Processing Techniques Research Articles

Influence of Processing Techniques on the Proximate Composition, Anti-Nutritional Factors, and Amino Acid Profile of Red Kidney Beans (Phaseolus vulgarisL).

The effects of traditional processing techniques-cooking, autoclaving, and roasting-on the physicochemical properties, anti-nutritional factors (ANF), and amino acid composition of red kidneybeans (RKB) were investigated. Significant alterations in proximate composition were observed: cooking generally increased protein levels, while roasting decreased fiber content. Autoclaving produced diverse effects. Evaluation of functional properties revealed changes in water activity, density, and color. Cooking increased water activity and lightness, whereas roasting and autoclaving led to darker colors and reduced water activity. Cooking effectively enhanced protein content and reduced ANF, while texture profile analysis (TPA) showed variations in hardness, adhesiveness, and resilience. Autoclaving resulted in softer beans, and cooking influenced adhesiveness and springiness. ANF analysis showed reductions post-processing, especially with cooking and autoclaving. Amino acid composition varied across processing methods, with notable effects from cooking and roasting. Microstructural changes observed via Scanning Electron Microscopy (SEM) suggested improved nutrient bioavailability. Fourier Transform Infrared (FTIR) spectroscopy indicated alterations in the chemical composition of proteins, lipids, and carbohydrates, attributed to the different processing methods. This comprehensive study highlights the complex interactions between cooking techniques and the physicochemical properties of legumes, providing valuable insights for optimizing their nutritional value. Traditional method of cooking RKB enhances the nutritional value.

The Effect of Processing Techniques on the Classification Accuracy of Brain-Computer Interface Systems

Background/Objectives: Accurately classifying Electroencephalography (EEG) signals is essential for the effective operation of Brain-Computer Interfaces (BCI), which is needed for reliable neurorehabilitation applications. However, many factors in the processing pipeline can influence classification performance. The objective of this study is to assess the effects of different processing steps on classification accuracy in EEG-based BCI systems. Methods: This study explores the impact of various processing techniques and stages, including the FASTER algorithm for artifact rejection (AR), frequency filtering, transfer learning, and cropped training. The Physionet dataset, consisting of four motor imagery classes, was used as input due to its relatively large number of subjects. The raw EEG was tested with EEGNet and Shallow ConvNet. To examine the impact of adding a spatial dimension to the input data, we also used the Multi-branch Conv3D Net and developed two new models, Conv2D Net and Conv3D Net. Results: Our analysis showed that classification accuracy can be affected by many factors at every stage. Applying the AR method, for instance, can either enhance or degrade classification performance, depending on the subject and the specific network architecture. Transfer learning was effective in improving the performance of all networks for both raw and artifact-rejected data. However, the improvement in classification accuracy for artifact-rejected data was less pronounced compared to unfiltered data, resulting in reduced precision. For instance, the best classifier achieved 46.1% accuracy on unfiltered data, which increased to 63.5% with transfer learning. In the filtered case, accuracy rose from 45.5% to only 55.9% when transfer learning was applied. An unexpected outcome regarding frequency filtering was observed: networks demonstrated better classification performance when focusing on lower-frequency components. Higher frequency ranges were more discriminative for EEGNet and Shallow ConvNet, but only when cropped training was applied. Conclusions: The findings of this study highlight the complex interaction between processing techniques and neural network performance, emphasizing the necessity for customized processing approaches tailored to specific subjects and network architectures.

