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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Syntax Literate: Jurnal Ilmiah Indonesia Buletin Ilmiah Sarjana Teknik Elektro An-Nadaa: Jurnal Kesehatan Masyarakat International Journal of Robotics and Control Systems Journal of Training and Community Service Adpertisi
Muryadi, Elvaro Islami
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Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Analisis Komunikasi Kesehatan Terhadap Partisipasi Masyarakat Dalam Program Vaksin Covid-19 Di Kota Jambi Tahun 2023 Muryadi, Elvaro Islami; Nasution, Sri Lestari Ramadhani; Girsang, Erni
An-Nadaa: Jurnal Kesehatan Masyarakat (e-Journal) Vol 10, No 2 (2023): AN-NADAA JURNAL KESEHATAN MASYARAKAT (DESEMBER)
Publisher : Universitas Islam Kalimantan Muhammad Arsyad Al Banjari Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31602/ann.v10i2.13319

Abstract

Dalam komunikasi kesehatan ada beberapa hal yang dapat mempengaruhi sesorang mau melakukan perubahan perilaku kesehatan, dimana hal tersebut dipengaruhi oleh kekuasaan/kekuatan, Diskusi dan partisipasi serta pemberian informasi. Vaksin covid 19 di Indonesia sudah ada sejak tahun 2020 dimana masyarakat diwajibkan untuk melakukan vaksinasi covid 19, guna untuk mencegah penyebaran covid 19 dan peningkatan derajat kesehatan masyarakat. Tujuan dari penelitian ini adalah menganalisis komunikasi kesehatan terhadap partisipasi masyakarat dalam program vaksin covid 19 di Kota Jambi tahun 2023.Penelitian ini adalah penelitian kualitatif non eksperimental yang menggunakan teknik survey untuk mendapatkan data, kemudian diuji korelasinya untuk mendapatkan hubungan pada variabel yang diteliti. Populasi dalam penelitian ini adalah 313.096 orang dengan sample sebesar 399 orang. Data ini di ambil dari seluruh masyakarat Kota Jambi yang telah melakukan vaksin ke 2 atau ke 3 dan kemudian diberikan quisoner baik online maupun offline. Hasil penelitian ini menunjukan bahwa adanya pengaruh pada perubahan perilaku masyarakat terhadap program vaksin covid 19 di Kota Jambi, dengan nilai p value 0,000 yang artinya < 0,005 hal ini menunjukan adalah pengaruh. Akan tetapi pengaruh yang paling besar nilainya adalah pemberian informasi berikutnya diskusi dan partisipasi dan yang terakhir adalah kekuasaan/kekuatan. Hal ini menunjukan bahwa masyarakat akan melakukan perubahan ketika mendapatkan informasi yang jelas dan diajak aktif untuk berdiskusi bukan melalui paksaan atau peraturan saja. Karna perubahan perilaku dengan menggukan paksaan tidak akan bertahan lama.Kata kunci : Komunikasi Kesehatan, partisipasi Vaksin Covid 19
Penyuluhan Deteksi Dini dan Penanganan Awal Kegawatdaruratan pada Anggota Perhimpunan Mahasiswa Indonesia di Thailand (Permitha) Simpul Khon Kaen Purwanti, indri astuti; Muryadi, Elvaro Islami; Rokhani
JOURNAL OF TRAINING AND COMMUNITY SERVICE ADPERTISI (JTCSA) Vol. 4 No. 1 (2024): Februari 2024
Publisher : ADPERTISI

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Abstract

The Association of Indonesian Students in Thailand (Permitha) Node Khon Kaen has a health and sports division, but there are no health promotion activities. In fact, there have been 3 cases of member morbidities and they were handled incorrectly without paying attention to the urgency, severity, or emergency. Therefore, community service implementers provide health education on early detection and initial handling of emergencies. The method of this activity is pretest-posttest design with an instrument in the form of a questionnaire. The participants were selected by accidental sampling as many as 11 people. The collected data were processed and analyzed using mean, standard deviation, median, minimum value and maximum value for univariate analysis. The bivariate analysis was carried out using paired sample t-test. The results of data analysis showed that the majority of participants were non-health graduate students (54.5%) living in off-campus apartments (72.7%), and aged less than 26.5 years (63.6%). The mean knowledge score after counseling was higher than before counseling by 3.36 points (95% CI: 2.11 to 4.61; p-value < 0.001). In conclusion, the health education on early detection and early treatment of emergencies was successful in increasing participants' knowledge.
Revolutionizing Anemia Classification with Multilayer Extremely Randomized Tree Learning Machine for Unprecedented Accuracy Saputra, Dimas Chaerul Ekty; Muryadi, Elvaro Islami; Futri, Irianna; Win, Thinzar Aung; Sunat, Khamron; Ratnaningsih, Tri
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1379

