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LAVOISIER: Chemistry Education Journal
Published by Universitas Islam Negeri Syekh Ali Hasan Ahmad Addary Padangsidimpuan
ISSN : 30252865     EISSN : 28306279     DOI : https://doi.org/10.24952/lavoisier.vxix
Core Subject : Science, Education,
Chemistry Education
LAVOISIER: Chemistry Education Journal, is a journal of chemistry education published by the Department of Chemistry Education, Faculty of Education and Teacher Training, UIN Syekh Ali Hasan Ahmad Addary Padangsidimpuan. This journal provides readers with present developments in chemistry education through the publication of articles and research reports. All articles will be reviewed (double-blind) by experts before being accepted for publication. Each author is responsible for the content of published articles. LAVOISIER: Chemistry Education Journal focused on the study result in the field of chemistry education. This journal encompasses original research articles, including: Teaching & Learning in Chemistry Education Material Learning in Chemistry Education Learning Media/Multimedia in Chemistry Education Evaluation & Assessment in Chemistry Education Higher Order Thinking Skills in Chemistry Education Science, Technology, Engineering, and Mathematics (STEM) Education Chemical Content Learning Strategy in chemistry education School Laboratory Experiment in chemistry education Integrating Islamic Values with Chemistry
Arjuna Subject : Kimia(semua kategori) - Kimia (Lain-Lain)
Articles 68 Documents
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COMPARISON OF UV-VIS SPECTROPHOTOMETRY, POTENTIOMETRIC TITRATION, AND CONDUCTOMETRY METHODS IN THE DETERMINATION OF VITAMIN C CONTENT IN COMMERCIAL BEVERAGES Haidhar Arvin Hidayatullah; Ahmad Wahyu Aprillianto; Arka Raditya Akmal; Faramufida Fricha Fricha; Venus Ramadhani Farichah; Meisela Rosalina Dewi
LAVOISIER: Chemistry Education Journal Vol 5, No 1 (2026)
Publisher : UIN Syekh Ali Hasan Ahmad Addary Padangsidimpuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24952/lavoisier.v5i1.19967

Abstract

Verification of vitamin C content in commercial packaged beverages is an important analytical need, given the chemical instability of ascorbic acid under various environmental factors that can cause the actual content to deviate from the declared label value. This study aimed to compare the analytical performance of three vitamin C determination methods, namely UV-Vis spectrophotometry, potentiometric titration, and conductometry, applied to a commercial beverage sample claiming 1000 mg vitamin C per 500 mL. UV-Vis spectrophotometric analysis was performed by constructing a five-point calibration curve at 265 nm, potentiometric titration was conducted using 0.1000 N NaOH with continuous pH-based equivalence point detection via a calibrated glass electrode, and conductometry was performed by monitoring changes in electrical conductivity during neutralization with volume correction applied at each point. Each method was performed in five replicates. The results showed that UV-Vis spectrophotometry provided the best performance with a mean measured content of 1024.0 mg/500 mL, %error of 2.48%, and %RSD of 0.85%. Potentiometric titration yielded a mean content of 1038.7 mg/500 mL with %error of 3.87% and %RSD of 1.65%. Conductometry produced the largest deviation with a mean content of 1072.3 mg/500 mL, %error of 7.23%, and %RSD of 4.22%. The high deviation in conductometry was attributed to matrix interference from various ions and organic acids in the beverage contributing to the total conductivity response, while variability in potentiometric titration was influenced by the sensitivity of equivalence point detection to titrant addition rate and the non-selective response to all titratable acids in the sample. UV-Vis spectrophotometry is recommended as the most reliable and practical method for determining vitamin C content in complex commercial beverage matrices under routine analytical laboratory conditions.
Implementation of Deep Learning for AI-Based Adaptive Chemistry Learning: Literature Review Method Winda Hudi Nurlatiffah; Nani Sukma Wati; Muhammad Dzafa Fathurrohman
LAVOISIER: Chemistry Education Journal Vol 5, No 1 (2026)
Publisher : UIN Syekh Ali Hasan Ahmad Addary Padangsidimpuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24952/lavoisier.v5i1.18182