Digitalization of Services through Mekari KlikPajak: Analysis of Public Private Partnerships

Digitalization in the field of tax administration needs more attention, where modern society today requires efficient and effective tax services, especially in Indonesia which uses a self-assessment tax system. Although it is the government's obligation to provide services according to the needs of the community, along with the development of approaches to improving the performance of public administration, the concept of public-private partnership has developed. One form of public-private partnership in tax administration services is Mekari Klik Pajak. This application is a digital application developed by a technology company to assist taxpayers in carrying out their tax obligations in a more practical, transparent, and in accordance with applicable provisions. This research article aims to analyze how the implementation of public-private partnerships in Mekari Klik Pajak can help accelerate the process of digitizing tax administration in Indonesia. The results of the analysis show that the implementation of public-private partnerships with Mekari makes it possible to reduce the burden on the government in the technical process, as well as develop user-friendly applications that make it easier for taxpayers, both individuals and companies, to fulfill their tax obligations. Applications such as Mekari Klik Pajak provide features that can be accessed anytime and anywhere, reducing geographical and time barriers that often occur in conventional tax systems.

A submesoscale eddy identification dataset in the northwest Pacific Ocean derived from GOCI I chlorophyll a data based on deep learning

Abstract. This paper presents a dataset on the identification of submesoscale eddies, derived from high-resolution chlorophyll a data captured by GOCI I in the northwest Pacific Ocean. Our methodology involves a combination of digital image processing, filtering, and object detection techniques, along with a specific chlorophyll a image enhancement procedure to extract essential information about submesoscale eddies. This information includes their time, polarity, geographical coordinates of the eddy center, eddy radius, coordinates of the upper left and lower right corners of the prediction box, area of the eddy's inner ellipse, and confidence score. The dataset spans eight time intervals, ranging from 00:00 to 08:00 (UTC) daily, covering the period from 1 April 2011 to 31 March 2021. A total of 19 136 anticyclonic eddies and 93 897 cyclonic eddies were identified, with a minimum confidence threshold of 0.2. The mean radius of anticyclonic eddies is 24.44 km (range 2.5 to 44.25 km), while that of cyclonic eddies is 12.34 km (range 1.75 to 44 km). This unprecedented hourly resolution dataset on submesoscale eddies offers valuable insights into their distribution, morphology, and energy dissipation. It significantly contributes to our understanding of marine environments, ecosystems, and the improvement of climate model predictions. The dataset is available at https://doi.org/10.5281/zenodo.13989785 (Wang and Yang, 2023).

A Framework for the Unsupervised Modeling and Extraction of Polarization Knowledge from News Media

Polarization poses global concerns for social cohesion and stability, making its understanding crucial for effective mitigation measures. In this paper, we introduce an unsupervised, domain-agnostic framework for computationally modeling, extracting, and measuring polarization in digital media. By leveraging Natural Language Processing and Graph Analysis techniques, the proposed framework creates a Polarization Data Model (PDM) that encompasses key elements of Polarization Knowledge (PK), such as entities, fellowships, dipoles, and discussion topics. To evaluate the effectiveness of the framework, we propose a multi-level PK evaluation methodology that assesses its ability to: i) capture entities’ attitudes toward various topics, ii) align politically cohesive fellowships with their respective party manifestos, and iii) identify domain-specific topics along with their degree of polarization. We applied this evaluation methodology to the use cases of Abortion, Immigration, and Gun Control. The results demonstrate our framework’s robust performance across these case studies, yielding promising outcomes compared to state-of-the-art and baseline methods.

Low-loss weakly coupled four-mode hollow-core antiresonant fiber based on centrosymmetric nested structure

Abstract This paper designs a novel centrosymmetric double-layer nested antiresonant fiber (CDNAF) with few-mode transmission characteristics. The cladding structure of the CDNAF incorporates glass plates and multilayer capillaries, effectively mitigating the coupling between various core modes and cladding modes. Simulation results show that the CDNAF can support four modes of low-loss transmission from LP01 to LP02 at 1550 nm. The confinement loss (CL) of the four modes is less than 0.008 dB/km, 0.07 dB/km, 0.42 dB/km, and 4.8 dB/km, respectively, and the CDNAF has a sizeable differential group delay (DGD) and weak bending sensitivity. The loss of high-order modes is much greater than that of the other four transmission modes, ensuring high-purity transmission of the four modes. In the wavelength range of 1200–1700 nm, the CDNAF ensures few-mode characteristics with at least two low-loss transmission modes. Furthermore, the effective refractive index difference (Δneff) between adjacent modes is much greater than 10^(-4), eliminating or significantly reducing the need for multiple-input multiple-output digital signal processing (MIMO-DSP) techniques in optical communication systems. Additionally, the results demonstrate that CDNAF can transmit three modes at an ultimate bending radius of 5 cm and can transmit four modes like a straight fiber at a bending radius of 18 cm, which demonstrates that CDNAF has excellent bending performance. These excellent characteristics give the CDNAF great potential for application in large-capacity optical communication systems.

