The quality of palm oil is strongly influenced by the maturity level of the fruit to be processed into palm oil. Many studies have been carried out for detecting and classifying the maturity level of oil palm fruit to improve the quality with the use of computer vision. However, most of these studies use datasets in the form of images of oil palm fresh fruit bunches (FFB) with incomplete categorization according to real conditions in palm oil mills. Therefore, this study introduces a new complete dataset obtained directly from palm oil mills in the form of videos and images with different categories in accordance with the real conditions faced by the grading section of the palm oil mill. The video dataset consists of 45 videos with a single category of FFB videos and 56 videos with a collection of FFB with multiple categories for each video. Videos are collected using a smart phone with a size of 1280 × 720 pixels with .mp4 format. In addition, this dataset has also been annotated and labelled based on the maturity level of oil palm fruit with 6 categories, which are unripe, under-ripe, ripe, overripe, empty bunches and abnormal fruit.

The classification of the ripeness degree of oil palm fruit has attracted the attention of numerous researchers. However, there are still many challenges due to constraints in the dataset, methodologies used, and variations in the use of data categories. Detecting oil palm fruit bunches accurately is crucial, given their complex shape and characteristics, particularly when different ripeness categories are present in a pile of oil palm. Most studies utilize oil palm images or the color spectrum of oil palm fruit to classify the level of ripeness. However, these methods are not real-time and lack efficiency. This study proposes a real-time model for determining the ripeness degree of oil palm using a smartphone and video data as input, incorporating modifications to the object detection approach. The research process involves collecting videos of palm oil piles using smartphones in the grading area of the palm oil industry. The videos are then pre-processed and labelled for the object detection and classification process. A detection and classification model is developed using the YOLOv4 approach with several performance improvements, enabling implementation on smartphones. The best-performing model is tested for detecting and classifying the ripeness of fresh fruit bunches using an android-based smartphone. The testing results, based on the mAP value, demonstrate that the YOLOv4 model with 16 quantization performs 12% better than YOLOv4 Tiny. Based on the test results at the grading location, this model can efficiently detect fruit bunches that do not meet the quality standards.

Sign Language Recognition (SLR) is a relatively challenging research field which allows opportunity for improvements. In this research, we propose sentence-level SLR using deep learning method by combining Convolutional Neural Network (CNN) and Bidirectional Recurrent Neural Network (Bi-RNN). Specifically, 3D CNN is implemented to extract features from each video frame and bidirectional-RNN is implemented to extract the unique features from the video frame's sequential behavior, which later generate a possible sentence. There are two key takeaways from this paper. The first is our proposed dataset of Indonesian Sign Language (SIBI) which is comprised of 30 sentences in SIBI. The second is our novel approach of using deep learning and Connectionist Temporal Classification (CTC) loss function in sentence-level SLR. The result shows that the hyperparameter used, in this case Hyperparameter 1, achieves the best result. Also, this research found that deeper network does not necessarily guarantee good results. A bigger number of dataset also affects the performance of the system.

The human population significantly increases in crowded urban areas. It causes a reduction of available farming land. Therefore, a landless planting method is needed to supply the food for society. Hydroponics is one of the solutions for gardening methods without using soil. It uses nutrient-enriched mineral water as a nutrition solution for plant growth. Traditionally, hydroponic farming is conducted manually by monitoring the nutrition such as acidity or basicity (pH), the value of Total Dissolved Solids (TDS), Electrical Conductivity (EC), and nutrient temperature. In this research, the researchers propose a system that measures pH, TDS, and nutrient temperature values in the Nutrient Film Technique (NFT) technique using a couple of sensors. The researchers use lettuce as an object of experiment and apply the k-Nearest Neighbor (k-NN) algorithm to predict the classification of nutrient conditions. The result of prediction is used to provide a command to the microcontroller to turn on or off the nutrition controller actuators simultaneously at a time. The experiment result shows that the proposed k-NN algorithm achieves 93.3% accuracy when it is k = 5.

Traditional palm oil production methods for evaluating fruit bunches (FFBs) are inefficient, costly, and have limited coverage. This study evaluates the performance of various YOLO models and other state-of-the-art object detection models using a novel dataset of oil palm fresh fruit bunches in plantations, captured in the plantation regions of Central Kalimantan Province, Indonesia. The dataset includes five ripeness classes (abnormal, ripe, underripe, unripe, and flower) and presents challenges such as partially visible objects, low contrast scenes, occluded and small objects, and blurry images. The proposed YOLOv8s Depthwise model was compared with other YOLO models, including YOLOv6s, YOLOv6l, YOLOv7 Tiny, YOLOv7l, YOLOv8s, and YOLOv8l. YOLOv8s Depthwise demonstrated a balanced performance, with a compact size (10.6 MB), fast inference time (0.027 seconds), and strong detection accuracy (mAP50 at 0.75, mAP50-95 at 0.481). Its rapid convergence and low training loss highlighted its efficiency, completing training in the shortest time of 2 hours, 18 minutes, and 30 seconds. Furthermore, it achieved low Mean Absolute Error (MAE) of 0.164 and Root Mean Square Error (RMSE) of 0.4, indicating precise counting capability. Hyperparameter tuning revealed that the YOLOv8s Depthwise model achieved optimal performance using the SGD optimizer with a batch size of 16 and a learning rate of 0.001, showing the best balance between accuracy and training efficiency. Data augmentation positively impacted model performance, resulting in improved performance metrics across various models. When evaluated against other state-of-the-art models on the same dataset, including Faster RCNN, SSD MobileNetV2, and YOLOv4, YOLOv8s Depthwise surpassed other state-of-the-art models, including Faster R-CNN, SSD MobileNetV2, YOLOv4, and EfficientDet-D0 from previous research, in terms of speed, accuracy, and efficiency, making it ideal for real-time palm oil harvesting applications.

Prediction and diagnosis of cardiovascular diseases (CVDs) based, among other things, on medical examinations and patient symptoms are the biggest challenges in medicine. About 17.9 million people die from CVDs annually, accounting for 31% of all deaths worldwide. With a timely prognosis and thorough consideration of the patient’s medical history and lifestyle, it is possible to predict CVDs and take preventive measures to eliminate or control this life-threatening disease. In this study, we used various patient datasets from a major hospital in the United States as prognostic factors for CVD. The data was obtained by monitoring a total of 918 patients whose criteria for adults were 28-77 years old. In this study, we present a data mining modeling approach to analyze the performance, classification accuracy and number of clusters on Cardiovascular Disease Prognostic datasets in unsupervised machine learning (ML) using the Orange data mining software. Various techniques are then used to classify the model parameters, such as k-nearest neighbors, support vector machine, random forest, artificial neural network (ANN), naïve bayes, logistic regression, stochastic gradient descent (SGD), and AdaBoost. To determine the number of clusters, various unsupervised ML clustering methods were used, such as k-means, hierarchical, and density-based spatial clustering of applications with noise clustering. The results showed that the best model performance analysis and classification accuracy were SGD and ANN, both of which had a high score of 0.900 on Cardiovascular Disease Prognostic datasets. Based on the results of most clustering methods, such as k-means and hierarchical clustering, Cardiovascular Disease Prognostic datasets can be divided into two clusters. The prognostic accuracy of CVD depends on the accuracy of the proposed model in determining the diagnostic model. The more accurate the model, the better it can predict which patients are at risk for CVD.