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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 16 Documents
 1   2 
NuminaMath 7B: Revolutionizing Math Solving with Integrated Reasoning Advanced Generative AI Tools and Python REPL Jufriansah, Adi; Akib, Irwan; Ishartono, Naufal; Khusnani, Azmi; Rahmawati, Tanti Diyah; Malahina, Edwin Ariesto Umbu; Maure, Osniman Paulina; Romadloni, Nova Tri
Jurnal Penelitian Sains Teknologi Vol. 2, No. 1, March 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/saintek.v2i1.15728

Abstract

The efficacy of NuminaMath 7B, an AI model that was created to address mathematical challenges, is assessed in this investigation. We evaluated the model's accuracy and efficiency against conventional methods through experiments that produced quantitative data. Qualitative data were collected through surveys and interviews with users to gain insight into their experiences and pinpoint areas for improvement. The survey results indicated that users found NuminaMath 7B to be pertinent, effective, and user-friendly, as evidenced by the exceptionally high average scores in user experience (95), perception of features and interface (90), and additional feedback (85). NuminaMath 7B was able to offer mathematical solutions with logical and detailed explanations as a result of the model's development through two phases of adjustments, which were conducted using the Chain of Thought (CoT) methodology and inspiration from the Tool-Integrated Reasoning Agent (ToRA) framework. Testing demonstrated that the model achieved a score of 29 out of 50 in the AI Math Olympiad competition, despite encountering difficulties in resolving more intricate problems. This study underscores the significance and urgency of AI technology, particularly in the field of mathematics, as well as the significant potential of AI models to facilitate a more comprehensive comprehension of mathematical concepts.
Temporal and Spatial Dynamics of Volcanic Aerosols: Absorbing Aerosol Index (AAI) Analysis During the Eruption of Mount Lewotobi Laki-laki Khusnani, Azmi; Jufriansah, Adi; Wahab, Dedi Suwandi; Samana, Fazaki Ramadhani Anwar; Bahruddin, Sitti Arafah; Anwar, Zaina; Nursilawati, Wingki; Arifin, Anggun Syafira
Jurnal Penelitian Sains Teknologi Vol. 2, No. 1, March 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/saintek.v2i1.15729

Abstract

In November 2024, the eruption of Mount Lewotobi Laki-laki on Flores Island, Indonesia, resulted in the release of substantial volcanic aerosols, including sulfur dioxide (SO₂) and volcanic debris. These aerosols impacted the environment, health, and aviation activities. The objective of this investigation is to examine the temporal and spatial dynamics of volcanic aerosols by employing the Absorbing Aerosol Index (AAI) in conjunction with TROPOMI satellite data (Sentinel-5P). The methodologies employed are as follows: spatial-temporal analysis with Google Earth Engine (GEE), aerosol dispersion simulation with the HYSPLIT model, and data processing with the Sentinel Application Platform (SNAP). The results indicated a substantial increase in volcanic activity from November 8th to 11th, 2024, as evidenced by an ash column that reached a height of as much as 10,945 m. The distribution of aerosols was influenced by atmospheric dynamics, with high concentrations observed in the vicinity of Mount Lewotobi Laki-laki and extending to the east-southeast. Although the level of volcanic activity declined in late November, aerosol concentrations were still detected in the atmosphere. This investigation offers critical insights into the distribution of volcanic aerosols during the eruption and its effects on disaster risk mitigation and air quality. It is anticipated that these discoveries will facilitate the implementation of more sustainable and effective risk management strategies for volcanic eruptions.
Reconstruction of the Ethics of Artificial Intelligence Development in Islamic Philosophy and Muhammadiyah Thought Ismiyanto, Mazwar; Anif, Sofyan; Prayitno, Harun Joko; Muhibbin, Ahmad; Kusumaningtyas, Dian Artha; Handayani, Trisakti
Jurnal Penelitian Sains Teknologi Vol. 2, No. 1, March 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/saintek.v2i1.16201

Abstract

This study aims to analyze the perspectives of al-Islam and Muhammadiyah on the development of Artificial Intelligence (AI) within the framework of ethics, epistemology, and the concept of blessing (barakah). The research employs a qualitative approach using a library research method, through the analysis of literature on Islamic philosophy, Muhammadiyah thought, and studies on technology ethics and AI. The data were analyzed using content analysis and hermeneutic techniques to identify normative principles relevant to responding to AI development. The findings indicate that Islam views technology as an instrument of the human mandate of khalifah (vicegerency), which must be directed toward public welfare (maslahah), justice, and balance between worldly life and the hereafter. The concept of Islamic ethics including tawhid, adl, moral character (akhlaq), and social responsibility serves as the normative foundation for evaluating and utilizing AI. Knowledge (ilm) is understood as a religious obligation that is not morally neutral; therefore, AI development must be oriented toward truth and benefit. Meanwhile, the concept of blessing (barakah) emphasizes sustainability and the spiritual dimension in the use of technology. From the Muhammadiyah perspective, the integration of religion and science, the strengthening of education, and community empowerment constitute the primary principles in AI development. AI is positioned as a means of civilizational renewal that must be guided by ethical values to prevent injustice or dehumanization. Thus, al-Islam and Muhammadiyah offer an integrative and normative philosophical framework for directing AI development in a responsible, just, and spiritually meaningful manner.
Utilization of Blockchain-Based Smart Contracts in Banking: A Systematic Review of Technical, Regulatory, and Systemic Risk Dimensions Pradana, Fajar Gemilang; Dipsatara, Taqiya
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.
Early Classification of Diabetes Risk in Productive Age Groups Using Machine Learning Zaki, Muhammad Ghifari; Imaduddin, Helmi
Jurnal Penelitian Sains Teknologi Vol. 2, No. 1, March 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/saintek.v2i1.16430

Abstract

This research aims to develop an early detection classification model for diabetes risk among the productive age group (18–44 years) using a machine learning approach. Implementing the CRISP-DM methodology, this study utilized the Diabetes Health Indicators Dataset from CDC BRFSS 2015, which was refined to 48,867 observations. The class imbalance issue (4.51% diabetes positive) was addressed using the Synthetic Minority Over-sampling Technique (SMOTE) to achieve a 1:1 class ratio in the training set. Elbow curve analysis and mutual information identified 10 optimal features that balance model performance and system usability. Three algorithms were evaluated Logistic Regression, Random Forest, and XGBoost and validated using Stratified 5-Fold Cross-Validation. The results demonstrate that Logistic Regression achieved the best performance for health screening purposes with a recall of 75.06% and ROC-AUC of 83.62%, capable of detecting three out of four diabetes cases with high consistency (cross-validation: recall 75.02% ± 2.35%). This model proved to be the most effective early screening tool for diabetes risk, supporting early detection and medical intervention for the productive age population.
Development of an Internet of Things (IoT) Based Grating Diffraction Experimental Device with Real-Time Light Intensity Data Acquisition Khairi, Arif Malik; 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.

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