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PELATIHAN DESAIN GRAFIS DENGAN METODE BERBASIS PROYEK BAGI ANAK BERKEBUTUHAN KHUSUS DI SLBN PURBALINGGA
Full text linkThe Graphic Design Training for Children with Special Needs at SLBN Purbalingga is a community service initiative through the Amikom Mitra Masyarakat (AMM) program aimed at improving inclusive education quality. It focuses on developing graphic design skills for students with special needs, helping them become more independent and prepared for future challenges. The program began with an in-depth situational analysis to identify students and the school’s potential and challenges. Structured solutions were implemented through intensive training sessions covering graphic design basics, software usage, and creative, applicable projects. As a result, students showed significant improvement, creating visual projects like posters and becoming more active and motivated. The assessment through the visual design rubric, which includes aspects of composition, color usage, typography, and other technical skills, shows a higher average score at the end of the learning period compared to the initial scores. Teachers reported increased student participation and enthusiasm. Evaluations with students, teachers, and the Amikom Purwokerto team confirmed the program’s effectiveness, highlighted areas for improvement, and provided feedback for future initiatives. This comprehensive, project-based approach not only enhances students technical skills but also strengthens the foundation of competitive, effective inclusive education in Purbalingga. The initiative aims to deliver lasting positive impacts, equipping students to face future challenges while supporting a sustainable, inclusive educational environment
KOPTIHUB: A WAREHOUSE APPLICATION PROTOTYPE FROM COOPERATIVE PERS PECTIVE
Full text linkEffective warehouse management is crucial for ensuring the availability of raw materials and smooth product distribution, particularly at Sentra Industri Kecil Somber (SIKS) Balikpapan, which specializes in soybean-based industries. Manual record-keeping has presented significant challenges, leading to recording errors, stock discrepancies, and delays in raw material procurement. To address these issues, a digital warehouse management prototype, "KoptiHub," was developed using a User-Centered Design (UCD) approach. This approach aimed to enhance inventory tracking efficiency, streamline raw material ordering, and improve overall product distribution. The prototype was evaluated using the System Usability Scale (SUS) with 15 cooperative administrators at SIKS Balikpapan. The evaluation yielded an SUS score of 82.17, resulting in an "A" grade, which indicates high usability and strong alignment with user expectations. Compared to previous warehouse management solutions, KoptiHub demonstrates superior usability, particularly in cooperative settings. However, further improvements, such as a simplified user interface and an AI-driven inventory forecasting feature, could enhance efficiency and accessibility. The results suggest that KoptiHub could serve as a scalable model for digitizing warehouse management in MSMEs and cooperatives, aligning with emerging trends in smart inventory management and supply chain optimization
OPTIMIZATION OF MACHINE LEARNING ALGORITHMS IN THE CLASSIFICATION OF VECTOR-BORNE DISEASES
Full text linkDeveloping a predictive model is the objective of this study, focusing on vector-borne diseases using various machine learning methods, including Random Forest (RF), Logistic Regression (LR), k-nearest Neighbors (kNN), Tree (DT), and XGBoost. The main goal is to use oversampling techniques like SMOTE and Random Oversampling to correct the dataset's class imbalance. The dataset was obtained from Kaggle and literature references published in Frontiers in Ecology and Evolution (Endo and Amarasekare 2022), consisting of approximately 9,490 entries with environmental, demographic, and clinical attributes. Dengue Fever is one of the diseases that this study focuses on. Aedes aegypti mosquitoes spread it, and it is a significant health risk in tropical areas. The DT and XGBoost models had the highest accuracy, at 99.2%. Logistic Regression and Random Forest also did well, with 99.1% accuracy. KNN did well, too, but with a lower recall, at 99.0%. The ROC curve gave a complete picture of how well each model classified things. These findings indicate that when combined with proper data handling, machine learning models can significantly improve early detection of vector-borne diseases and support more accurate and timely decision-making in public health interventions
OPTIMIZED DEEP AUTOENCODER WITH L1 REGULARIZATION AND DROPOUT FOR ANOMALY DETECTION IN 6G NETWORK SLICING
