Melanoma is one of the most malignant forms of skin cancer, with an incidence rate of 7.9% in Indonesia. Traditional biopsy-based diagnosis, though crucial, is invasive and time-consuming, creating barriers for early detection. To address this issue, this research compares two Convolutional Neural Network (CNN) models for digital image-based melanoma classification. The study utilized a publicly available dataset from Kaggle, consisting of 17,805 images (melanoma and non-melanoma), which were divided into training, validation, and testing subsets. The models were trained using the Adamax and SGD optimizers for 100 epochs. The performance of the models was evaluated based on accuracy, loss, precision, recall, and F1-score. The CNN model with the best architecture, which consisted of two fully connected layers, achieved an accuracy of 93.18% and a loss of 0.1636, outperforming the alternative model. These results confirm the effectiveness of CNN models in classifying melanoma images and support the development of a web-based platform that allows users to upload or capture images for rapid and non-invasive detection.

Accurate forecasting of the lowest and highest prices in financial markets poses a considerable challenge due to the inherent nonlinear behaviour, non-stationarity, and high noise levels of financial time series data. Most prior studies focus only on closing prices, with limited attention to the simultaneous prediction of high and low prices. Yet, predicting the lowest and highest prices is essential for investors to make informed trading decisions. To address this gap, this study proposes a hybrid DL framework that integrates VMD and LSTM networks for predicting daily high and low prices simultaneously. This study used 12 years of daily data from three diverse assets: AUD/USD, TLKM, and XAU/USD. The data underwent preprocessing, VMD-based decomposition, and were input into the LSTM. The dataset was split 80% for training and 20% for testing. Experiments varied the number of decomposition modes (K = 7, 10, 12) and sliding window sizes (5, 15, 30, 45, 60, 90). Results show that the VMD-LSTM model exhibits improved performance in most of the tested scenarios compared to traditional LSTM. These findings underscore that the use of VMD decomposition can help enhance the accuracy of forecasting the highest and lowest prices in the financial market.

Abstract Tourism plays a vital role in driving economic growth, and Minahasa holds strong potential to optimize this sector. However, challenges remain in digital advertising, particularly regarding transparency and consumer trust. This study investigates the impact of blockchain technology on transparency, trust, and the effectiveness of digital advertising in Minahasa’s tourism industry. A quantitative explanatory design was employed using Partial Least Squares Structural Equation Modeling (SEM-PLS), with data collected from 150–250 respondents through purposive and snowball sampling techniques.The findings reveal that blockchain significantly influences all key variables. It enhances advertising transparency (T-statistic = 36.738, p = 0.000), strengthens consumer trust (T-statistic = 33.164, p = 0.000), and improves advertising effectiveness (T-statistic = 28.400, p = 0.000). These results highlight blockchain’s capacity to provide immutable records, ensure data authenticity, and optimize ad performance through verifiable real-time information. This study confirms that blockchain can serve as a strategic tool to promote transparent, trustworthy, and effective digital advertising in tourism. The findings provide practical insights for tourism stakeholders and contribute to academic discussions on technology-driven marketing innovation.

The implementation of Learning Management Systems (LMS) in higher education institutions continues to increase in line with the growing demand for flexible digital learning, with the assumption that LMS is an easy-to-use platform that will be naturally accepted by lecturers. This study aims to analyze the factors that influence the adoption of LMS among lecturers at higher education institutions in Eastern Indonesia. This study uses a quantitative cross-sectional survey. The research instrument, comprising 25 items classified into five constructs —Constructivist Pedagogical Beliefs, Traditional Pedagogical Beliefs, Perceived Ease of Use, Perceived Usefulness, and LMS Use —was administered to lecturers at a private university in North Sulawesi. Using the Partial Least Squares-Structural Equation Modeling approach, this study incorporates the Technology Acceptance Model with a constructivist and traditional pedagogical belief orientation. The results show that three of the eight variables significantly influence LMS usage. The findings indicate that constructivist pedagogical beliefs and perceived usefulness have a significant influence on LMS adoption, whereas traditional pedagogical beliefs do not have a significant impact. These results have practical implications for universities in designing training policies and strategies to optimize LMS usage.

Preterm birth is a major cause of neonatal mortality in Indonesia and is influenced by multiple maternal factors. Early prediction models are crucial for supporting timely clinical decision-making and reducing adverse maternal–infant outcomes. The method of this study developed a linear regression–based predictive model using 915 pregnancy medical records from Dr. H. M. Ansari Saleh Regional Hospital, Banjarmasin (2020–2022). The workflow included data preprocessing, feature selection, Min-Max normalization, and experimentation with various train–test split ratios (90:10 to 50:50). Model performance was evaluated using R², Adjusted R², MAE, MSE, RMSE, and MAPE metrics. As the results, the 70:30 split ratio achieved the best accuracy of 89.05% and AUC of 98.10%, with low prediction errors. Optimizations with Adamax and Nadam enhanced stability and reduced MAPE to 1.95%. The optimized linear regression model reliably predicts preterm birth risk and is suitable for clinical decision support, particularly in resource-limited settings.

