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All Journal Scientific Journal of Informatics JURNAL MEDIA INFORMATIKA BUDIDARMA ABDIMAS: Jurnal Pengabdian Masyarakat Jurnal Informatika dan Rekayasa Perangkat Lunak
Syahrani Lonang
Universitas Ahmad Dahlan
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Klasifikasi Status Stunting Pada Balita Menggunakan K-Nearest Neighbor Dengan Feature Selection Backward Elimination Lonang, Syahrani; Normawati, Dwi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3312

Abstract

The main problem regarding nutrition faced by Indonesia is stunting, where Indonesia is ranked fifth in the world with the largest stunting prevalence rate in 2017, which is 29.6% of all Indonesian children. Stunting is a child under five years who has a z-score value of less than -3 standard deviations (SD). Stunting has a negative impact, namely it can disrupt the physical and intellectual development of toddlers in the future. In this case, the examination of stunting status by medical personnel is still carried out manually which takes a long time and is prone to inaccuracies. This study aims to classify stunting status in toddlers by applying the K-Nearest Neighbor method using the Backward Elimination feature selection to get fast and accurate results. Based on the results of this study, the average accuracy produced by the K-Nearest Neighbor algorithm at k=5 is 91.90% with 9 attributes and the average accuracy produced by the K-Nearest Neighbor algorithm with the addition of Backward Elimination is 92.20%. with 8 attributes. These results indicate that the application of Backward Elimination can increase the accuracy value of the K-Nearest Neighbor algorithm and also perform attribute selection.
Training on how to use Social Media Wisely and Ethically Herman Herman; Imam Riadi; Dikky Praseptian M; Faiz Isnan Abdurrachman; Syahrani Lonang
ABDIMAS: Jurnal Pengabdian Masyarakat Vol. 5 No. 2 (2022): ABDIMAS UMTAS: Jurnal Pengabdian Kepada Masyarakat
Publisher : LPPM Universitas Muhammadiyah Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (803.308 KB) | DOI: 10.35568/abdimas.v5i2.2686

Abstract

Nowadays, the behavior of users in social media arguably represent human behavior in the real world. Training on how to use social media wisely and ethically to young ages is needed to grow the good behavior. Based on a preliminary study, the ages of students in SMK Kesehatan Binatama is considered a terget of such training. 15 years old dominates with 63.6% followed by 16 years old with 29.3% according to age which has the highest penetration rate of social media users reaching 99.16%, namely ages 13-18 years. The activeness of students in social media reaches 99%. The number of hours students use social media where 10.1 % stated between 0-2 hours, 40.4% stated 2-5 hours, 36.4% stated 5-10 hours and 11.1 % more than 10 hours. Knowledge training on social media has been carried out several times but must continue to be carried out along with the development of social media technology and the shift in the age of its users. Training activities with wise and ethical materials using social media have been successfully held with the expected results. Participants' knowledge and insight, namely students can increase with information regarding what can and should not be done when using social media, information about hoaxes and cyberbullying and the ITE Law can be understood properly. The survey results also show an increase in the knowledge provided from the criteria of understanding to criteria of very understanding with an increase from the average score on the pre-test 2.96 with a percentage of 59.2% to the average score on the post-test 3.64 with a percentage of 72.8%.
Rancangan Sistem Klasifikasi Kekurangan Gizi Balita Dengan Metode K-Nearest Neighbor Syahrani Lonang; Anton Yudhana; Muhammad Kunta Biddinika
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 5, No 1 (2023): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v5i1.7834

Abstract

 Malnutrition in toddlers is a serious problem faced by developing countries like Indonesia, and the resulting long-term effects can reduce the intelligence of toddlers. The classification of the nutritional status of children under five is still carried out conventionally in community health centers. The K-Nearest Neighbor algorithm is included in a machine learning algorithm that can be used to classify one of the nutritional status classification problems. K-NN is used as a class determination algorithm for new data to be input according to the format. This research begins with a literature study, then identifies needs, followed by data collection that is planned to be used in the system to be built as well as a reference for making the design and the final stage of system design. This research succeeded in creating a system design using the Unified Model Language (UML), one use case that contains four functional systems, including uploading dataset files, displaying datasets, testing the accuracy of datasets, predicting new data, and designing system interfaces that will make system development easier..
Analisis Komparatif Kinerja Algoritma Machine Learning untuk Deteksi Stunting Syahrani Lonang; Anton Yudhana; Muhammad Kunta Biddinika
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6553

Abstract

Stunting is a serious problem caused by chronic malnutrition in children under five, causing stunted growth and having a negative impact on long-term health and productivity. Therefore, early detection of stunting is very important to reduce its negative impacts. Previous studies utilizing machine learning have proven the success of this method in various health applications, such as disease detection and the prediction of medical conditions. The results of this research are a comparative evaluation of five classifications, namely Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), in classifying stunted toddlers. The dataset used contains four important attributes: age, gender, weight, and height of toddlers, as well as a binary class label that differentiates between toddlers who are stunted and those who are not. The evaluation results show that KNN at K = 3 produces the highest accuracy of 94.85%, making it the best model for classifying stunting in toddlers. Apart from accuracy, other metrics such as precision, recall, and F1-score are used to analyze the algorithm's ability to solve this problem. KNN stands out as the best model, with the highest F1-score of 89.47%. KNN also manages to maintain a balance between precision and recall, making it an excellent choice for treating stunting in toddlers. Apart from that, the use of the AUC metric from the ROC curve also shows the superiority of KNN in differentiating between stunted and non-stunting toddlers. With a combination of consistent evaluation results, both in terms of accuracy and other evaluation metrics, this research proves that KNN is the best choice for overcoming the task of classifying stunting in toddlers.
Performance Analysis for Classification of Malnourished Toddlers Using K-Nearest Neighbor Syahrani Lonang; Anton Yudhana; Muhammad Kunta Biddinika
Scientific Journal of Informatics Vol 10, No 3 (2023): August 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i3.45196

Abstract

Purpose: Malnutrition in toddlers is a nutritional issue that Indonesia is still dealing with. Toddlers can suffer from decreasing cognitive and physical abilities, as well as being categorized as having a high risk of death. Early detection is crucial for preventing this and providing appropriate treatment if malnutrition is detected. Classification is a machine-learning technique widely used in disease detection. Because it is simple and easy to implement, K-Nearest Neighbor is the most used classification algorithm. Detecting malnutrition can be done automatically and more quickly by utilizing classification and machine learning algorithms. The aim of this study was to analyze performance to find out which model is best for detecting malnutrition by evaluating the performance of classification using KNN with the Euclidean distance function.Methods: The dataset used in this study is the nutritional status of toddlers from Puskesmas Ubung. The classification method proposed in this research is the KNN algorithm with Euclidean distance. There are three scenarios for the classification model that will be used. Performance classification will compare each model in terms of accuracy, precision, recall, f1-score, and mean absolute error.Results: The experimental results show that KNN k = 15 using the first model generates excellent classification when classifying malnourished toddlers using the Euclidean distance function. The model obtains 91% accuracy, 86.6% precision, 83.8% recall, 85.2% recall, and a mean absolute error of 0.09.Novelty: In this experiment, we analyzed the performance of the KNN to classify malnourished children using a nutritional status dataset, which resulted in an excellent classification that could be used for early detection.
Co-Authors Anton Yudhana Anton Yudhana Dikky Praseptian M Dwi Normawati, Dwi Faiz Isnan Abdurrachman Herman Herman Imam Riadi Muhammad Kunta Biddinika Muhammad Kunta Biddinika Muhammad Kunta Biddinika