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This study designs and implements an up-down counter system based on an AT89C2051 microcontroller programmed in assembly using the MC-51 application. The system modifies an existing digital clock board by mapping the display selector pins, seven-segment segment pins, pushbuttons, and buzzer to the microcontroller ports. The research method consists of literature review, hardware identification, algorithm design, assembly programming, program downloading, and functional testing using a 5 V DC supply. The implementation uses a four-digit common-cathode seven-segment display and a multiplexing routine to show the counter value in real time. The functional test shows that the system can display the initial value, increase the value through the up button, and decrease the value through the down button. The display is readable during operation, and the program can run on the target circuit after being downloaded to the AT89C2051. This study confirms that assembly programming on MC-51 can be applied to implement a simple counter system on a reused digital clock circuit. The main limitations are the absence of explicit button debouncing, overflow and underflow protection, quantitative response-time measurement, and non-volatile data retention.

. The effort to re-visualize regional architectural elements combined with modern touches is known as neo-vernakular architecture. In North Sumatra, there are various ethnic groups, including Batak, Mandailing, Malay, and others, each possessing rich traditional architectural values. This study aims to identify and analyze traditional architectural elements applied to the façade of the North Sumatra Regional House of Representatives (DPRD) building. The DPRD North Sumatra building is one of the government buildings that functions as a workplace for representatives of the people, council members, and the secretariat in carrying out governmental duties and public services. The building is located on Jalan Imam Bonjol with an area of approximately 8,000 square meters. This study uses a qualitative descriptive method with data collection techniques through direct field surveys and literature studies related to the façade design of the DPRD North Sumatra building. The results show that the neo-vernakular character on the building façade is represented through several architectural elements, such as exposed columns, sunshading, opening forms, and other supporting elements that reflect a combination of traditional and modern architectural features.  

Penelitian ini bertujuan untuk menguji pengaruh profesionalisme ASN, independensi ASN, dan komitmen organisasi ASN terhadap kualitas laporan keuangan daerah pada Badan Keuangan dan Aset Daerah (BKAD) Provinsi Sumatera Utara. Penelitian ini menggunakan pendekatan kuantitatif dengan jenis data primer yang diperoleh melalui penyebaran kuesioner kepada 45 responden. Populasi dalam penelitian ini adalah ASN pada BKAD Provinsi Sumatera Utara, dengan teknik pengambilan sampel menggunakan purposive sampling. Hasil penelitian menunjukkan bahwa profesionalisme ASN dan independensi ASN berpengaruh positif dan signifikan terhadap kualitas laporan keuangan daerah. Sementara itu, komitmen organisasi ASN berpengaruh positif tetapi tidak signifikan terhadap kualitas laporan keuangan daerah. Secara simultan, profesionalisme ASN, independensi ASN, dan komitmen organisasi ASN berpengaruh positif dan signifikan terhadap kualitas laporan keuangan daerah pada BKAD Provinsi Sumatera Utara.

Malware represents an evolving cybersecurity threat that demands more effective detection methods. Conventional signature-based detection systems have limitations in identifying new variants, driving the development of deep learning-based approaches. This research implements and evaluates four variants of the YOLOv11 algorithm (n, s, m, l) for malware classification based on visual image representation. The dataset consists of 22,056 malware and benign images, divided into 70% training, 15% validation, and 15% testing across 8 classes (adware, backdoor, benign, downloader, spyware, trojan, virus, worm). Each model was trained for 100 epochs with batch size 32 using Google Colab with GPU support. Results demonstrate that all variants achieve high accuracy (97.8%-98.1%) with YOLOv11m as the best performer (98.1%). YOLOv11n offers optimal balance between accuracy (97.9%) and efficiency (1.5M parameters, 0.3 ms/img inference) ideal for real-time applications. This research surpasses previous methods such as K-NN (97.18%) and hybrid CNN (96.55%) with superior inference speed (0.3-0.9 ms/img vs tens to hundreds of ms/img), proving the effectiveness of YOLOv11 for fast, accurate, and scalable malware detection.

The Free Nutritional Meal Program (MBG) is a government policy that is widely discussed by the public through social media, especially TikTok. Various comments that have emerged indicate differences in public opinion towards the program, so an analysis is needed to determine the tendency of public sentiment. This study aims to analyze TikTok user sentiment towards the Free Nutritional Meal Program using the Naive Bayes method. The research method is carried out through several steps, namely collecting TikTok comment data, preprocessing text, labeling sentiment data into positive, negative, and neutral, feature transformation using TF-IDF, and classification using the Naive Bayes algorithm. Based on the analysis of 500 comment data, the results show that positive sentiment dominates public opinion by 42% (210 data), followed by negative sentiment by 36% (180 data), and neutral sentiment by 22% (110 data). Testing the classification model using Naive Bayes produces excellent performance with an accuracy rate of 86%, precision of 84%, recall of 85%, and F1-score of 84%. The conclusion of this study shows that the Naive Bayes method is effective as an approach in social media sentiment analysis to map public responses to government policies.