Full text linkThe increasing complexity of 6G network slicing introduces new challenges in identifying abnormal behavior within highly virtualized and dynamic network infrastructures. This study aims to address the anomaly detection problem in 6G slicing environments by comparing the performance of three models: a supervised random forest classifier, a basic unsupervised autoencoder, and an optimized deep autoencoder enhanced with L1 regularization and dropout techniques. The optimized autoencoder is trained to reconstruct normal data patterns, with anomaly detection performed using a threshold- based reconstruction error approach. Reconstruction errors are evaluated across different percentile thresholds to determine the optimal boundary for classifying abnormal behavior. All models are tested on a publicly available 6G Network Slicing Security dataset. Results show that the optimized autoencoder outperforms both the baseline autoencoder and the random forest in terms of anomaly sensitivity. Specifically, the optimized model achieves an F1- score of 0.1782, a recall of 0.2095, and an accuracy of 0.714. These results indicate that introducing regularization and dropout significantly improves the ability of autoencoders to generalize and isolate anomalies, even in highly imbalanced datasets. This approach provides a lightweight and effective solution for unsupervised anomaly detection in next- generation network environments
MAPPING RESEARCH OPPORTUNITIES INNOVATION CAPABILITY SMALL MEDIUM ENTERPRISES: A BIBLIOMETRIC ANALYSIS AND NETWORK VISUALIZATION
Full text linkSmall and medium-sized business innovation research has a wide range of themes, and mapping innovation research is necessary to get an idea of the topics that are and will be developing. Bibliometric analysis of small and medium business innovation is one of the themes of trend research in the fields of business, economics, management, and computer science. Bibliometric analysis of the effects of innovation and open innovation on small and medium enterprises is one of the themes discussed, there has been no bibliometric analysis of the innovation ability of small and medium enterprises so the purpose of this study is to map research on the innovation ability of small and medium enterprises and to find opportunities for information technology innovation research themes for small and medium enterprises. The research method employed a bibliometric analysis, with data collection from the Scopus database, resulting in 542 documents. The data analysis stage, with the help of the enhancement software, is open for the main data cleansing. Vos Viewer for network visualization and overlay visualization. Tableau for data visualization and descriptive analysis. The results of the bibliometric analysis, combined with network visualization, successfully map four research clusters of innovation capabilities and provide direction to researchers to determine emerging topics and future research. The use of big data analytics to increase innovation, business, and technology can improve organizational performance is one of the research themes that has the opportunity to be studied in the futur
ENHANCING SENTIMENT ANALYSIS ACCURACY WITH BERT AND SILHOUETTE METHOD OPTIMIZATION
Full text linkThis research is based on the emergence of ChatGPT technology, which has significant implications in various fields. This research aims to design a model that improves sentiment analysis classification accuracy. The methods applied include the use of the Silhouette Coefficient to determine the best cluster parameters before performing data grouping with the Self-Organizing Map (SOM) method. Additionally, the Bidirectional Encoder Representations from Transformers (BERT) model is utilized to perform precise and convergent sentiment classification. The research methodology encompasses several phases, including data preprocessing through natural language processing techniques. Textual data is converted into vector representations, which are then processed using the Silhouette Coefficient to identify the optimal cluster parameters. These parameters are subsequently applied in the Self-Organizing Map method to cluster data, while the Bidirectional Encoder Representations from Transformers model determines public sentiment, categorized as positive, negative, or neutral. The findings of this study indicate that the best cluster parameter is 9, using a batch size of 64 and a maximum sequence length of 128. The highest accuracy achieved using the confusion matrix is 92.06%. Further tests with varying parameters confirm that the Silhouette Coefficient method significantly enhances the convergence and accuracy of classification outcomes. The conclusion of this research is that integrating the Silhouette Coefficient and Bidirectional Encoder Representations from Transformers is effective in optimizing sentiment analysis on large datasets, achieving both accurate and reliable results
A DECISION SUPPORT SYSTEM USING ROC-TOPSIS TO SPECIFY ELIGIBILITY IN THE FAMILY HOPE PROGRAM
