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The integration of Artificial Intelligence (AI) into precision agriculture has significantly improved plant disease recognition; however, many existing deep learning models remain computationally expensive and feature-redundant, limiting their deployment on low-power and edge devices. To address these limitations, this study proposes a lightweight framework for maize leaf disease recognition based on serial deep feature extraction, dimensionality reduction, and machine-learning–based classification. A pre-trained MobileNetV2 network is employed as a fixed feature extractor to obtain discriminative visual representations, while Principal Component Analysis (PCA) is applied to reduce feature dimensionality by approximately 76%, retaining 95% of the original variance and improving computational efficiency. The compressed features are subsequently classified using a Radial Basis Function Support Vector Machine (RBF-SVM), optimized via grid search and cross-validation. Experiments conducted on a four-class maize leaf disease dataset (Northern Leaf Blight, Common Rust, Gray Leaf Spot, and Healthy), with class imbalance handled during training, demonstrate that the proposed MobileNetV2–PCA–SVM pipeline achieves 97.58% accuracy, 96.60% precision, 96.59% recall, and 96.59% F1-score, outperforming the DenseNet201 + Bayesian-optimized SVM baseline (94.60%, 94.40%, 94.40%, and 94.40%, respectively). This improvement corresponds to a 2.98% accuracy gain, a 55% reduction in error rate, an 86% reduction in model parameters (20.31M to 2.75M), and an 85% reduction in model size (81 MB to 12 MB). These results indicate that the proposed framework provides a compact and efficient solution with strong potential for deployment in resource-constrained agricultural environments.

The increasing consumption of packaged food products in Indonesia reflects modern lifestyle changes but simultaneously raises public health concerns related to high calorie, sugar, and fat intake. Nutritional information presented on food labels consists of multiple interrelated variables, making it difficult to identify dominant nutritional factors that characterize packaged food products. This study aims to apply Principal Component Analysis (PCA) to reduce the dimensionality of nutritional data and to map the nutritional characteristics of packaged food products in Indonesia. The research employs a quantitative exploratory approach using secondary data obtained from nutrition facts labels of 1,651 packaged food products. Seven nutritional variables were initially analyzed, namely total energy, protein, total fat, total carbohydrates, sugar, sodium, and dietary fiber. Data preprocessing included data cleaning, Z-score standardization, and iterative variable selection based on the Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s Test of Sphericity to ensure sampling adequacy and sufficient correlation among variables. Variables with low sampling adequacy and perfect multicollinearity were eliminated, resulting in five variables retained for the final PCA model. Principal components were extracted using the eigenvalue greater than one criterion and confirmed through a scree plot, followed by Varimax rotation to enhance interpretability. The results indicate the formation of two principal components explaining approximately 69.7% of the total variance. The first component represents energy density and macronutrient richness, while the second component reflects carbohydrate-related characteristics, particularly the contrasting pattern between sugar and dietary fiber. Biplot visualization further illustrates product distribution based on these components. The findings demonstrate that PCA effectively simplifies complex nutritional information and provides a clear nutritional mapping of packaged food products, offering practical insights for consumers, producers, and policymakers in supporting healthier food choices in Indonesia.

This study develops a computational-based system to identify individual potential through the analysis of signature patterns using Artificial Neural Networks (ANN) and the Backpropagation algorithm. The research aims to explore and examine the effectiveness of applying ANN in recognizing and identifying signature patterns that are assumed to be related to an individual’s potential. In the data processing stage, Principal Component Analysis (PCA) is employed as a dimensionality reduction and feature extraction technique to optimally obtain the main characteristics of signature images. The system performance evaluation is conducted using a total of 80 signature images, consisting of 60 training data and 20 testing data. This study analyzes two network architecture configurations, namely a model with one hidden layer and a model with two hidden layers. The experimental results show that both network configurations achieve the same accuracy level of 92.5%. These findings indicate that the use of Artificial Neural Networks with the Backpropagation algorithm is effective in producing high accuracy in the signature pattern recognition process. Furthermore, the developed system has broad potential applications in the field of personal identification, such as employee evaluation, selection systems, and other applications across various organizational and industrial sectors.

