Judul: Model Plant-Disease Relation Menggunakan Pendekatan
BERT-BiLSTM-CRF dan Transfer Learning
Abstrak: A Plant-Disease Relation (PDR) is a relation extraction sub-task that aims to reveal the relationship between plants and disease entities in a sentence. Research on the PDR was first proposed by Kim et al. in 2019. They developed PDR as a gold-standard corpus by manually labeling the plant and disease entities with help of the experts. The corpus has four classes: treatment of disease, cause of disease, negative, and associate. Subsequently, they proposed a short dependency path-convolutional neural network (SDP-CNN) method to analyze the relationship between plants and disease entities. The SDP algorithm identifies the shortest path to find plant and disease entities while CNN analyzes the relationship between the two entities in the classification. The experimental results show that the model performance achieved an F1 score of 0.764 for imbalanced class data. The SDP-CNN combination effectively extracts local features from dependency paths and efficiently captures syntactic and semantic information. By concentrating on the most relevant parts of the sentences, SDP-CNN can improve the accuracy of relation extraction tasks, particularly in simpler sentences. On the other hand, the SDP-CNN method has several limitations: 1) Does not take class balance into account when building the PDR model; 2) the SDP-CNN method is only optimal for short sentences, but is not effective for complex English; and 3) rely on Experts when building ground truth datasets. Therefore, this research aims to 1) to select the best techniques in oversampling, undersampling, and SMOTE to deal with imbalance classes; 2) to improve the F1 score of plant-disease relation model proposed by Kim et al. (2019) by combine BERT-BiLSTM-CRF algorithm; and 3) to propose a multi-source transfer learning method for automatic labeling for replaced rely on the expert. Meanwhile, the novelty of this research is as follows: 1) BERT-BiLSTM-CRF method to predict plant-disease relation, and 2) multi-source transfer learning method for automatic labeling. We performed preprocessing and applied stop_word to realize the first goal. Then, the text was converted to numeric text using a Count Vectorizer with the addition of n-grams. In both the training and testing stages, we utilized an 80:20 ratio and 5-fold cross validation. We used machine learning classifiers (multinomial naïve bayes, k-nearest neighbour, support vector machine, and random forest) and Deep Learning (LSTM). Additionally, we employed undersampling, oversampling, and the Synthetic Minority Oversampling Technique (SMOTE) to address with unbalanced classes. Overall, experiments were conducted on all classifiers using the eight schemes. The experimental results show that, the LSTM classifier with the undersampling technique is superior to all the machine learning classifiers. To achieve the second goal, we normalized the PDR corpus. Each sentence was arranged in a single line and ended within a period. Next, the tokenisation process used the BERT Tokenizer. In the training stage, the training and testing data were divided in an 80:20 ratio. Hyperparameter tuning was performed using GridSearch to obtain the optimal model configuration. It took approximately 28 hours to obtain optimal parameters. The results of the GridSearch recommendation yielded the following parameters: epoch=40, dropout=0.3, batch_size=64, and num_units=128. To obtain the optimal model, we conducted the experiments ten times
Keyword: automatic labeling, conditional random field, long short-term memory, plant-disease relation, transfer learning

Judul: Deep Learning Model for the Detection and Classification of Banana Disease Based on Leaf Images
Abstrak: Fungal diseases are among the main reasons for low productivity in banana farming. Black Sigatoka and fusarium wilt race 1 are the major fungal diseases that threaten banana production. black Sigatoka is a fungal disease caused by a wind-bone fungus, Mycosphaerella fijiensis Morelet. On the other hand, fusarium wilt is another fungal disease originating from soil caused by a fungus named Fusarium oxysporum f. sp. cubense (Foc). The spread of these diseases is accelerating in many countries and threatens the production of bananas globally. Early detection of fungal diseases is essential and one of the possible approaches is using machine vision. Due to its high accuracy, deep learning is the most widely used algorithm in machine vision for many solutions. Its ability to model the data into multiple levels of abstraction makes it suitable for many agricultural solutions. Furthermore, deep learning can be trained with a vast amount of data. However, deep learning requires a high computational resource, challenging many agricultural solutions implemented on low-computing devices such as edge, mobile, and IoT devices. Therefore, this research proposes deep-learning models for detecting and classifying banana diseases based on leaf images. The study proposes the use of lightweight deep learning algorithms instead of off-shelf algorithms. Lightweight deep learning algorithms have a small architecture which make them suitable for low computing resources devices. The research is conducted in two stages: the first stage focuses on identifying the diseases using deep learning where default algorithms are trained. In this stage, four lightweight deep learning utilized namely mobileNetv2, mobileNetv3-small, ShuffleNetv2, and SqueezeNet. The choice of algorithms is based on the popularity of the algorithm in the research community. The second stage focuses on modification of the algorithms to increase the performance of the models. Modifications introduced include the addition of a convolutional block attention module (CBAM). CBAM is a lightweight attention module that enhances both channel and attention features. In both stages, sequential model based optimization (SMBO) is used for automatic hyperparameter tuning (HPO). SMBO is a variant of Bayesian optimization that selects the parameters sequentially. This makes SMBO to be faster than other optimization algorithms. The study used a dataset of images representing three classes: healthy, black Sigatoka, and fusarium wilt race 1. The dataset was collected in Northern Tanzania for a period of six months by the Mandela African Institution of Science and Technology (NM-AIST) and the International Institute of Tropical Agriculture (IITA). The preprocessing conducted after acquiring the dataset includes resizing, removing the stem images, splitting, and augmentation. The model's performance is measured by calculating accuracy, precision, recall, and f1-score as well as measuring model complexity. The results indicated that for stage one, mobileNetv2 outperforms all other models with 97,73% accuracy,97,74% precision, 97,73% recall, and 97,73% f1 score, while shuffleNetv2 had poor performance with an accuracy of 84,21%, precision of 84,76%, recall of 84,21%, and f1-score of 84,03%. SqueezeNet is a light model with a size of 2,78 MB, while mobileNetv3-small is the heaviest model with a size of 8.74 MB. However, all models had trouble distinguishing between black sigatoka and fusarium wilt race
Keyword: banana, CBAM, deep learning, HPO, SMBO

Judul: Konsumsi Pangan dan Status Gizi Ibu Hamil di Kecamatan Danurejan, Kodya Yogyakarta, Daerah Istimewa Yogyakarta
Abstrak: Hasil penelitian menunjukkan bahwa rata-rata konsumsi energi per orang per hari adalah 1 525 Kalori, konsumsi protein 45,l g, konsumsi zat besi 12,8 mg dan konsumsi vitamin A 2 264 SI. Rata-rata kecukupan energi per orang per hari adalah 2 213 Kalori, kecukupan protein 75,6 g, kecukupan zat besi 29,O mg dan kecukupan vitamin A 4 500 SI.
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