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Journal : International Journal of Engineering, Science and Information Technology

Supervised models to predict the Stunting in East Aceh Eva Darnila; Maryana Maryana; Khalid Mawardi; Marzuki Sinambela; Iwan Pahendra
International Journal of Engineering, Science and Information Technology Vol 2, No 3 (2022)
Publisher : Master Program of Information Technology, Universitas Malikussaleh, Aceh Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (439.949 KB) | DOI: 10.52088/ijesty.v2i3.280

Abstract

Nowadays, Undernutrition is the main cause of child death in developing countries. There are many people and organizations try to mitigate or minimize case of child death. Thus, this paper aimed to has excellent method to handle undernutrition case by exploring the efficacy of machine learning (ML) approaches to predict Stunting in East Aceh administrative zones of Indonesia and to identify the most important predictors. The study employed ML techniques using retrospective cross-sectional survey data from East Aceh, a national-representative data is collected from government by using 2019 about stunting data. We explored Random forest commonly used ML algorithms. Random Forest (RF) as an extension of bagging that in addition for taking random sample of data and also uses random subset of features which mitigates over fitting. Our results showed that the considered machine learning classification algorithms by random forest can effectively predict the stunting status in East Aceh administrative zones. Persistent stunting status was found in the east part of Aceh. The identification of high-risk zones can provide more useful information and data to decision-makers for trying to reduce child undernutrition.
Implementation of Organic and Inorganic Waste Selection System Based on Internet of Things Using MQTT Protocol at Abby Lhokseumawe Hospital Julita, Rina; Darnila, Eva; Risawandi, Risawandi
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.826

Abstract

The waste sorting system designed for Abby Hospital in Lhokseumawe aims to improve the efficiency and effectiveness of waste management by automatically separating organic and inorganic materials. This system integrates Proximity sensors as the primary detectors, capable of detecting organic objects within a spatial range of 4 cm and inorganic objects within a range of 5 cm. The main feature of this system is its ability to automatically sort waste, which helps reduce the potential for human error in waste categorization and improve operational efficiency in the waste disposal process. During the testing phase, which focuses on assessing the trash bin's capacity when complete, the system uses ultrasonic sensors to measure and monitor the waste filling levels. The test results show an average data transmission delay of 445.33 ms, which is within the acceptable tolerance for this system. Additionally, the prototype is equipped with an operational status notification feature for users. This notification is delivered with an average delay of just 402.5 ms, ensuring that system status information is provided to users in real time. The combination of sensor detection precision and response speed in the waste sorting process highlights the system's effectiveness in improving waste management at the hospital. This system is expected to support the hospital's efforts in maintaining a clean environment and contribute to a more environmentally friendly and organized waste management program.
Data Mining Analysis for Clustering the Number of Tb Patients in North Aceh Health Centers Using the Spectral Method Clustering Khainesya, Khainesya; Darnila, Eva; Risawandi, Risawandi
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.847

Abstract

Tuberculosis (TB) is one of the infectious diseases that is a significant concern in the world of health, especially in the North Aceh region. Grouping the number of TB patients based on severity and region is very important to support decision-making in further prevention and treatment efforts. This study applies the Spectral Clustering method to cluster the number of TB patients at Baktiya Health Center, Bayu Health Center, and Lhoksukon Health Center to identify patient distribution patterns based on severity categories. The system built is a web-based data mining analysis system using PHP and MySQL as a database. Clustering is done by dividing patients into three categories, low, medium, and high, based on five main criteria, namely age, gender, month of treatment, diagnosis results, and patient address. The results showed that Lhoksukon Health Center had the highest number of TB patients, with 136 patients (37.06%), an average age of 48.6 years, and the most cases occurred in December 2022. Bayu Health Center was at a moderate level with 130 patients (35.42%), most of whom were 45.5 years old, and most cases occurred in November 2023. Meanwhile, Baktiya Health Center had the lowest number of patients, 101 (27.52%), with the most cases occurring in November. From the clustering results, it can be concluded that the Spectral Clustering method can group TB patients well to help medical personnel and related parties develop more effective intervention strategies based on the region and severity of the patient.