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 20 Documents
Search

 1   2 
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.
Implementasi Pembelajaran Jarak Jauh di Fakultas Sains dan Teknologi Pasca Covid-19 Sukmana, Husni Teja; Rozy, Nurul Faizah; Eiji, Arta
Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Vol 2 No 2 (2024): Maret
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/mentari.v2i2.487

Abstract

Pandemi Covid-19 yang melanda dunia telah mengakibatkan transformasi mendalam dalam sektor pendidikan, mendorong pertumbuhan pembelajaran jarak jauh sebagai alternatif penting saat pembelajaran tatap muka terhambat. Meskipun menawarkan fleksibilitas, transisi ke pembelajaran daring memerlukan adaptasi yang bertahap. Kendala yang dihadapi siswa, seperti koneksi internet yang tidak stabil dan dukungan finansial, menyoroti kebutuhan akan aksesibilitas teknologi yang lebih baik. Strategi pembelajaran yang berpusat pada siswa, melalui integrasi Teknologi Informasi dan Komunikasi (TIK) serta teknik pembelajaran aktif, dapat meningkatkan keterlibatan dan hasil pembelajaran. Penelitian ini mengevaluasi berbagai aspek pembelajaran jarak jauh, termasuk proses pembelajaran, sarana-prasarana, dan aspek psikologis siswa di Fakultas Sains dan Teknologi (FST). Tujuan penelitian adalah memberikan gambaran menyeluruh, mengevaluasi dampak, dan menyusun rekomendasi. Metodologi penelitian menggunakan pendekatan survei dengan 500 responden mahasiswa FST melalui kuesioner elektronik. Analisis data dilakukan dengan metode statistik deskriptif. Temuan penelitian menunjukkan preferensi terhadap model pembelajaran hibrid yang menggabungkan pembelajaran di kelas dan daring dan menegaskan pentingnya perencanaan yang matang serta dukungan infrastruktur untuk menjaga kualitas pendidikan di era pasca-Covid-19. Penelitian ini memberikan pemahaman yang mendalam tentang implementasi pembelajaran jarak jauh di FST, menyoroti tantangan yang dihadapi serta rekomendasi untuk perbaikan dan pengembangan ke depannya.
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.
A Comparative Study of Naive Bayes, SVM, and Decision Tree Algorithms for Diabetes Detection Based on Health Datasets Nurwicaksana, Satria; Oh, Lee Kyung; Sukmana, Husni Teja
International Journal of Informatics and Information Systems Vol 7, No 4: December 2024
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v7i4.230

Abstract

Diabetes is a chronic, progressive condition whose global prevalence continues to rise, creating substantial public health and economic burdens. Early diagnosis and timely intervention are critical to preventing severe complications and improving long-term patient outcomes. In recent years, artificial intelligence (AI) particularly machine learning (ML) has emerged as a powerful tool in medical diagnostics, offering capabilities in automated pattern recognition and disease classification. This study aims to evaluate and compare the predictive performance of three supervised ML algorithms such as Naïve Bayes, Support Vector Machine (SVM), and Decision Tree for classifying and predicting diabetes based on two primary physiological indicators: glucose level and blood pressure. The dataset employed was sourced from Kaggle, comprising 995 patient records containing relevant clinical attributes. The research methodology involved several stages, including data preprocessing to ensure quality and consistency, data partitioning into training and testing subsets using an 80:20 split ratio, model training, and performance evaluation. Each algorithm’s effectiveness was measured using accuracy, precision, recall, and F1-score metrics. The experimental findings demonstrate that the Decision Tree algorithm achieved the highest classification accuracy (94.47%), outperforming SVM and Naïve Bayes, both of which recorded 92.96% accuracy. Moreover, the Decision Tree exhibited balanced precision and recall values, underscoring its robustness in identifying both diabetic and non-diabetic cases with minimal misclassification. These outcomes indicate that the Decision Tree model provides an optimal balance between predictive accuracy and interpretability, making it particularly suitable for clinical decision-support applications.
The Development of ITSM Research in Indonesia: A Systematic Literature Review Hayadi, B.Herawan; Sukmana, Husni Teja; Shafiera, Eghar; Kim, Jin-Mook
International Journal of Artificial Intelligence Research Vol 5, No 2 (2021): December 2021
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (631.666 KB) | DOI: 10.29099/ijair.v5i2.233

