Ornamental cork fish is a type of fish that is in great demand among the public as an ornamental fish. Ornamental cork fish have various types and colors; each variation has its own name and is a selling point among ornamental cork fish lovers. With a good motif, ornamental cork fish will have an expensive market value. However, for the most part, there are still many who do not know for sure what type of ornamental cork fish is included in the variation type classification because the colors are varied and seem similar. Because of this, this research created a system that can classify types of ornamental cork fish automatically based on data while still paying attention to the level of accuracy of the classification. The algorithm used for the initial classification process is KNN, which is chosen for its accuracy comparison level value. This algorithm does not consider the weight of each data point to be classified. The data processing process carried out only looks at the highest number of classes, which becomes the benchmark for labels from the classification results. In the classification process method using the KNN algorithm, there are still shortcomings in the classification process, so this research carried out a process of comparing classification accuracy using the Weight-KNN algorithm to increase the classification accuracy value. The process of the Weight-KNN algorithm stages is to carry out classification based on nearest neighbors first but still paying attention to the weight of each data. So that the classification process of determining the type of ornamental cork fish variation will be more accurate. Based on the results of experiments conducted, this research will focus on comparing the classification results between the KNN and Weight-KNN algorithms on ornamental cork fish. The results obtained state that the Weight-KNN algorithm has a higher level of accuracy with a weight of 83.6%, whereas using the KNN algorithm, it is only 80.6%.

Tuberculosis (TB) is a contagious disease caused by the bacterium Mycobacterium tuberculosis. If not adequately managed, TB can become a fatal, life-threatening condition. In Indonesia, TB remains a critical public health issue, with millions affected and the country ranking third globally in TB cases, following India and China. Symptoms of TB include persistent cough lasting more than three weeks, hemoptysis (bloody sputum), fever, chest pain, and night sweats. The widely used diagnostic method in Indonesia is the Ziehl-Neelsen stained sputum smear technique, which processes sputum samples with specific reagents, allowing acid-fast bacilli to be visualized through microscopic examination. However, this process is labor-intensive and time-consuming, often requiring between half an hour and several hours for an accurate diagnosis. To address these challenges, there is a crucial need to develop technology that accelerates the TB diagnosis process, facilitating easier labor for healthcare workers. This study focuses on employing YOLOv8 to automate the detection of acid-fast bacilli. The system acquires sputum sample images from a microscope, and the acquired data is then used to train the model for detecting tuberculosis bacteria. The proposed real-time approach, employing the YOLOv8 algorithm, has demonstrated adequate performance for one of our specialized models, achieving a precision score of 0.88, a recall score of 0.77, and an F1 score of 0.82. This research aims to enhance TB case detection and increase treatment coverage, thereby improving overall public health outcomes in Indonesia.

The surge in global internet traffic, primarily fueled by the rise of streaming platforms, cloud computing, and data-intensive applications, has posed significant challenges in effectively managing network resources. Ensuring consistent Quality of Service (QoS) across various types of traffic, such as video streaming, online gaming, and real-time communications, is becoming an increasingly challenging task. Traditional routing techniques, such as best-effort and shortest-path methods, are increasingly falling short in meeting these demands due to their inability to account for different traffic requirements. To address these limitations, this paper introduces QSroute, a novel QoS-based routing (QBR) scheme specifically designed for Software Defined Networking (SDN) environments. QSroute leverages the global view provided by SDN to dynamically optimize routing decisions based on critical QoS metrics, including available bandwidth, packet delay, and packet loss ratio. Our approach computes the optimal path between source and destination nodes by minimizing a composite link cost derived from these metrics. Extensive simulation results show that QSroute significantly outperforms existing QBR schemes, particularly in terms of reducing end-to-end delays, minimizing jitter, and improving overall throughput. These performance gains underscore QSroute’s potential as a highly effective solution for addressing the complex demands of modern network environments, offering enhanced scalability and network efficiency. Future research will investigate the integration of additional QoS metrics, as well as the scheme's scalability in more complex, multi-domain SDN environments, to further enhance its applicability.

