Article Per Year (5 Year)
p-Index From 2021 - 2026
3.212
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) Cyber Security dan Forensik Digital (CSFD) 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) International Journal of Cyber and IT Service Management (IJCITSM) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Blockchain Frontier Technology (BFRONT) International Transactions on Artificial Intelligence (ITALIC) Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain International Journal Research on Metaverse
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 : 29 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 29 Documents
Search

 1   2   3 
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.
Using K-Means Clustering to Enhance Digital Marketing with Flight Ticket Search Patterns Sukmana, Husni Teja; Oh, Lee Kyung
Journal of Digital Market and Digital Currency Vol. 1 No. 3 (2024): Regular Issue December
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v1i3.22

Abstract

This study explores the application of K-Means clustering to enhance digital marketing strategies by analyzing flight ticket search patterns. Utilizing a dataset containing 4,000 search engine results related to flights to Hong Kong, the research identifies five distinct user clusters based on search terms, titles, snippets, and other relevant features. The dataset's key features include search terms, ranks, titles, snippets, display links, and direct links, providing a comprehensive view of user interactions and preferences. The cluster analysis reveals significant variations in user intent and preferences across the identified segments. For instance, Cluster 1 is characterized by users searching for "cheap flights" and "discount tickets," indicating a price-sensitive segment. In contrast, Cluster 2 users prefer "premium flights" and "business class," highlighting an interest in luxury travel options. The study also examines the behavioral patterns within each cluster, such as Cluster 3 users who search for flights well in advance and prioritize flexible booking options. The findings underscore the effectiveness of K-Means clustering in enhancing digital marketing strategies. By leveraging the insights from the clustering analysis, marketers can design highly targeted advertising campaigns and personalized offers. For example, budget airlines can target Cluster 1 with promotions and discounts, while premium airlines can focus on Cluster 2 with exclusive service highlights. This targeted approach is expected to improve user engagement and conversion rates significantly. The study also highlights the advantages of behavior-based segmentation over traditional demographic methods, offering a more accurate representation of user preferences and intentions. The identified clusters provide a framework for understanding different user groups, enabling more efficient resource allocation and campaign design. Future research should explore the integration of additional data sources, such as social media interactions and user reviews, to enhance clustering accuracy. Additionally, advanced clustering techniques like hierarchical clustering and Gaussian Mixture Models could be investigated to provide further insights. The ongoing refinement and enhancement of segmentation processes are crucial for maintaining effective and impactful digital marketing strategies in the dynamic travel industry. Key results include the identification of five user clusters, the importance of personalized marketing strategies, and the potential for improved engagement and conversion rates through targeted advertising and offers.
Study of Bitcoin Market Efficiency Using Runs Test and Autocorrelation Sukmana, Husni Teja; Khairani, Dewi
Journal of Current Research in Blockchain Vol. 1 No. 1 (2024): Regular Issue June
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v1i1.9

Abstract

This paper presents a comprehensive statistical analysis of Bitcoin's daily returns, focusing on their unique characteristics and implications for financial modeling and market behavior. The descriptive statistics reveal a mean daily return of 0.001912 and a standard deviation of 0.044069, highlighting high volatility. The skewness of -1.297892 and kurtosis of 22.099740 indicate a left-skewed, leptokurtic distribution with frequent extreme price movements. The Jarque-Bera test statistic of 95428.68, with a p-value of 0.0, strongly rejects the null hypothesis of normality, suggesting that traditional financial models assuming normally distributed returns may be inappropriate for Bitcoin. The ADF test statistic of -12.303, with a p-value of 7.36e-23, confirms the stationarity of Bitcoin's daily returns, validating their suitability for time series analysis techniques such as ARIMA and GARCH models. Autocorrelation analysis uncovers significant short-term predictability in Bitcoin returns, challenging the weak form of market efficiency, though this predictability diminishes over time. The Runs Test, with a z-score of 2.56 and a p-value of 0.01, further supports the presence of short-term non-random behavior. Additional visualizations, including the daily closing price plot, histogram, and boxplot of daily returns, illustrate the high volatility and substantial variability in Bitcoin's market behavior. The findings underscore the need for specialized risk management strategies and financial models tailored to the cryptocurrency market's unique dynamics. While Bitcoin offers opportunities for high returns, it also poses significant risks due to its volatile nature and frequent extreme price movements. Future research should explore advanced models accounting for heavy tails and volatility clustering and examine the impact of external factors such as regulatory changes and macroeconomic events on Bitcoin's statistical properties. Understanding these characteristics is crucial for informed investment decisions and effective trading strategies in the evolving cryptocurrency market.
A Comprehensive Study on Public and Private Blockchain Performance Oh, Lee Kyung; Sukmana, Husni Teja
Journal of Current Research in Blockchain Vol. 2 No. 1 (2025): Regular Issue March
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i1.25

