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Letter management is one of the important aspects in supporting the efficiency of academic administration in schools. This study aims to explore and understand how letter management at SMA Al-Hidayah Medan can be optimized to improve the efficiency of academic administration. The study used a descriptive qualitative approach with data collection through direct observation and interviews with parties involved in the correspondence administration process. The research findings show that SMA Al-Hidayah Medan implements a combined letter management system, namely combining manual and digital management. Incoming and outgoing letters are recorded manually in the agenda book and expedition book, and archived digitally in the school computer system. However, the system used is still limited and has not been fully integrated. The obstacles found include limited human resources who have not fully mastered administration and information technology, weak document supervision, and archiving of physical letters that have not been systematically arranged. In addition, digital archiving which is still conventional without automatic categorization makes the archive search process less efficient. As an optimization effort, the school has implemented a semi-digital system, provided training for administrative staff, compiled letter classification codes, compiled SOPs for letter management, and utilized cloud storage and digitization of old archives. These steps demonstrate institutional awareness to build a more efficient and modern administration system. In conclusion, effective letter management plays a very important role in supporting academic administration, but requires continuous coaching, better digital integration, and strong managerial commitment.

The rapid advancement of information technology has had a significant impact in various fields, particularly in pattern recognition and image processing. One of the ongoing challenges is accurately recognizing handwritten digits, which plays a crucial role in document digitization, automated form reading, and other intelligent systems. This study aims to implement and evaluate the K-Nearest Neighbors (KNN) algorithm, a simple yet effective classification technique, in recognizing handwritten digit images. The data used comes from the public dataset load_digits from Scikit-learn, which contains 1,797 grayscale images of handwritten digits sized 8x8 pixels. Each image is represented as a 64-dimensional feature vector. The dataset is split into training and testing data with an 80:20 ratio, and the model is trained using KNN with k=3. The experimental results show a classification accuracy of 96.94%, with minimal prediction errors that typically occur in digits with similar visual shapes, such as 5 and 9. This study demonstrates that KNN, despite its simplicity, can provide high accuracy in handwritten digit recognition when supported by proper preprocessing and parameter selection. The implications of this research highlight the potential for developing intelligent applications in education, data entry automation, and identity verification.

Information system security is a crucial aspect in maintaining the confidentiality and integrity of user data. The IITC Intermedia website of Amikom Purwokerto University serves as an information system for national events and stores participants' personal data, necessitating a security evaluation. This study aims to analyze vulnerabilities on the website using the Vulnerability Assessment method with the OWASP ZAP tool. The research process involves data collection, vulnerability scanning, result analysis based on the OWASP Top 10 2021 categories, and providing technical recommendations. The scan results revealed 23 vulnerabilities, consisting of 1 high-risk, 4 medium-risk, 9 low-risk, and 9 informational findings. Among these, 15 vulnerabilities fall under the OWASP Top 10 classification. Key vulnerabilities identified include the use of outdated JavaScript libraries, security header misconfigurations, and weaknesses in session management and access control. Based on these findings, several mitigation measures are recommended to strengthen system security. This study emphasizes the importance of implementing OWASP standards in the development and management of web-based information systems.

Oil palm is a key commodity supporting Indonesia’s economy through exports and employment. The industry’s success depends heavily on the selection of superior seedlings, which determine productivity, crop quality, and resistance to pests and diseases. Manual selection, however, often leads to subjectivity and inconsistency due to limited human resources and genetic variation. To address this, the study applies the Naïve Bayes algorithm for classifying oil palm seedlings based on seven variables: height, stem diameter, number of leaves, leaf color, disease resistance, root growth, and fruit yield. Using an explanatory quantitative method, the study follows seven stages: identifying problems, literature review, collecting 1,000 data entries from PT Intitama Berlian Perkebunan, data pre-processing, system modeling (UML), algorithm implementation, and evaluation using a confusion matrix and black box testing. Data was split into 80% training and 20% testing. The Naïve Bayes-based classification achieved 95% accuracy and perfect recall (1.00) for the superior seedling class. However, its performance on the minority class (non-superior seedlings) was weaker due to dataset imbalance. Black box testing verified all system functions worked correctly, enabling effective and efficient use by administrators. The study concludes that Naïve Bayes improves objectivity, efficiency, and accuracy in seedling selection. Nonetheless, attention is needed on data balancing and optimization to maintain consistent performance across classes. This system shows strong potential as a decision-support tool in plantations and promotes digital transformation in agricultural processes.

