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Pengabdian ini bertujuan memperkuat akuntabilitas pengelolaan dana pembangunan masjid melalui penyusunan informasi keuangan berbasis ISAK 335. Pengabdian menggunakan pendekatan Participatory Action Research (PAR) melalui observasi, wawancara, dan pendampingan penyusunan laporan keuangan. Kegiatan dilakukan melalui klasifikasi transaksi, penyusunan laporan keuangan, serta analisis efisiensi dan partisipasi donatur. Hasil pengabdian berupa tersusunnya laporan keuangan pembangunan masjid yang lebih sistematis dan informatif. Takmir memperoleh pemahaman mengenai efisiensi penggunaan dana dan pola partisipasi masyarakat. Transparansi laporan keuangan juga memperkuat kepercayaan jamaah terhadap pengelolaan dana pembangunan. Pengabdian ini berdampak pada penguatan tata kelola, akuntabilitas, dan keberlanjutan partisipasi masyarakat dalam memakmurkan masjid.

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Along with the times, computer technology is developing very rapidly. The increasingly rapid development of computer technology means that everyone is required to utilize computer technology in their daily lives. Utilization of technology is one of the implementation roles of scientific disciplines. The reason behind the formation of this research is so that in the future it will become a fun learning concept in the introduction of objects and shapes in children and the motor development of children. children are usually more interested in seeing pictorial text, or pictures that contain lots of color. The Viola Jones method itself was chosen as the research completion algorithm. The Viola Jones method is usually used as a method in research that discusses the detection of objects, faces and others. The Viola Jones method was chosen because it has a high level of accuracy that can reach 100% probability.

This study aims to determine the soil classification of rock disintegration products based on the Unified Soil Classification System (USCS) and analyze its relation to sample depth variations on the lowwall slope of Pit North, PT Karya Putra Borneo, Kutai Kartanegara Regency, East Kalimantan. Soil samples were obtained through the Slake Durability test, simulating rock weathering from wetting and drying cycles, producing fine particles classified as weathered soil. These samples were analyzed for physical properties using Atterberg Limits tests and Grain Size Analysis. Observation point coordinates were X 508523.011 m, Y 9922791.186 m, at an elevation of 87.548 m. Drilling indicated soil material at 0–1.5 m depth; claystone with coal fragments at 2.97–4.44 m; siltstone with coal fragments at 4.44–10.55 m; and claystone at 12.05–29.36 m. USCS classification showed the materials were dominated by fine-grained soils: clay (CL) and silt (ML), with minor silty sand (SM). Correlation with borehole depth revealed no significant changes in soil classification, indicating that depth variations primarily affect soil physical properties rather than its classification type.  

The proliferation of online gambling promotional comments on Indonesian social media has become a serious issue requiring fast and accurate automated handling. This study aims to implement a Hybrid Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) method to classify online gambling comments and compare its performance with standalone RNN and LSTM models. The research utilized a dataset of 10,230 comments subjected to comprehensive preprocessing stages, including the normalization of non-standard language using a slang dictionary. Testing was conducted across three data-splitting scenarios: 90:10, 80:20, and 70:30. Experimental results demonstrate that the standalone LSTM model achieved the highest average accuracy of 97.45%. However, the Hybrid RNN–LSTM model showed significant superiority in terms of performance stability, yielding the lowest standard deviation (0.0027) and the smallest Coefficient of Variation (0.28%) across all scenarios. These findings indicate that while the LSTM architecture is highly effective at capturing short-text context, the Hybrid approach provides better robustness against fluctuations in data proportions, making it highly relevant for implementation as an automated detection system on social media.

Private vehicles are a frequently used mode of transportation because they are considered more practical. However, using private vehicles carries several risks, such as traffic accidents due to drivers losing focus on the road due to other activities, such as making calls on smartphones, drinking, or operating the radio. Approximately 90% of accidents are caused by human error. Convolutional Neural Network (CNN) is a type of neural network commonly used on image data. CNN is often used for image classification due to its high performance and accuracy. Therefore, this study aims to analyze the performance of CNN for the classification of distracted driving activities. The results show that the CNN model is able to effectively classify images of distracted driving activities, with an accuracy of approximately 99% across all datasets and across all input image size variations. Furthermore, the results of this study also show that differences in right-hand and left-hand drive datasets do not significantly affect model accuracy. Variations in input image size also do not significantly affect model accuracy, but do affect the training duration.

