Daftar Jurnal Penerbit Universitas Negeri Semarang
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Information Technology in Marketing: Implications in Marketing of Equality Education Programs
No full textBeground - The equality education program in Banjarmasin has an important role in providing learning opportunities for people who cannot access formal education. However, challenges in terms of marketing this program are still an obstacle, such as the lack of public understanding of the benefits of equality education and limitations in reaching prospective students. Along with the development of information technology, the implementation of digital-based marketing strategies is a potential solution to increase the reach and effectiveness of the promotion of this program.
Purpose - This study aims to explore the implications of the use of information technology in the marketing of equality education programs in Banjarmasin.
Method/approach - The research method used is a qualitative approach with data collection techniques through in-depth interviews, observations, and document analysis. The research informants consisted of the manager of the Center for Community Learning Activities (PKBM), educators, and students involved in the equality program.
Findings - Research findings show that the use of information technology, such as social media, websites, and mobile-based applications, has had a positive impact on increasing the visibility of equality education programs. The use of digital platforms allows for a wider reach, increased interaction with prospective students, and more effective and efficient delivery of information. However, challenges such as limited internet access in some regions and the lack of digital literacy among the community are still obstacles that need to be overcome.
Conclusions - The conclusion of this study confirms that the application of information technology in the marketing of equality education programs in Banjarmasin can increase community participation and the effectiveness of information delivery. A more targeted and sustainable digital marketing strategy is needed to overcome existing obstacles.
Novelty/Originality/Value - The novelty of this study lies in the development of an information technology-based marketing model that is tailored to the demographic and social characteristics of the Banjarmasin community, so that it can be a reference for equality education program managers in increasing public participation and awareness of the importance of non-formal education
Aesthetic Photography Analysis on Instagram: A Visual Study of Social Media using ATLAS.ti
No full textPurpose: This study aims to analyze the dominant trends in color and composition within aesthetic photography on Instagram and explore their influence on user interaction, specifically likes and comments. Given the growing role of visual aesthetics in digital marketing, understanding these elements is crucial for content creators, brands, and businesses aiming to maximize engagement. Unlike previous studies that focus on general social media engagement, this research integrates technology-driven qualitative analysis using ATLAS.ti, enabling structured coding and thematic identification of visual elements.
Methods: A qualitative content analysis was conducted on 591 Instagram posts tagged with #AestheticPhotography and #VisualAesthetic. Data was collected using Instagram scraping (PhantomBuster), extracting both visual (color palettes, composition techniques) and textual (captions, metadata) elements. The ATLAS.ti software was used to analyze recurring visual patterns and color extraction was performed via Google Colab and Python for accuracy.
Result: The results show that natural colors (48.18%) and pastel tones (30.90%) are dominant in aesthetic photography, contributing to higher engagement due to their harmonious and calming effect. Composition techniques such as center alignment (40.51%) and the Rule of Thirds (23.27%) significantly correlate with user interaction, as they align with cognitive load theory and visual perception principles. Additionally, short captions (≤10 words) were more effective in enhancing engagement, receiving 8,876 likes and 4,432 comments on average, compared to longer captions.
Novelty: This study bridges the gap between visual aesthetics and computational analysis, using ATLAS.ti to systematically examine social media trends. Unlike previous studies that focus solely on quantitative metrics, this research provides qualitative insights into how color and composition influence engagement. The findings offer practical guidance for content creators, designers, and marketers, suggesting that strong visual composition and color harmony can enhance audience engagement
Classification Performance of Stacking Ensemble with Meta-Model of Categorical Principal Component Logistic Regression on Food Insecurity Data
No full textPurpose: Stacking is one type of ensemble whose base-models use different algorithms. The classification results from its base-models are categorical and tend to be associated with each other. They then become input for the stacking meta-model. However, there are no currently definite rules for determining the classifier that becomes the meta-model in stacking. On the other hand, recent research has found that CATPCA-LR can work well on categorical predictor variables associated with each other. Therefore, this study focuses on the classification performance of the stacking algorithm with the CATPCA-LR meta-model.
Methods: The study compared the classification performance stacking with CATPCA-LR meta-model to stacking with other meta-models (random forest, gradient boost, and logistic regression) and its base-models (random forest, gradient boost, extreme gradient boost, extra trees, light gradient boost). This research used food insecurity data from March 2022.
Result: The stacking algorithm with the CATPCA-LR meta-model performs better insecurity data regarding sensitivity, balanced accuracy, F1-Score, and G-Means values. This model offers a sensitivity of 46.28%, a balanced accuracy of 59.82%, an F1-Score of 37.82%, and a G-Means of 58.26%. Meanwhile, regarding specificity values, the light gradient boost (LGB) algorithm gives the highest value compared to other algorithms. This model provides a specificity value of 88.40%. Generally, the stacking with the CATPCA-LR meta-model algorithm provides the best performance compared with other algorithms on food insecurity data.
