Journal of Mathematical Sciences and Informatics

FRACTAL MOTIF OF ‘GARUDA NGUPUK’ IN LOKATMALA BATIK DESIGN WITH SUNDANESE SCRIPT

YEFFRY HANDOKO PUTRA, TAUFAN HIDAYATULLAH, RAHMA WAHDINIWATY — Sun, 13 Oct 2024 00:00:00 +0800

In traditional Indonesian batik, intricate designs reflect the nation’s rich heritage. This study explores the use of fractal geometry, specifically the ‘Garuda Ngupuk’ motif, in evolving Lokatmala Batik designs known for their complexity and elegance. Inspired by Sundanese script, this research paper highlights how fractal curves can rejuvenate traditional batik artistry. Using digital design techniques alongside traditional craftsmanship, the study creates intricate Lokatmala Batik patterns that merge Sundanese cultural essence with modern geometric motifs. This fusion of tradition and innovation showcases Lokatmala Batik as a testament to the enduring beauty and adaptability of this Indonesian cultural heritage on a global stage.

STATIC SIGN LANGUAGE TRANSLATOR USING HAND GESTURE AND SPEECH RECOGNITION

EKO BUDI SETIAWAN, AGUS DARMAWAN, BUDI HERDIANA — Sun, 13 Oct 2024 00:00:00 +0800

Communication between ordinary and deaf people often has issues because ordinary people lack knowledge of sign language. This research aims to help ordinary people communicate with hearing impaired (deaf) people using sign language. This research aims to produce an Android-based mobile app that can translate static sign language using hand movements into text and also convert the spoken voice into sign language using speech recognition. The framework in this research for hand gesture detection uses the MediaPipe software program. This framework allows the creation of apps that translate hand movements into text that help ordinary people understand sign language. Speech recognition in this research uses the Android speech library. This research succeeded in detecting static letters of the alphabet from the a-z and numbers 0-9. Tests of 540 hand gestures carried out in the morning, afternoon, evening, and night had an average detection time of 4.37 seconds. The fastest object detection times were in the morning at a distance of 30 cm with an average detection time of 2.5 seconds. Based on acceptance testing, 83.13% of the features in this static sign language translator have met the users ’users’ needs when communicating with the deaf using sign language.

DEEP LEARNING APPROACH FOR ASPECT CATEGORY DETECTION: A BIBLIOMETRIC ANALYSIS

Sun, 13 Oct 2024 00:00:00 +0800

This article presents a quantitative and qualitative assessment of current research trends by conducting a bibliometric analysis of the sentiment analysis literature from 2020 to March 2024 using the Scopus database. Our focus is on the review of scientific documents, the arrangement of subject categories, the research trend in aspect category detection, the top 10 scholars that write the most number articles in aspect category detection and keyword trends. Our research shows that specialists in computer science, engineering, mathematics, medicine, decision science, material science, social sciences, business and management accounting, energy, and health are the most common topic groups in this industry. A study of keywords shows that terms like “BERT” and “deep learning” are frequently used together. This highlights the use of sophisticated models like BERT in this field and suggests a tendency to use innovative architecture to achieve better results. Conversely, although terms such as “sentiment analysis” and “aspect-based sentiment analysis” have modest frequency, their link strengths indicate a significant correlation with the main theme, emphasising the relationship between aspect category detection and sentiment analysis in research projects. we also provide the deep-learning technique used by the author for aspect category detection.

KAWANKULINER: PERSONALISED FOOD RECOMMENDATION APP USING BMR AND TDEE FOR OPTIMAL DAILY NUTRITION

RICHI DWI AGUSTIA, ANGGA CAYHA ABADI — Sun, 13 Oct 2024 00:00:00 +0800

People often have difficulty in determining the type of food that is suitable for the composition of their body. This is due to the many types of food available, as well as the lack of information about the nutritional content of each type of food. By fulfilling the daily calorie and macro-nutrient needs required by the body, its metabolism can be maintained, preventing various health problems such as malnutrition. Based on the results of the Basic Health Research in 2018 malnutrition is one of the most serious problems in Indonesia. Several studies related to calculating calorie estimates using the BMR approach with the utilization of technology have been conducted.
However, these studies were limited to only displaying the amount of daily calorie needs that were not accompanied by what foods were needed to meet these daily needs. The purpose of this research is to build an app to help individuals understand their daily calorie needs based on their Basal Metabolic Rate (BMR) and total daily expenditure energy (TDEE), using smart scale to provide body composition information and spoonacular API to provide food recommendations that match their energy needs and give them recipes on how to make their food meanwhile waterfall model is used as a software development method. The test results show that the app meets the functional and usability requirements well, with a high accuracy rate of 99.86% for calories, protein, fat, and carbohydrates. In terms of user response, 13 respondents had a very positive response, and considered the app useful for making it easier to get information about their calorie and macro-nutrient needs, as well as provide food recommendations based on their daily needs.

