Article Per Year (5 Year)
p-Index From 2020 - 2025
2.461
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering TEKNIK INFORMATIKA TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Pseudocode JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research ADI Journal on Recent Innovation (AJRI) IJIIS: International Journal of Informatics and Information Systems ADI Bisnis Digital Interdisiplin (ABDI Jurnal) Journal of Applied Data Sciences IAIC Transactions on Sustainable Digital Innovation (ITSDI) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi
Husni Teja Sukmana
Universitas Islam Negeri Syarif Hidayatullah Jakarta
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Other

Published : 20 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : Journal of Applied Data Sciences

 1 
The Success Factors of E-Philanthropy are Determined Based on Perceived Trust, Perceived Usefulness, Subjective Norms, Enjoyment and Religiosity: A Case Study on a Charity Site Sukmana, Husni Teja; Nanang, Herlino; Agustin, Fenty Eka Muzayyana; Aristoi, Zidny Fiqha; Azizah, Khansa
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.310

Abstract

The rapid development of information technology and social media has significantly influenced people's behaviors and preferences in various activities, including philanthropy. Traditionally, philanthropic activities necessitated direct interpersonal interactions. However, the advent of ephilanthropy has enabled more practical and accessible ways to engage in charitable activities anytime and anywhere using electronic technology. This study examines the perceived role of e-philanthropy users in Indonesia and their intention to make actual donations through crowdfunding for humanitarian purposes. The research integrates the Technology Acceptance Model (TAM) and the IS success model, supplemented by additional variables like trust, usefulness, subjective norms, and religiosity. Data were collected from 231 respondents across Indonesia using online questionnaires and analyzed using the PLS-SEM method. The findings indicate significant relationships between perceived quality and trust (t-value = 7.156, path coefficient = 0.681), trust and perceived usefulness (t-value = 31.724, path coefficient = 0.886), and religiosity and intention to use (t-value = 3.206, path coefficient = 0.360). However, perceived enjoyment (t-value = 1.100, path coefficient = 0.140), subjective norms (t-value = 1.448, path coefficient = 0.162), and perceived trust (t-value = 1.023, path coefficient = 0.128) did not significantly influence the intention to use e-philanthropy platforms. These insights can inform strategies to enhance user participation and trust in e-philanthropy initiatives in Indonesia.
Comparative Analysis of SVM and RF Algorithms for Tsunami Prediction: A Performance Evaluation Study Sukmana, Husni Teja; Durachman, Yusuf; Amri, Amri; Supardi, Supardi
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.159

Abstract

This study explores the use of machine learning algorithms, specifically SVM and RF, for predicting tsunamis, a crucial aspect of disaster management. The research utilized earthquake data from 2001 to 2023, evaluating these models based on accuracy, precision, recall, F1-score, and ROC AUC, emphasizing features like magnitude, depth, and alert levels. The SVM model demonstrated an accuracy of 65.61%, precision of 70.59%, recall of 19.67%, F1-score of 30.77%, and ROC AUC of 62.15%. In comparison, the RF model showed an accuracy of 61.15%, precision of 50.00%, higher recall of 36.07%, F1-score of 41.90%, and ROC AUC of 63.84%. These results highlight the distinct strengths of each model: SVM's precision makes it suitable for minimizing false positives, while RF's higher recall indicates its effectiveness in detecting actual tsunamis. The findings underscore the significance of selecting the appropriate model for tsunami prediction based on specific disaster management needs and the inherent trade-offs in model selection. The research concludes that SVM and RF models provide valuable yet distinct contributions to tsunami prediction. Their application should be customized to disaster management requirements, balancing accuracy and operational efficiency. This study contributes to disaster risk management and opens avenues for further research in enhancing the accuracy and reliability of machine learning in natural disaster prediction and response systems.
Survey Opinion using Sentiment Analysis Hariguna, Taqwa; Sukmana, Husni Teja; Kim, Jong Il
Journal of Applied Data Sciences Vol 1, No 1: SEPTEMBER 2020
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v1i1.10

Abstract

Sentiment analysis or opinion mining is a computational study of the opinions, judgments, attitudes, and emotions of a person towards an entity, individual, issue, event, topic, and attributes. This task is very challenging technically but very useful in practice. For example, a business always wants to seek opinion about its products and services from the public or the consumers. Additionally, potential consumers want to learn what users think they have when using a service or purchasing a product. To get public opinion on food habits, ad strategies, political trends, social issues and business policy, this is a very critical factor. This paper will explain a survey of key sentiment-extraction approaches.
Transformer Architectures for Automated Brain Stroke Screening from MRI Images Abstract Sukmana, Husni Teja; Hasibuan, Zainal Arifin; Rahman, Abdul Wahab Abdul; Bayuaji, Luhur; Masruroh, Siti Ummi
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.736

Abstract

Early and accurate detection of stroke is critical for timely medical intervention and improved patient outcomes. This study explores the application of deep learning models, particularly the Vision Transformer (ViT), for the automated classification of brain stroke from medical images. A curated dataset of brain scans was used to train and evaluate the ViT model, which was benchmarked against a widely used convolutional neural network (CNN), ResNet18. Both models were trained using transfer learning techniques under identical preprocessing and training configurations to ensure fair comparison. The results indicate that the ViT model significantly outperforms ResNet18 in terms of validation accuracy, class-wise precision, and recall, achieving a peak accuracy of 99.60%. Visual analyses, including confusion matrices and sample prediction comparisons, reveal that ViT is more robust in detecting subtle stroke patterns. However, ViT requires more computational resources, which may limit its deployment in real-time or low-resource settings. These findings suggest that transformer-based architectures are highly effective for medical image classification tasks, particularly in stroke diagnosis, and offer a viable alternative to traditional CNN-based approaches.
Co-Authors A'ang Subiyakto Abd. Rahman Ahlan Abdul Rahman Ahlan Aditya Mardiansyah Agustinus Purna Irawan Alamsyah, Aryajaya Amitkumar Dudhat Amri Amri Aripiyanto, Saepul Aristoi, Zidny Fiqha Azizah, Khansa B. Herawan Hayadi Chung-Wen Hung Diana Putri Lazirkha Eiji, Arta Febrian Wahyu Ramadhan Fenty Eka Muzayyana Agustin Henry, Amir Acalapati Herlino Nanang Isyak Meirobie Khairani, Dewi Khodijah Hulliyah Kim, Jin-Mook Kim, Jong Il Lee Kyung Oh Luh Kesuma Wardhani Luhur Bayuaji, Luhur Marviola Hardini Mira Kartiwi Muhtadibillah, Achmad Nandaputra, Riandi Nuke Puji Lestari Santoso Nurwicaksana, Satria Oh, Lee Kyung Rahman, Abdul Wahab Abdul Ramadhan, Febrian Wahyu Rayi Pradono Iswara Rohadi Rohadi Rozy, Nurul Faizah Shafiera, Eghar Siti Atinah Siti Ummi Masruroh Supardi Supardi Taqwa Hariguna Taqwa Hariguna Widiyanti, Nur Febriana Yusuf Abdurachman Zainal Arifin Hasibuan