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Muhammad Syahriandi Adhantoro
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Jurnal Penelitian Sains Teknologi
Published by Universitas Muhammadiyah Surakarta
ISSN : 14115174     EISSN : 2830067X     DOI : 10.23917/saintek
Core Subject : Science, Engineering,
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Physics Transportation
Jurnal Penelitian Sains Teknologi is a peer-reviewed scientific journal dedicated to publishing high-quality, original, and methodologically rigorous research in the fields of science and technology. The journal aims to serve as a scholarly forum for the dissemination of theoretical and applied research that advances scientific knowledge, technological innovation, and evidence-based solutions to contemporary challenges. Its scope covers applied science and computational studies, information technology and intelligent systems, engineering and applied technology, as well as the development, evaluation, and implementation of technological models, methods, and systems across various sectors, including education, industry, healthcare, government, energy, and the environment. By emphasizing originality, academic integrity, interdisciplinary perspectives, and ethical research practices, Jurnal Penelitian Sains Teknologi seeks to foster scholarly dialogue, support innovation-driven research, and enhance the impact and visibility of scientific and technological contributions at both national and global levels.
Arjuna Subject : Umum - Umum
Articles 6 Documents
 1 
Search results for , issue "vol. 2, no. 2, september 2026" : 6 Documents clear
Utilization of Blockchain-Based Smart Contracts in Banking: A Systematic Review of Technical, Regulatory, and Systemic Risk Dimensions Fajar Gemilang Pradana; Taqiya Dipsatara
Jurnal Penelitian Sains Teknologi Vol. 2, No. 2, September 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/saintek.v2i2.16341

Abstract

The advancement of blockchain technology has created significant opportunities for transforming the financial sector, particularly banking. One of the most transformative applications of this technology is the smart contract—self-executing digital agreements that automatically enforce predefined conditions without the need for intermediaries. This study presents a systematic literature review of 10 academic publications published between 2020 and 2025 to evaluate the utilization of smart contracts in the banking sector. The analysis focuses on three main dimensions: technical implementation, regulatory compliance, and systemic risk implications. The findings indicate that smart contracts have been implemented in automated lending systems, decentralized identity verification (KYC), asset tokenization, real-time auditing, and blockchain-based payment infrastructures. Despite these advantages, adoption remains constrained by security vulnerabilities in contract code, scalability limitations, interoperability challenges, and regulatory uncertainty across jurisdictions. This study also proposes a conceptual experimental framework for evaluating smart contract performance, security robustness, and compliance readiness within banking environments. The results contribute to a more integrated understanding of how smart contracts can be adopted safely and sustainably in highly regulated financial ecosystems.
Development of an Internet of Things (IoT) Based Grating Diffraction Experimental Device with Real-Time Light Intensity Data Acquisition Arif Malik Khairi; Ishafit Ishafit
Jurnal Penelitian Sains Teknologi Vol. 2, No. 2, September 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/saintek.v2i2.16343

Abstract

This study aims to develop an Internet of Things (IoT)-based diffraction grating experimental apparatus and to examine its feasibility and performance in physics laboratory activities. The research employs the Research and Development (R&D) method with the ADDIE development model, which includes the stages of analysis, design, development, implementation, and evaluation. The experimental apparatus developed consists of a laser diode light source, a diffraction grating, an LDR light sensor, a stepper motor as an automatic scanning system, and a NodeMCU ESP8266 microcontroller that functions as an IoT-based control and data acquisition system. The validation of the apparatus was conducted by subject-matter experts and media experts using an assessment instrument based on a Likert scale. The validation results indicate that the experimental apparatus falls into the very feasible category for use in physics laboratory activities. The performance testing of the apparatus shows that the system is capable of detecting the distribution of light intensity and the positions of diffraction maxima consistently. The calculation of the wavelength based on experimental data produces values in the range of 647–652 nm, which are close to the theoretical value of the light source of 650 nm with a low level of relative error. In addition, the use of an IoT-based system allows the data acquisition process to be conducted automatically and visualized in real time, thereby improving the efficiency and objectivity of measurements. The results of this study indicate that the developed experimental apparatus has the potential to support the modernization of physics laboratories and improve the quality of digital data based laboratory learning.
An AI-Driven Policy Intelligence Framework for Transforming National Data into Evidence-Based Public Policy Deva Yohand Pangestu; Laizza Natta Fatdaja; Hery Siswanto; Dzikrina Aqsha Mahardika
Jurnal Penelitian Sains Teknologi Vol. 2, No. 2, September 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/saintek.v2i2.16988

Abstract

The increasing complexity of national development requires public policies that are adaptive, data-driven, and evidence-based. However, many governments, particularly in developing countries, still face significant challenges in utilizing national data effectively due to data fragmentation, limited analytical capabilities of information systems, and the underutilization of Artificial Intelligence (AI). These limitations hinder the formulation of accurate and proactive public policies. This study aims to propose a conceptual framework that integrates national data, Information Systems, and AI to support intelligent policymaking. This research adopts a Design Science Research (DSR) approach to develop an artifact in the form of the National AI-Driven Policy Intelligence Framework (NAPIF). The framework is designed using a layered architecture consisting of data, processing, and output layers, supported by AI capabilities such as pattern recognition, predictive analytics, and policy recommendation generation. The proposed model transforms fragmented data into actionable insights through an integrated system that supports decision-making processes. The results indicate that the proposed framework enhances data integration, improves analytical capabilities, and enables predictive and adaptive policymaking. Compared to conventional systems, the framework provides more comprehensive decision support and supports continuous policy improvement through a feedback-driven mechanism. The study contributes theoretically by integrating the domains of Information Systems, AI, and public policy into a unified framework, and practically by offering a strategic approach for governments to implement data-driven governance aligned with long-term development goals. This study is limited by its conceptual nature; therefore, future research is recommended to validate the framework through empirical implementation and real-world case studies.
SisCek: A Deep Learning-Based Face Recognition System for Real-Time Exam Impersonation Detection Shandy Yusril Fadlullah; Afifah Nur Hidayah; Yuanda Eka Saputra; Uslan; Santosa Pradana Putra Setya Negara
Jurnal Penelitian Sains Teknologi Vol. 2, No. 2, September 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/saintek.v2i2.16998