Image processing is a branch of computer science that is growing rapidly and is widely used in various fields, including in security systems. Face identification is one of the main applications of image processing that aims to recognize and distinguish individual faces in a system. The methods used in face identification involve various techniques, such as facial feature detection, characteristic extraction, and classification using machine learning algorithms. This article discusses the application of image processing in a security system based on face identification and the technology used to improve the accuracy and reliability of the system. The results of the study show that the combination of deep learning algorithms with image pre-processing techniques can increase the success rate of face identification in security systems.

Mathematical connection ability is a crucial competence that should be developed from the elementary level, as it helps students understand the interrelationships between mathematical concepts and their applications in daily life. This study aims to analyze the mathematical connection abilities of first-grade elementary school students in learning flat shapes. The research employed a descriptive quantitative method using cognitive and psychomotor assessment instruments. The cognitive test consisted of multiple-choice and essay questions measuring students' ability to recognize, understand, classify, and apply flat shapes. The psychomotor test involved tasks such as assembling flat shapes into meaningful objects and classifying shapes by type. The results showed that students were fairly capable of identifying and grouping flat shapes, but had difficulty explaining classification reasons or relating shapes to real-life objects. The psychomotor results also indicated that fine motor skills and the ability to make real-world connections need improvement. These findings highlight the importance of contextual learning approaches and the use of concrete media to strengthen students’ mathematical connections from an early age.    

Premature neonate will experience rapid loss of body heat immediately after birth. Mortality of neonate is caused by changes in body temperature at birth, namely hypothermia.. The purpose of this study was to analyze the correlation of incubator temperature and light intensity with the classification of premature neonate body temperature. The research design used a correlational approach with a cross-sectional approach. The population of this study were premature neonates in the Perinatology ward and NICU of Waled Hospital, Cirebon Regency. The sample in this study were 32 neonates using purposive sampling. The instruments used were observation sheets, thermometers, luxmeters, thermohygrometers. Data analysis in this study used Chi square. The results showed that a correlation between incubator temperature and body temperature in premature neonates. (p value = 0.05; α = 0.05) and there is a correlation between light intensity with body temperature in premature neonates. (p value = 0.05; α = 0.05). Measuring the neonates body temperature and resetting the incubator temperature needs to be done periodically by health workers.

Film serves not only as entertainment but also as a medium for conveying messages through verbal interactions between characters. In pragmatics, illocutionary acts are central to understanding a speaker’s intention within a given context. This study aims to describe and analyze the types and functions of illocutionary speech acts in the Indonesian film Komang (2025) by Naya Anindita, which portrays a love story across religious and cultural boundaries. Using a descriptive qualitative approach, data were collected through observation and transcription of dialogue from the film. The analysis is based on Searle’s classification of illocutionary acts: representatives, directives, expressives, commissives, and declaratives. The findings reveal that all five types appear in the film, each playing a role in shaping interpersonal dynamics, emotional tension, and cultural values. Directive and expressive acts are the most dominant, reflecting intense emotional exchanges and social interactions. Commissive and declarative acts highlight commitment and authority, contributing to the narrative progression and cultural identity negotiation. The study concludes that illocutionary acts in films can represent social conflicts and cultural expressions, and recommends further research on other films with varied genres and contexts. The integration of multimodal elements is also suggested to enrich the analysis of meaning in audiovisual texts.  

