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CONTENT-BASED FILTERING CULINARY RECOMMENDATION SYSTEM USING DEEP CONVOLUTIONAL NEURAL NETWORK ON TWITTER (X)
Get PDFAlong with the development of technology, social media has become integral to everyday life, especially for sharing content like culinary reviews. Social media platform X (formerly Twitter) is often used for sharing culinary recommendations, but the abundance of information makes it difficult for users to find relevant suggestions. In order to improve rating prediction performance, this study suggests a recommendation system model that is more thoroughly created utilizing Content-Based Filtering (CBF) combined with Deep Convolutional Neural Network (CNN) and optimised with Particle Swarm Optimization (PSO). Data was collected from PergiKuliner and Twitter, totaling 2644 reviews and 200 cuisines. The preprocessing involved text processing, translation, and polarity assessment. Post-labeling, 7438 data were labeled with 0 and 1562 with 1. Label 0 means not recommended while label 1 means recommended. The imbalance is handled by applying the SMOTE method after observing that the fraction of data labeled 0 and 1 is 65.2%. CBF employed TF-IDF feature extraction and FastText word embedding, while Deep CNN handled classification. PSO optimisation was applied to enhance the accuracy of culinary rating predictions. The results showed an initial accuracy of 76.32% with the baseline Deep CNN model, which increased to 86.06% after Nadam optimisation with the best learning rate, and further reached 86.18% after PSO optimisation on dense units. The 9.86% accuracy improvement from the baseline model demonstrates the effectiveness of the combined methods
UNLEASHING THE POWER OF SVM AND KNN: ENHANCED EARLY DETECTION OF HEART DISEASE
Get PDFHeart disease is a fatal illness responsible for approximately 36% of deaths in 2020. Therefore, it is important to pay attention to and better anticipate the risk of heart disease. One technological contribution that can be made is through information related to the risk of heart disease. Classification techniques in data mining can be used to diagnose and identify the risk of heart disease earlier by processing medical data and making predictions. This study compares the effectiveness of two classification algorithms, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), in predicting the risk of heart disease using a Kaggle dataset consisting of 303 records with 14 attribute columns. The data is divided into 70% for training and 30% for testing. The software used in this study is Orange Data Mining to build the SVM and KNN models. The results show that the SVM accuracy is 85.6%, while KNN achieves 81.1%. Based on the confusion matrix, the SVM algorithm has a lower error rate compared to KNN. In conclusion, the SVM algorithm is superior to KNN in predicting the risk of heart disease. These findings indicate that SVM has a better potential in identifying individuals at high risk of experiencing a heart attack. This research can contribute to the development of a more accurate medical decision support system for early detection of heart disease
DAMPAK KUALITAS PELAYANAN TERHADAP KEPUASAN PELANGGAN DI RICHEESE CABANG GRAND DEPOK CITY
Get PDFThe fast-food industry, particularly Richeese Factory, plays a significant role in the global culinary landscape. This study aims to evaluate the impact of service quality on customer satisfaction at Richeese Factory GDC using a combination of library and field research methods. The study employs a cross-sectional design, with data collected through questionnaires from 99 randomly selected respondents. Service quality was assessed based on aspects such as speed, order accuracy, staff friendliness, and restaurant cleanliness. Linear regression methods were applied, including instrument tests for validity, reliability, and classical assumptions such as normality, heteroscedasticity, and multicollinearity. The validity and reliability tests showed that the questionnaire was reliable and valid. The classical assumption tests indicated that the data distribution was not normal; however, the regression model did not show heteroscedasticity or multicollinearity. Correlation analysis demonstrated a strong and positive relationship between the two variables. The coefficient of determination revealed that 29.2% of customer satisfaction variability was influenced by service quality, with the regression equation Y = 24.180 + 0.449X, indicating a positive effect. The T-test also showed a positive and significant effect of service quality on customer satisfaction, with a t-value of 6.322, which is greater than the t-table value of 1.988. These findings highlight the importance of improving service quality to achieve optimal customer satisfaction in the fast-food industry