Characterization of the SCR-1 Stellarator Physics: Investigating Plasma Discharge, MHD Equilibrium Calculations, and O-X-B Mode Conversion Feasibility

Abstract The Stellarator de Costa Rica 1 (SCR-1) is a small modular stellarator that serves as a valuable research and training tool for plasma magnetic confinement. This study aimed to analyze the characteristics of SCR-1, including its peripheral systems, technical plasma discharge processes, and advancements in plasma characterization. In addition, this study explored a new heating mechanism and the factors that influence it. The current state of the device and plasma discharge are initially presented. Subsequently, the measurement process was utilized to determine the electronic density and plasma temperature using a single Langmuir probe and the results were compared with theoretical predictions based on the particle and energy balance. Additionally, the VMEC code was employed to calculate magnetic flux surfaces with characteristics such as a low aspect ratio, low beta parameter, negative magnetic shear, and decreasing rotational transform along magnetic flux surfaces. The Mercier criterion was employed to conduct a linear stability analysis, which identified a magnetic well that played a crucial role in the linear stability of the majority of magnetic flux surfaces. Feasibility studies of electron Bernstein waves were conducted using the IPF-FMDC full-wave code. The results obtained from the IPF-FMDC code revealed that the O-X conversion percentage reached a maximum of 63 % when considering radiation reflection in the vacuum vessel. Significant effects of plasma curvature on the O-X wave conversion and normalized electron density scale length were observed, while the change in the SCR-1 heating position did not produce a significant impact. Three damping mechanisms affecting O-X conversion were studied, and one of the principal effects was the SX-FX conversion due to steep electron density gradient. Additionally, stochastic electron heating showed a low electron field amplitude, which is important for limiting the electron Bernstein wave propagation.

Nutrients and Sensory Qualities of Moi Moi Developed from Flours of Broad Bean (Vicia faba) Seeds Grown in South Eastern Nigeria as Affected by Different Processing Treatments

Background: Broad bean is a lesser-known and underutilized legume with excellent source of protein. This nutritious but underutilized bean is difficult to cook, requiring lots of energy to prepare and is on its way to extinction. This study assessed the impacts of different processing techniques which includes soaking, sprouting, cooking as well as combined effect of soaking and cooking on the nutritional and sensory qualities of moi moi made from processed broad bean flour. Methods: Broad bean seeds were processed into flours using standard procedures. Conventional methods were used to assess the nutritional properties of processed and unprocessed flours. Sensory investigation was performed on moi moi produced from broad bean flour versus moi-moi developed from cowpea. Antinutrient and functional characteristics were also analyzed and thus, varied significantly across processing treatments. Result: The results demonstrated that the combination of soaking prior to cooking approach significantly reduced the antinutrients while increasing the flour’s protein, fibre and ash levels. Minerals and vitamins contents increased significantly across all processing treatments, with sample SPBBF having the highest values, except for vitamin C. The sensory qualities of the moi-moi prepared from soaked/cooked broad bean showed the highest values in terms of aroma (7.10), taste (6.50) and overall acceptance (7.12) whereas the sprouted broad bean Moi-moi had the lowest values in terms of appearance (7.02), aroma (5.90), taste (5.90)and overall acceptability (5.83) when compared to the control (7.50) which is moi-moi made from cowpea. The study’s findings revealed that effective processing techniques improved the nutrient profile of the flours such as proteins, ash and fibre contents with an enhanced sensory profile of the moi-moi produced from the flours. Soaking broad beans before cooking increased the nutritional value of the flour and sensory characteristics of the moi-moi.

