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All Journal Jurnal Kimia Riset Sistemasi: Jurnal Sistem Informasi Journal of Halal Science and Research
Putri, Riski Dwimalida
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Chemistry Computer Science & IT Electrical & Electronics Engineering Other

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Mapping Machine Learning Trends in Chemistry Research using LLM with Multi-Turn Prompting Yudertha, Andreo; Putri, Riski Dwimalida
Sistemasi: Jurnal Sistem Informasi Vol 14, No 2 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i2.4961

Abstract

A review of research in the field of chemistry that incorporates machine learning is essential to identify recent developments and explore its potential applications. Published research articles provide an opportunity to analyze emerging research trends. The use of natural language processing (NLP) technology not only accelerates text data analysis but also enhances accuracy in understanding the content and context of scientific articles. Previously, trend analysis in ophthalmology research had been conducted using Zero-Shot Learning. In this study, an analysis of chemistry-related articles focusing on machine learning was carried out using a multi-turn prompting technique. The process began with data collection through web scraping of abstracts containing the keywords "machine learning" and "chemistry." The retrieved data was then tabulated and analyzed using a Large Language Model (LLM) with a Multi-Turn Prompting approach, where general prompts were initially used, followed by deeper exploration based on previous responses. Additionally, statistical descriptive analysis was performed using targeted prompts. Analysis of 200 article abstracts identified seven key terms related to the use of machine learning in chemistry: chemical (138 articles), protein (119 articles), drug (107 articles), structure (100 articles), molecular (96 articles), chemistry (91 articles), and quantum (84 articles). Furthermore, three dominant research topics were found in the intersection of chemistry and machine learning: protein and molecular structure, quantum chemistry, and drug discovery. The number of articles on machine learning in chemistry began to rise in 2012 and saw a significant increase in 2019. The findings suggest that there are still many opportunities for developing machine learning applications in chemistry, particularly in quantum chemistry. This field only began to gain attention in 2013, and the number of published articles remains relatively low each year, indicating that it is still in the early stages of exploration.
Sensitivity of commercial rapid test kit to pork contamination in processed foods Putri, Riski Dwimalida; Safitra, Suciana
Journal of Halal Science and Research Vol. 5 No. 1 (2024): February
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jhsr.v5i1.9688

Abstract

Guaranteeing halal products has now become a necessity, especially for food products. This is intended to ensure that the food produced is not contaminated with non-halal ingredients, including pork. Pork contamination in processed meat foods such as meatballs is still often found. Various tests can be done to detect the presence of pork in processed foods. One of them is a rapid test using the LFIA method. This test is widely used because it is more efficient, economical, and easy to prepare samples. A rapid pork contamination test kit (XEMA) has been circulating in Indonesia. In the research, the sensitivity of this rapid test kit was tested on processed meat foods with various concentrations of pork and variations in the main ingredients. The color test shows that the simulated samples of beef meatballs without added pork are dark greyish white, as are the simulated samples with concentrations of 1% and 10%. Meanwhile, samples with concentrations of 20% and 40% have a paler color. Meanwhile, there was no significant difference in the variation in pork concentration in meatballs with the main ingredients of chicken and fish. For smell and texture, there were no significant differences in the simulated samples, both the control and samples with varying concentrations. From testing, it is known that the test kit can detect the presence of pork up to a concentration of 10% in samples, with the main ingredients being beef, chicken, or fish. These results indicate that this rapid test kit can well detect pork contamination in processed food samples.
Isolation and Screening of the Enzymatic Potential of Lactic Acid Bacteria from VCO Oil Cake Putri, Riski Dwimalida; Balkis, Atia
Jurnal Kimia Riset Vol. 10 No. 2 (2025): December
Publisher : Universitas Airlangga, Campus C Mulyorejo, Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jkimris.v10i2.68610

Abstract

Virgin coconut oil (VCO) has various health benefits. In the VCO production process using fermentation, a by-product is produced as a solid called oil cake. In this study, lactic acid bacteria were isolated from oil cake VCO, characterized and tested for antimicrobial activity against E. coli and S. aureus bacteria. Screening of enzymatic activity was also carried out as amylolytic, proteolytic, and lipolytic activities. From the study, four isolates were obtained, namely BL1, BL2, BL3, and BL4. The isolates have the characteristics of lactic acid bacteria in the form of circular colonies, white and milky white, gram-positive and do not have catalase activity. Based on observations, it is suspected that the bacterial isolates belong to the Lactobacillaceae and Streptococcaceae families. Four isolates have moderate antibacterial activity against S. aureus. The largest inhibition zone owned by isolate BL1 about 9.5 mm in diameters. Four isolates have antibacterial activity against E. coli with a weak category. The enzymatic potential test shows that isolate BL 1 has amylase and protease enzyme activity, while isolates BL2 and BL3 only have amylase enzyme activity.  
Co-Authors Balkis, Atia Safitra, Suciana Yudertha, Andreo