SYSTEMATIC LITERATURE REVIEW: CHALLENGES AND SOLUTIONS ON AGILE PROJECT MANAGEMENT IN PUBLIC SECTOR
Get PDFThe public sector is transforming by adopting an agile approach to overcome bureaucratic rigidity and lagging the private sector. The aim is to overcome the limitations of traditional approaches by encouraging flexibility in planning, operations, and service delivery. In the face of diverse, agile characteristics, further research is required on the challenges and best practices other public sector organizations can adopt. This research identifies key challenges in agile implementation within the PMBOK 7th edition project performance domains with the most issues: Development Approach and Life Cycle and Project Work Domain. Using a systematic literature review (SLR), 35 of 680 reviewed papers were selected as references. The biggest challenges were in the Project Work Domain, dominated by the context of monitoring new work and changes, project processes, and procurement processes. Best practices were identified to address these challenges and guide other public sectors in supporting more flexible and responsive public service delivery
MOBILENET PERFORMANCE IMPROVEMENTS FOR DEEPFAKE IMAGE IDENTIFICATION USING ACTIVATION FUNCTION AND REGULARIZATION
Get PDFDeepfake images are often used to spread false information, manipulate public opinion, and harm individuals by creating fake content, making developing deepfake detection technology essential to mitigate these potential dangers. This study utilized the MobileNet architecture by applying regularization and activation function methods to improve detection accuracy. ReLU (Rectified Linear Unit) enhances the model's efficiency and ability to capture non-linear features, while Dropout and L2 regularization help reduce overfitting by penalizing large weights, thereby improving generalization. Based on experimental results, the MobileNet model optimized with ReLU and Dropout achieved an accuracy of 99.17% in the training phase, 85.34% in validation, and 70.60% in testing, whereas the MobileNet model optimized with ReLU and L2 showed lower accuracy in the training and validation phases compared to Dropout but achieved higher accuracy in testing at 72.18%. This study recommends MobileNet with ReLU and L2 due to its better generalization ability when testing data (resulting from reduced overfitting)
IMPLEMENTATION OF MULTIPLE LINEAR REGRESSION ALGORITHM IN PREDICTING RED CHILI PRICES IN GARUT REGENCY
Get PDFVegetables, including red chili peppers, play an important role in food and economic balance. Significant price fluctuations and inflation are often problems for farmers and traders. Garut Regency, as the center of red chili production in West Java, faces similar challenges. This research aims to implement a Multiple Linear Regression algorithm to predict the price of red chili peppers in the Garut Regency, highlighting the novelty of using a combination of One Hot Encoding, Feature Engineering, Standard Scaler, and Hyperparameter Tuning techniques. The method used is CRISP-DM with 6 stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The data used is the price and production of red chili peppers per week in 2018-2023, with a total of 702 records. This research involved 8 trials with data transformation and normalization scenarios. The model evaluation used MSE, RMSE, MAPE, R-squared, and statistical hypothesis testing metrics. Results showed 5 significantly influential attributes: year, month, production, net harvested area, and productivity. The best model yielded MSE 202,134,650, RMSE 14,217, MAPE 29.16%, and R-squared 0.320. This approach is simpler yet effective and is able to provide fairly accurate predictions. This research is expected to contribute to providing predictive models that help farmers and traders anticipate price fluctuations, as well as provide insights for policymakers in price management
PENGARUH INVESTASI PUBLIK TERHADAP PERTUMBUHAN PERUSAHAAN LOGISTIK
Get PDFIn Indonesia, the growth of logistics companies has experienced significant dynamics in line with the increasing volume of domestic and international trade. This research explores the impact of public investment on the growth of logistics companies in Indonesia, with a focus on infrastructure and information technology. The research subjects include managers or leaders of logistics divisions from each logistics company. Data collection is conducted through in-depth interviews, observation, and document analysis. The data obtained is analyzed using thematic analysis techniques. The research findings indicate that public investment in toll road construction and port facility improvements enhances operational efficiency by accelerating goods delivery and reducing travel time. Additionally, information technology support through transportation management systems and GPS tracking reduces operational costs by optimizin g routes and managing fleets more effectively. The findings also show that improvements in infrastructure and technology synergistically enhance the operational capacity of logistics companies, enabling them to handle larger volumes of goods and expand their service coverage. This study provides valuable insights for policymakers and company management on the strategic benefits of public investment in the logistics sector
