Indonesian Journal of Modern Science and Technology
Indonesian Journal of Modern Science and Technology (IJMST), ISSN: 3090-3831, is an Indonesian academic journal with three issues per year (January, May, September) by CV. Abhinaya Indo Group in collaboration with Universitas Qamarul Huda Badaruddin that specializes in various modern research in the field of science and technology relevant to development. The journal is designed as a platform for researchers, academics, and practitioners to share their latest discoveries and innovations in various fields, including artificial intelligence, Internet of Things (IoT), information technology, robotics, electrical, biotechnology, engineering, and environmental technology. With a focus on the application of modern technology in Indonesia, the journal also covers interdisciplinary research that combines technology with social, economic, and environmental sciences. Journal title Indonesian Journal of Modern Science and Technology Abbreviation IJMST Frequency 3 issues per year (January, May, September) Type of Review Double Blind Review Online ISSN ISSN: 3090-3831 Advisory Editor Prof. Abdeltif Amrane (Scopus ID: 7003955640), CNRS Centre National de la Recherche Scientifique, France Editor in Chief Asno Azzawagama Firdaus (Scopus ID: 58667521800), Universitas Qamarul Huda Badaruddin, Indonesia Publisher CV. Abhinaya Indo Group, in collaboration with Universitas Qamarul Huda Badaruddin DOI https://doi.org/10.64021/ijmst Indexing Google Scholar | Garuda | Crossref | Dimensions
Vibrational, Optical Band Gap, Urbach Energy, and Thermal Activation Analysis of TiO2/CaTiO3 Perovskite
TiO₂/CaTiO₃ is an oxide perovskite with considerable potential for photocatalytic, optical-sensing, and semiconductor-based functional-material applications. This study aimed to evaluate the vibrational characteristics, optical properties, band gap energy, Urbach energy, and kinetic and activation thermodynamic parameters of TiO₂/CaTiO₃.. The material was characterized using FTIR, UV–Vis spectroscopy, Kubelka–Munk analysis, Tauc plots, and Differential Thermal Analysis (DTA). The FTIR results revealed characteristic absorption bands in the low-wavenumber region associated with Ti–O, Ti–O–Ti, and Ca–O–Ti vibrations, indicating the formation of the TiO₂/CaTiO₃ perovskite framework. The UV–Vis spectrum showed dominant absorption in the ultraviolet region with an extension toward the visible region. Kubelka–Munk analysis yielded direct and indirect band gap energies of 2.896 and 2.897 eV, respectively, whereas the Tauc-plot method produced values of 2.894 and 2.896 eV. The Urbach energies obtained using the Kubelka–Munk approach ranged from 0.173 to 0.189 eV, while the Tauc-plot approach yielded 0.449–0.686 eV. These differences demonstrate the sensitivity of the estimation method to the baseline and fitting region and indicate the presence of tail states associated with optical disorder. DTA analysis showed a peak temperature of 623.31 K, with Ea = 7.853 kJ mol-1, ΔH* = 1.511 kJ mol-1, ΔG* = 233.89 kJ mol-1, ΔS* = −301.26 J mol-1 K-1, and ln A = −5.238. Overall, TiO2/CaTiO3 exhibited vibrational, optical, and thermal characteristics that support its potential as an oxide-perovskite functional material.
Copyright (c) 2026 Kormil Saputra, Dian Wijaya Kurniawidi , Aws M Aseer Nejres (Author) https://creativecommons.org/licenses/by-sa/4.0
Effects of Natural and Synthetic Preservatives on the Microbial, Physicochemical, and Shelf-Life Characteristics of Dried Beef Under Ambient Storage Conditions
This study investigated the effects of natural (ginger and garlic) and synthetic (potassium sorbate and sodium nitrite) preservatives on the microbial quality, physicochemical quality, and shelf life of dried beef stored under ambient conditions. Over a 9-day storage period, samples were analyzed for total bacterial counts, coliforms, staphylococcal and fungal counts, moisture content, and total volatile base nitrogen (TVB-N). The results revealed that untreated samples deteriorated rapidly, showing high microbial proliferation (total viable count increasing from 0.00 to 5.5 log CFU/g) and significant biochemical spoilage, with TVB-N rising from 7.25 to 24.60 mg N/100 g. In contrast, treated samples, particularly those preserved with potassium sorbate and sodium nitrite, showed markedly lower microbial counts (3.55 to 5.5 log CFU/g), controlled TVB-N values (6.70 to 15.00 mg N/100 g), and reduced moisture loss (30.40 ± 0.12% to 27.00 ± 0.18%) compared to the control. Microorganisms isolated revealed Bacillus subtilis, Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa as the dominant bacteria, while Aspergillus niger, Saccharomyces cerevisiae, and Candida spp. were the major fungi. Natural preservatives (ginger and garlic) exhibited antimicrobial activity, especially when combined, while synthetic preservatives demonstrated superior inhibitory and stabilizing effects (p < 0.05). Overall, the study concludes that both natural and synthetic preservatives significantly enhanced the safety and shelf life of dried beef, with synthetic treatments proving most effective. Nonetheless, natural preservatives such as ginger and garlic represent promising, health-friendly alternatives that can be optimized for sustainable meat preservation.
