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All Journal Desimal: Jurnal Matematika Jurnal Sisfokom (Sistem Informasi dan Komputer) Aptisi Transactions on Management ProBisnis : Jurnal Manajemen Journal Of World Science Jurnal Pengabdian Masyarakat International Transactions on Artificial Intelligence (ITALIC)
Febiansyah, Hidayat
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Analyzing the effects of social media influencers and advertising on consumer buying interests to determine the elasticity of digital marketing strategies Amri; Febiansyah, Hidayat; Yulianti; Isnanto, Burham
ProBisnis : Jurnal Manajemen Vol. 15 No. 2 (2024): April: Management Science
Publisher : Lembaga Riset, Publikasi dan Konsultasi JONHARIONO

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

By incorporating consumer behaviour theory and marketing communication theory, the research intends to investigate the impact of social media advertising and influencers on purchasing preferences and to evaluate the efficacy of digital marketing strategies. Data acquisition via online surveys and statistical analysis, including assessments of validity, reliability, normality, multicollinearity, heterogeneity, and double regression, are components of the research methodology. The findings indicate that purchasing interests are significantly and positively impacted by advertising and influencers; advertising and influencers account for 51.4% of the variance in purchasing interests. This research contributes to the body of knowledge by demonstrating how integrating influence and social media advertisements can increase the efficacy of digital marketing. Practical implications encompass the significance of discerning appropriate advertising and influencer strategies in order to augment consumer interest in making purchases. Additional investigation is warranted in order to comprehend the intricate interplay among social media, influencers, and consumer behaviour in the era of digitalization.
Advanced Predictive Models for the Startup Ecosystem Using Machine Learning Algorithms Febiansyah, Hidayat; Rahardja, Untung; Adiyarta, Krisna; Anderson, James; Kanivia, Aan
APTISI Transactions on Management (ATM) Vol 8 No 3 (2024): ATM (APTISI Transactions on Management: September)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v8i3.2345

Abstract

The startup ecosystem, characterized by its dynamism, presents significant challenges in predicting its future trajectory. Traditional analytical methods often fall short in comprehensively addressing the myriad factors that shape this ecosystem. This research aims to enhance the predictability of trends within the startup landscape by integrating the Technology Acceptance Model (TAM) with the advanced Random Forest algorithm. While existing literature has extensively explored the challenges startups face and the nuances of stakeholder interactions, the integration of TAM's constructs with key empirical attributes, specifically Investment Dynamics, Startup Metrics, Stakeholder Interactions, Entrepreneurial Challenges, and Technological Infrastructure, is a pioneering approach. Drawing from a comprehensive dataset that spans a diverse array of startups, this study operationalizes TAM's constructs in conjunction with the specified attributes. The subsequent application of the Random Forest algorithm offers a novel predictive methodology. Initial results highlight the superior predictive capabilities of this integrated model compared to traditional approaches. The findings provide insights into the intricate relationship between technological perceptions, as framed by TAM, and the tangible realities of the startup domain. The fusion of TAM with state-of-the-art machine learning signifies a groundbreaking direction in startup ecosystem research. This innovative approach offers stakeholders an enhanced analytical tool, ensuring more informed decision-making and a deeper grasp of the multifaceted nature of startup ecosystems.
Integrating Machine Learning with Web Intelligence for Predictive Search and Recommendations Santiago, Maria; Febiansyah, Hidayat; Dinarwati, Dini
International Transactions on Artificial Intelligence Vol. 3 No. 1 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v3i1.654

Abstract

This study examines the integration of Machine Learning (ML) with Web Intelligence (WI) as a transformative approach for enhancing web-based search and recommendation systems. The objective is to utilize the combined strengths of ML and WI to significantly increase the accuracy, precision, and relevance of predictions, providing personalized and context-aware results that adapt in real-time. Employing a hybrid model that leverages both the predictive capabilities of ML and the dynamic adaptability of WI, this research methodologically assesses the performance against traditional models through rigorous testing. Results indicate that the integrated system substantially outperforms conventional models, demonstrating enhanced performance metrics across accuracy, precision, and recall. Theoretically, this integration contributes to the advancement of WI frameworks, while practically, it offers significant improvements for real-world applications, especially in optimizing user interactions and satisfaction. However, the study also recognizes limitations related to the scalability of the data and models used. Future research should focus on refining model complexity and enhancing real-time data processing capabilities. Additionally, the integration of these technologies supports several Sustainable Development Goals (SDGs), particularly Goal 9 (Industry, Innovation, and Infrastructure) by promoting sustainable industrialization through advanced technologies, Goal 8 (Decent Work and Economic Growth) by fostering economic growth and employment in the tech sector, and Goal 12 (Responsible Consumption and Production) by enabling more informed consumer choices through better recommendations. These connections underline the role of innovative technologies in achieving sustainable development and enhancing global economic and social frameworks.
Local Wisdom Meets Modern Conservation: Welcome To Selindung's Ecotourism Amri, Amri; Febiansyah, Hidayat; Sari, Lili Indah; Jamhari, Jery
Journal of World Science Vol. 4 No. 7 (2025): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v4i7.1448