Microbial functional diversity and redundancy: moving forward.

Microbial functional ecology is expanding as we can now measure the traits of wild microbes that affect ecosystem functioning. Here, we review techniques and advances that could be the bedrock for a unified framework to study microbial functions. These include our newfound access to environmental microbial genomes, collections of microbial traits, but also our ability to study microbes' distribution and expression. We then explore the technical, ecological, and evolutionary processes that could explain environmental patterns of microbial functional diversity and redundancy. Next, we suggest reconciling microbiology with biodiversity-ecosystem-functioning studies by experimentally testing the significance of microbial functional diversity and redundancy for the efficiency, resistance, and resilience of ecosystem processes. Such advances will aid in identifying state shifts and tipping points in microbiomes, enhancing our understanding of how and where will microbiomes guide Earth's biomes in the context of a changing planet.

Enhancing Ultrasound Image Quality Across Disease Domains: Application of Cycle-Consistent Generative Adversarial Network and Perceptual Loss.

Numerous studies have explored image processing techniques aimed at enhancing ultrasound images to narrow the performance gap between low-quality portable devices and high-end ultrasound equipment. These investigations often use registered image pairs created by modifying the same image through methods like down sampling or adding noise, rather than using separate images from different machines. Additionally, they rely on organ-specific features, limiting the models' generalizability across various imaging conditions and devices. The challenge remains to develop a universal framework capable of improving image quality across different devices and conditions, independent of registration or specific organ characteristics. This study aims to develop a robust framework that enhances the quality of ultrasound images, particularly those captured with compact, portable devices, which are often constrained by low quality due to hardware limitations. The framework is designed to effectively process nonregistered ultrasound image pairs, a common challenge in medical imaging, across various clinical settings and device types. By addressing these challenges, the research seeks to provide a more generalized and adaptable solution that can be widely applied across diverse medical scenarios, improving the accessibility and quality of diagnostic imaging. A retrospective analysis was conducted by using a cycle-consistent generative adversarial network (CycleGAN) framework enhanced with perceptual loss to improve the quality of ultrasound images, focusing on nonregistered image pairs from various organ systems. The perceptual loss was integrated to preserve anatomical integrity by comparing deep features extracted from pretrained neural networks. The model's performance was evaluated against corresponding high-resolution images, ensuring that the enhanced outputs closely mimic those from high-end ultrasound devices. The model was trained and validated using a publicly available, diverse dataset to ensure robustness and generalizability across different imaging scenarios. The advanced CycleGAN framework, enhanced with perceptual loss, significantly outperformed the previous state-of-the-art, stable CycleGAN, in multiple evaluation metrics. Specifically, our method achieved a structural similarity index of 0.2889 versus 0.2502 (P<.001), a peak signal-to-noise ratio of 15.8935 versus 14.9430 (P<.001), and a learned perceptual image patch similarity score of 0.4490 versus 0.5005 (P<.001). These results demonstrate the model's superior ability to enhance image quality while preserving critical anatomical details, thereby improving diagnostic usefulness. This study presents a significant advancement in ultrasound imaging by leveraging a CycleGAN model enhanced with perceptual loss to bridge the quality gap between images from different devices. By processing nonregistered image pairs, the model not only enhances visual quality but also ensures the preservation of essential anatomical structures, crucial for accurate diagnosis. This approach holds the potential to democratize high-quality ultrasound imaging, making it accessible through low-cost portable devices, thereby improving health care outcomes, particularly in resource-limited settings. Future research will focus on further validation and optimization for clinical use.