Copyright (c) 2026 Omorordion Nnenna , Wami Princess C (Author) https://creativecommons.org/licenses/by-sa/4.0
Blockchain-based Decentralized Authentication and Authorization for IoT Applications
The rapid proliferation of Internet of Things (IoT) devices has introduced significant challenges in ensuring secure and efficient authentication and authorization mechanisms. Traditional centralized approaches are increasingly inadequate due to their single points of failure, scalability issues, and vulnerability to attacks. This paper explores a blockchain-based decentralized framework for authentication and authorization in IoT applications, leveraging the inherent security features of blockchain technology. The proposed solution employs smart contracts to automate and enforce access control policies, ensuring that IoT devices can securely interact without relying on a trusted third party. By integrating blockchain with IoT, this approach enhances data integrity, transparency, and auditability while mitigating common security risks associated with centralized systems. The paper provides a comprehensive analysis of the architecture, implementation details, and performance evaluation, demonstrating the feasibility and advantages of the proposed decentralized authentication and authorization scheme. Experimental results indicate improved security, scalability, and operational efficiency, positioning blockchain as a promising solution for secure IoT environments.
Copyright (c) 2025 Dr. Hewa Majeed Zangana, Hakem Beitollahi , Sabat Salih Muhamad , Marwan Omar , Firas Mahmood Mustafa, Aquil Mirza Mohammed , Sharyar Wani , Shuai Li (Author) https://creativecommons.org/licenses/by-sa/4.0
Option Pricing with Periodic Volatility : A Modified Black-Scholes Model Using Jacobi Elliptic Functions
In this work, a modified Black-Scholes model for European option pricing is presented, in which the volatility function exhibits a periodic pattern determined by the Jacobi elliptic sine function. This method maintains smoothness and mathematical tractability while capturing organized, oscillatory activity frequently seen in financial markets. We develop the relevant parabolic partial differential equation and confirm regularity and uniform ellipticity criteria to confirm that it is well-posed. The complete model is solved nu- merically using a Crank-Nicolson finite difference method after a formal solution is derived using the Fourier transform under some assumptions. The findings of the simulation show how volatility patterns and option prices are impacted by changes in the elliptic modulus. Specifi- cally, for short-term and at-the-money options, the model produces rippling price surfaces and observable deviations from traditional Black-Scholes pricing. This methodology, which remains rooted in the traditional option pricing model while offering insight into periodic risk dynamics, provides a useful and understandable alternative for stochastic volatility models.
Copyright (c) 2026 Chinwe N. Obi, Bright O. Osu , Promise Azor (Author) https://creativecommons.org/licenses/by-sa/4.0
Artificial Intelligence in Glaucoma Screening: Advances, Challenges and Future Directions
Glaucoma is a leading cause of irreversible blindness worldwide, with many cases remaining undiagnosed due to limitations in traditional screening methods. Conventional diagnostic approaches rely on specialized equipment, trained clinicians, and subjective interpretation, restricting large-scale and early detection, particularly in resource-limited settings. Artificial intelligence (AI)-based screening methods have emerged as scalable and objective solutions for automated glaucoma detection using retinal imaging data. This review provides a comprehensive overview of recent advances in AI-driven glaucoma screening, focusing on methodological innovations, diagnostic performance, fairness considerations, and real-world implementation challenges. A systematic analysis of studies published up to early 2025 was conducted, covering AI applications in fundus photography, optical coherence tomography (OCT), and multimodal imaging. Approaches including deep learning-based classification, segmentation, and progression prediction are evaluated. Recent AI models demonstrate high diagnostic performance, with reported accuracies of 95–98% and strong sensitivity and specificity. Multimodal fusion enhances early detection and progression monitoring, while explainable AI techniques improve transparency by highlighting clinically relevant retinal regions. Fairness-aware strategies further address demographic disparities to support equitable screening. Lightweight architectures enable portable and mobile deployment for large-scale community screening. AI significantly improves the accuracy, accessibility, and scalability of glaucoma detection. Continued emphasis on data diversity, interpretability, and clinical validation is essential for sustainable integration into real-world ophthalmic practice.
