This research was motivated by the development of an Android-based practical work e-module, considered the most relevant solution to address the limitations of existing teaching materials. This e-module integrates learning content, practical procedure steps, circuit visualizations, and supporting media—such as images and videos—into a single, easily accessible platform. Furthermore, the fact that most students already own Android devices allows for flexible use of the e-module both inside and outside the classroom. The objectives of this research were to develop an Android-based practical work e-module on the topic of Remote Servers for Grade XI TKJ (Computer and Network Engineering) students at SMK Negeri 1 Solok, and to determine the validity and practicality of the developed e-module. The study employed the Research and Development (R&D) method, which aims to produce and test the effectiveness of specific products—whether software, hardware, learning media, curricula, or instructional models. Media validation results showed a score of 91.53% (categorized as "highly valid"), while content validation results showed a score of 92.73% (also "highly valid"). The media's strength lay in the readability of text and images, which achieved the highest score of 97.92%, while the content's strength lay in the use of easily understandable language, scoring 95.83%. Regarding practicality, teacher assessments yielded a score of 87.50% ("highly practical"), and student assessments yielded 89.51% ("highly practical"). Based on student assessments, the e-module's greatest strength was the clarity of practical steps (90.36%), while the aspect with room for further improvement was the visual appeal (88.48%). Thus, the developed Android-based practical work e-module is deemed highly valid and highly practical for use as a learning support tool for the Remote Server topic.
The rapid development of information technology has encouraged the application of recommendation systems to help users select culinary places according to their personal preferences. This study aims to design and build a web-based dessert place recommendation system in the Renon area, Denpasar, using the Content-Based Filtering (CBF) method. A total of 61 dessert place data were collected through Google Maps scraping using Apify, covering price, location, and category attributes. The category attribute was transformed using One-Hot Encoding, the price attribute was normalized using Min-Max Normalization, and the distance between the user and dessert places was calculated using the Haversine formula. All attributes were represented as feature vectors, and the similarity between user preferences and dessert place data was calculated using Cosine Similarity to generate the most suitable recommendations. The system was developed using the Laravel framework with the Waterfall method and tested using the Blackbox Testing method. System performance was evaluated using a confusion matrix with 70:30 and 60:40 data-split schemes. The 70:30 scheme testing produced an accuracy of 90.00%, precision of 50.00%, recall of 50.00%, and F1-Score of 50.00%, while the 60:40 scheme produced an accuracy of 92.59%, precision of 75.00%, recall of 75.00%, and F1-Score of 75.00%. The relatively low precision, recall, and F1-Score values were caused by the very small number of truly relevant items in the test data, so a single misclassification had a large impact on these percentages, while accuracy remained high because it was dominated by clearly irrelevant items. These results indicate that the Content-Based Filtering method is reasonably able to provide relevant dessert place recommendations according to user preferences.
The growth of e-commerce has driven an increase in transactions for Muslim fashion products, generating a vast number of consumer reviews containing information about product experiences and perceptions. Analyzing these reviews is crucial, as sentiment information allows for a more systematic understanding of consumer satisfaction trends compared to manual analysis. This study aims to classify consumer sentiment regarding Muslim fashion products and compare the performance of several machine learning algorithms in this task. The research dataset was obtained from PRDECT-ID, initially comprising 5,400 reviews; filtering for the Muslim fashion category yielded 200 reviews. Sentiments were determined based on review content, and "Neutral" labeled data were excluded, resulting in a final set of 135 reviews (97 negative and 38 positive). The research process involved text preprocessing, feature weighting using Term Frequency-Inverse Document Frequency (TF-IDF), classification using Multinomial Naive Bayes (MNB), Logistic Regression (LR), and Support Vector Machine (SVM), and evaluation using Stratified 5-Fold Cross-Validation. This study contributes an empirical comparison of the three algorithms applied to Muslim fashion product reviews, accounting for class imbalance and multiple evaluation metrics. Experimental results indicate that SVM achieved the best performance, with an accuracy of 84.44%, precision of 81.43%, recall of 60.36%, and an F1-score of 68.00%. These findings demonstrate that the combination of TF-IDF and SVM is more effective than MNB and LR for sentiment classification on the study's dataset.
MSMEs in the culinary sector, such as BLE'E Coffee Cakung, often struggle to manage raw material inventory due to inaccurate product demand estimation, leading to overstocking or stockouts. This research aims to implement machine learning algorithms to build an accurate demand prediction model, compare algorithm performance, and design a web-based prediction information system for business owners. The research uses a Research and Development (R&D) approach with the Waterfall development model, following the CRISP-DM data analysis framework. Linear Regression (Ordinary Least Squares) was implemented natively in PHP as the primary method, with Support Vector Machine (SVM) discussed as a theoretical comparator. Model evaluation used MAE, RMSE, MAPE, and R² metrics; the system was validated through black-box testing and structured user acceptance interviews. Results for the Cold Brew Leci product show excellent accuracy (R² = 0.933; MAPE = 0.28%), a 100% success rate across 20 black-box test scenarios, and a Highly Feasible user acceptance rating (100%). This research provides a practical technology solution for culinary MSMEs in data-driven decision making.
As part of the nation’s cultural heritage, traditional Indonesian music has grown and developed throughout almost all regions of the archipelago. It has been passed down from generation to generation and continues to be practiced today. Each traditional musical instrument is an important element of Indonesian culture that reflects the identity, philosophical values, and history of a particular region. Basically, a musical instrument is an instrument specifically designed or modified to produce musical sounds. However, public interest in learning about and recognizing traditional musical instruments has gradually declined, resulting in a lack of interest in studying them. This research aims to develop an educational medium that utilizes Augmented Reality technology to introduce traditional Indonesian musical instruments to students in an interactive way. The research method used is Research and Development (R&D) with the ADDIE model, which consists of the stages of Analysis, Design, Development, Implementation, and Evaluation. This research produced a digital book integrated with Augmented Reality technology, enabling the presentation of three-dimensional (3D) objects and audio of traditional musical instruments through marker scanning. The results of media expert validation showed a percentage of 89%, categorized as very feasible, while the testing results indicated that the learning media was feasible for use, with a satisfaction level of 63%. Based on these results, the interactive learning media is considered feasible to support the introduction of traditional Indonesian musical instruments. It is expected that students will gain a better understanding of and appreciation for the diversity of local culture, particularly traditional musical instruments.