Abstract

IT Service Management (ITSM) is a framework used to support businesses by increasing IT service quality. Several studies have tried to examine the development of ITSM based on their respective interests. However, the development of ITSM in Indonesia has not been widely studied, such as the types of research that are most often investigated, what domains are often researched, the areas and types of companies being studied. The things above are the main objectives of this research. The method used in capturing data, screening, and analysis is the systematic literature review method. There are many findings obtained from this research. One of them is the domination of the service operation research area (45%) among other areas. Meanwhile, applied research had been researched quite consistently over the last five years. From these results,  it can be noticed that a deeper understanding of the synchronization between business and IT is needed. This is in accordance with the objectives of ITSM implementation so that future research is expected to provide balance in other areas, such as service strategy, design, transition, operation, and continuous service improvement.
Improving Indonesian Named Entity Recognition for Domain Zakat Using Conditional Random Fields Widiyanti, Nur Febriana; Sukmana, Husni Teja; Hulliyah, Khodijah; Khairani, Dewi; Oh, Lee Kyung
JOIN (Jurnal Online Informatika) Vol 8 No 2 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i2.898

Abstract

In Indonesia, where the majority of the population is Muslim, one of the obligations of a Muslim is zakat. To reduce illiteracy about zakat among Muslims, they need to have access to basic information about it. In order to facilitate the acquisition of this information, this study utilized named entity recognition (NER) and defined 12 named entity classes for the zakat domain, including the pillars of Islam, various types of zakat, and zakat management institutions. The Conditional Random Fields method was used for testing Indonesian-NER in three scenarios. In the specific context of the Zakat domain, NER can extract information about organizations, individuals, and locations involved in collecting and distributing Zakat funds. This information can improve the Zakat system’s efficiency and transparency and support research and analysis on Zakat-related topics. The average performance evaluation of the Indonesian-NER model showed a precision of 0.902, recall of 0.834, and an F1-score of 0.867.
Performance Improvement for Hotspot Prediction Model Using SBi-LSTM-XGBoost and SBi-GRU-XGBoost Sukmana, Husni Teja; Aripiyanto, Saepul; Alamsyah, Aryajaya; Henry, Amir Acalapati; Nandaputra, Riandi
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3047

Abstract

Forest fires damage ecosystems and harm all living beings, often triggered by low rainfall that worsens fire spread. Climatic factors such as the El Nino–Southern Oscillation (ENSO) also contribute to reduced rainfall and prolonged dry seasons. This study aims to enhance the performance of fire prediction models to support forest fire mitigation. Modified artificial neural network algorithms—Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) with bidirectional stacked layers—are employed as baseline models. An experimental approach was used to compare the performance of LSTM and GRU models with their ensemble versions, where XGBoost was added to improve prediction accuracy. The results show that the proposed ensemble algorithms significantly outperform the baseline models in multivariate fire prediction. The SBi-LSTM-XGBoost and SBi-GRU-XGBoost models demonstrated more than a 40% performance improvement compared to the original SBi-LSTM and SBi-GRU models. In multivariate modelling, the ensemble models achieved an R-value of 1.0000, with an average MAE of 0.0007, RMSE of 0.0009, and MAPE of 0.0008. This study also identified limitations of the LSTM and GRU models in processing ENSO data due to their non-linearity and weak correlation with hotspot data. As a contribution, our experiments show that integrating XGBoost into LSTM and GRU models effectively overcomes these limitations, significantly improving hotspot prediction accuracy and supporting better forest fire mitigation strategies.
An Evaluation Of Helpdesk With Gamification Using Indeks Kepuasan Masyarakat (IKM) Muhtadibillah, Achmad; Sukmana, Husni Teja; Rozy, Nurul Faizah
IAIC Transactions on Sustainable Digital Innovation (ITSDI) Vol 1 No 1 (2019): October
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/itsdi.v1i1.8

