Publication Search

ASTM A36 steel has relatively low hardness and corrosion resistance, making surface treatment necessary to improve its material properties. This study aims to determine the effect of temperature and holding time variations in the pack carburizing process on the hardness, corrosion rate, and microstructure of ASTM A36 steel. The pack carburizing process was carried out using coconut shell charcoal as the carburizing medium with temperature variations of 850°C, 900°C, and 950°C and holding times of 20, 40, and 60 minutes, followed by quenching in distilled water. Hardness testing was conducted using the Rockwell B scale (HRB) method, corrosion rate testing was performed according to the ASTM G31 method, and microstructural observations were carried out using Scanning Electron Microscopy (SEM). The results showed that increasing the temperature and holding time improved the hardness and corrosion resistance of ASTM A36 steel. The highest hardness value was obtained at a temperature of 950°C with a holding time of 60 minutes, reaching 114.1 HRB. Microstructural analysis revealed the formation of a martensitic phase on the specimen surface after the carburizing process.

Income inequality is a structural development problem that can trigger social conflict, affect long-term economic growth, and hinder the achievement of SDGs. The purpose of this study is to analyze the factors that contribute to income inequality in East Java Province by combining demographic, social, and economic factors. The data used includes panel data from 38 districts and cities in East Java Province between 2017 and 2023 period. The analysis method used is panel data regression with a fixed effect model. To measure the effect of variables on income inequality, the testing was conducted partially and simultaneosly. The individual test results show that elderly dependency and poverty have a positive and significant relationship, while the human development index and economic growth are proportional but not significantly, and the open unemployment rate is not significant negative. Simultaneously, all variables have a significant effect on income inequality of 55.67 percent, with the remaining 44.37 percent influenced by variables outside the scope of this study.

Non-Communicable Diseases (NCDs) such as Diabetes Mellitus and Gout pose a serious threat to the health of the elderly due to their often asymptomatic nature. Cost barriers hinder the elderly community from accessing laboratory services. This community service activity aims to improve health standards and early detection of NCDs through monitoring blood glucose and uric acid levels in the Generasi Kaleb community of the JKI Injil Kerajaan Church, Semarang. The Participatory Health Screening and Education method used includes health education, screening using the Point of Care Testing (POCT) tool, and personal consultation on the results. The service subjects were 50 elderly respondents. The examination results showed a prevalence of abnormal random blood glucose (GDS) of 8%, 32%, had high uric acid levels with a maximum value of 10.3 mg/dL. This activity successfully transformed the elderly's awareness from subjective perception to awareness based on objective data, and encouraged community independence in managing a healthy lifestyle to prevent further NCD complications. .

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.

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

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.

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 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..

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.

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.

Allergic rhinitis (AR) is a chronic inflammatory disorder of the nasal mucosa mediated by immunoglobulin E (IgE) in response to allergen exposure. This condition has become a significant global health concern because of its increasing prevalence and substantial impact on quality of life, sleep, cognitive function, and work productivity. AR is also commonly associated with other atopic diseases, including asthma and atopic dermatitis. This article aims to review the current understanding of allergic rhinitis, focusing on epidemiology, pathophysiology, clinical manifestations, diagnostic approaches, and evidence-based management strategies. This study used a narrative literature review method by analyzing scientific articles, clinical guidelines, and peer-reviewed journals published between 2015 and 2025. Literature sources were obtained from PubMed, Google Scholar, and relevant medical databases using keywords related to allergic rhinitis, IgE, diagnosis, and immunotherapy. The findings indicate that allergic rhinitis involves complex immune mechanisms characterized by Th2 lymphocyte activation, IgE production, and inflammatory mediator release. Diagnosis is primarily established clinically and supported by allergy testing such as skin prick tests or serum-specific IgE measurement. Intranasal corticosteroids remain the first-line therapy for moderate to severe cases, while antihistamines and leukotriene receptor antagonists are used as adjunctive therapies. Allergen immunotherapy has shown effectiveness in modifying disease progression and improving long-term outcomes. In conclusion, allergic rhinitis is a manageable chronic disease that requires a comprehensive and individualized treatment approach involving environmental control, pharmacotherapy, and immunotherapy.

