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All Journal Jurnal Medika Malahayati Computer Science (CO-SCIENCE) Journal Medical Informatics Technology
Ernawati, Muji
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Dentistry Engineering Health Professions Immunology & microbiology Medicine & Pharmacology Public Health

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Advances in Machine Learning and Deep Learning towards Medical Data Analysis Vebiyatama, Andicha; Ernawati, Muji
Journal Medical Informatics Technology Volume 2 No. 1, March 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i1.32

Abstract

Artificial intelligence uses advanced algorithms such as deep learning and machine learning methods to help doctors make more accurate diagnoses, identify potential health risks, and customize personalized treatment plans for patients. This literature review explores machine learning and deep learning methods applied to medical datasets over the past five years. The paper discusses the advancements, challenges, and future directions in utilizing ML and DL techniques for medical data analysis. It synthesizes recent research findings, highlighting key methodologies, datasets, and outcomes.
Penerapan: Penerapan Metode SMOTE Untuk Mengatasi Imbalanced Data Pada Klasifikasi Ujaran Kebencian Ridwan, Ridwan; Heni Hermaliani, Eni; Ernawati, Muji
Computer Science (CO-SCIENCE) Vol. 4 No. 1 (2024): Januari 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i1.2990

Abstract

Hate speech is the spread of hatred towards individuals or groups on the basis of ethnicity, religion, race, and other characteristics that can lead to discrimination, violence, and social conflict. Unbalanced data can cause negative results in classification results. The Synthetic Minority Oversampling Technique (SMOTE) method is used to deal with unbalanced data. Feature extraction uses Bag of Words and TD-IDF, then the training data are oversampled using the SMOTE, SVM-SMOTE, Kmeans-SMOTE, and Borderline-SMOTE methods. This classification uses the Random Forest, Support Vector Machine, Logistic Regression, and Naive Bayes algorithms using Twitter data. The research results show that the application of the Borderline-SMOTE method to handle imbalanced data produces better performance than other SMOTE methods based on accuracy, recall,precision and F1-Score values with respective values of 84.09%, 85.25%, 84,55% and 81.16%. The Random Forest algorithm produces higher performance values than other algorithms.
Analysis of Service Quality on User Satisfaction in BPJS Kesehatan Website Trihardo, Rendra; Jumadi, J; Ernawati, Muji
Journal Medical Informatics Technology Volume 2 No. 4, December 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i4.56

Abstract

BPJS Kesehatan plays a vital role in providing health insurance to millions of Indonesians, making it essential to assess the quality of service on their website to ensure efficient and accessible healthcare delivery. This study evaluates of service quality on user satisfaction with the BPJS Kesehatan website by analyzing 10 hypotheses related to information quality, system usability, and service effectiveness. The research employed a quantitative approach, utilizing a structured questionnaire and regression analysis with data from 32 respondents. Significant findings include a strong positive effect of service quality on system use (β = 0.928, p = 0.002) and a notable impact of system use on net benefits (β = 0.337, p = 0.014). The model's high R² value of 0.796 indicates that nearly 80% of the variance in net benefits is explained by the predictors, demonstrating that improved service quality and increased system use substantially enhance user satisfaction and perceived benefits. These results underscore the importance of focusing on service quality and user engagement to optimize outcomes from the BPJS Kesehatan website.
Penerapan: Penerapan Metode SMOTE Untuk Mengatasi Imbalanced Data Pada Klasifikasi Ujaran Kebencian Ridwan, Ridwan; Heni Hermaliani, Eni; Ernawati, Muji
Computer Science (CO-SCIENCE) Vol. 4 No. 1 (2024): Januari 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i1.2990

Abstract

Hate speech is the spread of hatred towards individuals or groups on the basis of ethnicity, religion, race, and other characteristics that can lead to discrimination, violence, and social conflict. Unbalanced data can cause negative results in classification results. The Synthetic Minority Oversampling Technique (SMOTE) method is used to deal with unbalanced data. Feature extraction uses Bag of Words and TD-IDF, then the training data are oversampled using the SMOTE, SVM-SMOTE, Kmeans-SMOTE, and Borderline-SMOTE methods. This classification uses the Random Forest, Support Vector Machine, Logistic Regression, and Naive Bayes algorithms using Twitter data. The research results show that the application of the Borderline-SMOTE method to handle imbalanced data produces better performance than other SMOTE methods based on accuracy, recall,precision and F1-Score values with respective values of 84.09%, 85.25%, 84,55% and 81.16%. The Random Forest algorithm produces higher performance values than other algorithms.
Efektivitas Konsumsi Coklat Dan Pijat Endorphin Terhadap Penurunan Nyeri Dismenore Pada Remaja Putri Lestari, Restu Senja; M, Maryam Syarah; Jayatmi, Irma; Ariyanti, Novi; Lestari, Fitri Wahyu; Kustari, N Amelinda; Ernawati, Muji; Aryanti, Tanty
Jurnal Medika Malahayati Vol 9, No 3 (2025): Volume 9 Nomor 3
Publisher : Universitas Malahayati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33024/jmm.v9i3.19997

Abstract

Dismenorea termasuk salah satu keluhan terkait menstruasi yang banyak dialami oleh wanita. Dismenorea merupakan gangguan menstruasi dengan angka kejadian tertinggi sebesar 89,5%, diikuti oleh ketidaknyamanan menstruasi sebesar 31,2% dan menstruasi berkepanjangan sebesar 5,3%. Pada beberapa penelitian, ditemukan prevalensi dismenorea bervariasi antara 15,8% dan 89,5%. Desain penelitian yang akan dipakai dalam penelitian ini merupakan pendekatan Study Case Literature Review (SCLR) menggunakan 6 sampel eksperimen yang dibagi kepada 2 pemberian intervensi di mana 1-3 responden remaja diberikan intervensi cokelat, 4-6 remaja putri lagi diberikan intervensi pijat endorphin, ekperimen dilakukan dengan memberikan intervensi selama 3 hari lalu kemudian dilakukan observasi intensitas penurunan nyeri menggunakan Numeric Rating Scale (NRS). Hasil dari pemberian intervensi didapatkan Responden 1-3 sebelum diberikan intervensi cokelat memiliki Tingkat nyeri parah, dan setelah diberikan intervensi cokelat terdapat penurunan nyeri dengan hasil responden 1 memiliki Tingkat nyeri 0 atau tidak nyeri dan responden 2,3 memiliki Tingkat nyeri ringan. Responden 4-6 sebelum diberikan intervensi pijat endorphin memiliki Tingkat nyeri parah, dan setelah diberikan intervensi pijat endorphin terdapat penurunan nyeri dengan hasil Tingkat nyeri 0 atau tidak nyeri. Berdasarkan hasil Intervensi pijat endorphin lebih efektif dibandingkan dengan konsumsi cokelat. Maka disarankan klien atau masyarakat khususnya Remaja Putri dapat melakukan tindak lanjut yang tepat dan mampu memberikan intervensi non farmakologis berupa konsumsi cokelat dan pijat endorphin untuk menurunkan masalah dismenorea atau Kesehatan reproduksi Wanita.
Co-Authors Ariyanti, Novi Aryanti, Tanty Heni Hermaliani, Eni Jayatmi, Irma Jumadi, J Kustari, N Amelinda Lestari, Fitri Wahyu Lestari, Restu Senja Maryam Syarah Mardiyah Ridwan, Ridwan Trihardo, Rendra Vebiyatama, Andicha