Copyright (c) 2026 Virendra Kumar Tiwari, Jitendra Agrawal, Sanjay Bajpai (Author) https://creativecommons.org/licenses/by-sa/4.0
Mathematical Modelling of Endosulfan Adsorption Using Activated and Unactivated Boiler Fly Ash and Maize Cob
The application of pesticides to control plant pests and increase yield introduces issues of pesticide contamination and toxicity. Endosulfan, a persistent organic pollutant, poses serious ecological and public health risks due to its toxicity, persistence, and bioaccumulative nature. Conventional remediation methods are often costly and generate secondary waste, hence the need for sustainable, low-cost alternatives. This work investigated the mathematical modelling of endosulfan adsorption from aqueous solutions using activated and unactivated boiler fly ash and maize cob. Batch adsorption experiments were conducted to examine the effects of contact time, initial concentration, and activation of adsorbents. The behavior of pesticide adsorption data against certain isotherm and kinetic equations was also investigated. The physicochemical properties of the activated and unactivated maize cob and boiler fly ash, as well as FT-IR, XRD, and SEM of the adsorbents, were determined. Both initial pesticide concentration and contact time affected the adsorption capacity of the adsorbents. As the initial concentration increased, the amount of pesticide adsorbed also increased. An increase in contact time rapidly increased the amount adsorbed until it reached a maximum at 60 minutes, after which it decreased; hence, equilibrium was reached after 60 minutes. Activation enhanced the porosity, cation exchange capacity, and surface functional groups of the adsorbents. Activated maize cob exhibited the highest adsorption capacity, followed by activated boiler fly ash, while unactivated forms showed comparatively lower performance. Equilibrium data fitted best to multilayer and heterogeneous surface models, while kinetic analysis suggested chemisorption tendencies. Activated maize cob and boiler fly ash are effective, eco-friendly, and low-cost adsorbents for the removal of endosulfan from aqueous systems. Mathematical modelling provided predictive insights into adsorption behavior, supporting process optimization. The findings demonstrate the potential of agricultural and industrial residues as sustainable alternatives for pesticide remediation and environmental protection.
Copyright (c) 2025 Bright Osu, Igwe J.C, Olunkwa C, Aghalibe C.U , Onwuka K.E (Author) https://creativecommons.org/licenses/by-sa/4.0
Innovative Strategies Using Digital Technologies to Enhance Performance in Racket Games
This study examines the impact of innovative strategies using digital technologies on enhancing performance in racket games, such as badminton and table tennis. An experimental approach was used on a sample of 50 students from the fourth stage in colleges of physical education. The sample was divided into two groups: an experimental group that used digital strategies and a control group that used traditional methods. The results showed a statistically significant positive relationship between the use of digital technologies and the players' performance in badminton, where p-value = 0.021. The results also showed that the group that used digital technologies was superior in the level of technical skills, with a p-value = 0.005 in shot accuracy and 0.012 in speed. The results indicate that digital technologies enhance physical and mental performance, which calls for their integration into training programs.