Online maps applications have become an essential tool for modern society in finding the fastest and most efficient routes. However, these applications often fail to detect current road conditions such as flooding, demonstrations, accidents, or public events, causing users to get stuck in problematic routes. This study aims to develop a prototype of a community-based road condition reporting system, visualized through a web-based digital map. The system allows users to directly report road conditions by providing photo evidence, descriptions, and event categories. It is also equipped with features for designing event routes such as carnivals and suggesting alternative paths based on community reports. The development process was carried out using a simulation-based approach with scenario testing that reflects real field conditions, without involving direct user data. The implementation results show that all core features work properly. The technologies used include Leaflet.js, OpenStreetMap, and the Nominatim geolocation API. This research produces an adaptive community-based GIS model that can be further developed as an intelligent navigation solution at the city scale

Phishing is one of the most common and dangerous cyber attacks as it can trick users into providing sensitive information such as usernames, passwords, and personal data. This study aims to simulate Phishing techniques using the Zphisher tool as an educational medium to improve cybersecurity awareness. The method used is a quasi-experimental approach involving 30 participants exposed to Phishing scenarios in a controlled environment using Kali Linux. The results show that 83% of participants clicked the Phishing link and 60% entered their credentials, while 40% demonstrated awareness by avoiding further interaction. These findings indicate that cybersecurity awareness is still relatively low, but Phishing simulation is effective as an experiential learning tool.

Digitalization in the fitness industry is increasingly essential to improve efficiency and service quality. However, many fitness centers still manage data manually, including KRESNA GYM, which faces challenges in member registration, workout scheduling, and facility information delivery. This study aims to design a web-based e-fitness application using the Laravel framework to support digital service management. The development follows the Waterfall model through stages of requirement analysis, design, implementation, testing, and evaluation. The application includes features such as a landing page, admin/member login and registration, management dashboards, workout schedules, equipment guides, and supplement listings. Black-box testing confirms that all features function as expected. This system simplifies administrative processes and enhances member experience, making it effective in supporting digital transformation at KRESNA GYM.

Bank stock price prediction is an important topic in the application of information technology because stock price movements are dynamic, sequential, and influenced by historical market patterns. This study aims to predict Indonesian banking stock prices using the Long Short-Term Memory method and evaluate the effect of Bayesian Optimization on model performance. The data used in this study consists of daily historical stock data of BBCA, BBNI, BBRI, BBTN, and BMRI from May 4, 2020, to May 4, 2026, obtained from Yahoo Finance. The input features include opening price, highest price, lowest price, closing price, and trading volume, while the prediction target is the stock closing price. The results show that the baseline model produced MAPE values ranging from 1.892% to 3.147%. The best baseline performance was obtained on BBCA with an R² value of 0.933, followed by BBTN with an R² value of 0.902. After optimization, performance improvement occurred on BBTN, with MAPE decreasing from 3.147% to 2.482% and R² increasing from 0.902 to 0.935. For BMRI, MAPE decreased from 2.385% to 2.206%, and R² increased from 0.687 to 0.743. This study concludes that Long Short-Term Memory can be used to predict Indonesian banking stock prices, while Bayesian Optimization can selectively improve model performance depending on the characteristics of each stock dataset.

Python programming has become a fundamental competence in the digital era, yet students often struggle to transform algorithmic logic into functional code. This gap between conceptual understanding and practical implementation skills requires a thorough investigation into learning challenges within the Industrial Informatics Engineering Technology (TRIN) program at Politeknik Manufaktur Bandung. Grounded in Bloom's Revised Taxonomy and Cognitive Load Theory, this descriptive quantitative study utilized a Likert-scale questionnaire and an objective comprehension test administered to 87 third-year students. Data were analyzed using descriptive statistics to map performance across three aspects: conceptual understanding, syntactic comprehension, and implementation ability. Results indicate the conceptual aspect achieved the highest average of 4.15, followed by syntax at 3.56 and implementation at 3.54, with objective test accuracy rates of 76.09%, 65.52%, and 67.36%, respectively. Major obstacles identified include difficulties with looping, debugging, and comparison operators. Therefore, enhanced structured practice and Project-Based Learning approaches are recommended to strengthen students' implementation competencies.

