Article Per Year (5 Year)
p-Index From 2021 - 2026
2.583
P-Index
This Author published in this journals
All Journal Jurnal Epidemiologi Kesehatan Komunitas Prosiding Semhavok Poltekita : Jurnal Ilmu Kesehatan Science Midwifery Journal of Applied Data Sciences Jurnal Bina Komputer IJOEM: Indonesian Journal of Elearning and Multimedia Jurnal Pengabdian Masyarakat Bangsa International Journal of Advances in Artificial Intelligence and Machine Learning
Misinem, Misinem
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

Published : 19 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 19 Documents
Search

 1   2 
KLB Keracunan Makanan Di Dusun Kacepit, Desa Wulungsari, Kecamatan Selomerto, Kabupaten Wonosobo, 2024 Gosari, Kevin Rayes; Adi, Mateus Sakundarno; Martini, Martini; Misinem, Misinem
Jurnal Epidemiologi Kesehatan Komunitas Vol 10, No 3: Agustus 2025
Publisher : Master of Epidemiology, Faculty of Public Health, Diponegoro University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jekk.v10i3.26833

Abstract

Background: A food poisoning incident occurred on Saturday, February 24, 2024, during a religious gathering in Kecapit Hamlet, Wulungsari Village, Selomerto Sub-district, with a total of 44 cases. The objective of this investigation was to identify the source of the outbreak and the risk factors associated with the food poisoning incident.Methods: Food poisoning was defined as a condition in individuals experiencing illness with symptoms and signs of poisoning caused by consuming food suspected of containing biological or chemical contaminants. An outbreak investigation using a cross-sectional study design was conducted. Research questionnaires were used to collect data on risk factors as well as signs and symptoms. A total of 58 individuals who attended the event were included as samples. These factors were analyzed descriptively, and attack rates were calculated for each factor. Fecal samples from clinically ill cases were collected for laboratory testing.Results and Discussion: Out of the 58 individuals, 44 experienced symptoms of diarrhea >3 times (72.4%), abdominal cramps (69%), fever (62.1%), vomiting (22.4%), and nausea (58.4%). More cases were detected in males (52%) with an age range of 5-81 years (average 41.7 years). The incubation period ranged from 6-15 hours (average 11.41 hours). The investigation results indicated that individuals who consumed durian coconut syrup became ill (44/58; Attack Rate 93.6%). Stool laboratory test results showed positive for Salmonella. However, laboratory testing for the durian coconut syrup was not conducted in this study due to the unavailability of samples.Conclusion: Based on the findings of the investigation, it can be concluded that the cause of the food poisoning was durian coconut syrup contaminated with Salmonella bacteria. This contamination may have occurred because the food spoiled as it was prepared at 8:00 AM and served in the afternoon (3:00 PM).
Pelatihan Penggunaan Sistem Informasi Kerja Praktek dan Tugas Akhir Digital di PT Pertamina Hulu Rokan Zona 4 Prabumulih Ulfa, Maria; Azizah, Shavira Nur; Misinem, Misinem; Bakti, Ahmad Mutakin; Suryayusra, Suryayusra; Udariansyah, Devi
Jurnal Pengabdian Masyarakat Bangsa Vol. 3 No. 5 (2025): Juli
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v3i5.2662

Abstract

Kegiatan pelatihan ini bertujuan untuk meningkatkan efektivitas dan efisiensi proses administrasi Kerja Praktik (KP) dan Tugas Akhir (TA) di PT Pertamina Hulu Rokan Zona 4 Kota Prabumulih melalui penggunaan sistem informasi berbasis website. Sistem ini menggantikan metode manual sebelumnya yang menggunakan email dan Excel, dengan menyediakan fitur registrasi online, unggah dokumen, verifikasi, hingga monitoring kegiatan. Metode pelaksanaan dilakukan melalui pelatihan langsung, simulasi penggunaan, dan evaluasi pemahaman. Hasil kegiatan menunjukkan peningkatan pemahaman peserta terhadap penggunaan sistem serta efisiensi kerja administratif. Sistem ini juga memberikan kemudahan pelacakan data dan mengurangi risiko kehilangan data. Kesimpulannya, pelatihan ini memberikan dampak positif dalam peningkatan kompetensi pegawai dan optimalisasi proses administrasi KP/TA.
AI-Enhanced Gross Pollutant Traps: A Smart Approach to River Health and Pollution Control Ying, Chang Shi; May , Bong Peak; Fang , Soo Ting; Yi, Lee Wai; Misinem, Misinem
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 1 No. 1 (2024): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v1i1.285