SEISMICITY of the URALS and WESTERN SIBERIA in 2020

The article summarizes information about seismic network in the Urals and Western Siberia region, describes processing technique, presents short analysis of seismic activity for 2020. Seismic network ca pability is shown for whole territory under control. It was found that about 84 % of registered events are explosions in mines and open pits, 29 events are natural and 36 are induced ones. For the strongest induced events, variants of source parameters obtained by different agencies are shown.

Modern Thyroid Cancer Diagnosis: A Review of AI-Powered Algorithms for Detection and Classification

Thyroid nodules are one of the most common abnormalities in the thyroid gland, which are often harmless in nature (benign), but in a few unfortunate instances, they may be fatal (malignant). This review explores recent advancements in artificial intelligence (AI) applied to thyroid cancer detection and classification, with a focus on machine learning, deep learning, and image processing techniques. We provide a comprehensive evaluation of AI applications across key imaging modalities—Ultrasonography (USG), Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET/CT)—as well as cytopathological analysis using Fine Needle Aspiration Biopsy (FNAB). By critically examining studies on AI-driven preoperative assessments, we highlight improvements in diagnostic accuracy, sensitivity, specificity and efficiency. This review also identifies current limitations in AI applications, including technical challenges and unresolved issues that hinder widespread clinical adoption. Although significant progress has been achieved, the integration of AI in clinical settings remains limited, as AI-based outputs currently serve as supportive tools rather than definitive diagnostic evidence. We discuss the potential of AI to transform thyroid cancer diagnostics by enhancing reliability and accessibility, while addressing the need for further research to develop a unified, robust and clinically trustworthy AI framework for thyroid cancer diagnosis.

A Bibliometric Analysis of Islamic Parenting Trend in Indonesia

This study aims to analyze the development, trends, and identify the novelty of scholarly publications on Islamic Parenting in Muslim families in Indonesia from 2019 to 2024. The research method used is a Systematic Literature Review with Bibliometric Analysis data processing technique. A total of 30 articles from 220 indexed in Sinta 1 and Sinta 2 were included and visualized using VOSViewer 1.6.20. The results show that the highest number of publications occurred in 2022, with the most citations recorded in 2019. Of the 25 journals contributing to articles on Islamic Parenting, 64% employed qualitative methods. Based on the overlay visualization co-occurrence, research topics related to the “Sandwich Generation” and the “Digital Era” emerged as the most recent developments in the field.

Unveiling the research landscape of planetscope data in addressing earth-environmental issues: a bibliometric analysis

The PlanetScope (PS) satellite constellation, developed by Planet Labs Inc., represents a significant advancement in Earth observation, offering high spatial resolution and daily revisit capabilities. This study provides a comprehensive bibliometric analysis of PS satellite imagery, exploring its utilization in scientific research from 2017 to 2023. Using data extracted from the Scopus database, 582 documents were analyzed to uncover the publication trends, key research disciplines, collaboration networks, and research themes related to PS imagery. The results highlight the increasing use of PS data in Earth and Planetary Sciences, Environmental Science, and Computer Science, with a notable concentration of research outputs from the United States, China, and Brazil. Furthermore, our findings indicate that PS data is applied in diverse fields, including land use/land cover classification, agriculture, environmental monitoring, and disaster assessment. Notably, machine learning techniques are increasingly applied to PS data, enhancing analysis capabilities. Despite the growing adoption of PS imagery, challenges related to data access, particularly in low-income regions, were identified, and PS data often plays a secondary or supplementary role in many studies. Recommendations for enhancing interdisciplinary collaboration, expanding open-access data programs, and integrating advanced processing techniques are proposed to maximize the impact of PS data in addressing global environmental challenges. This study provides valuable insights into the evolving landscape of PS-based research, emphasizing the potential of PS data and identifying areas for future exploration.

