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All Journal International Journal of Engineering, Science and Information Technology Jurnal Indonesia Sosial Teknologi
Iwan Pahendra
Universitas Sriwijaya
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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.
Shellcode Classification with Machine Learning Based on Binary Classification Jaka Naufal Semendawai; Deris Stiawan; Iwan Pahendra
Jurnal Indonesia Sosial Teknologi Vol. 6 No. 2 (2025): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v6i2.3233

Abstract

The Internet can link one person to another using their respective devices. The internet itself has both positive and negative impacts. One example of the internet's negative impact is malware that can disrupt or even kill a device or its users; that is why cyber security is required. Many methods can be used to prevent or detect malware. One of the efforts is to use machine learning techniques. The training and testing dataset for the experiments is derived from the UNSW_NB15 dataset. K-Nearest Neighbour (KNN), Decision Tree, and Naïve Bayes classifiers are implemented to classify whether a record in the testing data is Shellcode or non-Shellcode attack. The KNN, Decision Tree, and Naïve Bayes classifiers achieve accuracy levels of 96.82%, 97.08%, and 63.43%, respectively. The results of this research are expected to provide insight into the use of machine learning in detecting or classifying malware or other types of cyber attacks.
Co-Authors Deris Stiawan Eva Darnila Jaka Naufal Semendawai Khalid Mawardi Maryana Maryana Sinambela Marzuki