Abstract

Flooding and river pollution pose significant challenges in Malaysia, exacerbated by the inefficiencies of Gross Pollutant Traps (GPTs), which rely on manual monthly cleaning processes. These conventional methods are inadequate for addressing the dynamic influx of pollutants, particularly during adverse weather conditions. This research proposes an innovative AI-powered framework that integrates logistic regression for weather prediction and Convolutional Neural Networks (CNNs) for real-time garbage classification. By predicting weather patterns and classifying pollutants, this system optimizes GPT maintenance, enhancing its effectiveness and efficiency. The proposed system leverages real-time data from sensors, cameras, and weather forecasts, enabling authorities to implement proactive maintenance strategies based on accurate weather predictions and pollutant types. Logistic regression models forecast adverse weather conditions, while CNNs accurately classify garbage types, allowing targeted GPT cleaning during periods of increased pollutant buildup. The logistic regression model achieved an accuracy of 86.41%, and the CNN model attained a classification accuracy of 79.37%, showcasing strong performance in predicting weather conditions and categorizing pollutants. The integration of AI technologies in GPT maintenance significantly enhances environmental planning, mitigates flooding risks, and improves the accuracy of pollution monitoring. This solution provides valuable insights for decision-makers, helping them allocate resources effectively and maintain sustainable water management practices. In conclusion, the AI-driven system offers a robust and efficient approach to optimizing GPT operations, contributing to better environmental protection and urban sustainability.
AI-Powered Face Mask Detection Utilizing MobileNetV2 for Health Monitoring Misinem, Misinem; Agustini , Eka Puji; Ulfa, Maria
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 1 No. 1 (2024): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v1i1.286

Abstract

The COVID-19 pandemic has highlighted the critical need for face masks to prevent virus transmission. Ensuring consistent mask usage in crowded public spaces remains a challenge, especially with manual monitoring methods that are inefficient and prone to error. To address this, this research introduces a real-time face mask detection system leveraging MobileNet-V2, a lightweight and efficient deep learning model known for its high performance in image classification tasks. The system utilizes a dataset from Kaggle comprising 11,792 images, divided into training (10,000), validation (800), and testing (992) sets. MobileNet-V2 was fine-tuned for this task, using its inverted residual layers to extract features and enhance performance effectively. Data augmentation techniques were applied to improve the model’s ability to generalize across diverse scenarios. The MobileNet-V2 model achieved an impressive 98.69% accuracy on the testing dataset, demonstrating exceptional reliability in identifying individuals wearing masks versus those without. Standard evaluation metrics, including precision, recall, and a confusion matrix, confirmed its robustness. This system’s ability to operate in real-time makes it ideal for public health surveillance in environments such as airports, shopping malls, and public transport. The proposed face mask detection system is both accurate and scalable, offering an efficient solution for enforcing mask-wearing protocols in public spaces. The system’s integration of advanced deep learning techniques ensures its reliability in real-time monitoring, contributing to better public health management. Future work will focus on further optimizing the model and expanding its application to other health-related monitoring tasks, enhancing its value for public health surveillance.
Scalability and Efficiency: A Comparative Study of Face Recognition Technologies Zakaria, Zaki; Misinem, Misinem; Sopiah , Nyimas; Efrizoni , Lusiana
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 1 No. 1 (2024): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v1i1.296