This study investigates the implementation of the Gradient Boosting Machine (GBM) algorithm for network intrusion detection using the CICIDS2017 dataset within the CRISP-DM framework. The process encompasses Business Understanding, Data Understanding, and Data Preparation including data cleaning, categorical feature encoding, normalization, and data split (80 % training, 20 % testing). In the Modeling phase, GBM Hyperparameters (learning_rate = 0.1; max_depth = 5; n_estimators = 150) were optimized via Grid Search with 2-fold Cross Validation, and F1-Score  was selected as the primary metric due to class imbalance. Evaluation on the test set yielded accuracy of 99.99 %, precision of 100 %, Recall of 99.98 %, and F1-Score  of 99.99 %, demonstrating exceptional detection capability with minimal false negatives and false positives. Compared to previous studies, this GBM model outperforms in accuracy and stability without overfitting. These findings confirm GBM’s effectiveness for modern Intrusion Detection Systems and its suitability for Deployment in resource-constrained operational environments.

The thesis supervision process in higher education institutions is still frequently conducted manually, which may lead to inefficiencies in recording supervision data. This study focuses on the development and implementation of a web-based thesis supervision attendance application designed to facilitate attendance documentation, supervision session administration, and systematic monitoring of thesis supervision history. The methodology applied in this study is the Waterfall model, which includes the phases of requirements analysis, system design, implementation, and testing. The application was developed using the Laravel framework and a MySQL database. The system design was modeled using Unified Modeling Language (UML), while the validation process was conducted through Black Box Testing techniques. The research findings indicate that the developed application is capable of performing real-time supervision attendance recording, managing supervision information, and generating attendance reports effectively and efficiently. It can be concluded that the web-based thesis supervision attendance application improves the efficiency and accuracy of supervision record management and supports a more effective thesis supervision monitoring process..

Batik motif classification has attracted growing attention in visual computing due to its role in cultural heritage preservation, textile informatics, museum documentation, and automated cataloging. Although many studies report high classification accuracy, robustness under real-world acquisition conditions remains insufficiently understood. Batik images are frequently affected by illumination variation, blur, folds, watermark overlays, wearable deformation, scale inconsistency, and background clutter, creating challenges that extend beyond conventional image-noise assumptions. Existing studies largely focus on improving classification performance, while the interactions among acquisition variability, feature representation, evaluation practice, and deployment constraints remain fragmented. This systematic literature review addresses this gap by synthesizing batik classification research through a robustness-aware perspective. Using query expansion, backward and forward citation chaining, relevance screening, and thematic coding, 116 candidate records were identified, resulting in 50 highly relevant studies for detailed analysis. The review reveals that robustness is shaped less by denoising alone than by the combined effects of acquisition conditions, representation design, evaluation realism, and deployment context. Handcrafted descriptors remain competitive for small datasets and structured motifs due to their data efficiency and interpretability, whereas deep learning models achieve the highest reported accuracy when supported by sufficient data diversity and realistic augmentation. Hybrid representations emerge as the most consistently balanced approach, combining local texture stability with higher-level abstraction across heterogeneous acquisition settings. The review further identifies recurring robustness failure patterns, including background dependency, illumination instability, motif-scale inconsistency, wearable deformation, and source-shift vulnerability. Based on these findings, a robustness-oriented research agenda is proposed, emphasizing cross-acquisition evaluation, representation-stability analysis, batik-specific robustness benchmarks, acquisition-aware augmentation, and deployable lightweight or hybrid architectures. The study contributes a domain-specific synthesis that reframes batik motif classification from an accuracy-centric task toward a robustness-aware visual recognition problem.