A Comprehensive Survey on Physical Layer Authentication Techniques: Categorization and Analysis of Model-Driven and Data-Driven Approaches

The open and broadcast nature of wireless mediums introduces significant security vulnerabilities, making authentication a critical concern in wireless networks. In recent years, Physical-Layer Authentication (PLA) techniques have garnered considerable research interest due to their advantages over Upper-Layer Authentication (ULA) methods, such as lower complexity, enhanced security, and greater compatibility. The application of signal processing techniques in PLA serves as a crucial link between the extraction of Physical-Layer Features (PLFs) and the authentication of received signals. Different signal processing approaches, even with the same PLF, can result in varying authentication performances and computational demands. Despite this, there remains a shortage of comprehensive overviews on state-of-the-art PLA schemes with a focus on signal processing approaches. This paper presents the first thorough survey of signal processing in various PLA schemes, categorizing existing approaches into model-based and Machine Learning (ML)-based schemes. We discuss motivation and address key issues in signal processing for PLA schemes. The applications, challenges, and future research directions of PLA are discussed in Part 3 of the Appendix, which can be found in supplementary materials online.

Image Text to Speech Conversion in Desired Language and Summarization with Raspberry PI

In recent years, the integration of artificial intelligence and embedded systems has gained significant traction, enabling the development of efficient and user-friendly solutions. ourproject focuses on building a system capable of extracting text from images, converting the extracted content into speech in a desired language, and providing concise summaries, all powered by a Raspberry Pi. The system employs Optical Character Recognition (OCR) for text extraction, a text-to-speech engine for audio synthesis, and natural language processing techniques for summarization. Designed with accessibility and versatility in mind, the system can assist individuals with visual impairments, language barriers, or those seeking quick comprehension of extensive information. By leveraging the computational efficiency and affordability of the Raspberry Pi, the proposed solution aims to deliver a portable, cost- effective, and scalable platform for text and audio processing applications. Advancements in embedded systems and artificial intelligence have enabled the development of innovative solutions that enhance accessibility and improve user experience. ourproject presents a multifunctional system designed to process images, extract text, convert the text into speech in the desired language, and summarize the content, utilizing the Raspberry Pi as a cost-effective and portable platform. The system integrates Optical Character Recognition (OCR) for accurately identifying text within images, a text-to-speech synthesis engine for converting text to audio output, and natural language processing (NLP) algorithms to generate concise and meaningful summaries. Our approach offers significant benefits, including aiding visually impaired individuals, breaking down language barriers, and streamlining content consumption for users seeking quick comprehension of extensive material. The choice of the Raspberry Pi ensures affordability, energy efficiency, and scalability, making it suitable for various real-world applications. The proposed solution incorporates multilingual support, enabling audio output in a language of the user’s choice, thereby enhancing its versatility. The project also explores optimization techniques to ensure real-time performance, despite the hardware constraints of the Raspberry Pi. Applications of INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (IJSREM) VOLUME: 08 ISSUE: 12 | DEC - 2024 SJIF RATING: 8.448 ISSN: 2582-3930 © 2024, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM39818 | Page 2 oursystem span education, accessibility tools, document digitization, and language learning. By leveraging open- source tools and libraries, the project underscores the potential of embedded systems in democratizing technology for a broader audience. Keywords Artificial Intelligence (AI),Embedded Systems,Raspberry Pi,Optical Character Recognition (OCR),Text-to-Speech (TTS),Natural Language Processing (NLP),Multilingual Support,Accessibility,Real-timeOptimization,Image Processing,ContentSummarization,Cost-effective Solutions,Scalable Systems,Open-source Tools,Document Digitization,Language Learning

Fungsi Dinas Pemberdayaan Perempuan dan Perlindungan Anak Kota Kupang Terhadap Eksploitasi Anak Dibawah Umur Sebagai Pekerja Informal