Freshwater fish diseases significantly affect aquaculture productivity and economic sustainability, while accurate visual classification remains challenging due to interclass similarity and image variability. This study presents a comparative evaluation of three deep learning architectures—DenseNet201, ResNet50, and EfficientNetV2-S—using a stepwise optimization strategy combined with Gradient-weighted Class Activation Mapping (Grad-CAM) for freshwater fish disease classification. Models were trained through three phases: baseline, optimized, and fine-tuned. Performance was evaluated using accuracy, precision, recall, F1 score, Matthews correlation coefficient (MCC), Cohen’s kappa, and per-class ROC–AUC. Results show consistent performance improvement across all architectures, with EfficientNetV2-S achieving the highest accuracy (97.14%), followed by ResNet50 (96.11%) and DenseNet201 (94.40%). High ROC–AUC values (>0.98) indicate strong discriminative capability. Grad-CAM analysis confirms that all optimized models focus on biologically relevant lesion regions, enhancing model transparency and reliability.

This research develops a machine learning model to classify customer loyalty using the Random Forest algorithm. Customer churn is a critical issue that reduces revenue and increases acquisition costs. A dataset of 50,000 customers from global e-commerce and subscription platforms was processed through data cleaning, imputation, outlier handling, and class balancing with SMOTE. The Random Forest model was built as a baseline and optimized with hyperparameter tuning. Evaluation using accuracy, precision, recall, and F1-score shows that the optimized model achieved 90.81% accuracy and 83.87% F1-score, outperforming previous Naïve Bayes approaches. Feature importance analysis highlights customer service interactions, lifetime value, and demographic factors as key predictors of churn. These findings demonstrate Random Forest’s effectiveness in churn prediction and provide practical insights for customer retention strategies

The spread of hoax news in digital media is a serious problem because it can affect public opinion and social stability. This study aims to classify hoax news using the Support Vector Machine (SVM) algorithm. The dataset used is a hoax clarification dataset from the Ministry of Communication and Digital (Komdigi) of the Republic of Indonesia, totaling 1,872 data. The research process includes data collection, text pre-processing, feature extraction using TF-IDF, and classification using the SVM algorithm. Implementation was carried out using Google Colaboratory (Google Colab). Test results show that the SVM algorithm is able to provide good performance in classifying hoax news based on its topic with satisfactory accuracy, precision, recall, and F1-score values.

The increasing sedentary lifestyle in the digital era has the potential to cause various health problems due to lack of physical activity. One approach that can be taken to encourage physical activity is through the use of digital games with body movement-based control mechanisms. This study aims to develop a body gesture-based game character control system using a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. CNN is used to extract spatial features from each video frame, while LSTM serves to model the temporal relationship between frames so that movement patterns can be recognized sequentially. The research method used refers to the Machine Learning Lifecycle stages, starting from data collection, preprocessing, model development, to implementation in the endless runner game genre. Testing results show that the CNN–LSTM model is capable of classifying body gestures and generating outputs that can be used as commands to control game characters. The implementation of this system enables more natural and interactive game interactions without conventional input devices, and has the potential to encourage players to lead a more active lifestyle.

The rapid development of digital technology has led to an increased adoption of digital payment methods in online transaction-based businesses. However, in practice, failures and limitations in the implementation of digital payment systems still occur, potentially disrupting transaction processes and reducing customer convenience. Payment related obstacles may result in transaction cancellations and increase the risk of customer churn. This study aims to analyze the impact of failures and limitations in digital payment methods on customer churn using a classification-based approach. The data used in this research are secondary e-commerce customer data obtained from the Kaggle platform, including transaction information, payment methods, customer behavior, and historical transaction records. The research methodology consists of data preprocessing, time-based feature engineering, and classification modeling using logistic regression, decision tree, and random forest algorithms. Model performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results indicate that the decision tree model demonstrates superior capability in identifying churn customers compared to the other models, although it does not always achieve the highest accuracy. In addition to digital payment methods, other factors such as purchase value, transaction frequency, purchase timing patterns, and product return rates also influence customer churn. The findings highlight the importance of optimizing digital payment systems as part of customer experience enhancement strategies and customer retention efforts in online transaction–based businesses.

This study aims to analyze consumer purchasing behavior on e-commerce platforms using the Decision Tree algorithm as an easily interpretable classification method. The dataset used consists of 12,330 transaction records with 18 attributes representing visitor characteristics and user activities during interactions with the e-commerce platform. The research stages include data exploration to identify initial patterns, data preprocessing to handle missing values and class imbalance, splitting the data into training and testing sets, training the Decision Tree model, evaluating model performance, and visualizing the tree structure to analyze decision rules.The test results show that the Decision Tree model with a maximum depth of 3 achieves fairly good performance, with an average accuracy of 89.78%, precision of 69.82%, recall of 59.95%, and an F1-score of 64.51% for the buyer class. The visualization of the decision tree provides clear interpretation of the main attributes influencing purchasing decisions, thereby facilitating understanding for non-technical decision makers. Overall, this study demonstrates that the Decision Tree method is effective in modeling consumer purchasing behavior in e-commerce and can be utilized as a basis for data-driven business decision making, particularly in marketing strategies and improving sales conversion rates.