Abstract

This article addresses the challenge of selecting the most effective machine learning algorithm for face recognition tasks, a common problem in academic research and practical applications. To tackle this issue, we conducted a comparative analysis of five widely used algorithms: Linear Discriminant Analysis (LDA), Logistic Regression, Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The study involved implementing each algorithm on a standardized dataset, followed by a rigorous evaluation of their performance based on accuracy metrics. The results revealed that LDA, Logistic Regression, and SVM significantly outperformed the other models, each achieving an impressive accuracy of 97%. This high accuracy indicates that these algorithms are well-suited for handling datasets with linearly separable classes. Naive Bayes also showed a strong performance with 90% accuracy, proving effective under the feature independence assumption. However, KNN lagged, with an accuracy of 70%, highlighting its sensitivity to data scale and local structure, which affects its applicability in larger datasets or real-time scenarios. The findings suggest that while LDA, Logistic Regression, and SVM are optimal for datasets with clear class distinctions, the choice of an algorithm should still be guided by specific data characteristics and computational constraints. This study underscores the necessity for carefully considering each algorithm’s strengths and limitations, ensuring that the selected model aligns with the unique demands of the application. Future work could explore ensemble methods and advanced parameter tuning further to enhance the performance and robustness of these models.
The Fight Against Fiction: Leveraging AI for Fake News Detection Misinem, Misinem; Komalasari, Dinny; Adha Oktarini Saputri, Nurul
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 1 (2025): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v2i1.367

Abstract

This study aims to evaluate the performance of three machine learning algorithms namely Logistic Regression, Naïve Bayes, and Random Forest in classifying fake news. The research methods include data collection from various news sources, text preprocessing to improve data quality, and context-based feature engineering that considers temporal, linguistic, and named entity aspects. Furthermore, the model is developed using a machine learning approach that integrates ensemble techniques to improve prediction accuracy. Evaluation was conducted using accuracy, precision, accuracy, and F1 score metrics. The experimental results showed that Random Forest performed best with an accuracy of 93.00%, superior to Naïve Bayes (89.96%) and Logistic Regression (91.00%). This analysis confirms that algorithm selection should be tailored to the specific needs of the project, with Random Forest being a more reliable choice for scenarios that require high accuracy and robustness to data complexity. The findings are expected to contribute to the development of fake news detection systems that are more effective and adaptive to the dynamics of information in the digital world.
A Comparative Evaluation of Predictive Models for Lung Cancer: Insights from Logistic Regression, Naive Bayes, and Random Forest Hafiz Kurniawan, Muhammad; Misinem, Misinem
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 1 (2025): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v2i1.378

Abstract

This study aims to evaluate the performance of three machine learning models-Logistic Regression, Naive Bayes, and Random Forest-in predicting lung cancer using a publicly available dataset from Kaggle. The data used included demographic information, risk factors, and diagnostic imaging features, with significant class imbalance between benign and malignant cases. To address this imbalance, the Synthetic Minority Sampling Technique (SMOTE) was applied. In addition, Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE) were used for dimensionality reduction and feature selection to improve model performance. The results showed that Random Forest, especially when combined with PCA, outperformed the other models with the highest accuracy of 96.77% and a balanced F1 score of 0.50 for the minority class. Although Logistic Regression achieved high accuracy, it was less effective in predicting minority classes, especially when combined with RFE. Meanwhile, Naive Bayes showed moderate performance but was limited by the assumption of feature independence. The application of SMOTE significantly improved the model's ability to handle class imbalance, while PCA proved more effective than RFE in improving model performance. This study highlights the importance of selecting appropriate machine learning models and preprocessing techniques for lung cancer prediction. Random Forest, with its ability to model complex relationships and handle imbalanced data, emerged as the most effective model for this task. These findings underscore the potential of machine learning in medical diagnostics and provide valuable insights for future research.
Advanced Anomaly Detection in ECG Signals Through Convolutional Autoencoders Henderi, Henderi; Misinem, Misinem; Hamdani, Hamdani; Zakaria, Mohd Zaki; Kasim, Shahreen Binti
IJOEM Indonesian Journal of E-learning and Multimedia Vol. 3 No. 3 (2024): Indonesian Journal of E-learning and Multimedia (October 2024)
Publisher : CV. Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijoem.v3i3.308