Abstract

This literature review study aims to analyze and synthesize the implementation of Deep Learning in adaptive and inclusive chemistry learning based on Artificial Intelligence (AI). The background of this research is based on the demands of the digital era and Ki Hajar Dewantara's educational philosophy regarding guidance that humanizes humans, which requires pedagogical innovation to overcome the abstract nature of chemistry subjects. Chemistry, which involves microscopic concepts and high visual representation, is often a source of learning difficulties for students. Therefore, Deep Learning is presented as a solution to create a personalized and responsive learning system. The method used was a systematic literature study by analyzing ten relevant articles published between 2015 and 2025, with the main keywords “Deep Learning,” “Artificial Intelligence/AI,” and “Chemistry Learning.” The synthesis of the ten articles shows that the implementation of AI and Deep Learning has three main impacts: (1) Improved Understanding of Chemical Concepts: AI/Deep Learning, through differentiated learning systems, virtual laboratories, and digital tutors, effectively addresses the abstract nature of chemical concepts (A2, A4). This technology facilitates the visualization of molecular structures and provides targeted automated feedback (A10), thereby improving student understanding, (2) Strengthening Teacher Competence: AI integration assists teachers in providing simulations, digital media (A6), and rapid feedback, which significantly improves teachers' pedagogical abilities in designing innovative learning (A1, A3), (3) Development of 21st Century Skills: The use of Deep Learning (A8) and Generative AI (A10) models has been proven to develop multiple creativity, critical thinking, and collaboration among students in the context of chemistry, in line with sustainability demands (SDGs). However, consistent implementation challenges include infrastructure limitations, low digital literacy, and the need for teacher training (A1, A7). Overall, this study concludes that Deep Learning and AI are important and strategic components in shaping an adaptive, personalized, efficient, and inclusive chemistry learning environment that is capable of aligning the complexity of the material with the unique needs of each student in the digital age.
The Effect of Work Card Media Integrated with the Group Investigation (GI) Cooperative Learning Model on Students' Learning Outcomes Improvement in the Colloidal System Topic Silvia Elastari Matondang; Putri Junita Sari Nst
LAVOISIER: Chemistry Education Journal Vol 5, No 1 (2026)
Publisher : UIN Syekh Ali Hasan Ahmad Addary Padangsidimpuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24952/lavoisier.v5i1.19700

Abstract

This study aims to determine the improvement of learning outcomes by using work card media in the GI (Group Investigation) type cooperative learning model on colloidal system material at Al-Hidayah Medan Private High School. The population used in this study were all students of class XI IPA Al-Hidayah Medan Private High School consisting of two classes. The research sample was randomly sampled from both classes, namely the experimental class I was given GI (Group Investigation) treatment with work card media and the experimental class II was given GI (Group Investigation) treatment without work card media. The improvement of student learning outcomes was calculated using the normalized gain form and the percentage of student learning success in the experimental class I was 80.2% while the percentage of student learning success in the experimental class II was 52.1%. The hypothesis test was conducted using the right-hand t-test and obtained thitung which was 3.124 while ttabel was 1.670 for α = 0.05 and db = 61. Thus thitung > ttabel, then Ha was accepted, namely the improvement in chemistry learning outcomes by using work card media in cooperative learning type GI (Group Investigation) was better than without using work card media in the application of the cooperative learning model type GI (Group Investigation).
The Effect of Problem-Based Learning Model on Student Learning Outcomes in Acid-Base Material at MAN 3 Mandailing Natal Rico Apryanto Rangkuti; Mariam Nasution; Nur Azizah Putri Hasibuan
LAVOISIER: Chemistry Education Journal Vol 5, No 1 (2026)
Publisher : UIN Syekh Ali Hasan Ahmad Addary Padangsidimpuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24952/lavoisier.v5i1.19311