This study aims to determine the production process and the level of consumer acceptance of Bandung nagasari cake utilizing stevia as a natural sweetener and butterfly pea flower extract (Clitoria ternatea) as a natural coloring agent. The research employed a quantitative approach with an experimental method through organoleptic testing involving 20 panelists. Data collection techniques consisted of literature review, questionnaires, and organoleptic evaluation covering taste, texture, aroma, and appearance. The obtained data were analyzed using descriptive quantitative analysis with percentage calculations. The findings indicated that the Bandung nagasari formulation containing stevia and butterfly pea flower extract was more preferred than the formulation using granulated sugar. Approximately 90% of panelists preferred the taste attribute due to its healthier perception, while 80% of panelists favored the texture, aroma, and appearance attributes because of the softer texture and the attractive natural coloration produced by the butterfly pea flower extract. Furthermore, the product demonstrated a shelf life of 12 hours at room temperature and up to 3 days under refrigerated storage conditions. The study concludes that the incorporation of stevia and butterfly pea flower extract may serve as an innovative development of traditional Bandung nagasari cake without eliminating its traditional characteristics and shows favorable consumer acceptance.

Hydrocephalus is defined as active distension of the brain’s ventricular system, resulting from inadequate flow of cerebrospinal fluid from its site of production to its site of absorption into the systemic circulation. Hydrocephalus can affect anyone at any age; pediatric hydrocephalus affects 1 in 1,000 live births and is the most common cause of brain surgery in young patients. Hydrocephalus is a pathological condition characterized by abnormal accumulation of cerebrospinal fluid (CSF) due to increased production, impaired flow, or reduced absorption. Ventricular enlargement occurs in response to increased CSF volume and can lead to structural damage to the brain parenchyma. This condition can be congenital or acquired. One key point in prenatal diagnosis is the differentiation between fetal hydrocephalus and non-hypertensive ventriculomegaly. The former qualifies for intrauterine treatment with good outcomes. However, the latter can result in either favorable or catastrophic outcomes due to a damaging etiology, as seen in viral infections such as Zika virus. For an accurate diagnosis, fetal MRI is performed to detect brain anomalies, in addition to fetal ultrasound (to detect common complications), karyotype testing, and TORCH testing (toxoplasma, rubella, cytomegalovirus, herpes simplex). Obstetric management of fetal hydrocephalus depends on the gestational age at diagnosis and the presence of other anomalies. Treatment options include termination of pregnancy before the fetus is viable, placement of a ventriculoamniotic shunt, cephalocentesis before delivery, and/or cesarean section.

Coal hauling activities are an important part of mining operation because they affect production continuity, cycle time efficiency, and operational safety. This study aims to analyze the requirements of road support equipment on the coal hauling road from Sector 4 to the new Coal Processing Plant (CPP) at PT. Asmin Bara Bronang, Central Kalimantan. Based on road geometry, traffic density, California Bearing Ratio (CBR), and Unsurfaced Road Condition Index (URCI). The research method used was applied research with a quantitative approach. Primary data ware collected through field measurements consisting of road geometri observations, traffic density observations, Dynamic Cone Penetrometer (DCP) testing to obtain CBR values, and road surface condition assessment using the URCI method. Secondary data were obtained from the company records. The results showed that the hauling road has a total length of 9.1 km with an average width of 16 m, and grade values ranging from -7.68% to 10.52%, which are still below the maximum standard of 12%. Traffic density reached 184 dump trucks/day, for coal hauling and 62 units/day for construction material transportation, indicating high traffic intensity. CBR values ranged from 7% to 100%, showing variations in subgrade bearing capacity. The URCI value ranged from 72,50 to 91.00, indicating fair to good road conditions. Based on the analysis of road conditions and maintenance area requirements, the recommended support equipment for maintaining the hauling road consists of 1 motor grader unit, 1 compactor unit, 1 bulldozer unit, and 1 water truck unit.

The high volume of fishery waste often poses an environmental burden if not managed appropriately. Conversely, the circular economy concept offers sustainable solutions aligned with Maqasid Shariah principles in environmental preservation (Hifz al-Alam). This study aims to educate and implement the processing of fish waste into high-quality animal feed that meets halal standards (halalan thayyiban). This research employs a qualitative approach using the Asset-Based Community Development (ABCD) method. The stages include socializing the concept of Sharia circular economy, technical training on waste processing, and nutritional content testing of the feed. The findings indicate an 80% increase in community understanding regarding the economic value of waste. Technically, the fish waste processing successfully produced an alternative animal feed with high protein content, free from najis (impurity) through a purification process according to Islamic jurisprudence (fiqh). This education proves that the integration of circular economy and Sharia principles not only reduces environmental impact but also creates sustainable economic added value for local communities.