EXPLORING AI WITH SENSE THROUGH APPLYING THE GRAVITY IN MIND MECHANISM

LULU GAO, HIROYUKI IIDA — Sun, 13 Oct 2024 00:00:00 +0800

The study of the laws of motion has been advancing, with significant contributions from key figures like Galileo and Newton. Analogous to the gravitational forces observed in the natural world, individuals occasionally find themselves irresistibly drawn to specific entities. The gravity in mind, the basis of free-fall motion in one’s mind, acts as a sensor to make an individual sense subtle judgments about things like common sense, as if it were whispering to our minds. Since it has been said for more than half a century that judging common sense is the most difficult task for AI, this paper explores whether AI can possess true intelligence by applying this mechanism. Empirical data from many different types of games show that Game Refinement (GR) zone is located in 0.07-0.08, which respectively corresponds to the lower limit (fairness) and upper limit (engagement). In other words, there is a border between objectivity and subjectivity in a thing, and this is the minimal objectivity, or the resignation in game context. Based upon this, in unconventional circumstances, when a greater gravitational acceleration operates within the mind, a sense of “playfulness” is generated, disrupting the harmony of comfort and discomfort sustained by the gravity in mind. The study concludes that applying the “gravity in mind” mechanism to AI could significantly blur the line between human and artificial intelligence, enhancing AI decision-making capabilities.

INNOVATIVE MATHEMATICAL MODELLING IN PREDATOR-PREY DYNAMICS: A SYSTEMATIC REVIEW

Sun, 13 Oct 2024 00:00:00 +0800

Advancements in mathematical ecology have greatly expanded predator-prey models beyond the classical Lotka-Volterra framework, incorporating complex factors such as role reversal, fractional calculus, and the Allee effect. This review highlights innovative models and advanced mathematical techniques that enhance our understanding of predator-prey dynamics and their applications across disciplines. It analysed recent studies introducing new ecological factors, including maturation delays, disease dynamics, and spatial heterogeneity. Techniques like fractional calculus and network theory were examined for their effectiveness in capturing complex behaviours. Models addressing role reversal between adult prey and juvenile predators or incorporating generalized fractional derivatives reveal significant impacts on population stability. Additionally, maturation delays, handling time, and gestation periods markedly influence oscillatory dynamics. The reviewed models demonstrate versatility in guiding pest control, understanding disease spread, and optimizing biotechnological processes. This review shows that modern predator-prey models, enriched by complex ecological factors and advanced mathematics, provide profound insights into system dynamics, with practical applications across various fields. As global challenges grow, these models offer crucial guidance for developing more resilient and sustainable systems, underscoring their potential to address
issues like food security, disease outbreaks, and ecosystem degradation. Future research should integrate emerging factors, such as anthropogenic noise and industrial pollutants, to further enhance the models’ real-world relevance.

EXPLORING THE BENEFITS, CHALLENGES AND GUIDELINES OF DEVOPS ADOPTION: A SYSTEMATIC LITERATURE REVIEW AND AN EMPIRICAL STUDY

USMAN HAMZA, SHARIFAH MASHITA SYED-MOHAMAD, NASUHA LEE ABDULLAH — Sun, 13 Oct 2024 00:00:00 +0800

This study aims to explore the benefits and challenges of DevOps adoption in the rapidly evolving landscape of DevOps and Information Technology (IT) firms. DevOps is a software development approach that emphasises communication and collaboration between software developers and IT operations teams, aiming to streamline processes and enhance software delivery. Despite the growing popularity of DevOps, there are several challenges to its adoption, including stakeholder confusion, a lack of clear processes and guidelines, and a lack of empirical studies that discuss the challenges of DevOps. To establish a foundation of understanding and provide insights into the benefits, challenges, and guidelines for DevOps adoption, this study uses a two-fold approach, including a systematic literature review and conducts semi-structured interviews involving six organisations of various sizes. The interviews aimed to obtain evidence of DevOps adoption in practice and to detail real scenarios and explain the role of each category during DevOps adoption. The study provides insights into the challenges faced by software organisations in adopting the DevOps culture and the benefits of DevOps adoption. The study also proposes DevOps adoption guidelines based on the findings. The findings contribute to the existing literature on DevOps adoption and provide valuable recommendations for software organisations.

A REVIEW ON MACHINE LEARNING AND DEEP LEARNING TECHNIQUES FOR TEXTUAL EMOTION ANALYSIS ON SOCIAL NETWORKS

RAHMAT ULLAH KHAN, ARIFAH CHE ALHADI, NORAIDA ALI — Sun, 13 Oct 2024 00:00:00 +0800

In recent times emotion detection has achieved significant attention in the field of Natural Language Processing (NLP) due to the abundance of text data available on different social network platforms like Twitter, LinkedIn and Reddit. This paper presents a thorough review of existing emotion detection techniques on text analysis. The methodology involves a comparative analysis of different machine
learning and deep learning models, approaches and datasets utilised for emotion detection. The article discusses the limitations and challenges of traditional methods and delves into the theoretical foundations of machine learning and deep learning techniques such as SVM, CNN and BERT. By exploring the current state-of-the-art in emotion detection from social networks, this review aims to provide insight for researchers to advance the field of emotion detection.