Criminal responsibility is constructed through the elements of homicide offences, the assessment of mens rea, accomplice liability, and sentencing rationality grounded in individual culpability. In judicial practice, however, cases involving the killing of women are frequently framed at the outset through the labels of “honour” or “intimate relationship,” leading honour killing and intimate partner femicide to be treated as interchangeable categories. This practice shifts the assessment of intent toward the perpetrator’s narrated motive and narrows accountability to the last physical actor. This research aims to formulate, first, legal criteria and evidentiary indicators for distinguishing the two categories through a staged judicial classification test, and second, to assess the implications of such classification for the construction of intent, the attribution of responsibility to non-executing actors, and sentencing rationality through disciplined reason-giving. The study employs a normative legal method with a conceptual approach, based on library research of primary and secondary legal materials. The findings demonstrate that the core deficiency lies in the absence of an operational classification device, allowing honour narratives to displace structured mens rea analysis and to obscure the causal contributions of non-executors. The article proposes working definitions and a stepwise indicator-based test—focusing on the presence of determinative social pressure or sanctioning, provable role allocation within perpetrator networks, and prior threats framed in terms of honour restoration—and links these indicators to concrete doctrinal consequences for intent, accomplice liability, and sentencing. Through this framework, judicial reasoning is redirected from label-driven interpretation toward accountability, while restraining the use of honour as a mitigating rationale and preventing femicide patterns from being concealed by reputational narratives.

Thіs studу aіmеd to examіnе the eligibilitу оf tеасhеr honorаrium bу іmрlemеntіng a classifiсatіon method usіng thе Decіsіon Тree algorithm. Тhе primary іssuе addressеd in thіs rеsеаrсh is the absеnce of а fair and dаtа-driven salarу sуstеm аt SМA YPKPР. А сlаssificatіon aрproach was emрloуеd to сatеgorіze teасhers intо "Elіgіble" and "Not Elіgiblе" grоuрs basеd оn attributеs such аs tеachіng hоurs, hourly wаge, eduсatiоn lеvel, jоb роsіtіon, сеrtіfіcаtіоn allowаnсe, аnd schoоl status.Thе сlаssifiсatіon model was dеveloрed usіng RаріdМiner sоftwаrе. Тhе datasеt was dіvidеd into trainіng and tеsting sets usіng a sрlіt datа technique. Тhe modеl wаs еvaluatеd usіng metrісs such as acсuraсy, рrecisіоn, rеcall, and сonfusiоn matrіх. The rеsults indіcаtеd that thе Dесіsiоn Тree model аchіеved аn асcurасy оf 93.75% in сlаssіfуing tеаchеr honorarium еligіbіlity. Тeасhing hours and hourlу wаge werе idеntifіеd as thе twо most іnfluеntial variables іn the сlаssіfісаtіоn рroсеss.Аs a form of vаlіdatіоn, addіtionаl statistіcal аnalysis was соnducted usіng SРSS. The Рeаrsоn cоrrelаtіon tеst showеd а sіgnіficаnt relаtionshір bеtween teaching hours and hourlу wаge wіth thе tоtаl honоrаrіum rеcеivеd. Мultіplе .Lineаr rеgressiоn аnalysis resulted іn аn R Squаre valuе of 0.860, indicаting that 86% оf thе varіation in hоnorarium сan be eхрlаіnеd bу thе twо vаrіаblеs.Тhis study іs expесtеd tо serve аs а foundаtіоn for mоre objеctive аnd dаtа-drіven dеcisіоn-mаking іn thе tеaсhеr comреnsation sуstem. Тhe findіngs dеmоnstrаte thаt а combinаtiоn of datа minіng аnd stаtіstісаl аnаlуsis aрprоаches сan bе usеd to devеlop a trаnsparent, fair, аnd efficient sаlаrу system.

The increasing intensity of traffic object movement in urban areas has not been accompanied by adequate road infrastructure, resulting in traffic congestion, air pollution, and a higher risk of traffic accidents. One of the primary causes of accidents is traffic violations, particularly wrong-way driving behavior. This study develops a video-based automated traffic violation detection system using the YOLOv5 algorithm. A computer vision approach is employed to detect, classify traffic objects, and count wrong-way violations in real time. Due to limited access to real-world traffic violation footage, simulated traffic scenarios are used as testing data. The system is evaluated on four traffic object classes: motorcycles, cars, buses, and trucks. Experimental results demonstrate strong performance, achieving a precision of 90%, a recall of 92%, and an F1-score of 91%, while the traffic object counting accuracy reaches 89%. These findings indicate that the proposed system has significant potential to support traffic analysis and assist authorities in making more effective decisions to reduce congestion and traffic accidents.

