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All Journal Sinergi International Journal of Research and Applied Technology (INJURATECH) Prosiding SNTTM
Sari, Annisa Wulan
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Comparative Analysis of Machine Learning Techniques for Cryptocurrency Price Prediction Sari, Annisa Wulan
International Journal of Research and Applied Technology (INJURATECH) Vol. 4 No. 2 (2024): Vol 4 No 2 (2024)
Publisher : Universitas Komputer Indonesia

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Abstract

The increasing volatility and complexity of cryptocurrency markets have led to the growing application of machine learning (ML) techniques for accurate price prediction. This study presents a comparative analysis of eleven recent research papers on cryptocurrency forecasting using various ML and deep learning models, including Support Vector Machines (SVM), Random Forests (RF), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and ensemble methods. The findings highlight that deep learning models, particularly GRU and LSTM, often outperform traditional statistical models in capturing non-linear patterns and temporal dependencies. Moreover, feature diversity—such as on-chain data, market sentiment, and macroeconomic indicators—has been shown to significantly enhance predictive performance. However, many studies still lack comprehensive validation strategies and rely solely on historical price data, limiting generalizability. This review identifies key gaps in model benchmarking, feature integration, and evaluation consistency, providing a foundation for future research focused on hybrid models and interpretable AI for financial decision-making.
Experimental investigation of HHO blending in combustion engine performance Martin, Awaludin; Hidayatullah, Abda; Ginting, Yogie Rinaldy; Sari, Annisa Wulan
SINERGI Vol 29, No 3 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.3.006

Abstract

The transition to renewable energy sources has become increasingly critical due to the adverse effects of greenhouse gas emissions. One alternative to reducing fossil fuel dependence is hydrogen. Hydrogen technology can be integrated into internal combustion engines without major design modifications. This study investigates the effects of HHO gas blending on engine performance under varying brake load conditions. The carburetor was modified to allow HHO gas from electrolysis to enter the combustion chamber. The results indicate that HHO blending led to a 4.9% increase in brake power, a 1.66% improvement in thermal efficiency, and a 3% reduction in brake-specific energy consumption (BSEC). Additionally, among different potassium hydroxide (KOH) concentrations, the 30% wt solution exhibited the lowest power consumption for electrolysis.
A Systematic Literature Review of User Acceptance Factors in E-Government Services Sari, Annisa Wulan
International Journal of Research and Applied Technology (INJURATECH) Vol. 4 No. 2 (2024): Vol 4 No 2 (2024)
Publisher : Universitas Komputer Indonesia

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The rapid digital transformation in the public sector has led to the widespread implementation of e-government services. However, the success of these systems heavily depends on citizen adoption and persistent usage. This study aims to explore and analyze the critical factors influencing user acceptance of e-government services through a qualitative approach. The thematic analysis reveals that user experiences closely align with the core constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technology Acceptance Model (TAM). Key determinants driving adoption include performance expectancy, social influence, and a strong foundational trust in government, which emerged as a pivotal factor for users in developing regions. Furthermore, the qualitative findings highlight significant real-world challenges regarding the accessibility of e-government platforms for elderly and disabled users. These insights provide a strategic roadmap for policymakers and developers to enhance inclusive and user-centric digital public services.
The Role of Cloud Computing and Big Data in Enhancing E-Learning Service Quality Sari, Annisa Wulan
International Journal of Research and Applied Technology (INJURATECH) Vol. 5 No. 2 (2025): December 2025
Publisher : Universitas Komputer Indonesia

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The transition to digital education has exponentially increased the demand for robust, scalable, and personalized e-learning platforms. Legacy educational systems often struggle with server overloads, limited storage capacity, and the inability to process massive amounts of student data. This study explores the integration of Cloud Computing and Big Data analytics as a strategic solution to enhance e-learning service quality. Through a qualitative approach and thematic analysis of recent literature, this paper identifies that Cloud Computing provides a highly scalable, cost-effective infrastructure that ensures continuous system availability. Concurrently, Big Data empowers educational institutions to analyze student learning behaviors, predict academic outcomes, and deliver personalized learning experiences. The findings suggest that the synergy between these two technologies not only resolves technical bottlenecks but also transforms passive e-learning environments into adaptive, student-centric ecosystems. This study provides a comprehensive framework for higher education institutions aiming to modernize their IT governance and instructional delivery.
Ethics, Trust, and Adoption: A Literature Review on Student Perceptions of Generative AI in Higher Education Sari, Annisa Wulan
International Journal of Research and Applied Technology (INJURATECH) Vol. 5 No. 2 (2025): December 2025
Publisher : Universitas Komputer Indonesia

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Abstract

The rapid emergence of Generative Artificial Intelligence (GenAI) has sparked a paradigm shift in higher education, placing students at the intersection of technological innovation and ethical ambiguity. This study provides a qualitative systematic review of existing literature to explore the intricate relationship between ethics, trust, and adoption in student perceptions of GenAI. Utilizing a thematic synthesis approach, the research analyzes diverse academic studies to identify recurring patterns in how students navigate these tools. Findings reveal that while GenAI is highly valued for its ability to enhance productivity and personalized learning, adoption is significantly hindered by "ethical anxiety"—concerns regarding academic integrity, data privacy, and the potential loss of critical thinking skills. Trust is identified as a multi-dimensional construct, heavily dependent on institutional transparency and the clarity of AI-usage policies. This review concludes that for GenAI to be successfully integrated, higher education must move beyond functional training toward a framework of ethical literacy. The results offer strategic insights for educators and policymakers to foster a responsible AI-driven academic environment.
Design of smart food container using thermoelectric cooler and heat pipe integrated with internet of things Istiqomah, Adinda Diana Suci; Sari, Annisa Wulan; Martin, Awaludin
Prosiding SNTTM Vol 23 No 1 (2025): SNTTM XXIII October 2025
Publisher : BKS-TM Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71452/sxnxjf15

Abstract

The increasing demand for food delivery systems that ensure product quality, freshness, and safety has driven the development of a Smart Food Container (SFC) integrated with Internet of Things (IoT) technology, utilizing a combination of Thermoelectric Cooler (TEC) and heat pipe as the primary temperature control mechanism. The design employs a TEC2-25408 module coupled with a copper heat pipe containing water as the working fluid, which efficiently transfers heat from the hot side of the TEC to maintain temperature stability while reducing the TEC’s workload. The integration of IoT technology using a NodeMCU ESP8266 enables real-time temperature monitoring and remote system control. The research process includes design, prototype fabrication, and experimental testing to evaluate system performance. The SFC prototype, with dimensions of 63 cm × 34 cm × 37 cm, consists of two compartments: a cooling section operating at 5–10 °C and a heating section operating at 50–70 °C. Based on experimental results indicate that the system achieves a design COP of 0.383 and an average experimental COP of 0.339 with a performance deviation of 11.38%.
Co-Authors Awaludin Martin Ginting, Yogie Rinaldy Hidayatullah, Abda Istiqomah, Adinda Diana Suci