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PEMBUATAN E-KATALOG SEBAGAI MEDIA PROMOSI DIGITAL UMKM WARGA RW 03 KELURAHAN MANYAR SABRANGAN
Get PDFIndonesia, with a population of around 270 million, has great potential to develop its economy through the utilization of existing resources. However, the challenges in strengthening the economy are still great, especially with the rapid development of technology 4.0 which affects the dissemination of information. Digital marketing strategies are key to driving economic growth, especially for MSMEs that often still use conventional marketing methods. Observation results show that many MSMEs face difficulties in implementing digital marketing due to lack of knowledge and skills. This community service activity was carried out in RW 03, Manyar Sabrangan Village, Surabaya, to help MSMEs switch to digital marketing by creating an e-catalog that can be accessed via QR code. The method used is a participatory method with a community approach. Community service activities involve collecting product data, creating designs using digital devices, and handing over and assisting in the use of e-catalogs to MSMEs. Evaluation was carried out to assess the effectiveness of the use of e-catalogs by MSMEs. The implementation of e-catalogs showed an increase in operational efficiency and market reach for MSMEs. This program is expected to provide a significant positive impact, strengthen the position of MSMEs through effective promotional strategies, and increase the capacity of human resources in business management and digital marketing
IMPLEMENTASI APLIKASI WEBSITE SEKOLAH TAMAN KANAK-KANAK DEWI SARTIKA BANDUNG
Get PDFCommunity service activities held at Dewi Sartika Bandung Kindergarten School are based on several areas for improvement in publicizing the school's activities to the community. The school only had social media such as Instagram and Facebook during its establishment. Another shortcoming is the documentation of new student registration, which still uses registration forms, hard copies of family cards, and identity cards of prospective students' parents. The effectiveness of document storage could be improved. It is still prone to data loss, so it is necessary to have digital data storage to overcome these shortcomings by creating a website that is integrated with digital prospective student data collection. The method applied to measure success in this community service activity is quantitative method, namely giving a questionnaire which is then calculating to obtain a percentage value of success from this activity. The results of community service activities carried out at the school are in the form of a website that can be accessed by teachers, parents of students, prospective school students, and public society and is expected to help with administrative work at the Dewi Sartika Bandung Kindergarten. The percentage of success of the website that has been created is in the “sufficient/normal” category, which is represented by a percentage value of 46.5 %
PELATIHAN LITERASI DIGITAL DI SMAN 15 SERAM BAGIAN BARAT
Get PDFLatu Village in West Seram faces challenges in accessing quality education, particularly in digital literacy. To address this issue, digital literacy training at SMAN 15 West Seram was implemented as a concrete step. The activity aims to enhance the digital understanding and skills of teachers and students so they can optimally utilize technology in the learning process. The training was held on August 21, 2024, from 09:00 to 12:00, using a training approach that included needs assessment, digital literacy socialization, discussions, and evaluation. The training materials covered basic software such as Microsoft Office and various online learning platforms. The results showed a significant increase in students’ knowledge of digital literacy, including internet safety, hoax identification, and the importance of verifying information. A total of 86.7% of students better understood the meaning of digital literacy, 96.7% became aware of its positive impact, and 93.3% knew how to respond to messages from strangers on social media, reflecting greater awareness of cybersecurity. Although the program successfully improved students’ skills and awareness, it still faced challenges such as limited internet access and inadequate school facilities. Nevertheless, this initiative is expected to serve as a model for developing education in remote areas and contribute meaningfully to improving the quality of education in Indonesia
MODEL OF CYBERBULLYING DETECTION ON SOCIAL MEDIA USING MULTI-LABEL DEEP LEARNING: A COMPARATIVE STUDY