User reviews on the Shopee e-commerce platform represent an important source of information for understanding consumer perceptions of products and services. Sentiment analysis is commonly applied to classify user opinions into positive, neutral, and negative sentiment categories based on textual data. This study aims to analyze the performance of the Long Short-Term Memory (LSTM) method in sentiment classification of Shopee user reviews. The dataset used in this study consists of Indonesian-language user reviews that have undergone preprocessing stages, including case folding, text cleaning, tokenization, and stopword removal. The LSTM model was trained using preprocessed text represented as word sequences. Model performance was evaluated using overall accuracy and class-wise classification results. The experimental results indicate that the LSTM method achieved an overall accuracy of 87.62%. In addition, the classification performance for the positive sentiment class reached 95.27%, the neutral class achieved 4.96%, and the negative class reached 74.26%. These results demonstrate that the LSTM method performs well in classifying sentiment in Shopee user reviews, particularly for positive sentiment. This study is expected to provide insights and references for the application of deep learning methods in sentiment analysis of Indonesian e-commerce review data.

This research aims to develop a multiclass flower image classification system using the Convolutional Neural Network (CNN) algorithm with the EfficientNet architecture. The main problem addressed is the difficulty of manual identification of flower species that share high visual similarity. The research stages include collecting 17,299 flower images across 19 classes, performing data preprocessing such as image resizing, pixel normalization, and augmentation, followed by model training using the EfficientNet transfer learning approach. The model was trained for 10 epochs with an 80:20 training-validation data split. The evaluation results show that the model achieved a validation accuracy of 98.05% with a loss value of 0.0968, and an average precision, recall, and F1-score of 0.98. The trained model was then implemented into a web-based application built using the Next.js framework, enabling users to upload flower images and obtain real-time classification results via the Hugging Face API. The system successfully identified flower species with a confidence level of 99.87%. These findings demonstrate that combining a modern CNN architecture with transfer learning provides efficient and highly accurate flower classification performance, which can be effectively implemented for educational and digital conservation purposes.

Bintan Island has abundant bauxite soil resources; however, its utilization as road construction material remains limited. The scarcity of high-quality granular material in the region necessitates the use of available local resources, particularly for pavement subgrade layers. This article aims to analyze the classification and mechanical properties of native soils in Bintan Island through a systematic literature review. The reviewed literature includes laboratory test results of bauxite soil. The findings indicate that bauxite soil exhibits low plasticity, relatively high CBR values (±35%), and is classified as CL (USCS) and A-2-4 (AASHTO). These results suggest that bauxite soil is suitable for subgrade applications, although require stabilization with binding agents. The implication of this review highlights that the utilization of local materials could support sustainable infrastructure development in island regions by reducing dependency on imported materials.  

Private vehicles are a frequently used mode of transportation because they are considered more practical. However, using private vehicles carries several risks, such as traffic accidents due to drivers losing focus on the road due to other activities, such as making calls on smartphones, drinking, or operating the radio. Approximately 90% of accidents are caused by human error. Convolutional Neural Network (CNN) is a type of neural network commonly used on image data. CNN is often used for image classification due to its high performance and accuracy. Therefore, this study aims to analyze the performance of CNN for the classification of distracted driving activities. The results show that the CNN model is able to effectively classify images of distracted driving activities, with an accuracy of approximately 99% across all datasets and across all input image size variations. Furthermore, the results of this study also show that differences in right-hand and left-hand drive datasets do not significantly affect model accuracy. Variations in input image size also do not significantly affect model accuracy, but do affect the training duration.

The rapid development of smartphone technology today creates challenges for consumers and manufacturers in determining an objective price range based on highly varied technical specifications. This study aims to implement the Random Forest algorithm in classifying smartphone price ranges into four main categories, namely low, mid-range, high, and flagship. The research method was carried out systematically through the stages of loading a dataset of 2,000 entries, exploratory data analysis (EDA) to ensure data integrity, and model training with a training and testing data split of 80:20. The results showed that the Random Forest model achieved a significant overall accuracy rate of 89%. Based on feature importance analysis, it was found that RAM capacity was the most dominant determining factor, contributing 47% to prediction accuracy, followed by battery power and screen resolution as supporting features. These findings have strategic implications for manufacturers to prioritize memory capacity upgrades in determining product pricing in the market, as well as providing guidance for consumers in assessing the fairness of a device's price based on its technical capabilities.