Abstract

Anemia is a prevalent global health issue that is characterized by a deficit in red blood cells or low levels of hemoglobin. This condition is influenced by various causes, including nutritional inadequacies, chronic diseases, and genetic predisposition. The incidence of the phenomenon exhibits variation across different geographical regions and demographic groups. This pioneering research investigates the identification and classification of anemia, potentially leading to transformative advancements in the discipline. The classification of anemia encompasses four distinct groups, namely Beta Thalassemia Trait, Iron Deficiency Anemia, Hemoglobin E, and Combination. This comprehensive categorization offers clinicians a more refined and detailed comprehension of the condition. The integration of deep learning and machine learning in the Multilayer Extremely Randomized Tree Learning Machine (MERTLM) model represents a departure from traditional approaches and a significant advancement in the field of medical categorization accuracy. The MERTLM approach integrates randomized tree with multilayer extreme learning machine (M-ELM) representation learning, hence emphasizing the possibility of interdisciplinary collaboration in the field of diagnostics. In addition to its impact on anemia, artificial intelligence (AI) is playing a significant role in revolutionizing medical diagnosis by emphasizing the integration of innovative methods. This study utilizes the combined capabilities of machine learning and deep learning to improve accuracy. Notably, recent developments have resulted in an exceptional accuracy rate of 99.67%, precision of 99.60%, sensitivity of 99.47%, and an amazing F1-Score of 99.53%. This study represents a significant advancement in the field of anemia research, providing valuable insights that may be applied to intricate medical issues and enhancing the quality of patient care.
HNIHA: Hybrid Nature-Inspired Imbalance Handling Algorithm to Addressing Imbalanced Datasets for Improved Classification: In Case of Anemia Identification Saputra, Dimas Chaerul Ekty; Ratnaningsih, Tri; Futri, Irianna; Muryadi, Elvaro Islami; Phann, Raksmey; Tun, Su Sandi Hla; Caibigan, Ritchie Natuan
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i3.11306

Abstract

This study presents a comprehensive evaluation of three ensemble models designed to handle imbalanced datasets. Each model incorporates the hybrid nature-inspired imbalance handling algorithm (HNIHA) with matthews correlation coefficient and synthetic minority oversampling technique in conjunction with different base classifiers: support vector machine, random forest, and LightGBM. Our focus is to address the challenges posed by imbalanced datasets, emphasizing the balance between sensitivity and specificity. The HNIHA algorithm-guided support vector machine ensemble demonstrated superior performance, achieving an impressive matthews correlation coefficient of 0.8739, showcasing its robustness in balancing true positives and true negatives. The f1-score, precision, and recall metrics further validated its accuracy, precision, and sensitivity, attaining values of 0.9767, 0.9545, and 1.0, respectively. The ensemble demonstrated its ability to minimize prediction errors by minimizing the mean squared error and root mean squared error to 0.0384 and 0.1961, respectively. The HNIHA-guided random forest ensemble and HNIHA-guided LightGBM ensemble also exhibited strong performances.
Bibliometric Analysis of Explainable AI in Advance Care Planning: Insights, Collaborative Trends, and Future Prospects Futri, Irianna; Muryadi, Elvaro Islami; Saputra, Dimas Chaerul Ekty
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i4.11641

Abstract

The increasing complexity of healthcare systems has led to a growing need for Advance Care Planning (ACP) to ensure personalized care for patients. Explainable Artificial Intelligence (XAI) has emerged as a promising solution to enhance ACP by providing transparent and interpretable decision-making processes. However, the current landscape of XAI in ACP remains unclear, necessitating a comprehensive bibliometric analysis. This study employed a systematic review of existing literature on XAI in ACP, using a bibliometric approach to analyze publication trends, collaboration patterns, and research themes. One hundred sixty articles were selected from prominent databases, and their metadata were extracted and analyzed using Biblioshiny, the analysis revealed a significant growth in ACP XAI-related publications, focusing on deep learning and natural language processing techniques. The top contributing authors and institutions were identified, and their collaborative networks were visualized. The results also highlighted the prominent themes of patient-centered care, decision support systems, and healthcare analytics. The study's findings have implications for developing more effective XAI-based ACP systems. This bibliometric analysis provides valuable insights into the current state of XAI in ACP, highlighting the need for further research and collaboration to address the complex challenges in healthcare. The study's outcomes can inform policymakers, researchers, and practitioners in developing more effective ACP systems that leverage the potential of XAI.
An Innovative Artificial Intelligence-Based Extreme Learning Machine Based on Random Forest Classifier for Diagnosed Diabetes Mellitus Saputra, Dimas Chaerul Ekty; Muryadi, Elvaro Islami; Phann, Raksmey; Futri, Irianna; Lismawati, Lismawati
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.28690