Copyright (c) 2025 Ameera Sabry Hussein, Mayasa Abd Ali Kadhim, Lamyaa Hasan Aldewan, Mohammed Asim Ghazi (Author) https://creativecommons.org/licenses/by-sa/4.0
UI/UX Design for E-Commerce For Sesaot Village Using Figma and the User-Centered Design (UCD) Method
The swift expansion of e-commerce demands accessible digital platforms; nevertheless, numerous current applications lack intuitiveness, leading to an unsatisfactory user experience. This study creates a UI/UX for an online shopping platform utilizing Figma and the User Centered Design (UCD) approach to fulfill user requirements. This research aims to offer a structured method for creating e-commerce interfaces that improve user satisfaction. The techniques employed consist of user research, specification of requirements, development of prototypes, and testing for usability. The outcome is a model of an e-commerce site featuring a user-friendly interface that showcases an impressive System Usability Scale (SUS) score. Users mentioned a greater simplicity in usage and contentment, suggesting the success of the UCD method. In summary, utilizing UCD with Figma effectively created an interface that fulfills user requirements and boosts engagement.
Copyright (c) 2026 Rifqi Sulthon Abqari, Hendri Purnomo, Bayu Wibisana, Krisostomus Nova Rahmanto, Nuraqilla Waidhah Bintang Grendis (Author) https://creativecommons.org/licenses/by-sa/4.0
Development of Interactive Multimedia Applications for Local Culture Awareness
Local culture is a valuable heritage that needs to be introduced and preserved, especially in the face of globalization, which causes younger generations to become increasingly disconnected from their cultural identity. This study aims to develop an interactive multimedia application based on Android as a tool for educating and introducing local culture to high school students. The Research and Development (R&D) method with the ADDIE approach (Analysis, Design, Development, Implementation, and Evaluation) was employed. The test subjects were 60 eleventh-grade students from a high school in West Java. The developed application, named BudiLokal, contains various cultural content, including traditional music, dance, folk tales, musical instruments, and interactive audio-visual quizzes. Evaluation was conducted using a usability questionnaire based on the System Usability Scale (SUS) and pre- and post-tests to measure improvements in student understanding. The research results indicated an average SUS score of 82 (excellent category) and a post-test score increase of 21.8 points compared to the pre-test (p < 0.001). Additionally, 88% of the respondents stated that the application made them more interested in learning about local culture. These findings support the multimedia learning theory, which suggests that presenting material through a combination of text, images, sound, and animation can significantly improve understanding and motivation to learn. This study recommends further development of the application by incorporating Augmented Reality (AR) and Virtual Reality (VR) features for a more immersive learning experience. The BudiLokal application has proven to be an innovative solution in cultural preservation through an adaptive educational approach aligned with technological advancements.
Copyright (c) 2025 Muhammad Ziad Iqbal, Firmansyah, Yusuf Hendra Pratama, Setiawan Ardi Wijaya (Author) https://creativecommons.org/licenses/by-sa/4.0
Sentiment Analysis of User Reviews of TikTok App on Google Play Store Using Naïve Bayes Algorithm
In recent years, user interaction through mobile applications has grown rapidly, making user reviews an important source of feedback for improving service quality. This study explores sentiment analysis on 5,000 user reviews of the TikTok application, collected from the Google Play Store using the google-play-scraper library. The data underwent several preprocessing steps, such as case folding, text cleaning, and selecting relevant columns like review content and rating score. Sentiment labeling was based on rating values: scores of 4 and 5 were treated as positive, while scores of 1 and 2 were considered negative. From the results, it was observed that negative reviews appeared more frequently, indicating an imbalance in the dataset. Despite this, the Naïve Bayes classification algorithm still achieved a reasonably good performance in categorizing the sentiments. These findings suggest that even with simple models, valuable insights can be gained from user-generated content. Moreover, the results provide meaningful input for TikTok developers to better understand user concerns and emphasize the potential need for applying balancing techniques in future analysis. Further studies are encouraged to explore other algorithms that may improve sentiment classification accuracy on more complex datasets.
Copyright (c) 2025 Rakyatol Hasanah, Sahrul Sani SR, Misbahul Munzir, Asno Azzawagama Firdaus, Chaerus Sulton, Muhajir Yunus (Author) https://creativecommons.org/licenses/by-sa/4.0
Data Analysis of Student Monitoring Using the K-Means Clustering Method
This study aims to group student monitoring data by focusing on two main variables, namely anxiety level and mood score, using the K-Means Clustering method. The research data was obtained from the Kaggle platform, which contains 1000 rows of data with nine attributes, including Student ID, Date, Class Time, Attendance Status, Stress Level, Sleep Hours, Anxiety Level, Mood Score, and Risk Level. The research process involved several stages, from problem identification, data collection, data cleaning and preprocessing, to the application of the K-Means algorithm. The analysis results showed that the data could be divided into two main groups: Cluster 1 consists of students with low to moderate anxiety levels and high mood scores, while Cluster 2 includes students with high anxiety and low mood scores. These findings provide relevant information for schools or campuses to design more effective psychological support and emotional monitoring programs. Additionally, this clustering method can serve as a foundation for developing an early detection system for psychological issues among students.