Full text linkSelection committee at Jetis Village Sukoharjo Regency, Indonesia had difficulty to assign FHP assistance recipients priority. This is a problem must be resolved so that selection committee can be helped to determine which candidates are entitled to receive. This research is to develop a system using Rank Order Centroid (ROC) and Technique For Order Preference By Similarity to Ideal Solution (TOPSIS) methods and measure accuracy level of two methods used. Data used is 150 on potential 2024 FHP assistance recipients obtained from Jetis. From 150 real data in 2024, there were 71 people receiving FHP assistance, while a system developed in this research is produced 62 recipients. ROC method is used to specify each criterion importance level and TOPSIS method to process data which ultimately results in a potential ranking aid recipients. From comparison of original data and research results, there were 121 data had same system output as original data. From an accuracy rate of 81%, ROC and TOPSIS methods show the potential to increase accuracy and fairness in determining priority for candidates who are entitled to receive FHP assistance
FINE-GRAINED SENTIMENT ANALYSIS ON BIG DATA FROM MULTI-PLATFORM IN INDONESIA
Full text linkSentiment analysis on multi-platform big data in Indonesia presents a complex challenge, particularly in optimizing sentiment classification with higher granularity levels. This study aims to develop and optimize a sentiment classification model for analyzing public opinion on ChatGPT using a Fine-Grained Sentiment Analysis approach based on Indonesian Bidirectional Encoder Representations from Transformers (IndoBERT). The method is applied to big data collected from various social media platforms to improve accuracy and precision in identifying a broader spectrum of sentiments, including highly positive, positive, neutral, negative, and highly negative categories. A comparative analysis was conducted on different base models, including BERT, RoBERTa, and IndoBERT, to determine the most effective model. Experimental results show that the optimized IndoBERT model achieves an accuracy of 96% and outperforms other models in terms of precision and F1-score across all sentiment categories. Additionally, this study evaluates the model's computational efficiency and adaptability to diverse data. Thus, the developed model can serve as a more effective solution for gaining deeper insights into public opinion across various digital platforms in Indonesia
ENHANCING MACHINE LEARNING ALGORITHM PERFORMANCE FOR PCOS DIAGNOSIS USING SMOTENC ON IMBALANCED DATA
Full text linkPolycystic Ovarian Syndrome (PCOS) is one of the most frequently occurring endocrine disorders in women of reproductive age, distinguished by disruptions in hormonal regulation that can impact menstrual cycles, fertility, and physical appearance. Despite its high prevalence, PCOS is often diagnosed late and inaccurately, leading to inappropriate treatment and long-term health issues for patients. Machine learning can serve as an effective solution to enhance the accuracy of PCOS diagnosis. However, one of the primary challenges encountered is the class imbalance in the dataset, where the number of positive case data (PCOS) is often significantly lower than the negative case data. This imbalance can result in a biased model that is less effective in predicting the actual condition of patients. In this study, the Synthetic Minority Over-sampling Technique for Nominal and Continuous (SMOTENC) method is recommended to address the issue of imbalanced data, thereby improving the performance and accuracy of the machine learning model employed. The evaluation matrix test results clearly demonstrate that the accuracy of each machine learning model improved after applying the SMOTENC method. Specifically, the accuracy of the K-Nearest Neighbors (KNN) algorithm increased from 81.6% to 89.8%, the Support Vector Machine (SVM) algorithm from 90.6% to 92.5%, the Naive Bayes algorithm from 70% to 82.3%, and the C4.5 algorithm from 99.6% to 99.7%. This research provides a substantial contribution to advancing the development of diagnostic methods thatare both more precise and efficient
DRIP INFUSION MONITORING AND DATA LOGGING SYSTEM BASED ON YOLOv5
Full text linkIntravenous infusion (IV) functions to deliver medication or fluids directly into the patient’s body and requires an accurate drops-per-minute (TPM) calculation to ensure the correct dosage is administered. Manual calculation techniques, which are still widely used today, tend to be inefficient and carry a high risk of human error. Therefore, a more reliable and innovative automated approach is needed. In this study, we developed a prototype of an automatic infusion monitoring system based on the CNN-YOLOv5 architecture. The system records a one-minute IV drip video using a mobile device, then processes it through a server to automatically calculate the TPM, where YOLOv5 is used for drip detection, Deep SORT for object tracking, and a unique ID numbering scheme is applied to each droplet to ensure it is counted only once until it exits the frame. The calculation results are stored in a patient database that we designed. We also explored the effect of dataset background on accuracy. Testing was conducted on 48 videos (30 fps) with two background types—white (LBP) and black (LBH)—and drip variations of 20, 30, 40, and 50 TPM with varying durations. The results showed higher accuracy on the black background, reaching 0.79 compared to 0.58 on the white background, both with a precision of 1.00. The system demonstrated excellent performance in detecting drips with high precision and good accuracy, particularly on LBP for TPM <40 fps and on LBH for TPM <50 fps.