Golf participation has increasingly become a lifestyle-oriented recreational activity that combines physical exercise, social interaction, and personal identity. However, participation decisions are not only shaped by individual interest, but also by demographic readiness, psychographic orientation, digital promotional exposure, and psychological commitment to the sport. This study aims to examine the influence of demographic factors, psychographic factors, and digital promotion on golf participation decisions in Bandar Lampung, with sport commitment as a mediating variable. A quantitative survey approach was employed using purposive sampling. Data were collected from 287 golf participants through a structured questionnaire measured with a five-point Likert scale. The data were analyzed using multiple linear regression and Sobel mediation testing. The findings show that demographic factors, psychographic factors, digital promotion, and sport commitment have positive and significant effects on golf participation decisions. Sport commitment was found to be the strongest predictor and significantly mediated the relationship between demographic factors, psychographic factors, digital promotion, and golf participation decisions. These results indicate that golf participation is influenced not only by access, lifestyle, and digital promotion, but also by the level of commitment developed by participants. This study contributes to sport marketing literature by integrating individual, psychological, and digital factors into one empirical model of golf participation behavior.

This study discusses a student concentration detection system using Convolutional Neural Network (CNN) with the MobileNetV2 architecture. The dataset was adapted from Classroom Student Behaviors and mapped into four concentration categories: highly focused, focused, less focused, and unfocused. The system was tested with a 720p webcam and produced real-time detection data. The evaluation results show an overall accuracy of 75.85%, with the highest precision achieved in the focused class (0.9859) and the highest recall in the highly focused (0.9739) and unfocused (0.9811) classes. The confusion matrix indicates that the focused class was detected most consistently, while highly focused and unfocused classes were often misclassified as focused, resulting in lower precision. In real-time testing, the system operated at an average of 7 FPS and worked optimally when students faced the camera directly with sufficient lighting, but its performance decreased significantly at face angles greater than 45°. User evaluation shows that 75% of students rated the detection results as accurate/very accurate with an average satisfaction score of 3.6 out of 5, and 75% felt assisted in recognizing their concentration level. From the teachers’ perspective, most stated that the results were consistent with classroom observations, and all expressed willingness to reuse the system.

The development of social media, especially TWITTER, has become one of the main means for people to express opinions and criticism on various issues, including the performance of law in Indonesia. This study aims to analyze public sentiment towards the performance of law based on TWITTER user comments using the Naïve Bayes algorithm. The research data consists of 1004 comments collected from several videos related to legal topics. The analysis process includes the stages of data crawling, pre- processing (text cleaning, normalization, and tokenization), labeling sentiment into positive, negative, and neutral, and testing the Naïve Bayes model. The results show that the Naïve Bayes algorithm is able to classify sentiment with an accuracy level of 93.73%. The distribution of sentiment from 1004 comments shows that the majority of public opinion is (negative/positive/neutral), which indicates that public perception of the performance of law is still (critical/positive). These findings are expected to be input for related parties to understand public opinion and improve the quality of legal performance in

The high volume of fishery waste often poses an environmental burden if not managed appropriately. Conversely, the circular economy concept offers sustainable solutions aligned with Maqasid Shariah principles in environmental preservation (Hifz al-Alam). This study aims to educate and implement the processing of fish waste into high-quality animal feed that meets halal standards (halalan thayyiban). This research employs a qualitative approach using the Asset-Based Community Development (ABCD) method. The stages include socializing the concept of Sharia circular economy, technical training on waste processing, and nutritional content testing of the feed. The findings indicate an 80% increase in community understanding regarding the economic value of waste. Technically, the fish waste processing successfully produced an alternative animal feed with high protein content, free from najis (impurity) through a purification process according to Islamic jurisprudence (fiqh). This education proves that the integration of circular economy and Sharia principles not only reduces environmental impact but also creates sustainable economic added value for local communities.

QRIS (Quick Response Code Indonesian Standard) has become a widely used national digital payment standard. User satisfaction with this service needs to be monitored continuously to ensure its sustainability. This study aims to predict the level of QRIS user satisfaction based on their experiences and perceptions expressed organically on the Twitter social media platform. The method used is sentiment analysis with the Naive Bayes classification algorithm implemented using RapidMiner software. The research data was obtained from Twitter user comments collected through web scraping techniques. The text data then went through a preprocessing stage that included cleansing, stopword filtering, stemming, and tokenizing to be prepared as features ready to be processed by the model. The data was divided into training (80%) and testing (20%) subsets for model training and validation. The results showed that the Naive Bayes model was able to predict user satisfaction sentiment with an accuracy of 80.99%. These findings indicate that the model is highly accurate in identifying satisfied comments and sufficiently sensitive in detecting dissatisfaction. This study concludes that sentiment analysis of Twitter UGC data using Naive Bayes is an effective and efficient approach for predicting QRIS user satisfaction in real time. The practical implication of this study is to provide an automatic feedback system for service providers to monitor public sentiment and take targeted corrective actions.