Abstract

Based on research, students are unable to distinguish between the concepts of acids and bases, and are unable to apply the existing formulas for acids and bases, resulting in students being less active, less able to explore their knowledge, and less able to express their ideas, thereby leading to low learning outcomes. This study aims to determine the significant effect of the Problem-Based Learning model on student learning outcomes in acid-base material. This study was conducted at MAN 3 Mandailing Natal from June 16 to June 26 in the even semester of the 2024/2025 academic year. The research method used in this study was a quasi-experiment, and the research sample consisted of 32 students in each of the experimental and control classes. In the experimental class, there were 16 male students and 16 female students, while in the control class, there were 12 male students and 20 female students. The data collection techniques used were learning outcomes and documentation. The main instrument used was a multiple-choice test consisting of 15 questions, which were then analyzed using a t-test. The results of the hypothesis test using SPSS version 24 software obtained significant data, namely 0.000 0.05, so H0 was rejected and H1 was accepted. This indicates that there is a significant effect of the Problem-Based Learning model on student learning outcomes in acid-base material at MAN 3 Mandailing Natal. The implication of this study is that the application of the problem-based learning model affects learning outcomes and motivates students to learn at MAN 3 Mandailing Natal.
Literature Review: The Effect of Activated Carbon Adsorption on the Reduction of Iron (Fe) Concentrations in Peat Water Yogi Chandra; Heni Sugesti; Arya Wiranata; Abdul Rohman Wali
LAVOISIER: Chemistry Education Journal Vol 5, No 1 (2026)
Publisher : UIN Syekh Ali Hasan Ahmad Addary Padangsidimpuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24952/lavoisier.v5i1.19921

Abstract

Peatlands in Riau Province have great potential as a water source, but their quality often fails to meet standards due to high organic matter content and levels of heavy metals such as iron (Fe), which can pose health risks. This study aims to evaluate the effectiveness of various types of activated carbon derived from agricultural waste and biomass in reducing iron (Fe) levels in peat water through a Systematic Literature Review (SLR) based on the PRISMA 2020. A total of 8 relevant articles from the Google Scholar and ScienceDirect databases were analyzed, covering the use of materials such as empty oil palm fruit bunches, bintaro fruit shells, bamboo stalks, tofu residue, shrimp shells, corn cobs, coffee grounds, and natural zeolite. The results of the study indicate that the activation process, whether physical or chemical (using activators such as H₃PO₄, HCl, or KOH), is crucial in enhancing the porosity and surface area of activated carbon to optimize adsorption capacity. The highest iron (Fe) reduction efficiency reached 99.66% and was achieved using corn cob activated carbon activated with 0.25 M HCl at 400°C. Overall, the use of biomass waste as an adsorbent is not only effective and economical in improving peat water quality but also supports sustainable waste management.
The Effect of the EnglishScore Media-Assisted Deep Learning Approach on TOEFL Scores in the “Structure and Written Expression” Module of the Chemistry English Course Dimas Ridho; Lenni Khotimah Harahap
LAVOISIER: Chemistry Education Journal Vol 5, No 1 (2026)
Publisher : UIN Syekh Ali Hasan Ahmad Addary Padangsidimpuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24952/lavoisier.v5i1.19662