Copyright (c) 2025 Sulistiani, Ahmad Rizky Nusantara Habibi , Adrian Maulana , Hidear Talirongan , Anrom G. Abao , Ahmed Mahmoud Zaki Elmalky , Asno Azzawagama Firdaus (Author) https://creativecommons.org/licenses/by-sa/4.0
An Analysis of The C4.5 Decision Tree Algorithm Method Applied to The Play Tennis Dataset and Manual Calculation Approach
This study explores the use of the C4.5 decision tree algorithm on the Play Tennis dataset through two approaches: manual calculations and a Python-based program. As an improved version of the ID3 algorithm, C4.5 is capable of managing both categorical and numerical inputs, dealing with missing data, and utilizing entropy and information gain to determine the most important features. The dataset contains 14 entries with attributes such as Outlook, Temperature, Humidity, Windy, and the target variable PlayTennis. Entropy and information gain were calculated manually to construct the decision tree in a step-by-step manner. The resulting tree was then compared with one generated using Python tools like Pandas, NumPy, and Scikit-learn. Both trees were identical, confirming the accuracy of the method. A comparison with previous research highlights the flexibility and clarity of decision tree algorithms, making them suitable for various fields such as healthcare, finance, privacy-conscious machine learning, and materials science. These findings support the real-world usefulness of such algorithms. Overall, the study finds that C4.5 is highly effective for small classification problems and shows promise for use in larger, more complex datasets. Additionally, this research supports deeper learning of how decision tree algorithms work, making it a helpful reference for both educational and applied data science contexts.
Copyright (c) 2025 Minhajul Abidin, M. Hikari Aufa, M. Ilham Cahyo Saputra, Babatunde Bamidele Oyeyemi, Nuraqilla Waidha Bintang Grendis (Author) https://creativecommons.org/licenses/by-sa/4.0
Information Technology Governance Audit at the Communication and Information Office of Central Lombok Regency Using the COBIT 2019 Framework
Information technology is widely used to enhance the ability to deliver, manage, and distribute information. The objective of this study is to assess the capability and maturity levels as well as to provide recommendations and suggestions. This research was conducted using the COBIT 2019 framework standard. Among the process objectives, three relevant objectives were selected: APO07, APO08, and APO12. The results of the study indicate that APO07 is at capability level 1 with a maturity score of 79.62%. APO08 has a maturity score of 80%, while APO12 has a maturity score of 74.99%. All three objectives fall within the evidence work of product category “Largely Achieved” (50–84%).
Copyright (c) 2025 Sumiati, Joni Saputra, Ahmad Fatoni Dwi Putra (Author) https://creativecommons.org/licenses/by-sa/4.0
Internet of Things-Based Automatic Trash Can Prototype Using Arduino Mega 2560
The development of Internet of Things (IoT) technology encourages the creation of various smart device innovations that can be applied in everyday life, one of which is an automatic waste management system. This research aims to design and implement an IoT-based automatic trash can prototype using an Arduino Mega 2560 microcontroller that is able to detect the presence of people who will throw away garbage, open and close the lid of the tub automatically, and provide notification if the trash can is full. This research uses an experimental method by combining ultrasonic sensors, servo motors, and LED indicators as the main components. The test results show that the device works well and in accordance with the researcher's expectations. Ultrasonic sensor 1 can detect the presence of objects in front of the trash can and trigger the servo motor to open and close the lid automatically. Ultrasonic sensors 2 and 3 are also able to detect the height of the garbage and activate the servo motor while the indicator LEDs also function as designed: LED 1 blinks when someone approaches to take out the trash, while LED 2 and LED 3 light up when the sensors detect that the trash has reached a certain height limit. In addition, the system is energy efficient as it only activates when an object is detected, making it suitable for households and educational institutions.