Novelty: This research has explored a stacking classification performance with CATPCA-LR as meta-model
Improving Random Forest Performance for Sentiment Analysis on Unbalanced Data Using SMOTE and BoW Integration: PLN Mobile Application Case Study
No full textPurpose: This research aims to improve the accuracy of sentiment analysis on PLN Mobile app reviews by overcoming the challenge of data imbalance. This goal is important to provide a better understanding of user opinions and support PT PLN (Persero) in improving mobile application services.
Methods: This research uses the Random Forest algorithm combined with Synthetic Minority Over-sampling Technique (SMOTE) to handle imbalanced data. Data is collected through web scraping reviews from the Google Play Store, followed by preprocessing processes such as data cleaning, stopword removal, tokenization, and stemming. Feature extraction is performed using the Bag of Words (BoW) method, and the data is tested with four sharing schemes.
Result: The results showed that the 90%-10% sharing scheme gave the best performance with an accuracy of 81% and an average precision and recall of 0.79. This finding confirms that the larger the proportion of training data, the better the model performs sentiment classification.
Novelty: This research\u27s novelty lies in combining SMOTE with BoW and Random Forest to overcome data imbalance. This approach is a significant reference for future sentiment analysis research. It provides practical insights that PT PLN (Persero) can use to improve the quality of its application services
Neural Style Transfer and Clothes Segmentation for Creating New Batik Patterns on Clothing Design
No full textPurpose: Applying the original batik image style to other object images and generating new batik patterns that applied to clothing.
Methods: This research uses the Neural Style Transfer method to apply object images to batik to produce new batik patterns, and Clothes Segmentation is used to select areas of clothing in the image so the new batik patterns can be applied to clothing images. And Testing using SSIM, LPIPS and PSNR metrics. This research uses Google Colab, batik image data, and clothing mockup images taken from the internet.
Result: This study shows high average results on SSIM, LPIPS and fair results on PSNR. The results show that the similarity is relatively high with high detected noise.
Novelty: This research develops a new approach in the field of batik pattern innovation and its application to clothing design images. The novelty of this research lies in the implementation of Neural Style Transfer and Clothes Segmentation, which results in a method of exploring new batik patterns and applying them to clothing design images
Safety Stock and Reorder Point System for RF Media Stock Optimization
No full textPurpose: This study develops a web-based inventory management system by applying the Safety Stock and Reorder Point (ROP) methods to address inventory issues at RF Media small and medium enterprises (SMEs) in the printing sector. The system aims to improve operational efficiency and reduce the risk of stockouts, which frequently occur in SMEs due to their reliance on manual inventory processes.
Methods: This study develops a web-based inventory management system by applying the Safety Stock and Reorder Point (ROP) methods to address inventory issues at RF Media small and medium enterprises (SMEs) in the printing sector. The system aims to improve operational efficiency and reduce the risk of stockouts, which frequently occur in SMEs due to their reliance on manual inventory processes.
Result: The simulation showed a 21.38% increase in operational efficiency and a 16.10% reduction in the risk of stockouts. The system ensures complete inventory visibility, facilitates faster decision-making, and minimizes manual errors. Usability testing revealed high user satisfaction regarding interface clarity, ease of use, and quick access to inventory information.
Novelty: This study introduces an innovative integration of the Safety Stock and ROP methods into a lightweight, cost-effective web-based system specifically designed for SMEs. Inventory digitization plays a critical role in enhancing competitiveness. This system offers a practical and scalable solution for efficient inventory management in SMEs environments with limited resources
User Experience Improvement (MSMEs and Buyers) Mobile AR Using Design Thinking Methods
No full textPurpose: This research aims to improve the User Experience (UX) of Augmented Reality (AR) mobile applications for MSMEs and buyers through the Design Thinking method. This research solves the problem of suboptimal UX in AR-based mobile applications. This study hypothesizes that the application of Design Thinking can result in significant improvements in the UX of AR mobile applications, which is evidenced by an increase in heuristic evaluation scores.
Methods: The Design Thinking approach (Empathize, Define, Ideate, Prototype, Test) is implemented. Data were collected through interviews, observations, and heuristic evaluation questionnaires.
Result: Initial heuristic testing showed several usability problems in the developed AR mobile applications, such as Help and Documentation (H10), Recognition Rather than Recall (H6), and Error Prevention (H5). After the application of the Design Thinking method and design iteration, the heuristic testing showed that the results of the evaluation comparison before and after the improvement showed a high effectiveness of the corrective actions taken, with an average decrease in severity score of 37% based on the Nielsen scale (0–4), indicating that the most critical and major issues were successfully reduced to cosmetic or minor levels.