Abstract

This article aims to present a comprehensive study on convolutional autoencoders for advanced anomaly detection in ECG signals. Anomaly detection in complex datasets has become increasingly critical due to the rising need for systems that can effectively identify irregularities that may indicate fraud, system failures, or significant deviations from normal operations. Traditional methods often need help capturing nuanced patterns in high-dimensional data, necessitating more sophisticated approaches. This research uses an autoencoder-based model as a robust solution for anomaly detection, utilizing its capability to learn high-level representations in an unsupervised manner. The proposed model uses a convolutional autoencoder architecture to compress and decompress input data, thus highlighting anomalies through reconstruction errors. We outline detailed experiment strategies, including model training on average data to minimize reconstruction loss, setting an optimal threshold for anomaly sensitivity based on validation loss, and evaluating the model using precision, recall, F1-score, and AUC-ROC metrics. These experiments were conducted using a dataset with labeled normal and abnormal instances, allowing precise tuning and assessment of model performance. The results indicate that the autoencoder discriminates between normal and abnormal data, achieving high precision and recall at 99.22% and 98.98%, respectively. The confusion matrix and loss distribution analysis further validate the model's efficacy, clearly distinguishing between normal and abnormal data loss values concerning the defined threshold. This research shows the autoencoder model demonstrates high accuracy in anomaly detection and offers insights into the types of anomalies it can detect, supporting its application across various domains requiring reliable anomaly identification.
The Implications of Utilizing Artificial Intelligence-Based Parenting Technology on Children's Mental Health: A Literature Review Yunike, Yunike; Rehana, Rehana; Misinem, Misinem; Kusumawaty, Ira
Poltekita : Jurnal Ilmu Kesehatan Vol. 17 No. 3 (2023): November
Publisher : Poltekkes Kemenkes Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33860/jik.v17i3.2958

Abstract

The study aims to look at the development of artificial intelligence to support positive parenting and help improve children's mental health. This research method uses the PRISMA approach through a search process using the keywords "Parenting, Child Mental Health, Artificial Intelligence, Communication, and Discipline Enforcement on the Scopus, Google Scholar, Science Direct, and Pubmed databases in the period 2018 to 2023 with a five-step process to obtain data. After elimination, 1,223 journal articles were obtained that met the inclusion and exclusion criteria of 11 journal articles. Based on the results of the literature review, information was identified regarding the history of the development of artificial intelligence technology, artificial intelligence significantly has a positive impact on children's mental health, through the use of artificial intelligence parents gain meaningful literacy in providing positive care for children. However, the use of artificial intelligence can lead to parents' dependence on artificial intelligence, which is feared to replace human figures. In conclusion, studying the development and adequate use of artificial intelligence technology is urgently needed to improve the ability and quality of parenting to support the optimization of children's mental health.
Co-Authors Adha Oktarini Saputri, Nurul Agustini , Eka Puji Astried, Astried Azizah, Shavira Nur BAKTI, AHMAD MUTAKIN Dewi, Deshinta Arrova Efrizoni , Lusiana Fang , Soo Ting Firamon Syakti, Firamon Gosari, Kevin Rayes Hafiz Kurniawan, Muhammad Hamdani Hamdani Hannah, Merrieayu Puspita Henderi . Joan Angelina Widians, Joan Angelina Kasim, Shahreen Binti Komalasari, Dinny Kurniawan, Tri Basuki Maria Ulfa Martini Martini Mateus Sakundarno Adi, Mateus Sakundarno May , Bong Peak Mukti, Gerry Praja Nazmi, Che Mohd Alif Nirwana, Nirwana Praja Mukti, Gerry Putra, M. Soekarno Raden Mohamad Herdian Bhakti Ramadhon, Putra Rehana, Rehana Rezania Agramanisti Azdy, Rezania Agramanisti Saputri, Nurul Adha Oktarini Sopiah , Nyimas Suryayusra, Suryayusra Triarso, Hardian Alfin Udariansyah, Devi Wahyudin, Untung Yi, Lee Wai Ying, Chang Shi Yunike, Yunike Zakaria, Mohd Zaki Zakaria, Zaki