Abstract

In order to establish sustainable ecotourism in Selindung, this study examines the connections between policy regulation, infrastructure development, community involvement, and environmental awareness. Although prior research has demonstrated the significance of combining environmental preservation with the growth of the tourism industry, little is known about the mechanisms behind these relationships. Analyzing the impact of environmental consciousness, community engagement, infrastructure development, and policy regulations on the growth of sustainable ecotourism and environmental sustainability in Selindung is the primary goal of the study. The study employs a cross-sectional design and a quantitative methodology. Two hundred people were surveyed using probability sampling to collect data. Structural Equation Modeling-Partial Least Squares (SEM-PLS) was used to analyze the data. The study's findings indicate that the development of sustainable ecotourism is most influenced by environmental awareness (?=0.797), followed by policy regulation (?=0.443), infrastructure development (?=0.624), and community involvement (?=0.716). Environmental sustainability is significantly mediated by sustainable ecotourism (?=0.674).
Digital signature scheme with matrix-based approach Irawadi, Syafrul; Febiansyah, Hidayat; Maxrizal, Maxrizal
Desimal: Jurnal Matematika Vol. 7 No. 3 (2024): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v7i3.23956

Abstract

The use of digital signatures in various electronic services such as e-transactions, e-commerce, and e-learning is necessary for today's humans. All types of these services are highly dependent on the privacy, integrity, and authenticity between the sender and recipient of the data. Mathematically, many digital signature schemes such as Rivest Shamir Adleman (RSA), Elgamal, and Elliptic Curve Cryptography (ECC) are made using the concept of integer multiplication. Previous research introduced the RSA signature with a square matrix that changes data as a matrix instead of integers. The security of the scheme depends on the matrix with order . The larger  the digit chosen, the better the level of protection. This modification makes this digital signature system more secure than systems using integers because the randomization process is more random and complicated. However, the operating system involves matrix exponentiation, requiring a lot of computing time and space. In this study, researchers changed the matrix exponentiation to ordinary matrix multiplication. The advantage is that the proposed algorithm has a faster computing speed because it only involves ordinary matrix multiplication. In the first step, the researcher forms several rectangular matrices as random variables for the key generation algorithm. Next, the researcher models the signing and signature verification algorithms. After that, the researcher codes in Mathematica and simulates the proposed signature scheme. In the final stage, the researcher performs a mathematical attack test analysis on the algorithm. The results show that the proposed scheme can generate keys and sign and verify signatures well. In addition, the proposed scheme system has also been tested for possible mathematical attacks.
A Systematic Literature Review on the Application of Machine Learning for Predicting Stunting Prevalence in Indonesia (2020–2024) Indrisari, Emilda; Febiansyah, Hidayat; Adiwinoto, Bambang
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i3.2366

Abstract

Stunting remains a serious public health issue in Indonesia, with persistently high prevalence and long-term impacts on children's physical and cognitive development. The growing need for data-driven early detection systems has encouraged the adoption of technologies such as machine learning (ML) to more effectively predict stunting prevalence. This study employed a Systematic Literature Review (SLR) to examine 20 scientific articles published between 2020 and 2024, focusing on the application of ML algorithms in stunting research. Literature was sourced from Scopus and Google Scholar, with inclusion criteria covering studies relevant to the Indonesian context or comparable global settings. The analysis focused on the algorithms used, data types, model performance, and implementation challenges. The findings indicate that Random Forest, Support Vector Machine (SVM), and Artificial Neural Network (ANN) are the most frequently used algorithms, with prediction accuracy ranging from 72% to 99.92%. Dominant predictor variables include maternal education, economic status, sanitation, and spatial-temporal data. The main challenges include data imbalance, limited model interpretability, and a lack of external validation. In conclusion, machine learning holds strong potential to support predictive systems and data-driven policies for stunting prevention in Indonesia. This study recommends future research to focus on integrating spatial-temporal data, implementing Explainable AI (XAI), and conducting cross-regional validation to enhance model reliability and policy relevance.
PELATIHAN MANAJEMEN PEMBELAJARAN DIGITAL DAN PLATFORM EDUKASI BAGI PESERTA DIDIK SMA SMK DI BANGKA TENGAH Febiansyah, Hidayat; Lili Indah Sari; Probonegoro, Wishnu Aribowo; Isnanto, Burham; Nur, Muchamim
BESIRU : Jurnal Pengabdian Masyarakat Vol. 2 No. 10 (2025): BESIRU : Jurnal Pengabdian Masyarakat, Oktober 2025
Publisher : Lembaga Pendidikan dan Penelitian Manggala Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62335/besiru.v2i10.1908

Abstract

The issue of limited digital competence in learning at SMA Negeri Namang, Bangka Tengah, requires serious attention due to its impact on the quality of education and students' preparedness for the digital era. This community service project aimed to improve digital learning management competence and mastery of educational platforms through a comprehensive training approach for 11th-grade students from the science and social studies departments. The activity was conducted using an interactive learning and hands-on practice approach over 2 days, involving 35 students through theory, practice, and continuous evaluation sessions. The material covered included the fundamentals of digital learning, managing online educational platforms, and techniques for optimizing learning media, with a total duration of 16 learning hours. The results showed an average competence increase of 68.5%, with a participant satisfaction level of 89.7% and an improvement in educational platform management skills of 72.3%. Program evaluation indicated a significant transfer of knowledge, with direct implementation in digital learning projects. This community service activity successfully and measurably improved students' digital competence and provided tangible benefits in the form of practical skills in digital learning management
Co-Authors Adiwinoto, Bambang Adiyarta, Krisna Amri Amri Amri Anderson, James Dinarwati, Dini Indrisari, Emilda Irawadi, Syafrul Isnanto, Burham Jamhari, Jery Kanivia, Aan Lili Indah Sari Maxrizal Nur, Muchamim Probonegoro, Wishnu Aribowo Santiago, Maria Sari, Lili Indah Untung Rahardja Yulianti