Penelitian ini bertujuan untuk menganalisis implementasi strategi manajemen kesiswaan berbasis nilai Kristiani serta kontribusinya terhadap pembentukan karakter peserta didik dalam lingkungan pendidikan multikultural di SDS Unity Primary School. Latar belakang penelitian ini didasarkan pada meningkatnya kompleksitas keberagaman di lingkungan sekolah yang menuntut adanya pengelolaan peserta didik yang tidak hanya berorientasi administratif, tetapi juga mampu membangun sikap toleransi, penghargaan terhadap perbedaan, dan karakter yang kuat. Penelitian menggunakan pendekatan kualitatif deskriptif dengan metode studi kasus. Data diperoleh melalui observasi partisipatif, wawancara, dan analisis dokumentasi sekolah, kemudian dianalisis menggunakan kerangka SWOT (Strengths, Weaknesses, Opportunities, Threats). Hasil penelitian menunjukkan bahwa manajemen kesiswaan di SDS Unity Primary School diimplementasikan melalui integrasi nilai-nilai Kristiani, seperti kasih, integritas, tanggung jawab, kerendahan hati, dan pelayanan ke dalam berbagai kebijakan dan program kesiswaan. Keberagaman peserta didik yang terdiri atas berbagai agama dan etnis dikelola melalui pendekatan inklusif yang mendorong interaksi lintas budaya dan lintas agama. Analisis SWOT menunjukkan bahwa identitas spiritual sekolah, program pembinaan rohani, kegiatan ekstrakurikuler, serta dukungan orang tua menjadi kekuatan utama dalam pembentukan karakter peserta didik. Di sisi lain, keterbatasan pembina rohani dan tantangan sekularisasi menjadi faktor yang perlu diantisipasi. Temuan penelitian juga menunjukkan bahwa harmonisasi program kerohanian, ekstrakurikuler, dan program anti-perundungan berbasis pendekatan restoratif berkontribusi terhadap terciptanya iklim sekolah yang aman, toleran, dan harmonis. Penelitian ini menyimpulkan bahwa strategi manajemen kesiswaan berbasis nilai Kristiani efektif mendukung pembentukan karakter peserta didik serta dapat menjadi model pengelolaan pendidikan multikultural yang inklusif dan berorientasi pada pengembangan karakter secara holistik.

Perundungan di sekolah menengah pertama sering kali tidak muncul sebagai agresi verbal yang terang-terangan, melainkan tersembunyi dalam candaan yang telah menjadi norma di kalangan remaja. Bentuk dominan yang ditemukan adalah ejekan yang melibatkan nama orang tua siswa. Penelitian ini bertujuan untuk meneliti alasan di balik penggunaan ejekan nama orang tua sebagai candaan dalam interaksi remaja, serta memaknainya sebagai bentuk kekerasan simbolik dalam bullying verbal. Penelitian ini menggunakan pendekatan kualitatif dengan metode fenomenologis. Data diperoleh melalui wawancara mendalam dengan guru Bimbingan dan Konseling serta peserta didik, observasi langsung terhadap interaksi siswa di sekolah, dan dokumentasi pendukung. Hasil penelitian mengungkap bahwa pelaku memandang ejekan nama orang tua sebagai humor dan sarana membangun kedekatan sosial, namun korban mengalami dampak psikologis yang serius, seperti rasa malu, sedih, takut, dan ketidaknyamanan di lingkungan sekolah. Fenomena ini diperkuat oleh dinamika kelompok sebaya, agresi relasional, serta normalisasi bahasa yang dilegitimasi melalui respons lingkungan. Kesimpulan penelitian ini menyatakan bahwa ejekan nama orang tua adalah manifestasi kekerasan simbolik yang dinormalisasi dalam budaya pergaulan remaja, namun kerap luput dari perhatian institusional, sehingga memerlukan perhatian mendalam dalam layanan bimbingan dan konseling di sekolah.

Background: Adenoid hypertrophy is a condition characterized by enlargement of the adenoid tissue which can cause nasopharyngeal obstruction and potentially affect the function of the Eustachian tube. Dysfunction of the Eustachian tube may disrupt middle ear ventilation and increase the risk of otitis media. The degree of adenoid hypertrophy can be objectively assessed using a lateral nasopharyngeal radiograph by calculating the adenoid–nasopharyngeal ratio (A/N ratio). Objective: This study aimed to determine the relationship between adenoid hypertrophy based on the adenoid–nasopharyngeal ratio and the incidence of otitis media. Methods: This study was a quantitative research with an analytic observational design using a cross-sectional approach. Data were obtained from the medical records of patients with adenoid hypertrophy who underwent lateral nasopharyngeal radiographic examination at RSI Purwokerto from 2022 to 2025. The sample consisted of 31 patients who met the inclusion and exclusion criteria. Data analysis was performed using the Spearman correlation test because the data were not normally distributed based on the Shapiro–Wilk test. Results: The results showed that most of the subjects were male (17 subjects, 54.8%) and female (14 subjects, 45.2%). The median age of the subjects was 7 years with an age range of 4–9 years. The median adenoid–nasopharyngeal ratio was 0.90 with a range of 0.68–0.99. Otitis media was found in 11 subjects (35,5%), while 20 subjects (64,5%) did not experience otitis media. The Spearman correlation test showed a correlation coefficient of r = 0.015 with a p-value of 0.936 (p > 0.05), indicating that there was no significant relationship between the adenoid–nasopharyngeal ratio and the incidence of otitis media. Conclusion: There was no statistically significant relationship between adenoid hypertrophy based on the adenoid–nasopharyngeal ratio and the incidence of otitis media.

