Judul: Ekstraksi ciri morfologi (panjang dan lebar fisiologis) dan tekstur untuk temu kembali citra helai daun
Abstrak: In recent time, the ability to identify and classify leaves becomes a great need for taxonomist to know the diversity of plants (Hickey et al 1999). Identification can be done by identifying features of morphology and texture of the leaves or also with a combination of both. Annisa (2009) implemented the feature extraction approach to obtain the basic characteristic morphological image derived from the leaf blade and the co-occurrence matrix for texture feature extraction. Morphological features that obtained were diameter, leaf area, leaf perimeter, smoothing factor, form factor and perimeter ratio of diameter. Texture features were obtained energy, inverse difference moment, entropy, maximum probability, contrast, correlation, and homogeneity. This study used morphological features approach to obtain morphological features and co-occurrence matrix for texture features. Therefore, the evaluation of image retrieval was done by using more complete morphological features with additional morphological features of physiological length and physiological width. Both morphological features are very useful to help identify the characteristics of a leaf blade. This research approach has been successfully implemented morphological features. Value of image retrieval evaluation leaves increased with the addition of two morphological characters. In this research, with additional morphological features and physiological length and physiological width resulted in morphological traits 0.2083, the texture feature values 0.1864 and character of Bayesian Network 0.2055. So that the average value of precision increased by 0.0137 for morphological features and by 0.0069 for combined value of morphological and textural characteristics (Bayesian Network).
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Judul: Morphology and Texture Features Extraction for Leaf Sheet Image Retrieval.
Abstrak: In the biology and forestry science, there are many kinds of leaves with different features. It causes difficulty to make leaf identification. Colour feature is not too significantly different because generally they are green. So, the primary leaf identification depends on morphology and texture feature. The two of them makes leaf identification more accurate. But, the combination makes some varies. A lot of people even the scientist are difficult to recognize the leaf, especially in leaf image. The reason to make feature extraction automatically by computer will be used for leaf content based image retrieval. This research started with collected leaf images by taking some pictures of them with digital camera. The collected location was Bogor Agricultural University Darmaga area. Eleven kinds of fruit tree leaves were photoing, one hundred pictures for each kind. So, there are eleven classes of leaf on database. The next step was cleaning the images. Background images were cleaned to get white background. After that, Digital Morphological Features Extraction was used to extract the shape features and Cooccurrence Matrix for texture features. Three primary features of shape are extracted and calculated it to get three derivative features. Meanwhile, seven features of texture are extracted with Coocurrence Matrix. Those features were combined with Bayesian Network Model to improve content based image retrieval. Generally, Bayesian Network Model increases precision value of image retrieval. Average precision value for Bayesian Network Model on all class is 0.2733. This is the highest value. Average precision value using texture feature on all class is 0.2722. Then, average precision value using shape feature on all classes is 0.2531. This is the lowest value. Keyword : Content Based Image Retrieval (CBIR), feature extraction, Morphological Features Extraction, Co-occurrence Matrix, Bayesian Netrwork Model.
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Judul: SEIRS Stochastic Model with Quarantine on the Spread of Covid-19 Disease
Abstrak: Penyakit Covid-19 merupakan penyakit yang disebabkan oleh virus yang bernama Severe Acute Respiratory Syndrome Coronavirus-2 (SARSCoV-2). Virus ini menyebar dari manusia ke manusia melalui droplet akibat batuk dan bersin. Virus juga dapat ditularkan dari orang tanpa gejala sehingga sulit mendeteksi penyebarannya. Upaya yang dapat dilakukan untuk menekan penyebarannya yaitu mematuhi protokol kesehatan dan melakukan karantina jika dinyatakan terinfeksi, baik isolasi mandiri ataupun dirawat di rumah sakit. Untuk memodelkan penyebaran penyakit Covid-19, dapat menggunakan model epidemik stokastik. Penelitian ini bertujuan untuk (1) membangun model SEIRS dengan karantina pada penyebaran penyakit Covid-19, (2) menentukan peluang transisi dan peluang wabah dengan menggunakan pendekatan CTMC, (3) melakukan simulasi untuk mengetahui pengaruh peningkatan laju transmisi tanpa karantina dan laju karantina pada sistem. Data yang digunakan untuk menentukan parameter model yaitu data harian Covid-19 DKI Jakarta mulai dari 25 Maret 2020 sampai 24 Maret 2021. Data tersebut terdiri dari data kasus Covid-19 masing-masing kelurahan di DKI Jakarta. Peluang transisi ditentukan dengan mengasumsikan terdapat enam peubah acak dan satu peubah yang nilainya dapat ditentukan secara pasti. Peubah acak tersebut meliputi banyaknya individu pada subpopulasi rentan, rentan yang dikarantina, terinfeksi tanpa gejala, terinfeksi tanpa gejala yang dikarantina (isolasi mandiri), terinfeksi dengan gejala, serta terinfeksi dengan gejala yang dikarantina (dirawat), sedangkan banyaknya individu sembuh dapat ditentukan secara pasti. Peluang wabah diperoleh melalui proses bercabang berganda. Pada proses bercabang berganda, peluang wabah bernilai 0 ketika nilai harapan banyaknya individu terinfeksi (m)≤ 1, dan lebih dari 0 ketika nilai harapan banyaknya individu terinfeksi (m) > 1. Simulasi pengaruh peningkatan laju karantina untuk individu terinfeksi dilakukan dengan 100 kali pengulangan untuk memperoleh sebaran peluang waktu bebas penyakitnya, selanjutnya dapat ditentukan rata-rata waktu bebas penyakitnya. Berdasarkan hasil simulasi, untuk nilai harapan banyaknya individu terinfeksi (m)≤1, didapatkan bilangan reproduksi dasar (R_0)<1. Ketika nilai m>1, peningkatan nilai m membuat peluang wabah semakin besar. Adapun nilai R_0,m, dan peluang wabah diperoleh secara analitik. Terdapat tiga skenario dalam simulasi numerik, yaitu pengaruh peningkatan laju transmisi dari individu rentan yang dipengaruhi individu tanpa gejala (β_1) dan dengan gejala (β_2), peningkatan laju karantina untuk individu dengan gejala (θ_3 ), dan peningkatan laju karantina untuk individu tanpa gejala (θ_2 ). Hasil simulasi pada skenario pertama menyatakan bahwa peningkatan β_1 dan β_2 dapat menaikkan R_0,m, dan waktu bebas penyakit. Sebaliknya, hasil simulasi pada skenario kedua menunjukkan bahwa peningkatan θ_3 dapat menurunkan nilai R_0, m, dan rata-rata waktu bebas penyakit. Sama halnya dengan hasil simulasi pada skenario ketiga menyatakan bahwa peningkatan θ_2 dapat menurunkan nilai R_0,m, serta rata-rata waktu bebas penyakit. Hasil simulasi skenario ketiga juga menunjukkan bahwa peningkatan θ_2 dengan ditetapkannya nilai θ_3, menyebabkan nilai R_0,m, dan rata-rata waktu bebas penyakit lebih kecil dibandingkan tanpa θ_3., Covid-19 disease is a disease caused by virus called Severe Acute Respiratory Syndrome Coronavirus-2 (SARSCoV-2). This virus spreads from person to person through droplets caused by coughing and sneezing. The virus can also be transmitted from asymptomatic individuals, making it difficult to detect its spread. Efforts that can be made to suppress the spread are complying with health protocols and quarantine if declared infected, either self-isolation or hospitalized. To model the spread of Covid-19 disease, we can
Keyword: continuous time Markov chain, Covid-19 disease, outbreak probability, quarantine