PERAN HARGA DALAM MEMPENGARUHI MINAT PEMBELIAN KEBAYA DI BUTIK IMAS COLLECTION
Get PDFThe fashion industry in Indonesia continues to experience rapid growth, with traditional clothing such as kebaya gaining increasing popularity. Price is one of the key factors influencing consumer interest, particularly in niche markets like kebaya boutiques. This study aims to evaluate the role of price in influencing consumer purchase interest through a case study at Butik Imas Collection. A qualitative approach was used with a case study method, involving in-depth interviews with the boutique owner and 10 female consumers, as well as direct field observations. The findings reveal that although price is a significant factor, consumers also consider other aspects such as fabric quality, kebaya design, and customer service. These findings support the price perception theory by Kotler and Armstrong and align with previous research by Sawitri et al. (2024), which states that the combined application of price and product quality can enhance purchasing decisions. This study provides strategic insights for boutique managers on the importance of maintaining competitive pricing without compromising quality. A combination of competitive pricing, superior product quality, and excellent customer service can boost consumer purchase interest while strengthening Butik Imas Collection's position in the kebaya market
DEEP LEARNING FOR AUTOMATIC CLASSIFICATION OF AVOCADO FRUIT MATURITY
Get PDFAvocado (Persea Americana), a fleshy fruit with a single seed, has increased in popularity globally, especially in tropical and Mediterranean climates, thanks to its commercial and nutritional value. Rich in bioactive compounds, avocados contribute to the prevention and treatment of various diseases, including cardiovascular problems and cancer. Avocado production in Indonesia, for example, is showing a significant increase, reflecting the growing demand. Avocado ripeness affects shelf life and quality, making the determination of ripeness level a critical aspect of postharvest management. Skin color and pulp firmness change during storage, affecting quality and nutritional value. Proper classification of ripeness is important to reduce post-harvest losses, improve quality and optimize export costs. Recent research shows the use of technologies such as machine learning and YOLO (You Only Look Once) version 9 in real-time detection of avocado ripeness, offering innovative solutions to reduce post-harvest losses and improve distribution efficiency. This approach not only benefits farmers and consumers but also ensures consumer satisfaction and reduces economic losses. This study highlights the importance of real-time detection in monitoring avocado ripeness, where the training process was conducted for 89,280 iterations resulting in a new model for avocado ripeness detection. The final model has a mean Average Precision (mAP) validation value of 84.3%, mAP 84.3% signifies the optimal level of accuracy in object recognition in avocado fruit maturity images using the YOLO model that has undergone an intensive training process
DIAGNOSIS OF CUCUMBER PLANT DISEASES USING CERTAINTY FACTOR AND FORWARD CHAINING METHODS
Get PDFCucumber plants spread and can live in tropical climates like Indonesia. The cucumber plant has many benefits and can be a beauty ingredient. Cucumbers, like other plants, can also have disease attacks, which can threaten farmers. This expert system can help farmers discover diseases that attack cucumber plants and how to control them. The certainty Factor is a method used to measure the certainty of facts to describe an expert's confidence in facing a problem. Forward Chaining is an approach method monitored by data starting from information in the form of facts and supported by rules to reach conclusions. Implementing an expert system for diagnosing cucumber diseases using certainty factor and forward chaining methods will make it easier for farmers and the public to cultivate cucumber plants and get good results. Applying the forward chaining method and factor certainty in this expert system can produce an accuracy level of 95.918%