Forecasting student admissions remains a challenge due to fluctuating online engagement and complex administrative processes. Existing predictive models rarely integrate website behavioral data with institutional admission funnels, resulting in lower accuracy. This study bridges that gap by combining web analytics from Google Analytics 4 (GA4) with administrative enrollment funnel data from the admission of new students (Penerimaan Mahasiswa Baru/PMB) system to develop a unified predictive framework. The approach strengthens forecasting by aligning digital behavior with verified enrollment milestones. A quantitative explanatory design was employed, applying Pearson correlation to identify linear relationships and Seasonal ARIMA (SARIMA) to model cyclical admission trends. The dataset includes GA4 metrics sessions, engagement rate, bounce rate, and events per session and PMB funnel stages from account creation to confirmed enrollment. Results reveal strong correlations (r > 0.9, p < 0.001) between digital engagement and mid-funnel conversions, while SARIMA achieved its highest accuracy for early-stage predictions (MAPE ≈ 19%). Forecasts for final outcomes were less accurate, reflecting administrative variability. These findings confirm that web engagement metrics are reliable leading indicators of student interest and mid-stage commitment. This research establishes a reproducible pipeline unifying web analytics (GA4) with institutional funnel data (PMB), providing empirical evidence that digital engagement is a reliable leading indicator of early and mid-stage commitment, thereby forming a novel and adaptable foundation for data-driven enrollment planning.

Blindness or visual impairment restricts spatial awareness and increases the risk of collisions, falls, and mobility challenges. This study presents the design and development of a Smart Assistive Stick with Arduino and multidirectional ultrasonic sensors for intelligent obstacle detection and navigation. Unlike conventional white canes that provide only short-range tactile feedback, the proposed system employs multidirectional sensing to detect obstacles from various directions within a range of 0.1 to 4 meters. Intelligent feedback is delivered through both haptic and auditory signals, with an average response delay of only 200 ms, ensuring timely and reliable navigation assistance. Testing showed detection accuracy exceeding 85%, continuous battery life of 6–8 hours, and a total device weight of 600 grams, making it lightweight and suitable for daily use. While performance decreases in noisy environments due to ultrasonic interference, the system demonstrates novelty in extending detection range, incorporating multidirectional sensing, and providing intelligent real-time feedback. These contributions establish the smart assistive stick as a more effective and user-friendly mobility aid compared to traditional solutions.

The escalating threat of cyberattacks necessitates the development of intrusion detection models that are both accurate and computationally efficient for large-scale network traffic. To address this issue, this study proposes a hybrid approach combining Autoencoder, Convolutional Neural Network (CNN), and XGBoost as an adaptive and lightweight solution for network traffic classification. The key contribution of this research lies in the design of a multi-stage pipeline that performs dimensionality reduction, feature extraction, and final classification. The model was evaluated using the Moore Dataset, which contains complex and high-dimensional network traffic data. The experimental results indicate that the proposed hybrid model achieved a classification accuracy of 99.20% with a testing time of only 0.09 seconds. Furthermore, the pipeline significantly reduced computational load compared to single CNN or XGBoost models. These findings demonstrate that the hybrid approach not only offers high classification performance but also enhances scalability and efficiency, making it suitable for real-world implementation in modern network security systems. Overall, the proposed model presents a promising and practical solution for advancing future intrusion detection systems.

An academic information system is expected to provide information to lecturers, students, and administrators. There are several parts included in the academic information system, including lecturer teaching schedules, lecturer attendance systems (monitoring), and student lecture schedules. These three things are related to the results of the recapitulation of lecturer attendance for reporting on the academic section institution. Dipa Makassar University, the process of recapitulating lecturer attendance still uses a manual system, namely, officers record lecturer attendance at each session and then input it into the Excel application. This process is inefficient because the work is repetitive and time-consuming. When lecturers are delayed, there is no information provided to students, causing them to wait for long periods. In this study, the researcher developed an Android-based fingerprint application for recapitulating lecturer attendance and tardiness. The testing method applied to evaluate the application is the Black Box testing method. Based on the overall testing results, it can be concluded that the application functions properly in accordance with the system requirements and is free from errors. The testing focused on the application's input, process, and output features without examining the internal structure of the program. All tested features produced valid results and met the expected outcomes. With the presence of this application, both lecturers and students can access class schedules at any time through the Android platform. Additionally, the application provides notifications to students in case the lecturer arrives late, which significantly improves communication between lecturers and students.

This study presents the design, implementation, and evaluation of the 11ISC Conference Management System (CMS), a full-stack web application developed to address the fragmented administrative workflows of the 11th International Scholars Conference. Using the Design Science Research methodology, the system was created in response to recurring challenges such as manual registration, accommodation and transportation coordination, and the time-intensive preparation of Letters of Acceptance. The CMS was evaluated through blackbox functional testing covering twelve primary use cases, all of which passed successfully, including participant registration, payment verification, automated LoA generation, QR-based check-in, and accommodation assignment. Administrator feedback indicated substantial process improvements, with the automated LoA module reducing preparation time by up to 90 percent and integrated room and check-in management significantly decreasing errors associated with the previous spreadsheet-based workflow. Deployed during the conference, the system supported more than 220 participants and over 180 paper submissions, providing real-time dashboards and unified data management. The results demonstrate that the CMS enhances efficiency, accuracy, and coordination, offering a practical and replicable solution for academic event management in similar institutional contexts.