The era of abundant information and the continuous introduction of new products and services has made it increasingly challenging for users to navigate numerous options. Recommender systems have emerged as essential tools to help users find personalized and relevant information quickly. This paper proposes a hybrid recommender system that effectively processes online customer reviews using word embedding and clustering techniques. The system generates product-feature words, detects sentiment words and their intensity, analyzes word correlations, and extracts variables from the reviews for the product. Word embedding models, such as Word2Vec, are employed to capture the semantic content of product reviews and descriptions. The attributes extracted from the text data and word embeddings are combined to create a hybrid representation of products. Based on this hybrid representation, the system calculates the similarity among products using cosine similarity and other measures. Finally, it returns a ranked list of recommended best products based on how similar they are to either an inputted product or user preferences. We have implemented the system and experimental evaluations have been carried out on the “Datafiniti Electronics Product Data" dataset. We aim to provide personalized recommendations to users based on online reviews, ultimately enhancing the user experience and addressing the challenge of information overload in the digital age. The developed prototype will provide personalized recommendations to users, ultimately enhancing the user experience and addressing the challenge of information overload in the digital age.

Ensuring the sustainability of coffee supply chains in emerging economies is a growing concern due to complex systemic barriers and limited strategic direction. The Vietnamese coffee industry is ranked second in global coffee exports and plays a vital role in Vietnam's economy. The Vietnamese coffee supply chain encompasses a vast network of smallholder farmers, local processors, and exporters, presenting challenges related to sustainability, price volatility, and quality control. This study proposes the use of Interval Type-2 Fuzzy Sets (IT2FS), Decision-Making Trial and Evaluation Laboratory (DEMATEL), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to analyze and rank the barriers and strategic interventions in Vietnam's coffee sector. Using IT2FS-DEMATEL, we looked at the driving and dependent relationships between six main barriers and six solutions that focus on sustainability. The results showed that the DEMATEL-based structural analysis revealed that Unstable Market and Trade Conditions had the most substantial driving influence. At the same time, the Lack of Best Cultivation Quality Standards was the most dependent factor. The TOPSIS analysis ranked Establishing National Coffee Cultivation Standards as the top solution, which was remarkably close to the optimal solution vector. These results provide a thorough, evidence-based plan for determining the initial actions to take in stabilizing Vietnam's coffee supply chain during times of volatility. It gives policymakers and industry stakeholders a clear framework for developing targeted actions to enhance the sustainability and resilience of coffee supply chains.

In many developing countries, private universities face considerable financial challenges due to their limited sources of income. Unlike public universities, which receive substantial government subsidies, private institutions must rely primarily on tuition fees and student enrollment to sustain their operations. This financial dependency pushes private universities, particularly in Indonesia, to invest heavily in branding and marketing to attract prospective students. Social media platforms, especially Instagram, have emerged as crucial tools in these efforts. Instagram enables universities to engage with potential students directly, providing a space for showcasing academic programs, campus life, and student achievements. This paper’s goal is to explore the potential of Instagram-driven influence maximization (IM) techniques for enhancing university branding. Traditionally, these techniques are employed in product marketing to increase consumer engagement and sales, but their application in higher education remains relatively unexplored. We aim to address this gap by examining three promising IM methods that private universities can adopt: first, leveraging Instagram’s broadcast channel feature to communicate effectively with large audiences in real time; second, selecting key opinion leaders (KOLs) from influencer marketplace platforms to enhance credibility and broaden outreach; and third, optimizing the use of targeted hashtags to increase discoverability and engagement. As a result, we provide a review of recent evidence supporting the effectiveness of these strategies in higher education marketing. By adopting these techniques, private universities can improve their digital presence, enhance brand awareness, and increase student enrollment. IM with uncertainty is a challenging educational landscape for future research.