Get PDFCyberbullying is the deliberate act of using technology to harm others. This study aims to analyze 400 Instagram comments obtained via API from previous research. The data were labeled into three classes: negative (containing cyberbullying), positive (non-bullying, supportive), and neutral (neither positive nor negative). The data for experiment was divided into 70% for training and 30% for testing. The research methodology consists of three main stages. The first stage is text preprocessing, which includes tokenization (splitting comments into tokens), filtering (removing unimportant words or stop-words), and stemming (converting words with affixes into their root forms). The second stage is classification analysis using BiLSTM, LSTM, RNN, and CNN-1D methods. The third stage is evaluation by comparing the model's classification results with manually labeled data using accuracy as the evaluation metric. The results show that the BiLSTM model performed the best, achieving an accuracy of 98.51% on the training data and 81.82% on the testing data. The BiLSTM method used in this study can be further adapted to enhance the effectiveness of cyberbullying detection in various applications
THE ROLE OF THE INTERNET OF THINGS (IOT) IN ELECTRIC VEHICLE MANAGEMENT AND MAINTENANCE
Get PDFThe growing adoption of electric vehicles (EVs) as an eco-friendly alternative to fossil fuel-based vehicles necessitates more advanced management and maintenance systems. The Internet of Things (IoT) presents significant potential to enhance EV management by enabling real-time monitoring and data analysis through interconnected sensors and technologies. This research investigates the integration of IoT in electric vehicle systems, focusing on real-time battery health monitoring, early detection of technical issues, and route optimization for improved energy efficiency. The study employs a system design and testing approach, supported by descriptive-analytical analysis using data from case studies, literature reviews, surveys, and interviews. Findings indicate that IoT implementation in EVs yields notable advantages. Real-time battery health tracking provides accurate performance insights, achieving a 92% accuracy rate in predicting battery degradation. Technical problem detection through sensor analysis enables timely maintenance, leading to a 30% reduction in vehicle downtime. Furthermore, IoT-based route optimization improves energy efficiency, reducing energy consumption by 15% and extending battery lifespan by 20% compared to traditional systems. These results underscore the practical benefits of IoT in enhancing EV performance and operational efficiency. The system enables users and service providers to make informed decisions regarding vehicle maintenance and usage, promoting better understanding of battery conditions. Ultimately, the application of IoT technology contributes to extending battery life, minimizing vehicle downtime, and supporting broader efforts in energy efficiency and carbon emission reductio
DETECTION OF FRAUDULENT ATM TRANSACTIONS USING RULE-BASED CLASSIFICATION TECHNIQUES
Get PDFThe significant rise in ATM fraud—reflected in 130,472 suspicious transactions reported in Indonesia in 2022—highlights the urgent need for accurate and efficient real-time fraud detection systems. This study evaluates two complementary detection approaches using a dataset of 20,000 anonymized ATM transactions collected from XYZ Bank between January and December 2022, each labeled by internal fraud analysts as fraud or non-fraud. The models compared are a Rule-Based Classifier and a Decision Tree classifier. The Decision Tree demonstrates strong overall performance, achieving 98% accuracy, 75% precision, 79% recall, and a 77% F1-score, indicating a reliable ability to detect diverse fraud patterns. In contrast, the Rule-Based Classifier yields 60% accuracy, 97% precision, 60% recall, and a 74% F1-score, showing high precision with fewer false alarms but a limited ability to detect varied fraud cases. These results emphasize the trade-off between specificity and sensitivity in static versus adaptive models. To address this, a hybrid detection framework is proposed—combining rule-based screening to filter obvious non-fraud cases, followed by Decision Tree analysis to handle more complex patterns. This approach aims to reduce unnecessary transaction holds and improve detection reliability. This study contributes to the limited comparative research on fraud detection methods using real ATM transaction data within the Indonesian banking context. Future research will focus on adaptive learning models to maintain performance against evolving fraud behaviors in dynamic financial systems
APPLICATION OF MACHINE LEARNING MODELS FOR FRAUD DETECTION IN SYNTHETIC MOBILE FINANCIAL TRANSACTIONS
Get PDFThe financial industry faces challenges in detecting fraud. The 2023 Basel Anti-Money Laundering (AML) Index report shows a worsening money laundering risk trend over the last five years in 107 countries. And according to the Financial Action Task Force (FATF) in 2023, this is exacerbated by financial institutions which have problems with low reporting of suspicious financial transactions (Suspicious Transaction Report). Limited access to confidential financial transaction data is an obstacle in developing machine learning-based fraud detection models. To overcome this challenge, the research uses PaySim synthetic datasets that mimic real financial transaction patterns. The CRISP-DM approach is used, including the Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation and Deployment stages. The algorithms used are Decision Tree, Random Forest, and XGBoost. Model evaluation is carried out using accuracy, precision, recall, F1-score, specificity, cross-validation and ROC-AUC metrics. The results show that the Random Forest algorithm has the best performance with 99% accuracy, followed by XGBoost (98.9%) and Decision Tree (97%). Data analysis shows that cash-out and transfer transactions have the highest risk of fraud. This model has proven effective in detecting suspicious financial transactions with a high level of accuracy. This research makes a significant contribution to mitigating financial risks, supporting anti-fraud policies, and encouraging innovation in fraud detection using synthetic data
