Article Per Year (5 Year)
p-Index From 2021 - 2026
3.057
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics International Journal of Artificial Intelligence Research Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Ilmiah Keperawatan Sai Betik Aisyah Journal of Informatics and Electrical Engineering Journal of Information Systems and Informatics Wellness And Healthy Magazine Jurnal Aisyah : Jurnal Ilmu Kesehatan Jurnal Pengabdian Kepada Masyarakat Ungu ( Abdi Ke Ungu) Jurnal Ilmu Kesehatan Indonesia (JIKSI) Jurnal Maternitas Aisyah (JAMAN AISYAH) Kolaborasi Jurnal Pengabdian Masyarakat Jurnal Gizi Aisyah
Fitriana Fitriana
Universitas Aisyah Pringsewu
Computer Science & IT Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Medicine & Pharmacology Nursing Public Health Veterinary Other

Published : 22 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 22 Documents
Search

 1   2   3 
A Multi-Algorithm Approach for Predicting OSCE Exam Passing Status Zulkifli; Panji Bintoro; Fitriana; Muhammad Galih Ramaputra; Hafsah Mukaromah
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1518

Abstract

This study provides a paradigm for using a digital decision support system to automate OSCE evaluation. The effectiveness of this model is restricted to the scope of small-scale data and particular educational situations at Aisyah University, despite the results demonstrating great accuracy. As a result, additional modifications are needed for its practical implementation at other institutions. However, this research provides a crucial basis for the creation of digital assessment systems that might assist teachers in identifying students who want extra aid prior to final exams. Five machine learning algorithms Neural Network (NN), Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), and K-Nearest Neighbors (kNN) are assessed experimentally in this study. A dataset of 439 clinical competency data from Aisyah Pringsewu University midwifery students was used to create the model. Eight clinical skill factors were used as input, including baby massage, newborn care, and family planning services. To guarantee result stability, the 5-fold cross-validation approach was used for model validation. According to the test findings, every algorithm performs well, with an accuracy of more than 90%. On this particular dataset, SVM achieved a 100% classification accuracy, whereas Random Forest and SVM showed the most efficacy. With an average validation accuracy of 95%, neural networks also demonstrated excellent performance. This study provides a paradigm for using a digital decision support system to automate OSCE evaluation. The effectiveness of this model is restricted to the scope of small-scale data and particular educational situations at Aisyah University, despite the results demonstrating great accuracy. As a result, additional modifications are needed for its practical implementation at other institutions. However, this research provides a crucial basis for the creation of digital assessment systems that might assist teachers in identifying students who want extra aid prior to final exams.
Development and Evaluation of a Multi-Algorithm Application for Predicting Breast Cancer Patient Survival Zulkifli; Kraugusteeliana; Sukarni; Ikna Awaliyani; Nur Asini; Fitriana
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1665

Abstract

This study developed a multi-algorithm machine learning prototype for multiclass breast cancer survival prediction using 1,980 patient records, classifying patients as Living, Died of Disease, or Died of Other Causes. The framework integrated NN, SVM, RF, NB, and KNN algorithms within a decision-support monitoring application, with preprocessing steps including data cleaning, normalization, feature preparation, and dataset partitioning. To prevent target leakage, survival-related variables were excluded from the predictor set. The revised evaluation results indicated that NB and KNN delivered the strongest performance, achieving weighted average F1-scores of 0.93 and 0.92, respectively, while NN and RF showed comparatively lower results. These findings highlight the potential of machine learning for breast cancer survival status monitoring, although the proposed system is designed as a decision-support prototype rather than a clinical diagnostic tool. Therefore, before actual healthcare deployment, more research incorporating explainable AI techniques, external validation, and real-world clinical testing is needed.
Co-Authors Abdullah Abdullah Amali Rica Pratiwi Andini, Dwi Yana Ayu Anggraeni, Egi Ani Kristianingsih Aprilia Pratiwi Azri Afdhalluddin Beniqna Maharani Bertalina Bertalina Bintoro, Panji Desi Kumalasari Dewi Yunarti Fauziah, Nur Alfi Hafsah Mukaromah Hardiana, Erni Hellen Febriyanti Heni Pujiastuti Iis Tri Utami Ika Fitria Elmeida Ikna Awaliyani Juwita Desri Ayu Komalasari Komalasari Kraugusteeliana Kraugusteeliana Lia Puspita Lienggonegoro, Lisa Andriani Luthfiana Nurkusuma Ningtyas Marshanda, Novi Maulia Isnaini Monica Dara Delia Suja Muhammad Galih Ramaputra Nisrina, Zalfaa Nopi Anggista Nopi Anggista Putri, Nopi Anggista Nur Asini Nurul Badriyah Panjaitan, Novaria Sari Dewi Psiari Kusuma Wardani Putriani, Olif Delfia Rahmawati, Nira Rahmi Khalida Rika Agustina, Rika Salwa, Akhla Saras Cipta Casmi SEPTI WULANDARI Septika Yani Veronica Setiani Setiani Sfenrianto, Sfenrianto Sheilla, Putri Soeprihatini, Retno Ariza Sri Lita Yani Suci Lanavia Sudarmi Sudarmi Sukarni Sukarni Sukarni Sukarni Sukarni Sukarni Sulistiawati, Yuni Sunarno Sunarno Suriyani Tan, Suriyani Suyanto, Jipri Syarif Hidayat Tahta Herdian Andika Umar, Mareza Wiyanti, Zulvi Yani Veronica, Septika Yuliani Yuliani Yuliarti, Elis Yuni Sulistiawati Yuni Sulistiawatia Yuni Sulistyawati Yuniastini Yuniastini Yunita Anggriani Zulkifli