Abstract

Low TOEFL scores among students in the Structure and Written Expression section are often caused by conventional, superficial learning methods. This study aims to analyze the effect of a deep learning approach supported by the EnglishScore media on students’ TOEFL scores in the Chemistry English course. The research method used was a quasi-experimental study with a non-equivalent control group design. The research sample consisted of two classes of second-semester Chemistry Department students at the State University of Medan, selected through purposive sampling. Data were collected through pretests and posttests, which were analyzed using descriptive statistics and the independent samples t-test via SPSS software. The results showed that the average posttest score of the experimental class (28.829) was significantly higher than that of the control class (23.229). The results of the hypothesis test showed a significance value of 0.001 (p < 0.05), indicating a significant difference in achievement between the two groups. The deep learning approach proved effective in creating a more homogeneous distribution of scores and enhancing students’ conceptual understanding through reflective and analytical activities. The integration of the EnglishScore platform played a crucial role in providing an adaptive learning environment and enhancing cognitive engagement. This study highlights the importance of transforming technology-based learning strategies to strengthen students’ English language competencies in the field of science.
Advances in α-Amylase Technology: Production, Characterization, Engineering, and Functional Applications in Industrial and Biomedical Systems Rahma Noer Fauziah; Anti Zahrotul Abror; Naela Salma Khasani; Anggie Dhea Aprilia
LAVOISIER: Chemistry Education Journal Vol 5, No 1 (2026)
Publisher : UIN Syekh Ali Hasan Ahmad Addary Padangsidimpuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24952/lavoisier.v5i1.19608

Abstract

Abstract α-Amylase is a key industrial enzyme widely utilized in food processing, bioenergy production, and various biotechnological applications due to its ability to hydrolyze starch into simpler sugars. This study aims to comprehensively review recent advancements in α-amylase technology, including its production, characterization, mechanisms of action, engineering strategies, inhibition, and applications in industrial, biomedical, and sustainable systems. The research employed a descriptive-analytical literature review method by analyzing 69 relevant scientific publications from reputable databases such as ScienceDirect, SpringerLink, MDPI, and Google Scholar. The findings indicate that microbial-based production, particularly through the utilization of agro-industrial waste, offers a cost-effective and sustainable approach aligned with circular bioeconomy principles. Enzyme characterization reveals that α-amylase exhibits high adaptability to varying temperature and pH conditions, with performance strongly influenced by physicochemical factors and metal ions. Structural analysis highlights the importance of the GH13 domain in determining catalytic efficiency and substrate specificity. Furthermore, advancements in enzyme engineering, including genetic modification, protein engineering, and immobilization techniques, significantly enhance enzyme stability, reusability, and resistance to extreme conditions. The study emphasizes the importance of enzyme inhibition mechanisms, particularly in medical applications such as diabetes management. Overall, α-amylase technology demonstrates significant potential as a versatile and sustainable biocatalyst. Future developments should focus on optimizing enzyme engineering approaches, exploring novel microbial resources, and integrating advanced biotechnological innovations to enhance efficiency and broaden application potential.
Misconception identification in the buffer solutions: how to prepare the first-year student to learn general chemistry Asih Widi Wisudawati; Fidia Lukita; Sarima Saharani; Alviana Mahbubah
LAVOISIER: Chemistry Education Journal Vol 5, No 1 (2026)
Publisher : UIN Syekh Ali Hasan Ahmad Addary Padangsidimpuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24952/lavoisier.v5i1.18684

Abstract

Understanding Buffer solutions is required for university students who will take a general chemistry course. Conceptually, buffer solutions are needed to prepare chemical experiments. Due to the nature of the buffer concept, students often hold misconceptions. This study aims to analyze the misconceptions of Chemistry Education students on buffer solutions using a two-tier diagnostic test instrument. This study used a descriptive method with 43 Chemistry Education first-year students enrolled in 2025 at UIN Sunan Kalijaga Yogyakarta as participants. Data collection was conducted through Google Forms using a two-tier diagnostic test consisting of 10 multiple-choice questions with explanations. The results showed that misconceptions were still significantly found, especially in the basic concepts of buffer solution properties against pH changes, the formation of buffer solution systems, the role of stoichiometry in buffer reactions, and the effect of buffer component concentration on buffer capacity. The highest percentage of misconceptions was found in the sub-concepts of buffer solution formation and loss of buffer properties. These findings indicate that some students still understand buffer solutions procedurally and absolutely, without understanding the underlying acid-base equilibrium mechanism. Therefore, learning strategies that emphasize conceptual understanding, the use of multiple representations, and the use of diagnostic tests to identify and reduce misconceptions early on are needed.

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