Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnas Nasional Teknologi dan Sistem Informasi JAIS (Journal of Applied Intelligent System) JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Adhitya Nugraha
Universitas Dian Nuswantoro
Computer Science & IT Decision Sciences, Operations Research & Management

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
The Development of Berbakti: Elder Caring Mobile Application in Indonesia Septian Enggar Sukmana; Heru Agus Santoso; Fahri Firdausillah; Adhitya Nugraha; Farah Zakiyah Rahmanti; Arkav Juliandri
Journal of Applied Informatics and Computing Vol 3 No 2 (2019): Desember 2019
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (806.047 KB) | DOI: 10.30871/jaic.v3i2.1501

Abstract

Children must care their parent as their devotion to their parent. In Indonesia, that kind condition is a common situation. But, to handling this situation in this global era is more difficult because many children choose going to another city or another region to do some activity like taking a job or going to college. It gives an impact to their parent especially when their parent is too old and needs to be cared. This motivation in this paper is based on this kind problem. The development of application uses Waterfall. The system must meet the requirement so not just technichal development is performed, social study must be conducted in the process. We use several testings such blackbox testing, server testing, and usefulness identity. Commonly, we got unsatisfied result based on testing, so some repairement must be conducted.
Malware Detection Using Decision Tree Algorithm Based on Memory Features Engineering Adhitya Nugraha; Junta Zeniarja
Journal of Applied Intelligent System Vol 7, No 3 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i3.6735

Abstract

Malware is malicious software that can harm, manipulate, steal from victim's device system. Due to the diverse needs of using internet services, security threats are also increasingly difficult to detect. now attackers are starting to develop malware that can change their own signature which is referred to as polymorphism. Therefore, improvements in the traditional approach to detecting the presence of malware are needed to be improved. One of the malware detection approaches, memory-based analysis technique has proven to be a powerful and effective analytical technique in studying malware behavior. In this study, the implementation of a Decision Tree-based classification algorithm was carried out to analyze the data set. Classifier model was created for the purpose of classifying malware based on memory features engineering. The result shows that the Decision Tree machine learning algorithm has been well performed with accuracy to 99.982 %, a false positive rate equal to 0.1% and precision equal to 99.977%
Improving Multi-label Classification Performance on Imbalanced Datasets Through SMOTE Technique and Data Augmentation Using IndoBERT Model Leno Dwi Cahya; Ardytha Luthfiarta; Julius Immanuel Theo Krisna; Sri Winarno; Adhitya Nugraha
Jurnal Nasional Teknologi dan Sistem Informasi Vol 9, No 3 (2023): Desember 2023
Publisher : Jurusan Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v9i3.2023.290-298

Abstract

Sentiment and emotion analysis is a common classification task aimed at enhancing the benefit and comfort of consumers of a product. However, the data obtained often lacks balance between each class or aspect to be analyzed, commonly known as an imbalanced dataset. Imbalanced datasets are frequently challenging in machine learning tasks, particularly text datasets. Our research tackles imbalanced datasets using two techniques, namely SMOTE and Augmentation. In the SMOTE technique, text datasets need to undergo numerical representation using TF-IDF. The classification model employed is the IndoBERT model. Both oversampling techniques can address data imbalance by generating synthetic and new data. The newly created dataset enhances the classification model's performance. With the Augmentation technique, the classification model's performance improves by up to 20%, with accuracy reaching 78%, precision at 85%, recall at 82%, and an F1-score of 83%. On the other hand, using the SMOTE technique, the evaluation results achieve the best values between the two techniques, enhancing the model's accuracy to a high 82% with precision at 87%, recall at 85%, and an F1-score of 86%.
Co-Authors Ardytha Luthfiarta Arkav Juliandri Fahri Firdausillah Farah Zakiyah Rahmanti Heru Agus Santoso Julius Immanuel Theo Krisna Junta Zeniarja Leno Dwi Cahya Septian Enggar Sukmana Sri Winarno