Novelty: This research contributes in the form of a practical framework to improve the UX of AR mobile applications for MSMEs and buyers by utilizing the Design Thinking method. The results of this research can be a reference for developers in designing user-friendly AR mobile applications
Integrating UX Five Elements and Design Thinking to Design a Learning Management System
No full textPurpose: This study aims to enhance the user experience of Learning Management Systems (LMS) by integrating two established design frameworks: the UX Five Elements and Design Thinking. The research addresses the need for a more structured yet human-centered design process to improve the usability and engagement of LMS platforms in higher education.
Methods: The research adopts a design and development approach by combining the UX Five Elements, which offer a systematic structure across five user experience layers, with Design Thinking, which emphasizes empathy and iterative user involvement. This integration forms an Extended Model Design (EMD) used to guide the development of a new LMS interface. The final system was evaluated using usability testing involving students as target users.
Result: Evaluation of the LMS prototype using the User Experience Questionnaire (UEQ) showed positive perceptions on all six dimensions, with the highest scores on the Efficiency (1.644) and Attractiveness (1.634) aspects, reflecting a practical and attractive system design. Although the Novelty (1.203) aspect had the lowest score, its value was still above the positive threshold, indicating that the system was functionally good but could still be improved in terms of innovation to strengthen user engagement.
Novelty: This study introduces a novel design framework by integrating UX Five Elements with Design Thinking in the context of LMS development. Extended Model Design (EMD) offers a replicable model that balances structure and user empathy, contributing to user-centered e-learning system design
Integration of Random Forest, ADASYN, and SHAP for Diabetes Prediction and Interpretation
No full textPurpose: Diabetes is a chronic disease with a globally rising prevalence. Early detection of individuals at risk is essential to prevent long-term complications. This study aims to develop a diabetes prediction model that not only achieves high classification accuracy but also provides transparent explanations of the factors influencing its predictions.
Methods: The study utilized the Pima Indians Diabetes Dataset, which contains clinical data from 768 female patients aged over 21. The methodology included data preprocessing (handling of missing values and feature engineering, such as the creation of Age_BMI and Glucose_BMI features), a 70:30 train-test split, class imbalance handling using the ADASYN technique, model development using the Random Forest algorithm with hyperparameter tuning via GridSearchCV, and model interpretability analysis using SHAP.
Result: The proposed model achieved an accuracy of 79.2% and a recall of 85.2% on the test data. SHAP analysis revealed that Glucose, Age_BMI, BMI, and DiabetesPedigreeFunction were the most influential features in predicting diabetes. Furthermore, the SHAP heatmap indicated that individuals aged 30–50 years with obesity were at the highest risk. These findings align with existing medical literature, reinforcing the role of metabolic and age-related factors in diabetes development.
Novelty: This study presents an integrative approach combining class balancing (ADASYN), classification (Random Forest), and model interpretability (SHAP) in a unified framework for diabetes prediction. It emphasizes the importance of transparent model interpretation for healthcare professionals, enabling not only predictive outcomes but also actionable insights into risk factors. The findings support future research opportunities, including the integration of lifestyle variables and external validation using real-world clinical data from diverse populations
Optimizing LSTM-CNN for Lightweight and Accurate DDoS Detection in SDN Environments
No full textPurpose: This study optimizes the LSTM-CNN model to detect Distributed Denial of Service (DDoS) attacks in Software-Defined Networking (SDN)-based networks and improves accuracy, computational efficiency, and class imbalance handling.
Methods: We developed an Improved LSTM-CNN by removing the Conv1D layer, reducing LSTM units to 64, and using 21 features with a 5-timestep approach. The InSDN dataset (50,000 samples) was preprocessed with one-hot encoding, MinMaxScaler normalization, and sequence formation. Class imbalance was managed using class weights (0:2.0, 1:0.5) instead of SMOTE, with performance compared against Baseline LSTM-CNN and Dense-only models optimized with the Sine Cosine Algorithm (SCA).
Result: The Improved LSTM-CNN achieved 0.99 accuracy, 0.93 F1-score for Benign traffic, and 1.00 for Malicious traffic, with ~25,000 parameters and 125 ms inference time on Google Colab. It outperformed Baseline LSTM-CNN (0.08 accuracy) and was more efficient than Dense-only (46,000 parameters), with a false positive rate of ~1%.
Novelty: This research presents a lightweight, efficient DDoS detection solution for SDN, leveraging temporal modeling and class weights, suitable for resource-constrained controllers like OpenDaylight or ONOS. However, its generalization is limited by dataset diversity, necessitating broader validation