resource settings such as Indonesia through working on the improvement of diagnosis and on speedier detection of malaria.
FEATURE ALIGNMENT OF THE INTERNAL QUALITY AUDIT SYSTEM BASED ON PPEPP Jollyta, Deny; Hajjah, Alyauma; Mukhsin, Mukhsin; Prihandoko, Prihandoko
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 3 (2025): Juni 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i3.3896

Abstract

Abstract: The Ministry of Education, Culture, Research, and Technology, has developed guidelines for the Internal Quality Assurance System or known as SPMI, that is being implemented through the Internal Quality Audit (IQA) with the PPEPP cycle, namely Determination (P), Implemen-tation (P), Evaluation (E), Control (P), and Improvement (P). Some universities have implemented IQA with system. The problem is that the system does not line well with the PPEPP cycle, which results in unsatisfactory audit results. The purpose of this study is to evaluate how well the university-owned AQI system features in line the PPEPP cycle and to highlight development opportunities. The method used Feature Oriented Domain analysis (FODA) and Acceptance Testing. This study delivered an analysis of IQA system features that consistent with PPEPP. The FODA results were validated by expert and tested with User Acceptance Test (UAT) with 89.98% user response that the system is acceptable. The research contributes to universities' understanding of the features necessary in the AQI system, which has an impact on the perfection of the university AQI system design in accordance with the PPEPP cycle.            Keywords: FODA; IQA system; PPEPP cycle; SPMI  Abstrak: Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi telah menyusun pedoman Sistem Penjaminan Mutu Internal atau yang dikenal dengan SPMI, yang diimplementasikan melalui Audit Mutu Internal (AMI) dengan siklus PPEPP, yaitu Penetapan (P), Pelaksanaan (P), Evaluasi (E), Pengendalian (P), dan Peningkatan (P). Beberapa perguruan tinggi telah mengimplementasikan AMI dengan sistem. Permasalahannya, sistem tersebut tidak sejalan dengan siklus PPEPP, sehingga hasil audit kurang memuaskan. Tujuan dari penelitian ini adalah untuk mengevaluasi seberapa baik fitur sistem AMI milik perguruan tinggi sejalan dengan siklus PPEPP dan menyoroti peluang pengembangan. Metode yang digunakan adalah analisis Feature Oriented Domain (FODA) dan Acceptance Testing. Penelitian ini menghasilkan analisis fitur sistem AMI yang konsisten dengan PPEPP. Hasil FODA divalidasi oleh ahli dan diuji dengan User Acceptance Test (UAT) dengan 89,98% respon pengguna bahwa sistem dapat diterima. Penelitian ini memberikan kontribusi terhadap pemahaman universitas terhadap fitur-fitur yang diperlukan dalam sistem AMI, yang berdampak pada kesempurnaan desain sistem AMI universitas sesuai dengan siklus PPEPP. Kata kunci: FODA; siklus PPEPP; sistem AMI; SPMI
Co-Authors Adang Suhendra Agus Perdana Windarto Alyauma Hajjah Amri Jahi Asqia, Misna Chandra, Reza Darwis S. Gani Deny Jollyta Dewi, Alfa Yuliana Edy Prihantoro Erick Santoso Fawwaz, Muhamad Faishal Gusrianty, Gusrianty Handayani, Citra I Gusti Putu Purnaba Iwan Tjitradjaja Johan Johan Kusumaningtyas, Amelinda Luky Adrianto Muhammad Hadi Mukhsin Mukhsin Nurahman, Nurahman Nurul Huda Pratama, Abdul Hanif Putra, Anas Sofyan Azhar Surya Ramadhan, Muhamad Iqbal Ramdhan, William Setyawati, Dyah Mieta Siddik, Muhammad Sonya, Izatul Putri Sri Agustina Rumapea Sukrianto, Darmanta Yanto, Musli Yuhandri, Muhammad Habib Yuniar Banowosari, Lintang