Abstract

Gamification is a concept of game element that is applied to non-game contexts, Helpdesk is an IT (Information Technology) section. It is first contacted by a user when someone has questions or problems related to IT services. UIN Syarif Hidayatullah Jakarta as a tertiary institution also has a help desk facilities as the tools to solve problems related to IT service. Based on study, the problem that common occurs is, the user is aware of the helpdesk service facilities on campus, but they prefer to make complaints directly to the relevant division. The concept of gamification with elements of points, badges, levels, leaderboard, and rewards is applied to the helpdesk system through the RAD (Rapid Application Development) development method. The method of evaluating the helpdesk system is done in two stages, first pre-test and second post-test. It through two application which is game based helpdesk and non-game based help desk applications. Using Indeks Kepuasan Masyarakat (IKM) as the calculation method of gamification helpdesk and End User Computing Satisfaction (EUCS) as an indicator service of the IKM that will be tested.
Prototyping ITSDI Journal Center Menggunakan Tools Invision Untuk Mewujudkan Creative Innovation Soft Skill Di Era Industri 4.0 Sukmana, Husni Teja
ADI Bisnis Digital Interdisiplin Jurnal Vol 1 No 1 (2020): ADI Bisnis Digital Interdisiplin (ABDI Jurnal)
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/abdi.v1i1.119

Abstract

Berdasarkan Peraturan Menteri Riset, Teknologi, dan Pendidikan Tinggi Republik Indonesia Nomor 20 Tahun 2017, Tridharma Perguruan Tinggi adalah kewajiban perguruan tinggi untuk menyelenggarakan pendidikan, penelitian, dan pengabdian kepada masyarakat. Kegiatan penelitian ilmiah ini merupakan pencapaian hasil riset yang secara rutin dilaksanakan oleh Direktorat Jenderal Penguatan Riset dan Pengembangan yang dipublikasikan oleh pengelola jurnal ilmiah. penulisan sebuah karya ilmiah adalah salah satu hal yang sangat berpengaruh untuk meningkatkan kualitas pendidikan di Indonesia. Namun, didalam penerapannya kelemahan utama pada penulisan karya ilmiah disebabkan oleh kurang nya motivasi dosen untuk meneliti dan minimnya pengetahuan penulisan sesuai dengan standar yang telah telah ditetapkan. Selain itu, Sivitas akademika merupakan sumber daya yang dituntut untuk memiliki kemampuan yang lebih dari masyarakat biasa karena kapasitasnya yang lebih intens berinteraksi dengan ilmu pengetahuan. Hal tersebut sudah sepatutnya mampu mengaktualisasikan kompetensinya bukan sekedar kegiatan penelitian, tetapi mampu untuk menulis hasil penelitian tersebut dalam media publikasi baik yang bertaraf nasional, regional, maupun internasional. Dalam upaya meningkatkan kualitas, kuantitas penelitian dan pengabdian kepada masyarakat serta sebagai langkah untuk mendukung program PERMENRISTEKDIKTI Nomor 20 Tahun 2017, penulis berupaya memberikan hasil penelitian untuk mendorong pengembangan inovasi dengan mengedepankan teknologi berupa Penerapan ITSDI Journal Center sebagai platform penyedia layanan pelatihan dan materi penulisan karya ilmiah secara online. Pemanfaatan teknologi pembelajaran iLearning ini akan dipadukan dengan unsur entertainment yang diharapkan mampu membantu masyarakat dalam melaksanakan kegiatan pelatihan peningkatan keterampilan soft skill penulisan secara menyenangkan yang dapat diakses kapanpun dan dimanapun. Pada penelitian ini ditemukan 3 (tiga) permasalahan dan didukung dengan 3 (tiga) metode penelitian yaitu metode waterfall, studi pustaka, dan Analisis SWOT. Hasil akhir penelitian ini ialah adanya implementasi terhadap prototyping ITSDI Journal Center sebagai media pelatihan penulisan karya ilmiah secara online guna mewujudkan creative innovation di Era 4.0.
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