This research aims to analyze Shopee application user feedback using Google Colab-based information technology and a proportion distribution approach. The method used includes a Multinomial Naïve Bayes-based classification technique to categorize sentiment into positive and negative. The research results show that the model accuracy level is 90.84% ​​with an average precision for the positive and negative categories of 0.93 and 0.78 respectively. The statistical test produces a T-statistic value of 2759.50 with a p-value of 0.0, which shows significance in the difference in the distribution of positive and negative reviews.

This study explores the social and psychological dynamics of the collective expression of the Indonesian youth through the hashtag #KaburAjaDulu on TikTok social media, which reflects their concerns about inequality in access to work and lack of opportunities for self-development. This study uses qualitative methodology and a virtual ethnography approach to analyze data, covering social interactions and meaning construction in virtual communities. Primary data sources involve real-time monitoring using tools such as Brand24, which captures the frequency and sentiment of posts. Secondary data sources are obtained from literature related to labor migration, digital expression, and network society theory. Data collection techniques combine passive online observation and thematic classification, while data analysis is carried out through content analysis to identify dominant discourse patterns and sentiments. Data validity is tested using source triangulation, comparing netnography findings with scientific references and policy documentation. The results show that the hashtag #KaburAjaDulu is not just an expression of jokes, but a symbol of structural alienation and the aspirations of the young generation to seek better life opportunities abroad. The discussion highlights the implications of this social critique for migration policies and the protection of migrant workers. The conclusion emphasizes the importance of adaptive government responses and more inclusive policies to address the aspirations of the younger generation.

Islamic economics has been practiced since the time of the Prophet Muhammad. However, at that time until several centuries later there was no special classification of disciplines for economics, causing several Muslim works to be lost. The role of scholars in this case is very influential in the sustainability of Islamic economics both in terms of theory and implementation. This journal aims to review the thoughts of Islamic economics from scholars, one of whom is Imam Abu Hanifah. By matching whether Imam Abu Hanifah's thoughts are still relevant and can be applied in the modern era. The method used is research with the study method used is library research. Where researchers use books, journals, periodical articles, yerbooks, bulletins, annual reviews, historical records, magazines, and newspapers as the main subject of their study. The results of this study are that Imam Abu Hanifah's thoughts on the salam contract are still relevant and can be implemented in the modern era. although not all sales and purchase contracts can use the salam contract method because there are several conditions that must be met in this contract.

This study aims to analyze the types of illocutional speech acts contained in the lyrics of the song Hati-Hati di Jalan karya Tulus. The background of this research is based on the importance of understanding song lyrics as a form of communication that is full of pragmatic meaning, especially in the context of emotions and interpersonal relationships. The method used is qualitative descriptive with a content analysis technique based on the theory of classification of illocutional speech acts by Searle. Data were taken from all song lyrics and analyzed based on five types of illocution speech acts, namely representative, directive, expressive, commissive, and declarative. The results of the study show that in the lyrics of this song there are four types of illocutional speech acts, namely representative, directive, expressive, and commissive, with a dominance of representative and expressive types. The act of speech reflects various communication functions such as expressing beliefs, expressing feelings, giving hope, and showing commitment. This research implies that song lyrics can be a valuable object of pragmatic study, as well as enriching the understanding of communication strategies through language in popular literary works. This study has limitations on the scope of a single song and the interpretation of the researcher, so it is recommended that the next study examine other songs to broaden the perspective.