This study aims to apply the Principal Component Analysis (PCA) method to identify the main factors influencing poverty in North Sumatra Province. Poverty rates in this region show significant variations among districts and cities, influenced by differences in social, economic, educational, and basic facility availability. The data used in this study include eleven indicators related to population, education, health, access to basic services, and economic conditions. All variables were initially normalized to ensure they had comparable scales, then PCA feasibility tests were conducted using MSA, KMO, and Bartlett's test, which indicated that the data were eligible for further analysis. The results of the PCA revealed three main components explaining a total of 69.91 percent of the variation. The first component represents regional population and economic factors, with the largest contributions coming from population density, open unemployment rate, and per capita expenditure. The second component reflects household living conditions, such as access to clean water, adequate sanitation, and health complaints. The third component describes the educational dimension through indicators of the population aged at the primary and secondary school levels. These findings indicate that poverty in North Sumatra is influenced not only by economic factors but also by the quality of basic services and education levels among the population. Therefore, this research is useful for policymakers at the central and regional government levels to consider the factors influencing the increase in poverty in North Sumatra.

Credit scoring is an important element in decision-making for providing financing, especially for microfinance institutions. Several methods for predicting credit scoring include Decession Tree, Gradient Boosted, Neural Network, K-NN, and Rule Induction. This study aims to improve the accuracy of financing risk prediction by efficiently integrating historical data. The Neural Network (NN) algorithm is a machine learning algorithm consisting of neurons (nodes) connected to each other in several layers (input, hidden, and output). NN is used for pattern recognition, classification, regression, and complex non-linear modeling. The NN algorithm has the advantage of working well on large and diverse data and unstructured data. However, the NN algorithm has weaknesses such as overfitting and data dependence. In this study, the integration of the Sample Bootstrapping and Weighted Principal Component Analysis (PCA) methods is proposed to improve optimal accuracy in the NN algorithm. The Sample Bootstrapping method is used to reduce the amount of training data to be processed. The Weighted PCA method is used to reduce attributes. This study uses a financing customer dataset. The results of the study show that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA resulted in an accuracy increase of 1-3% (97%-99%) compared to other algorithms. Therefore, it can be concluded that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA produces better accuracy than other algorithms

This study discusses the introduction of digital signature patterns using the Backpropagation method on Artificial Neural Network (JST) to identify a person's characteristics and potential. The increasing use of digital identities demands a verification system that is more secure, accurate, and adaptive to the variations of each individual's signature. The main problem faced in the signature recognition system is the low level of accuracy when the visual features of the signature have similarities between users, both in terms of shape, size, and stroke pressure. In addition, variations of signatures made by the same individual are also a challenge in the identification process. As a solution, this study implements Principal Component Analysis (PCA) to extract important features from the signature image before the training process using JST. PCA is used to reduce the data dimension so that the learning process becomes more efficient and optimal. A total of 80 signature images were used in this study, consisting of 60 training data and 20 test data. The results showed that the system was able to achieve an accuracy level of 92.5%. These findings prove that the combination of PCA and JST methods is effective in recognizing digital signature patterns and has the potential to be applied to digital security-based biometric identification systems.

Attendance is an essential component of school management that serves as both proof of presence and a basis for evaluating the discipline of teachers, students, and staff. The manual attendance system that is still widely used often leads to several issues, such as delayed data recapitulation, potential attendance manipulation, and low recording accuracy. This study aims to design and develop a web-based attendance application at SD Negeri 19 Pasar Cubadak by integrating geolocation, timestamp, and face recognition features as attendance validation tools. The research method employed is the Research and Development (R&D) approach using the Waterfall software development model, which includes the stages of requirements analysis, system design, implementation, and testing. The application was developed using the Laravel framework with PHP and JavaScript programming languages and a MySQL database. The results show that the web-based attendance application can record attendance in real-time, automatically validate user location and identity, and generate accurate and transparent attendance reports. The User Acceptance Test (UAT) results indicate that the system is user-friendly, improves recapitulation efficiency, and assists the principal in objectively monitoring school members’ discipline. Therefore, this application is expected to serve as a modern solution to enhance the effectiveness of attendance administration in primary education environments.

The development of information technology has encouraged institutions, including hospitals, to adopt digital systems to improve operational efficiency. One important aspect is the employee attendance system, which previously relied on fingerprints. This method has limitations, such as difficulty detecting when fingers are not in ideal condition and causing queues during peak hours. This research aims to design and implement an Android-based attendance system using the Eigenface facial recognition method as a faster, safer, and more accurate alternative. Eigenface works by extracting facial features using Principal Component Analysis (PCA), thus being able to efficiently recognize individual identities. The system was developed with MySQL database integration and tested on employees of Khalishah General Hospital. The implementation results showed that the system can recognize faces with a good level of accuracy and increase the effectiveness of attendance recording. Furthermore, the use of facial-based attendance can minimize the potential for fraud and increase user comfort because it does not require physical contact. Thus, the Eigenface method has proven feasible to be implemented as a modern attendance solution to support employee attendance management in hospital work environments and other institutions.

