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: 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: Analisis Sifat Kristal Dan Sifat Optik Pada Keramik SrTiO3 Didadah RuO2
Abstrak: Sintesis keramik Stronsium Titanat (SrTiO3) didadah Rutenium Oksida (RuO2) metode reaksi padatan (solid state) telah berhasil dilakukan. Keramik di annealing dengan suhu 850C. Karakterisasi keramik Stronsium Titanat (SrTiO3) didadah Rutenium Oksida (RuO2) dengan variasi konsentrasi pendadah (0%; 0,5%; 1%) dilakukan untuk mengetahui pengaruh konsentrasi pendadah Rutenium Oksida (RuO2) terhadap sifat kristal dan sifat optik pada keramik Stronsium Titanat (SrTiO3). Struktur kristal keramik berbentuk kubus dengan ukuran parameter kisi a=b=c berturut-turut senilai 3,905 Å; 3,905 Å; dan 3,903 Å. Sifat optik keramik ditunjukkan dengan optik longitudinal dan transversal yang berada pada bilangan gelombang 261-937 cm-1 dan nilai energi celah pita sebesar 3,48 eV-3,56 eV., The synthesis of Strontium Titanate (SrTiO3) ceramic doped with Ruthenium Oxide (RuO2) using the solid-state reaction method has been successfully conducted. The ceramics were annealed at a temperature of 850°C. Characterization of Strontium Titanate (SrTiO3) ceramic doped with Ruthenium Oxide (RuO2) with varying dopant concentrations (0%; 0.5%; 1%) was performed to understand the influence of Ruthenium Oxide (RuO2) dopant concentration on the crystal properties and optical properties of Strontium Titanate (SrTiO3) ceramics. The ceramic's crystal structure is cubic with lattice parameter sizes of a=b=c, respectively, measuring 3.905 Å; 3.905 Å; and 3.903 Å. The optical properties of the ceramic are indicated by longitudinal and transverse optics in the wavenumber range of 261-937 cm-1 and a bandgap energy value ranging from 3.48 eV to 3.56 eV.
Keyword: ceramic, crystal properties, optical properties