This study was conducted to analyze the relationship between students’ critical thinking skills and mathematical problem-solving abilities through a literature review approach. The study is based on the importance of mastering higher-order thinking skills in the mathematics learning process, particularly when students are faced with complex problems related to real-life situations. The method used in this research was a literature review by examining various relevant scientific journals and academic books published between 2021 and 2026. The data analysis process was carried out through stages of identification, classification, evaluation, and synthesis of the collected sources. The findings revealed a significant and positive relationship between critical thinking skills and students’ mathematical problem-solving abilities. Critical thinking skills play an important role in helping students understand problems, process and analyze information, select appropriate solution strategies, and systematically review the results obtained. Students with strong critical thinking skills generally demonstrate more optimal mathematical problem-solving abilities. In addition, the implementation of learning models such as Problem-Based Learning and contextual approaches has been considered effective in improving both abilities. Therefore, critical thinking skills are regarded as an essential aspect that needs to be developed in mathematics learning in order to enhance students’ mathematical problem-solving abilities.

This study examines the concept of compassion-based education (rahmatan lil ‘alamin) from the perspective of Islamic educational philosophy and its relevance to bullying prevention in Islamic educational institutions. The study is motivated by the tendency of modern education to emphasize cognitive achievement while giving less attention to moral and affective development, resulting in the persistence of bullying behavior in educational settings. This study employs a qualitative approach using library research. Data were collected from the works of Muslim scholars, scientific journals, and relevant literature on Islamic education and analyzed through content analysis. The findings reveal that compassion-based education is built upon the integration of moral development, purification of the heart, and the cultivation of adab (proper conduct). These elements contribute to the development of empathy, social concern, and moral awareness among students. Therefore, compassion-based education (rahmatan lil ‘alamin) has strong relevance as a preventive approach to reducing bullying behavior and fostering a more humane and supportive educational environment in Islamic educational institutions.

The Integrated Primary Care Posyandu (ILP) program requires cadres to assume a much broader role compared to conventional Posyandu services. In the working area of Puskesmas Penusupan, ILP achievement reached only 20% in 2025, while merely 25 out of 225 cadres (11%) had received related training. This condition indicates that cadre readiness needs to be examined more comprehensively before the program can be implemented optimally. This study aimed to analyze the factors associated with the readiness of ILP Posyandu cadres using a descriptive-analytic method with a cross-sectional approach. The study involved 144 cadres selected proportionally from a population of 225 using the Slovin formula. Data were collected through structured interviews using a validated questionnaire and analyzed using univariate, bivariate (Spearman Rank), and multivariate (ordinal logistic regression) analyses. The results showed that among the eight variables examined, only skills (r = 0.171; p = 0.040) and resource availability (r = 0.225; p = 0.007) were significantly associated with cadre readiness. In the multivariate analysis, resource availability emerged as the most influential factor with an OR = 2.839 (95% CI: 1.311–6.149), indicating that cadres supported by adequate resources were 2.8 times more likely to demonstrate good readiness. In conclusion, resource availability was identified as the dominant factor associated with the readiness of ILP Posyandu cadres in the working area of Puskesmas Penusupan. Therefore, improving the availability of facilities and infrastructure, providing practice-based training, and strengthening operational support should become priority programs in the future.

Diabetes Mellitus is a non-communicable disease with an increasing prevalence and has become a major public health concern globally and nationally. The incidence of diabetes mellitus is influenced by various individual and environmental factors. This study aimed to analyze the relationship between age, gender, and residential proximity to landfill areas and the incidence of diabetes mellitus in Bendosari District. A quantitative analytic observational design with a cross-sectional approach was employed. The study population consisted of 1,890 patient visit records recorded in the Management Information System of Bendosari Public Health Center in 2025. A total of 363 respondents were selected using proportionate stratified random sampling. Data were analyzed through univariate analysis, bivariate analysis using the Chi-Square test, and multivariate analysis using multiple logistic regression. The findings showed that age was significantly associated with diabetes mellitus incidence (p=0.000; OR=79.790; 95% CI=53.511–118.974). Gender was also significantly related to diabetes mellitus incidence (p=0.000; OR=0.298; 95% CI=0.203–0.435), indicating that females had a higher risk than males. In contrast, residential proximity to landfill areas was not significantly associated with diabetes mellitus incidence (p=0.308; OR=1.247; 95% CI=0.439–3.540). Multivariate analysis revealed that age and gender were the dominant factors influencing diabetes mellitus incidence, with the model explaining 56.1% of the variation. The study concludes that age and gender are significant determinants of diabetes mellitus incidence, whereas residential proximity to landfill areas has not been proven to significantly affect its occurrence.