Vocational education in Indonesia faces a serious challenge in the form of a gap between the competencies of Vocational High School (SMK) graduates and the evolving needs of the workforce. Timika City, despite having the largest number of vocational high schools (SMK) in Papua, has not yet fully demonstrated optimal educational management quality, particularly in the aspect of school administration. This condition has resulted in low effectiveness in data recording, documentation of industrial work practices, and reporting and evaluation of partnerships with the business and industrial world (DUDI). This study aims to explore and analyze administrative management strategies that can improve educational quality while strengthening the work readiness of vocational high school students in Timika City. The research method used is a descriptive qualitative approach with content analysis of secondary data, including government policy reports, educational statistics, and the latest scientific literature. The results show that weaknesses in the administrative system are one of the factors inhibiting the achievement of competitive vocational education quality. The implementation of management strategies based on Total Quality Management (TQM) principles, particularly through the Plan–Do–Check–Act (PDCA) cycle, data-driven decision-making, and the involvement of all school elements, has proven effective in increasing the efficiency and accountability of administrative services. The discussion also emphasized the importance of transformational leadership from school principals, developing the competencies of administrative staff, utilizing information technology in administrative systems, and strengthening strategic partnerships with the industrial and industrial sectors (DUDI). Therefore, it can be concluded that school administrative management is not merely a technical function, but rather a strategic component in building a vocational education ecosystem that is adaptive, relevant, and oriented to the needs of the workplace. This research recommends that school policymakers and local governments strengthen administrative governance systems to support educational quality and improve the job readiness of vocational high school graduates.

Gangguan tidur pada mahasiswa merupakan permasalahan yang dapat berdampak pada kesehatan jantung, khususnya melalui perubahan aktivitas listrik jantung yang terekam dalam sinyal EKG. Penelitian ini bertujuan mengembangkan sistem klasifikasi otomatis untuk mendeteksi kondisi jantung berdasarkan sinyal EKG menggunakan algoritma K-Nearest Neighbor (KNN) dan reduksi fitur dengan Principal Component Analysis (PCA). Dataset yang digunakan terdiri dari 159 citra sinyal EKG yang dibagi menjadi dua kelas, yaitu Good Heart dan Bad Heart. Citra diproses melalui tahap preprocessing, reduksi dimensi menggunakan PCA, dan diklasifikasikan menggunakan KNN dengan berbagai nilai parameter. Model terbaik diperoleh pada kombinasi 20 komponen PCA dan nilai K = 6, dengan akurasi mencapai 96,23%, precision 98,46%, recall 92,75%, dan f1-score 95,50%. Hasil penelitian menunjukkan bahwa metode ini mampu mengenali kondisi jantung dengan baik dan efisien. Sistem ini berpotensi dikembangkan sebagai alat bantu deteksi dini gangguan jantung, khususnya pada kelompok mahasiswa yang mengalami gangguan tidur.

This study aims to evaluate the implementation of the English Club extracurricular program at UPTD SMP Negeri 1 Kupang using the CIPP evaluation model (Context, Input, Process, Product). The CIPP model was chosen because it provides a comprehensive overview of the various components of the program. The study employs a descriptive qualitative approach, with data collected through observation, interviews, and document analysis. Based on the evaluation results, in terms of context, the program was implemented in response to students' needs to improve their English language skills, supported by school policies and the Education Office. Regarding input, the program is supported by the school principal, program supervisors, and teachers, and is funded through the BOS Fund, although limitations in learning facilities and infrastructure still exist. In the process aspect, the activities are held regularly twice a month after school hours, involving 150 selected students from grades 7 to 9. Internal evaluations are periodically conducted by the school. As for the product aspect, the program has shown a positive impact, as evidenced by improvements in students’ English proficiency, increased self-confidence, and achievements in various English competitions. Overall, the English Club program at UPTD SMP Negeri 1 Kupang is considered successful in achieving its goals and is recommended to be continuously developed and sustained.

