Climate change is universal phenomena which is importantly anticipated including cocoa plantation. Drought tolerance cocoa seedling is urgently neededto develop cocoa plantation. This paper studied possible drought tolerance of cocoa seedling through crossing between female parent KKM 22 with three maleparents BAL 209, KW 641, and KW 614. Progeny test was conducted in green house based on four water availability conditions: 25, 50, 75, and 100%. Root condition was recorded as rootstock parameters of three crossings. Result showed that root characteristics varied among crossing samples studied. The longestand hight volume root were recorded from KKM 22 x BAL 209 crossing. Seedling of KKM 22 x BAL 209 crossing tended to have long and wide root, while seedling of KKM 22 x KW 641 crossing tended to have a wide root type and seedling of KKM 22 x KW 614 tended to have a long root type. Based on drought tolerancy, seedling of KKM 22 x KW 641 crossing could be classified as drought tolerance while other two group progenies could be classified as susceptible to drought.To conclude, seedling of KKM 22 x KW 641 can be recommended for cocoa plantation in drought area.
Vascular streak dieback (VSD) is one of the main diseases on cocoa. This disease can produce a heavy damage in susceptible plants. Agro-climatic condition influences the VSD disease severity level. A study on the relationship between agro-climatic condition and VSD disease severity was conducted in eight locations which were selected based on difference in agro-climatic conditions including altitude, rainfall, number of wet, and dry months. Randomized complete block design was used consisting of eight agro-climatic conditions as treatments which consisted of 200 trees samples, and scored for VSD intensity. A study was also conducted on the response of cocoa clones with different level of resistance at different altitude at Kendeng Lembu, Jatirono, Sungai Lembu, Banjarsari, and Sumber Asin Plantations. A split plot design was applied consisting of two factors. The first factor was location including Pager Gunung (highland) and Besaran (lowland). The second factor was clone resistance with two levels: PA 191 (resistant) and BL 703 (susceptible). VSD scores and stomatal characteristics (stomata number, stomata diameter, and stomata aperture) were determined. The results of experiment showed that VSD scoring differed significantly between the eight agro-climatic conditions. The highest VSD score occurred in the lowland (Gereng Rejo, Banjarsari Plantation, 38 m asl.), where the average annual rainfall was 2161 mm, with five dry months. Cocoa trees in Sumber Asin (580 m asl.), with the average annual rainfall of 2302 mm and 8.5 wet months/3.5 dry months were mostly free of VSD disease. Altitude was positively correlated with rainfall, and negatively correlated with VSD severity. Number of wet months was negatively correlated with VSD severity. Conversely, number of dry months was positively correlated with VSD. The result indicated that genotype, environment, or their interaction did not significantly affect number and aperture of stomata. Although stomatal diameter was significantly affected by environment, genotypes or their interaction with environment did not influence this character.
Deteksi tumor otak merupakan bidang penelitian yang menarik untuk diteliti. Perkembangan teknologi informasi menghasilkan berbagai metode yang dipergunakan antara lain menggunakan CT (Computed Tomography) scan atau dikenal dengan teknologi CT scan. CT Scan mempunyai berbagai macam keunggulan dalam mendeteksi tumor otak antara lain pada sisi kecepatan, kemampuan memvisualisasikan citra 3 dimensi dan kemampuan membedakan antar jaringan yang berbeda. Keunggulan CT Scan tersebut membuat para peneliti tertarik untuk mengembangkan berbagai jenis metode yang dipergunakan untuk menganalisis dan memprediksikan hasil CT scan tersebut. Salah satu metode yang dipergunakan adalah menggunakan pendekatan Machine Learning (ML). ML dapat digunakan untuk deteksi tumor otak dengan CT scan. Prosesnya melibatkan penggunaan algoritma ML untuk mengidentifikasi pola-pola yang terdapat pada gambar CT scan pasien dengan tumor otak. Dalam hal ini, CT scan pasien dengan tumor otak digunakan sebagai dataset pelatihan untuk membangun model ML. Namun penggunaan Machine Learning juga memiliki keterbatasan dalam hal kurang handal nya Model dan kesulitan hasil deteksi yang diinterpretasikan dokter. Metode ML akan mengalami ketidakakuratan prediksi dengan model training data yang semakin besar sehingga membutuhkan metode lain yang bisa menghasilkan tingkat akurasi yang tinggi. Deep Learning (DL) merupakan fenomena baru pada dunia teknologi informasi dan telah berhasil diimplementasikan pada berbagai macam bidang penelitian. DL memberikan tingkat akurasi yang semakin tinggi jika didukung data yang semakin besar. Penelitian ini mengaplikasikan salah satu metode DL yaitu Deep Neural Network (DNN) untuk memprediksi tumor otak dari hasil CT Scan yang akan disimpan pada cloud server sehingga bisa diakses kapanpun dan dimanapun juga sepanjang tersedia teknologi Internet. Hasil penelitian ini akan bermanfaat bagi para tenaga medis dalam memprediksi tumor otak dengan lebih akurat berdasarkan gambar citra dari CT scan.Detection of brain tumors is an interesting field of research to study. The development of information technology has resulted in various methods being used, including using a CT (Computed Tomography) scan or known as CT Scan technology. CT Scan has various advantages in detecting brain tumors, including in terms of speed, the ability to visualize 3-dimensional images and the ability to distinguish between different tissues. The superiority of the CT Scan makes researchers interested in developing various types of methods used to analyze and predict the results of the CT Scan. One of the methods used is the Machine Learning (ML) approach. ML can be used to detect brain tumors with CT scans. The process involves using ML algorithms to identify patterns present in the CT scan images of patients with brain tumors. In this case, CT scans of patients with brain tumors are used as a training dataset to construct the ML model. However, the use of Machine Learning also has limitations in terms of the lack of reliability of the model and the difficulty of interpreting the results of detection by doctors. The ML method will experience prediction inaccuracies with the larger training data model, requiring other methods that can produce a high level of accuracy. Deep Learning (DL) is a new phenomenon in the world of information technology and has been successfully implemented in various research fields. DL provides a higher level of accuracy if it is supported by larger data. This study applies one of the DL methods, namely Deep Neural Network (DNN) to predict brain tumors from CT Scan results which will be stored on a cloud server so that they can be accessed anytime and anywhere as long as Internet technology is available. The results of this study will be useful for medical personnel in predicting brain tumors more accurately based on images from CT scans.
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