Copyright (c) 2025 Sumarno Wijaya, Ahmad Fatoni Dwi Putra, Yuan Sa'adati, Ahmad Syahrul Hadi San, Muhajir Yunus, Florence Jean B. Talirongan, Diana May Glaiza G. Tangaro, Bernadine Grancho (Author) https://creativecommons.org/licenses/by-sa/4.0
Diabetes Mellitus Disease Analysis using Support Vector Machines and K-Nearest Neighbor Methods
Diabetes Mellitus (DM) is a chronic disease characterized by high blood sugar levels and can cause various serious complications if not treated properly. This study aims to analyze the effectiveness of Support Vector Machines (SVM) and K-Nearest Neighbor (KNN) methods in classifying diabetes mellitus patient data. The methodology used includes collecting diabetes datasets, preprocessing data, and applying SVM and KNN algorithms to perform classification. The performance of both methods is analyzed using evaluation metrics such as accuracy, precision, recall, and F1-score. The experimental results show that the SVM method provides more optimal performance in classifying diabetes data compared to KNN, with higher accuracy and lower error rate. This finding indicates that SVM is more suitable for early detection of diabetes mellitus in the dataset used in this study.
Copyright (c) 2025 Indonesian Journal of Modern Science and Technology https://creativecommons.org/licenses/by-sa/4.0
Classification of Stunting in Toddlers using Naive Bayes Method and Decision Tree
Child stunting is a health problem that has a major impact on their physical growth and brain development. This study aims to create a model that can predict the risk of stunting using machine learning technology, in order to provide assistance quickly. Using data from 7,573 children, which included information such as age, weight, height gender and breastfeeding status, we tried two methods, Naive Bayes and Decision Tree. As a result, Naive Bayes was more accurate and the success rate reached 92%, compared to Decision tree which was only 88%. With this model, it is hoped that health workers will find it easier to find children at risk of stunting, so that preventive action can be taken earlier. This research aims to provide technology-based solutions to overcome the problem of stunting in the community.
Classification of Heart (Cardiovascular) Disease using the SVM Method
Cardiovascular disease is one of the leading causes of death worldwide, with a high mortality rate, especially in developing countries like Indonesia. This highlights the importance of developing systems to identify and detect heart disease at an early stage. In this study, the Support Vector Machine (SVM) algorithm was used to classify cardiovascular diseases by utilizing a dataset consisting of 303 patient records obtained from Kaggle. The dataset was divided into 70% for training and 30% for testing. Before optimization using GridSearchCV, the SVM model achieved an accuracy of 79%, precision of 79%, recall of 73%, and F1-score of 76%. However, after adjusting the hyperparameters with GridSearchCV, the model's accuracy slightly decreased to 77%, with precision remaining at 79%, recall dropping to 66%, and F1-score at 72%. Despite this decline in performance after optimization, the results indicate that although SVM has potential for classifying heart disease, its performance is highly influenced by data quality and the selection of appropriate hyperparameters. Even with the performance decrease postoptimization, the model still provides useful predictions, showing consistent results and a proportional class distribution.
Play Store Data Scrapping and Preprocessing done as Sentiment Analysis Material
Sentiment analysis is a computational technique used to interpret user opinions about a product through textual reviews. This research aims to prepare useful data for further research, one of which is sentiment analysis. A total of 12000 recent reviews from July 2024 - January 2025 were collected through web scrapping. The research process includes data preprocessing steps such as case folding and data cleaning to transform the raw data into a usable format. The raw data up to the given changes have been uploaded to the mendeley data repository to be reprocessed into further research, one of which is the sentiment analysis approach.
The Role of Sentiment Analysis in Election Predictions Compared to Electability Surveys
Indonesia has just held the voting process for the Presidential Election. This has become a discussion of various media to social media, especially Twitter. However, when making predictions based on social media it will be so difficult if there is no specific technique or method for handling it. The prediction method we found in Indonesia often uses electability surveys in elections, but this research will compare it with sentiment analysis that utilizes social media in data collection. Another novelty is the data used during candidate campaign debates using the Support Vector Machine (SVM) method in class classification. The results obtained show that there are still differences between electability and sentiment, but this is due to several factors such as the amount of data, data objects, data collection time span, and methods. Overall, the SVM method has an accuracy of more than 0.75 on all three candidate datasets, proving that this method can be applied to similar cases.