Robo-advisors provide an alternative financial solution tailored for regular clients. Beyond the acceptability of technology, financial factors significantly influence the adoption of robo-advisors. While existing studies extensively discuss the stages involved in the intention to utilize robo-advisors, only a few offer insights into financial capabilities. The purpose of this study is to investigate the extent to which Indonesian investors embrace robo-advisors by incorporating financial variables such as financial goals and financial literacy into the technology adoption in Robo Advisor. Additionally, the study explores the relationship between application costs and data privacy on the adoption of robo-advisor technology. This research employs a quantitative approach using purposive sampling techniques. Data were collected through a survey of 431 robo-advisor users and analyzed using SmartPLS. The findings reveal a significant and positive correlation between financial goals, perceived technology usefulness, and application costs in the adoption of robo-advisors in Indonesia. These results contribute to the development of investment decision theory using technology-based approaches, specifically robo-advisors. Furthermore, companies in the financial sector, particularly in wealth management or investment management, can benefit from incorporating financial goal features, enhancing technological performance, and setting competitive fees to increase adoption rates. Future research should further explore robo-advisor adoption, focusing on additional financial variables and financial behaviors that drive technology adoption as an investment decision. These findings highlight the importance of considering both financial and technological factors in promoting the use of robo-advisors among investors especially in Indonesia.

The typical user creates documents over many years of computer usage. As people move from computer to computer, they tend to copy the files to the new computer, because "you never know when we might need to refer to something from the past." Hence, the collection grows larger and larger, expanding to hundreds and thousands. This collection soon exceeds the ability of most people to remember what each document was, even if they have been keeping them in some order in folders – and many people fail to anticipate how the folders and subfolders should be arranged as time passes – and by the time they realize it, most find it too daunting a task to reclassify them all manually. Therefore, we sought to solve this problem using a data mining-based solution, specifically multinomial naive Bayes. We developed a document classification program to automatically categorize all documents stored on a person's personal computer hard drive, eliminating the need for manual classification. The proposed algorithm achieved a score of 0.853 for accuracy, 9,833 for precision, 0.661 for recall, and 0.767 for the F1 metric. It should be possible, with further refinement and improvement, for example by balancing the dataset and increasing its size, for this technique to be applied in practical applications that enable automatic document classifications on the computers of most computer users.

Autism Spectrum Disorder (ASD) is a neuro-developmental disorder characterized by varying degrees of difficulty in social interaction and communication and repetitive behaviors. Early confirmation of the diagnosis of ASD leads to early appropriate treatment. However, confirming ASD diagnosis is challenging due to its wide spectrum and challenging behavior assessment. This research proposes a technology-based ASD diagnosis on children utilizing thermal facial analysis. This is conducted subject to the uniqueness of facial expression that is typically applied to children with ASD. A modified Inception V3 architecture did the intended thermal facial analysis for ASD diagnosis. Homomorphic filters and morphological filters are applied to the data pre-processing to improve the classification ability. The proposed identification method shows better sensitivity to the false-positive aspect. It is indicated by better performance in terms of precision, with a rate of 90% to 91%. This research is expected to support medical experts in confirming early diagnosis in children with ASD.

This study aims to analyze the characteristics of green space in reducing carbon dioxide (CO2) levels in Makassar City by integrating Structural Equation Modeling (SEM) and Nonparametric Spline. Data were collected from 251 observation points mapped using ArcGIS, along with satellite imagery taken from September 27 to October 1, 2024, to identify land use fractions such as shrub vegetation, non-shrub vegetation, roads, residential areas, and water bodies. The analyzed variables include net CO2, green space characteristics (shrub and non-shrub fractions), non-green space characteristics (residence, industry, commerce, roads, sea, and drainage), and meteorological variables (temperature, humidity and solar radiation). The results show that the best model for green space characteristics was found at the knot point of 36 with a minimum GCV of 27644.53. This model divides the area into two regions: areas with less than 36% green space and areas with more than 36% green space. An increase in green space tends to reduce CO2 levels. Meanwhile, the best model for non-green spaces was found at the knot point of 66 with a minimum GCV of 27644.18. An increase in non-green space above 66% leads to a sharper rise in CO2 levels. This study provides data-driven recommendations for urban planning and green space management, utilizing statistical modeling and spatial data visualization to support CO2 emission reduction in Makassar City