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All Journal TEKNIK INFORMATIKA Jurnal Edukasi dan Penelitian Informatika (JEPIN) Sistemasi: Jurnal Sistem Informasi IJCIT (Indonesian Journal on Computer and Information Technology) Indonesian Journal on Software Engineering (IJSE) Jurnal CoreIT Jurnal Pilar Nusa Mandiri Information Management For Educators And Professionals (IMBI) Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Sisfokom (Sistem Informasi dan Komputer) JurTI (JURNAL TEKNOLOGI INFORMASI) Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal INTI Nusa Mandiri Journal of Applied Data Sciences Jurnal Responsif : Riset Sains dan Informatika Computer Science (CO-SCIENCE) Jurnal Sosial dan Abdimas Jurnal Publikasi Ilmu Komputer dan Multimedia Journal Medical Informatics Technology Journal of Hypermedia & Technology-Enhanced Learning SWAGATI: Journal of Community Service
Mayangky, Nissa Almira
Universitas Nusa Mandiri
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Journal : Journal Medical Informatics Technology

 1 
Comparison Algorithm on Machine Learning for Student Mental Health Data Nuarini, Sri; Siti Fauziah; Mayangky, Nissa Almira; Nurfalah, Ridan
Journal Medical Informatics Technology Volume 1 No. 3, September 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i3.18

Abstract

The COVID-19 pandemic has posed unparalleled difficulties, encompassing substantial repercussions on the emotional well-being of students. This study utilises machine learning methodologies to forecast the mental health condition of students during and following the pandemic. The dataset consists of 11 distinct attributes and a total of 101 data points, which have been gathered from multiple sources. The preprocessing stage encompasses the removal of unnecessary characteristics, handling missing data, and partitioning the dataset into separate subsets for training and validation purposes. This study utilises three machine learning algorithms, namely RF, KNN, and NB, in order to make predictions regarding the potential need for psychiatric support among students. These algorithms are carefully optimised to enhance their predictive capabilities. Evaluation metrics commonly used in several fields of study. The findings suggest that the KNN and RF algorithms had outstanding performance, but the Naïve Bayes algorithm exhibited satisfactory accuracy and a balanced trade-off between precision and recall. The optimised models have practical consequences that may be applied at educational institutions and inform policymakers. These implications include the ability to provide tailored interventions and support services specifically designed for students who are facing mental health difficulties as a result of the epidemic. Future research endeavours encompass the need for additional improvement of existing models and the fostering of interdisciplinary collaboration. This study provides significant contributions to the field by examining the utilisation of machine learning techniques in addressing the mental health needs of students both during and after the epidemic.
Comparison of Naive Bayes and Decision Tree Methods in Breast Cancer Classification Sulistyowati, Daning Nur; Hadianti, Sri; Mayangky, Nissa Almira
Journal Medical Informatics Technology Volume 3 No. 4, December 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i4.112

Abstract

The early diagnosis of breast cancer is a critical factor in improving recovery rates and reducing cancer-related mortality. This study aims to compare the performance of two widely used machine learning algorithms in medical data classification Naive Bayes and Decision Tree in detecting breast cancer using the Breast Cancer Wisconsin (Diagnostic) dataset. The dataset consists of 569 samples with 30 numerical features and one target label. The methodology includes data preprocessing, model training, and performance evaluation using six metrics: accuracy, precision, recall, F1-score, AUC, and MCC. Naive Bayes achieved higher performance, with 96.5% accuracy, 97.6% precision, 93.0% recall, 95.2% F1-score, 0.997 AUC, and 0.925 MCC, compared to Decision Tree with 93.9% accuracy, 90.9% precision, 93.0% recall, 92.0% F1-score, 0.936 AUC, and 0.87 MCC. Confusion matrix and ROC curve analyses support these results, particularly in minimizing classification errors. While Decision Tree offers better interpretability, Naive Bayes may be more suitable for early breast cancer detection under similar dataset conditions. Future studies could explore ensemble approaches to combine the strengths of both methods.
Co-Authors Ade Chandra Arfhan Prasetyo Arfita Adikvika Aziz, Faruq Balla, Imanuel Dea Amelia Dedi Destio Irawan Desiana Nur Kholifah Destari Rahmadani Putri Dina Zakiyah Doni Purnama Alamsyah Dwiza Riana Dwiza Riana Fahrurrozi4, Nabil Ali Fiyah, Nur Gata, Windu Ida Zuniarti, Ida Imanuel Balla Isti Kharoh Jajang Jaya Purnama Jessika Aryani Juniarti Kholifah, Desiana Nur Kusumayudha, Mochammad Rizky Marcella Irsictia Marscal Panjaitan Mochamad Rizky Kusumayudha Mochammad Rizky Kusumayudha Muhamad Hasan Muhammad Faitullah Akbar Mutmainah Putri Nita Merlina Nita Merlina Nita Merlina Nita Merlina, Nita Nuarini, Sri Nur Fiyah Nurfalah, Ridan Panjaitan, Yogi Yosua Prasetio, Arfhan Pratama, Kasahana Indra Putra, Ananta Kusuma Rani Risdiawati Ridan Nurfalah Ridan Nurfalah Rousyati Rousyati Siti Fauziah Siti Nurdiani Sri Hadianti Sri Hadianti Sri Rahayu Suharyanto Suharyanto Sulistyowati, Daning Nur Thira, Indra Jiwana Tika Adilah M Tuti Haryanti Utami, Cheilla Cahya Utami Yuma Yaumaidzinnaimah Yuris Alkhalifi