Exploitation according to Law Number 35 of 2014 concerning Child Protection is divided into physical, social, sexual and economic exploitation. With the existence of children who work, this is a violation of the right to child protection and child growth and development, which should be at that age the child is still in school and gets a proper education. The phenomenon of underage labor almost occurs in all regions of Indonesia. One of the children's problems that must receive special attention is the issue of child labor. This issue has become global because so many children around the world are already working at school age. In fact, the issue of child labor is not just an issue of children carrying out work with wages, but it is very closely related to exploitation, dangerous work, inhibition of access to education and hindering the physical, psychological and social development of children, even in certain cases and forms of child labor have entered as the qualification of children who work in the most intolerable situations (the intolerable form of child labor). This research is an empirical research that uses primary data and secondary data collected using interview, observation and literature study techniques. The informants in this study are 10 people. The data processing techniques in this study use several techniques, namely editing, coding, tabulation and verification after which the data will be analyzed using qualitative descriptive techniques. The results of this study show that (1) the Kupang City Women's Empowerment and Child Protection Office needs to increase socialization, supervision, and cooperation with the community to effectively prevent and protect minors from exploitation. (2) Budget factors, apparatus resources, and socialization of child protection to the community are factors that hinder the implementation of the functions of the Kupang City Women's Empowerment and Child Protection Office in providing prevention, protection, coaching and empowerment of children.

Development of an Autonomous Drone-Based Irrigation Decision Support System Utilizing Image Processing and Machine Learning Techniques

Efficient management of water resources is essential for sustaining the global food supply amidst growing populations and climate change. Traditional irrigation methods are often plagued by inefficiencies, leading to significant water wastage. This paper presents the development and validation of an autonomous drone-based irrigation system that leverages advanced image processing and machine learning techniques to optimize water usage in agriculture. The system employs standard low-cost cameras to capture high-resolution aerial images, which are processed to accurately predict the water needs of the plants and inform irrigation decisions in real-time also it can do autonomous watering by controlling the electrical water valve in the specified irrigation areas. Comprehensive field tests conducted on pepper crops demonstrate the system's ability to enhance water use efficiency and improve crop yields. By integrating state-of-the-art technologies such as TensorFlow techniques for machine lear-nig, image analysis and autonomous navigation capabilities, the proposed solution represents a significant advancement in precision agriculture. The results indicate that the autonomous drone-based irrigation system can substantially reduce water consumption while maintaining or enhancing crop productivity, thereby promoting sustainable agricultural practices.

ANALISIS SENTIMEN PUBLIK TERHADAP KEBIJAKAN EKSPOR SEDIMEN LAUT INDONESIA MELALUI PLATFORM X.COM

Indonesia's marine sediment export policy has become a controversial topic among the public, especially in relation to environmental and socio-economic impacts. This research aims to analyze public sentiment towards Indonesia's marine sediment export policy via the X.com platform (formerly Twitter) using Natural Language Processing (NLP) techniques. Data was taken during the period September to October 2024 using the scraping method using the Twitter API. The dataset consisting of 72 tweets was processed through a data preprocessing step to remove irrelevant elements. Sentiment is analyzed using the BERT (Bidirectional Encoder Representations from Transformers) model, which allows deep detection of sentiment context. The analysis results show that 67% of tweets contain negative sentiment, 25% are neutral, and 8% are positive. This finding shows that the public is worried about the impact of marine sediment export policies.

Student Notebook Mansor

The Student Notebook MANSOR is an innovative web application designed to assist students in their academic and personal development. This application aims to provide a comprehensive solution for effective management of study tasks, personal time, and activities that contribute to holistic student development. The application not only enables efficient scheduling and organization of academic responsibilities but also fosters personal growth through features that encourage engagement in extracurricular activities. This paper discusses the technical architecture, development process, and user interface design of MANSOR, while also examining its potential impact on student productivity and overall educational experience. A comparative analysis with similar systems highlights MANSOR’s unique functionalities and user-centered design, positioning it as a valuable tool in contemporary educational environments.