This study aims to analyze the influence of digital transformation and change management on the performance of investigators at the Ditreskrimum Polda Bali in implementing the Electronic Investigation Management (E-MP) application. The research focuses on understanding the extent to which these factors affect investigative effectiveness and identifying key drivers of investigator performance improvement in a digital system. A quantitative approach was employed using a survey method. Data were collected from 128 investigators at Ditreskrimum Polda Bali through questionnaires and analyzed using multinomial logistic regression. The analysis included the Likelihood Ratio Test, Wald Test, and Pseudo R-Square to assess model significance and explanatory power. Principal Component Analysis (PCA) was applied to address multicollinearity and enhance regression model stability. The findings indicate that digital transformation and change management significantly influence investigator performance. The Nagelkerke R-Square value of 0.866 suggests that the model explains 86.6% of variability in investigator performance. Furthermore, the Likelihood Ratio Test results confirm that predictor variables have a significant impact on the dependent variable with a p-value < 0.05. the regression equation is as follows ln(P(Y=0)P(Y=1))=β0+β1X1+β2X2. In conclusion, effective implementation of digital transformation and change management positively contributes to improved investigator performance in the use of E-MP. Therefore, enhancing digital technology training and strengthening change management strategies are highly recommended to optimize the utilization of E-MP in investigative environments.

The Maluku Islands are a high-poverty region in Indonesia. The region consists of 2 provinces, namely Maluku province and North Maluku province. There are 21 districts/cities in the region, with 17 regencies and 4 municipalities. The poverty rate in this region is a challenge that always wants to be studied with a socio-population approach and a mathematical statistics approach. One method or approach in analyzing poverty is Principal Component Analysis (PCA).  PCA has the advantage of simplifying information from various variables to several principal components without losing much information and can overcome the problem of multiple linearity by changing variables that correlate with freely related components. The purpose of this research is to identify poverty in districts/municipalities in Maluku Islands using the PCA approach. The results showed that the components formed by the PCA method were formed in 2 factors. Factor 1 consists of GRDP (X2), Life Expectancy Rate (X3), Unemployment Rate (X4) and Percentage of Population (X6). Meanwhile, factor 2 consists of 2 variables, namely the Poverty Level (X1) and TPAK (X5).

Chitosan is a natural polysaccharide derived from chitin, commonly found in the shells of crustacean animals. The production of chitosan involves several stages: deproteinization, demineralization, and deacetylation, which require the use of acidic and alkaline solutions. This study aimed to evaluate the effectiveness of various types of organic acids and a natural acid source, Averrhoa bilimbi (bilimbi fruit) extract, in the chitosan extraction process from Vannamei shrimp shells. The study employed a completely randomized design with a single factor consisting of four acid treatments: acetic acid (AA), citric acid (CA), lactic acid (LA), and bilimbi fruit extract (BE), each replicated three times. The chitosan obtained from each treatment was analyzed for its chemical characteristics and mineral content, and the results were further analyzed using Principal Component Analysis (PCA). The best results were obtained from the citric acid treatment, which produced chitosan with a moisture content of 6.59%, a degree of deacetylation of 91.72%, ash content of 2.68%, and magnesium and calcium contents of 2.56 mg/100g and 0.15 mg/100g (dry basis), respectively. In contrast, the bilimbi extract treatment resulted in an ash content of 41.64%, with magnesium and calcium contents of 1456.52 mg/100g and 4.17 mg/100g (dry basis), indicating that the bilimbi fruit extract still has low demineralization effectiveness.

This study aims to optimize the implementation of the K-Nearest Neighbors (K-NN) algorithm for medical image classification by focusing on selecting the optimal KKK parameter and applying dimensionality reduction techniques to improve accuracy and efficiency. The data used was sourced from public medical image repositories such as The Cancer Imaging Archive (TCIA) and Medical Image Analysis datasets, covering various diseases, including brain tumors, lung cancer, and kidney lesions. The research process involves data collection, data preprocessing, dimensionality reduction using Principal Component Analysis (PCA), applying the K-NN algorithm with Euclidean, Minkowski, and Cosine distance metrics, and performance evaluation using accuracy, precision, recall, and F1-score. Experimental results demonstrate that K=5with the Euclidean distance metric provides the best performance, achieving an accuracy of 90%. Additionally, PCA effectively reduces computational time by 30% without significantly compromising accuracy. This study proves that K-NN is an effective method for medical image classification. However, further research is needed to integrate K-NN with deep learning models to enhance performance and feature extraction capabilities.