This research is motivated by the importance of improving the quality of passport making services at the Pontianak City Immigration Office which still faces obstacles such as complicated procedures, limited quotas, lack of officer direction, and mismatches in passport collection schedules that cause public dissatisfaction. This research aims to classify the level of satisfaction of passport making services using the Naïve Bayes algorithm, measure classification accuracy, and develop a website-based system that helps evaluate and improve service quality effectively and efficiently. The method used is a quantitative approach with data collection through questionnaires, interviews, and direct observation of 205 respondents, then the data is processed using the Naïve Bayes algorithm which assumes independence between variables to classify satisfaction levels based on variables such as officer friendliness, officer ability, ease of procedure, and timeliness of service. The main findings show that the Naïve Bayes algorithm is able to classify satisfaction levels with 73% accuracy, 76% precision, 70% recall, and 73% F1-score, signaling the effectiveness of this method in identifying community satisfaction patterns. However, the results also indicate the need for improvement in user interface aspects and system responsiveness so that the system can be widely accepted and provide optimal benefits. The implication of this research is that the application of Naïve Bayes-based data mining methods can be an effective tool in evaluation and decision-making to improve the quality of public services, especially in the field of passport making, and encourage the development of interactive and empirical data-based public service information systems.

Breast cancer is the most prevalent cancer among women worldwide, requiring early and accurate diagnosis to reduce mortality. This study proposes a hybrid classification pipeline that integrates Hybrid Statistical Feature Selection (HSFS) with unsupervised LSTM-guided feature extraction for breast cancer detection using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Initially, 20 features were selected using HSFS based on Mutual Information, Chi-square, and Pearson Correlation. To address class imbalance, the training set was balanced using the Synthetic Minority Over-sampling Technique (SMOTE). Subsequently, an LSTM encoder extracted non-linear latent features from the selected features. A fusion strategy was applied by concatenating the statistical and latent features, followed by re-selection of the top 30 features. The final classification was performed using a Support Vector Machine (SVM) with RBF kernel and evaluated using 5-fold cross-validation and a held-out test set. Experimental results showed that the proposed method achieved an average training accuracy of 98.13%, F1-score of 98.13%, and AUC-ROC of 99.55%. On the held-out test set, the model reached an accuracy of 99.30%, precision of 100%, and F1-score of 99.05%, with an AUC-ROC of 0.9973. The proposed pipeline demonstrates improved generalization and interpretability compared to existing methods such as LightGBM-PSO, DHH-GRU, and ensemble deep networks. These results highlight the effectiveness of combining statistical selection and LSTM-based latent feature encoding in a balanced classification framework.

This study analyzes the level of economic development disparity among regions in East Java Province during the period 2009–2023 using the Klassen Typology approach. The study aims to map the classification of regions based on economic growth rates and per capita GRDP to identify spatial disparities among the 38 districts/cities in the province. The analysis results indicate significant inequality, with regions such as Surabaya, Sidoarjo, and Gresik consistently categorized as advanced and rapidly growing areas (Quadrant I), while regions such as Sampang, Pamekasan, and Bondowoso fall into the underdeveloped category (Quadrant IV). This phenomenon shows that the economic spillover effect from growth centers to surrounding regions remains suboptimal and suggests the need for spatially-based policy interventions. This study provides important implications for formulating more inclusive regional development policies and recommends further quantitative analysis to identify the determinants of inequality in greater depth.

Traditional markets are characterized by fast-paced and diverse transactions, necessitating a reliable product monitoring system to enhance the efficiency of stock management and transactions. This study develops a monitoring system based on a loadcell sensor and a TCS230 color sensor to automatically classify product weight and type. The loadcell is used to measure product weight with high accuracy, while the TCS230 detects the color characteristics of products to distinguish between different types of commodities, such as various varieties of chili peppers. The development process includes sensor calibration, dataset collection, and the training and evaluation of a classification model. Experimental results show that the classification accuracy exceeds 90%, demonstrating the effectiveness of combining weight and color data for market product recognition.