Abstract

The digital transformation of educational assessment systems has accelerated the adoption of computer-based technologies; however, it still faces significant challenges related to security and identity verification of examination participants. One of the major issues is impersonation, where unauthorized individuals act as proxies during exams, thereby compromising academic integrity. This study aims to develop and evaluate SisCek (Sistem Pendeteksi Calo Ujian/ Exam Broker Detection System) based on face recognition and deep learning as a solution to automatically and in real time detect and prevent such practices. The research employs an experimental approach involving facial data collection, preprocessing, model training using a Convolutional Neural Network (CNN), and integrated system implementation. The evaluation is conducted using accuracy, False Acceptance Rate (FAR), and False Rejection Rate (FRR), as well as testing under real examination scenarios. The results show that the proposed model achieves an accuracy of 96.8%, with a FAR of 2.1% and an FRR of 3.4%. System-level testing demonstrates a detection success rate of 96% for both legitimate participants and impostors, with an average response time of 2.5 seconds, satisfying real-time system requirements. Comparative analysis indicates that SisCek outperforms conventional systems and previous studies, particularly in real-time impersonation detection and full integration with examination systems. This study provides a significant contribution to the development of AI-based examination security systems and has strong potential to enhance the integrity, fairness, and credibility of educational assessment in the digital era.
IndoBERT-Based Sentiment Analysis of Electric Motorcycle Policy in Indonesia Using Instagram Data Muhammad Syahriandi Adhantoro; Faris Athoil Haq; Dody Hartanto; Aninditawidagda Pandam Sudaryanto
Jurnal Penelitian Sains Teknologi Vol. 2, No. 2, September 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/saintek.v2i2.17021

Abstract

This study aims to analyze public sentiment toward the procurement of electric motorcycles within the Nutritional Service Fulfillment Unit/ Satuan Pelayanan Pemenuhan Gizi (SPPG) program in Indonesia by utilizing data from Instagram. The approach employed is a deep learning-based sentiment analysis using the IndoBERT model, which has been fine-tuned to classify data into positive, negative, and neutral categories. The research stages include data collection, preprocessing, labeling, model development, and model evaluation using accuracy, precision, recall, and F1-score metrics. The results indicate that public sentiment is predominantly negative at 80%, followed by positive sentiment at 15% and neutral sentiment at 5%. Further analysis reveals that negative sentiment is primarily driven by issues related to budget prioritization, infrastructure readiness, and policy effectiveness, while positive sentiment is associated with environmental benefits and improved service distribution efficiency. The model evaluation demonstrates that IndoBERT achieves high performance, with an accuracy of 0.89, precision of 0.88, recall of 0.90, and F1-score of 0.89. These findings indicate that IndoBERT is effective in capturing the contextual nuances of the Indonesian language in unstructured social media data. This study contributes to the advancement of transformer-based sentiment analysis methods and provides data-driven insights to support more responsive and evidence-based policymaking.
Forest Depletion in Indonesia: An Environmental Economics Perspective on Sustainability and Community Welfare Audi Kusumawardani Sarwowidodo; Bintang Anugrah Habibi; Sylviana Efelyn Santosa; Muhammad Zami Arham; Raihan Satya Putra Hariadi; Faridhatun Nikmah
Jurnal Penelitian Sains Teknologi Vol. 2, No. 2, September 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/saintek.v2i2.17910

Abstract

Forest depletion in Indonesia has become a major issue due to its environmental, economic, and social impacts. This study aims to analyze the trends and main causes of forest depletion in Indonesia and examine its economic, environmental, and social consequences. This research employed a descriptive qualitative approach using a literature review method. Data were collected from scientific journals, government reports, books, and relevant academic publications obtained through Google Scholar, ResearchGate, and Garuda. The data were analyzed systematically through data reduction, classification, validation, comparative interpretation, and integration with environmental economics theory. The findings indicate that forest depletion in Indonesia is primarily caused by plantation expansion, mining activities, illegal logging, infrastructure development, and weak environmental governance. Although forest exploitation contributes to short-term economic growth, it also generates long-term environmental losses that are often excluded from conventional Gross Domestic Product (GDP) calculations. Furthermore, deforestation contributes to biodiversity loss, climate change, soil degradation, water disruption, and increased risks of natural disasters such as floods and landslides. These impacts directly affect community welfare, particularly among rural and indigenous populations that depend on forest ecosystems. This study concludes that forest depletion in Indonesia is a multidimensional issue requiring integrated and sustainable policies involving environmental conservation, economic sustainability, social inclusion, and stronger governance to support long-term ecological and community resilience.

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