The Introduction to Schooling Field Program (PLP) is a key component in preparing prospective educators with the practical skills needed to face challenges in the world of education. This study aims to explore the role of PLP in improving the competence of prospective educators at SMP Swasta Daya Cipta Medan. Through the PLP program, prospective educators are able to apply the theories learned in college in a real context at school, from observing the learning process to participating in extracurricular activities. PLP also provides opportunities for prospective educators to develop pedagogical, professional, and social competencies through direct interaction with teachers, students, and school staff. This research uses a field research approach with a qualitative descriptive design that aims to describe the phenomena that occur. The main informant in this study was the principal, while supporting informants consisted of the vice principal for curriculum, the head of administration, teachers, and school staff. Data were collected through observation, interview, and documentation methods, which were then analyzed using data analysis techniques to review, reduce, draw conclusions, and use triangulation to ensure data validity. This study found that PLP not only enriched students' theoretical knowledge, but also strengthened their skills in managing classes, developing lesson plans, and adapting to various challenges in the field. The PLP program at SMP Daya Cipta Medan contributes significantly in shaping educators who are competent and ready to face the dynamics of the world of education, as well as being able to make a positive contribution to the development of education in Indonesia.

The determination of human skeletons and fragments has significant value from both a legal and humanitarian perspective, which generally includes the evaluation of teeth, bones, fingerprints, and genetic material. However, fingerprinting and DNA methods are not without their limitations. Sex determination is an important first step in biological profiling, which plays a role in victim matching. Research on sex identification using T12 vertebrae with Geometric Morphometric (GM) techniques is still relatively rare. The GM technique deeply explores the dimensions and shape of objects. This study used an analytic observational design with a retrospective cross-sectional approach, involving 100 CT scan samples from Dr. Kariadi Hospital. Gender was used as the independent variable, while size (centroid size) and shape (principal component) were the dependent variables. The results of independent t-test and Mann-Whitney analysis showed a significant difference in T12 vertebra size between men and women (p = 0.000), but not in shape (p = 0.439). Based on the GM technique, it was revealed that the difference in T12 vertebrae between men and women was in size, not shape.

Background: Every child has the right to receive education in a healthy environment in accordance with the 1945 Constitution Article 31 paragraph 1. The health of the school environment, especially the existence of a healthy canteen, is an important component in supporting student growth and development. This study aims to analyze the implementation of healthy canteens in elementary schools in the working area of the Paal V Health Center in Jambi City. Methods: This study is a qualitative study with an analytical descriptive approach. Research data was collected through in-depth interviews, FGDs, observations, and documentation, with informants consisting of school principals, health center staff, teachers, canteen sellers, and students. Results: At SDN 64/IV Jambi City, there is a written policy regarding school healthy canteens. The average canteen handler has used an apron as personal protective equipment (PPE) and maintained hand and clothing hygiene. School canteen facilities are equipped with adequate lighting and the availability of clean water. However, it is still found that plastic containers are used for food containers. The informants agreed that the existence of healthy canteens in schools is very important and suggested that canteens only sell food that is filling and free from harmful chemicals. Conclusion: Schools with higher accreditation have better canteen management than schools with lower accreditation, especially in policies, facilities, and supervision.

Nation-building requires long-term investment focused on improving human resource (HR) quality, where higher education levels play a crucial role in shaping superior HR. In Indonesia, the government continuously strives to expand access to and improve the quality of education across regions, including in Central Java Province. However, disparities in educational attainment remain a challenge, influenced primarily by social, economic, and demographic factors. This study aims to analyze the factors affecting education levels in Central Java in 2021, a post-pandemic year, using the Principal Component Analysis (PCA) approach. Suspected influential factors include poverty rates, gender ratios, population growth rates, gross enrollment rates, net enrollment rates, expected years of schooling, and average years of schooling. The analysis results show that the data characteristics of these factors indicate asymmetrical distributions and high variability, as observed from wide boxplots for most factors. Only the expected years of schooling exhibit lower variability, with the presence of outliers. Assumption tests reveal that the data follow a multivariate normal distribution, are sufficient for factor analysis, and are dependent. The principal component analysis results indicate that two components are sufficient to explain overall data variability. The factor analysis forms two new components, identified as the welfare and education factor and the education participation factor.

This research aims to optimize inclusive education supervision in addressing diversity in the classroom. The object of this research concerns inclusive educational supervision in addressing diversity in classes located in class 9 c of Madrasah Tsanawiyyah Al-Amiriyyah, Blokagung, Banyuwangi, East Java. This research method uses qualitative with a case study approach. Sources of informants in this research include; Principal, Teachers, Students. Data collection techniques used in this research include observation, document analysis, and interviews. Data analysis in this research uses the Miles and Huberman data analysis model, which consists of three main components: data reduction, data analysis, and data conclusions. The results of research on inclusive educational supervision in addressing diversity in the classroom include; Differentiated Approach in Supervision, Parental Involvement, Emotional Impact on Students.