Abstract

Since 2014, the World Health Organization has accumulated data indicating that 8.5% of 18-year-olds and older have been diagnosed with diabetes. In 2019, diabetes caused the lives of 1.5 million people worldwide, with those under the age of 70 accounting for 48% of all diabetes-related deaths. It is estimated that diabetes causes an additional 460,000 deaths each year due to renal failure and that hyperglycemia contributes to about 20% of all cardiovascular disease-related deaths. Diabetes may have contributed to a 3% rise in the age-adjusted death rate between the years 2000 and 2019. In recent years, the fatality rate attributable to diabetes has increased by 13% in low- and middle-income countries. Statistics collected by the World Health Organization indicate that the number of persons diagnosed with diabetes has increased from 108 million in 1980 to 422 million in 2014. The objective of this study is to construct a model capable of diagnosing persons with diabetes reliably, correctly, and consistently. This research used secondary data offered by Kaggle. The original data came from the National Institute of Diabetes and Digestive and Kidney Diseases. Each of the up to 768 data points consists of nine characteristics and two outputs, such as diabetes and non-diabetes in the provided example. In this study, a single algorithm is constructed by integrating two separate algorithms. Random forest algorithms, which are based on machine learning, and extreme learning machines, which are based on deep learning, have generated extraordinarily accurate results. When the confusion matrix is used, 98.05% accuracy is attained. Therefore, it is feasible to conclude that the suggested method was successful in completing an adequate analysis and classifying the data.
iGWO-RF: an Improved Grey Wolfed Optimization for Random Forest Hyperparameter Optimization to Identification Breast Cancer Muryadi, Elvaro Islami; Futri, Irianna; Saputra, Dimas Chaerul Ekty
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i4.29300

Abstract

The study focuses on improving the accuracy of breast cancer diagnosis by enhancing the predictive capabilities of a Random Forest model. This is achieved by utilizing an improved Grey Wolf Optimization algorithm for hyperparameter optimization. The main objectives are to enhance early detection, increase diagnostic precision, and reduce computational demands in clinical workflows. The work utilizes the Improved Grey Wolf Optimization (iGWO) algorithm to tune the hyperparameters of a Random Forest (RF) model, thereby improving its accuracy in diagnosing breast cancer. The methodology encompasses several steps, including data preparation, model training using iGWO-enhanced RF, performance evaluation compared to traditional methods, and validation using clinical datasets to confirm the reliability and effectiveness of the approach. The iGWO-RF model demonstrated exceptional performance in diagnosing breast cancer, achieving an accuracy of 96.4%, precision of 96.4%, recall of 98.0%, F1-score of 97.2%, and ROC-AUC of 0.988. The findings of iGWO-RF outperform those of SVM, original RF, Naive Bayes, and KNN models, indicating that iGWO-RF is effective in optimizing hyperparameters to improve prediction accuracy. The iGWO-RF model greatly enhances the accuracy and efficiency of breast cancer diagnosis, surpassing conventional models. Integrating iGWO-RF into clinical workflows is advised to improve early identification and patient outcomes. Additional investigation should focus on the utilization of this technology in various medical datasets and circumstances, highlighting its potential in a wide range of healthcare environments.
Hubungan Sikap Siswa Terhadap Kebiasaan Cuci Tangan Pakai Sabun di Era Pandemi Covid-19 pada Siswa SMP Negeri 2 Kota Sungai Penuh Tahun 2021 Azri, Alya Shaira; Muryadi, Elvaro Islami
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v9i10.16679

Abstract

Cuci tangan pakai sabun (CTPS) adalah tindakan sanitasi penting untuk mencegah penyakit, terutama selama pandemi COVID-19. Penelitian ini meneliti hubungan antara sikap siswa dan kebiasaan CTPS di SMP Negeri 2 Kota Sungai Penuh. Metode yang digunakan adalah penelitian kuantitatif dengan desain korelasional, melibatkan 87 siswa yang dipilih secara acak. Data diperoleh melalui kuesioner dan dianalisis menggunakan SPSS 21. Hasil menunjukkan nilai korelasi 0.001 (p<0.05), mengindikasikan hubungan signifikan antara sikap siswa dan perilaku CTPS, dengan koefisien korelasi 0.352 yang menunjukkan hubungan yang kuat. Penelitian ini menyimpulkan bahwa sikap siswa berpengaruh terhadap kebiasaan CTPS.
Co-Authors Azri, Alya Shaira Caibigan, Ritchie Natuan Dimas Chaerul Ekty Saputra Futri, Irianna Girsang, Erni Indri Astuti Purwanti Lismawati, Lismawati Nasution, Sri Lestari Ramadhani Phann, Raksmey Rokhani Sunat, Khamron Tri Ratnaningsih Tun, Su Sandi Hla Win, Thinzar Aung