DEEP GATED RECURRENT UNITS PARAMETER TRANSFORMATION FOR OPTIMIZING ELECTRIC VEHICLE POPULATION PREDICTION ACCURACY
Get PDFThe development of electric vehicles is an important innovation in reducing greenhouse gas emissions while reducing dependence on fossil fuels. The main problem in developing electric vehicles is the lack of adequate infrastructure. Inaccurate predictions regarding the number of electric vehicles hinder adequate infrastructure planning and development. This research proposes the use of the Gated Recurrent Units (GRU) algorithm to improve the accuracy of electric vehicle population predictions by carrying out GRU parameter transformations. This parameter transformation involves searching and adjusting the parameters of the GRU model in more depth to increase its ability to handle uncertainty in electric vehicle population data. After carrying out the training and testing process, the result was that the hyperparameter model using RandomizedSearchCV was the best model because it had the highest accuracy compared to other models tested with a combination of GRU_unit 64 and 128, dropout 0.5 and 0.6, batch size 64 and the number of epochs was 100 which had MAE results: 257.94, MSE: 66655.087, RMSE: 258.176, and Accuracy of 100%
DEVELOPMENT OF SKIN CANCER PIGMENT IMAGE CLASSIFICATION USING A COMBINATION OF MOBILENETV2 AND CBAM
Get PDFSkin cancer is one of the most common types of cancer worldwide, making early detection a crucial factor in improving patient recovery rates. This study compares three classification methods for pigmented skin cancer images using a combination of VGG16 with CBAM, MobileNetV2 with CBAM, and a hybrid VGG16-MobileNetV2 approach with transfer learning. The dataset used in this study is the Skin Cancer ISIC - The International Skin Imaging Collaboration (HAM10000) from Kaggle, which consists of 10,015 images covering seven types of skin cancer. After balancing, the dataset was reduced to 2,400 images with three main classes: Actinic Keratosis (AKIEC), Basal Cell Carcinoma (BCC), and melanoma (MEL), each containing 800 images. This study involves data preprocessing stages such as augmentation, normalization, and image resizing to ensure optimal data quality. The model training process was conducted using the Adam optimizer, a batch size of 16, and an Early Stopping mechanism to prevent overfitting. Evaluation results indicate that the MobileNetV2 with CBAM model achieved the best performance with a validation accuracy of 86%, followed by the VGG16-MobileNetV2 combination at 77%, while VGG16 with CBAM experienced overfitting with an accuracy of 54%. Additionally, the best-performing model demonstrated a precision of 86.53% and a recall of 86.46%, highlighting its superior stability in detecting skin cancer compared to previous single-model approaches. With these results, the developed system can serve as an effective tool for medical professionals in performing early and more accurate skin cancer diagnose
PENGEMBANGAN TAMAN GIZI SEBAGAI UPAYA PENCEGAHAN STUNTING DI DESA KLATAKAN KECAMATAN TANGGUL KABUPATEN JEMBER
Get PDFStunting is a significant health issue in Indonesia, including in Jember Regency, which still faces a high prevalence rate. Without proper intervention, stunting can hinder development and reduce the quality of life for affected children. Efforts to reduce stunting rates are being made through various programs, one of which is the development of a nutrition garden. This program is based on a Participatory Action Research (PAR) approach and aims to improve community knowledge and nutritional practices by utilizing household yard spaces. The nutrition garden employs a hydroponic farming system to grow various nutritious vegetables, such as chili, spinach, and mustard greens, which are distributed to families with young children, pregnant women, and breastfeeding mothers. Through intensive education and mentoring, the nutrition garden is expected to meet the nutritional needs of the local community, raise awareness about the importance of a balanced diet, and effectively reduce stunting rates in Klatakan Village. This study reveals that the "Nutrition Garden Development Program in Klatakan Village, Tanggul District, Jember Regency" has successfully enhanced community understanding and participation in stunting prevention efforts by utilizing household yards to grow nutritious vegetables through hydroponic methods