Judul: Stabilitas Minyak Ikan Komersial (Soft Gel) Impor di Beberapa Wilayah Jawa Timur
Abstrak: Produk minyak ikan (soft gel) komersial telah banyak diperdagangkan di Indonesia khususnya wilayah Jawa Timur. Penelitian ini dilakukan untuk menentukan mutu minyak ikan komersial (soft gel) serta stablilitasnya di beberapa wilayah Jawa Timur. Metode yang digunakan untuk mengidentifikasi kualitas minyak ikan komersial yaitu uji karakteristik minyak ikan berdasarkan International Fish Oil Standards (IFOS) yang meliputi analisis asam lemak bebas, analisis bilangan peroksida, analisis bilangan anisidin, analisis total oksidasi dan profil asam lemak serta pengujian stabilitas dengan metode Schaal oven test. Minyak ikan komersial soft gel impor dari beberapa wilayah di Jawa Timur yang sesuai dengan standar IFOS pada parameter FFA yaitu 87.5%. bilangan peroksida yaitu 12.5%, bilangan anisidin yaitu 75% dan total oksidasi yaitu 37.5%. Hasil pengujian stabilitas menunjukkan penurunan kualitas minyak ikan seiring penyimpanan dan hanya bilangan anisidin yang menunjukkan hasil sesuai standar IFOS. Minyak ikan dengan kandungan omega-3 tertinggi terdapat pada sampel S4, yakni 78.6%, EPA tertinggi terdapat pada S4 sebesar 45.53% dan DHA tertinggi terdapat pada sampel S3 sebesar 37.35%.
Keyword: metode Schaal oven test, minyak ikan komersial, stabilitas minyak ikan

Judul: Inventarisasi Kualitas Minyak Ikan Komersial (Softgel) Impor di Wilayah DKI Jakarta dan Sekitarnya
Abstrak: Permintaan minyak ikan yang semakin meningkat menjadi tantangan produsen untuk memproduksi minyak ikan yang berkualitas. Penelitian ini bertujuan menginventarisasi dan menentukan kualitas softgel minyak ikan komersial di wilayah DKI Jakarta dan sekitarnya berdasarkan standar International Fish Oil Standards (IFOS) dengan parameter uji bilangan peroksida, bilangan p-anisidin, total oksidasi, profil asam lemak, dan analisis asam lemak bebas. Hasil penelitian menunjukkan bahwa presentase asam lemak bebas, bilangan peroksida, bilangan p-anisidin, dan total oksidasi yang memenuhi standar IFOS masing-masing yaitu 90 % (19 dari 21 sampel), 29 % (6 dari 21 sampel), 90 % (19 dari 21 sampel), dan 33 % (7 dari 21 sampel). Kualitas minyak ikan komersial tidak semuanya sesuai dengan standar IFOS, hanya 19 % (5 dari 21 sampel) yang memenuhi standar IFOS. Minyak ikan terbaik dari 19 % yang memenuhi semua parameter IFOS yaitu minyak ikan sampel P dari Bogor. Minyak ikan berdasarkan kandungan omega-3, EPA, dan DHA tertinggi yaitu minyak ikan sampel U dari Jakarta dengan nilai masing-masing 74,3 %, 14,17 %, dan 59,73 %.
Keyword: asam lemak, minyak ikan, oksidasi, standar

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