The Majapahit Museum houses a significant collection of tosan aji, particularly keris, which holds important historical, technological, and symbolic value within Javanese culture. However, a substantial portion of the collection lacks clear provenance and stratigraphic context, resulting in data gaps and potential misinterpretations, especially regarding Majapahit-period attribution. This study aims to examine strategies for managing non-stratigraphic keris collections through standardized visual-morphological identification as an initial curatorial approach. The research employs direct artifact observation, morphological analysis of blades and ricikan, visual examination of pamor configurations, and a review of museological, keris studies, and archaeometallurgical literature. The findings demonstrate that standardized morphological identification provides a systematic framework for preliminary classification while preventing speculative chronological claims. The study also emphasizes the necessity of separating blade analysis from keris fittings and highlights the limitations of visual assessment in determining tangguh, which requires support from non-destructive metallurgical analyses. As a practical contribution, this article proposes strengthening curatorial standards through the development of morphology-based artifact labels (manual and digital), multidisciplinary scholarly catalogues, and the integration of documentation and material analysis technologies. This approach positions the Majapahit Museum as a knowledge-producing institution in tosan aji studies rather than merely a repository of artifacts.   Kata Kunci : Museum Majapahit, Keris, Identifikasi Morfologis, Ricikan, Tangguh Keris

This study aims to analyze the structure and shifts in the economy of the livestock subsector in South Lampung Regency to identify base and prospective commodities, concentrated and distinctive commodities, fast-growing and highly competitive commodities, and those with the highest economic shifts, collectively referred to as leading commodities. The data used are secondary data from the Central Bureau of Statistics and the Department of Livestock and Animal Health of South Lampung Regency. The commodities analyzed include 12 types of livestock distributed across 17 sub-districts, with a total of 204 observation units. The analytical methods used include Location Quotient (LQ) and Dynamic Location Quotient (DLQ) to identify base and prospective commodities; Localization Index (LI) and Specialization Index (SI) to assess commodity concentration and distinctiveness; and Shift Share Analysis (SSA) to measure growth, competitiveness, and net shifts. Quadrant classification at each stage was performed using the Klassen Typology. The results indicate that 61 observation units of commodity-subdistricts are classified as base and prospective commodities, and 10 of these also meet the criteria for concentration, distinctiveness, rapid growth, and high competitiveness. The commodity with the highest net shift value is broiler chickens in Tanjung Sari Sub-district, making it a leading commodity in South Lampung’s livestock subsector.

Image classification is a key field in digital image processing with broad applications, such as object recognition and disease detection. The use of artificial neural network architectures, such as MobileNetV2, has significantly advanced pattern recognition in large datasets. However, in small datasets, challenges related to accuracy and generalization are often encountered. This study explores an RGB-based approach utilizing MobileNetV2 for image feature extraction and Support Vector Machine (SVM) as the classifier. MobileNetV2 is applied to extract features from RGB images, which are then further processed by SVM to determine image classes. The results indicate that this model achieves an accuracy of 91.67%, precision of 0.9163, recall of 0.9167, and F1-score of 0.9161. Based on the confusion matrix analysis, the model effectively distinguishes between classes, despite slight overlaps. This research contributes to the development of intelligent image classification systems that can be applied in various fields, including the food industry. With these achievements, the RGB approach integrating MobileNetV2 and SVM has proven effective in enhancing image classification accuracy, even with relatively small datasets. These findings open opportunities for applying similar methods in other image processing tasks that require high accuracy in object or disease detection and classification.

Investment is an activity undertaken to allocate funds with the expectation of generating future returns. In a dynamic economic environment, identifying profitable investment opportunities can be a complex task. This study aims to determine potential investment opportunities in Semarang City using a classification method that facilitates business actors or investors in selecting appropriate business sectors. The study utilizes valid data to help investors make informed decisions when establishing a business in the region. Data collection was conducted through research at the Investment and One-Stop Integrated Services Agency (DPMPTSP) of Semarang City, employing a quantitative approach with the K-Nearest Neighbor (K-NN) method. The dataset was divided into training and testing sets with an 80:20 ratio. The experimental results show that the implementation of the K-NN algorithm, conducted using Google Colab, achieved an accuracy of 86% based on 60 testing data points. This demonstrates that the K-NN classification algorithm is effective and produces accurate predictions. Therefore, applying data mining classification techniques to identify investment opportunities can serve as a viable solution to support strategic decision-making for investors.their business development strategies with sector-specific prospects in Semarang City.