Abstract

Blockchain technology has emerged as a transformative innovation, with applications spanning diverse industries. This study provides a comprehensive comparison between public and private blockchains, focusing on six key dimensions: scalability, security, use case distribution, energy efficiency, developer ecosystem, and performance metrics. Data were collected from 30 blockchain systems, representing a wide range of consensus mechanisms and industry applications. The findings reveal significant trade-offs between the two blockchain types. Public blockchains, such as Bitcoin and Ethereum, excel in decentralization and transparency, making them ideal for open and trustless environments like cryptocurrency and decentralized finance (DeFi). However, they face limitations in scalability, high energy consumption, and slower transaction speeds. Conversely, private blockchains, such as Hyperledger Fabric and Corda, demonstrate superior scalability, energy efficiency, and privacy, making them more suitable for controlled environments like healthcare, supply chain management, and enterprise financial services. The study underscores the importance of aligning blockchain technology selection with specific application requirements. Furthermore, it highlights the potential of hybrid blockchain models to integrate the strengths of both public and private systems, addressing existing limitations. These findings provide valuable insights for organizations and developers in leveraging blockchain technologies effectively.
Exploring the Impact of Virtual Reality Experiences on Tourist Behavior and Perceptions Sukmana, Husni Teja; Kim, Jong Il
International Journal Research on Metaverse Vol. 1 No. 2 (2024): Regular Issue September
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i2.8

Abstract

This study explores the impact of virtual reality (VR) experiences on tourist behavior and perceptions, utilizing logistic regression and analysis of variance (ANOVA) to understand these relationships. The logistic regression analysis revealed that VR experience (coefficient = 0.432, p = 0.020) significantly enhances the likelihood of being a tourist. Demographic factors such as gender (coefficient = -0.512, p = 0.018), income (coefficient = -0.301, p = 0.001), and age (coefficient = 0.298, p = 0.003) also play crucial roles: females and higher-income individuals are less likely to be tourists, while older individuals are more likely to travel. ANOVA results indicated significant differences in emotional responses (EMO1: F = 6.40, p = 0.012; EMO2: F = 4.63, p = 0.032; EMO3: F = 7.77, p = 0.006; EMO4: F = 5.77, p = 0.017), flow states (FLOW1: F = 12.21, p = 0.001; FLOW2: F = 20.39, p < 0.001; FLOW3: F = 17.38, p < 0.001; FLOW4: F = 14.52, p < 0.001), and intentions to visit (INT2: F = 7.79, p = 0.006; INT4: F = 4.61, p = 0.032) based on VR experience. These findings suggest that VR significantly influences emotional and cognitive states, fostering engagement, satisfaction, and increased intentions to visit real-world destinations. The results underscore the potential of VR as a powerful tool in tourism marketing, capable of driving tourism interest and behavior. Future research should investigate the long-term effects of VR on tourist behavior and consider cultural and technological advancements to further optimize VR's application in tourism. This study offers actionable insights for tourism marketers to develop targeted, effective, and immersive VR promotional strategies.
Co-Authors A&#039;ang Subiyakto Abd. Rahman Ahlan Abdul Rahman Ahlan Aditya Mardiansyah Agung Yudo Ardianto Agustinus Purna Irawan Alamsyah, Aryajaya Amitkumar Dudhat Amri Amri Arini Arini Aristoi, Zidny Fiqha Azizah, Khansa B. Herawan Hayadi Chung-Wen Hung Dewi Khairani Diana Putri Lazirkha Dudhat, Amitkumar Eiji, Arta Febrian Wahyu Ramadhan Fenty Eka Muzayyana Agustin Henderi . Henry, Amir Acalapati Herlino Nanang Hery Juan Situmorang I Ketut Gunawan Isyak Meirobie Khairani, Dewi Khodijah Hulliyah Khodijah Hulliyah Kim, Jin-Mook Kim, Jong Il Lee Kyung Oh Lee Kyung Oh Lestari Santoso, Nuke Puji Luh Kesuma Wardhani Luhur Bayuaji, Luhur Luthfi Arsalan, Muhammad Mardiansyah, Aditya Marviola Hardini Mira Kartiwi Muhtadibillah, Achmad Nandaputra, Riandi Nuke Puji Lestari Santoso Nur Febriana Widiyanti Nurwicaksana, Satria Oh, Lee Kyung Rahman, Abdul Wahab Abdul Ramadhan, Febrian Wahyu Rayi Pradono Iswara Rohadi Rohadi Rozy, Nurul Faizah Saepul Aripiyanto Shafiera, Eghar Siti Atinah Siti Ummi Masruroh Supardi Supardi Taqwa Hariguna Taqwa Hariguna Widiyanti, Nur Febriana Widjaja, Andree E. Yusuf Abdurachman Zainal Arifin Hasibuan