Judul: Model Pembelajaran Mendalam untuk Deteksi dan Klasifikasi     Penyakit Pisang Berdasarkan Gambar Daun
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: 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: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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