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"Penyakit Gugur Daun Pestalotiopsis (PGDP) merupakan penyakit yang menyerang perkebunan karet Indonesia sejak akhir 2017. Patogen yang menyebabkan PGDP adalah kelompok fungi Pestalotiopsis. Dampak yang ditimbulkan oleh PGDP antara lain gugur daun hingga mencakup 75-90% kanopi dan penurunan produksi lateks hingga 45% dari berbagai tingkatan usia dan berbagai jenis klon. Penelitian bertujuan untuk mengetahui respons daun Hevea brasiliensis terhadap infeksi Pestalotiopsis sp. secara mikromorfologi dari klon RRIC 100 dan BPM 24. Sebanyak 3 individu dari klon RRIC 100 dan BPM 24 digunakan sebagai ulangan dalam penelitian. Inokulasi dilakukan di 3 area pada permukaan abaksial anak daun. Pengamatan dilakukan pada 1 sampai 3 hari setelah inokulasi (hsi). Sediaan mikromorfologi dibuat melalui 2 tahap, yaitu bleaching dan staining. Respons yang ditunjukkan anak daun Hevea brasiliensis klon RRIC 100 dan BPM 24 adalah terbentuknya lesi patogenik yang terdiri atas zona miselia dan zona nekrosis di sekitar area inokulasi. Hasil pengamatan mikromorfologi menunjukkan bahwa kecenderungan arah kolonisasi hifa Pestalotiopsis sp. teramati pada area pertulangan daun. Respons yang ditunjukkan klon RRIC 100 terhadap infeksi Pestalotiopsis sp. adalah pembentukan zona nekrosis berwarna cokelat dengan garis batas yang jelas dan mencegah penyebaran miselia Pestalotiopsis sp.. Sementara pada BPM 24, pembentukan zona nekrosis memunculkan warna cokelat dengan tepi tidak beraturan dan gari batas yang samar sehingga kurang dapat mencegah penyebaran miselia Pestalotiopsis sp.. Hasil tersebut menunjukkan bahwa klon RRIC 100 mengembangkan sistem pertahanan diri yang lebih baik dalam menghambat penyebaran infeksi melalui pembentukan zona nekrosis yang lebih tebal dan tegas dibandingkan dengan klon BPM 24.

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Pestalotiopsis leaf fall disease (PLFD) is a disease that has been affecting Indonesian rubber plantations since late 2017. The pathogen that causes PLFD is Pestalotiopsis group of fungi. The impacts caused by PLFD include leaf fall covering 75-90% of the canopy and a decrease in latex production of up to 45%. PLFD can attack H. brasiliensis plants of various ages and various types of clones, including clones with moderate resistance such as RRIC 100 and susceptible clones such as BPM 24. The research aimed to determine the response of Hevea brasiliensis leaves to Pestalotiopsis sp. infection micromorphologically from clones RRIC 100 and BPM 24. Total of 3 individuals from clones RRIC 100 and BPM 24 were used as replicates in the research. Each individual was represented by 3 compound leaves that treated with wounding, inoculation, and wounding+inoculation. Inoculation was given in 3 areas on abaxial surface of the leaflets. Observations were made at 1 to 3 days post inoculation (dpi). Micromorphology preparations were made through 2 stages, which are bleaching and staining. Micromorphological observations showed that the tendency of Pestalotiopsis sp. hyphae colonization was observed in the area of leaf veins. The response shown by clone RRIC 100 to Pestalotiopsis sp. infection is the formation of a brown necrosis zone with clear boundaries and is blocking the spread of infection. While in BPM 24, the formation of necrosis zones gave rise to a brown color with irregular edges. These results indicate that the RRIC 100 clone developed a better self-defense system in inhibiting the spread of infection through the formation of a thicker and more defined necrosis zone compared to the BPM 24 clone."

"ABSTRAKSifat koloid lateks yang cenderung mengalami koagulasi mengakibatkan lateks sulit untuk dimodifikasi pada saat proses hidrogenasi. Hal ini disebabkan oleh sifat koloid lateks yang cenderung tidak stabil terhadap perlakuan temperatur, kecepatan pengadukan, dan penambahan zat kimia. Penelitian ini bertujuan mempelajari suatu kondisi proses yang optimum dengan mempertahankan kestabilan koloid untuk meningkatkan derajat hidrogenasi. Uji turbiditas dilakukan untuk mengetahui tingkat kekeruhan larutan lateks yang mengindikasikan terdispersinya koloid didalam larutan lateks. Sedangkan, uji viskositas dilakukan sebagai uji pendukung. Berdasarkan hasil penelitian dalam menentukan kondisi optimum stabilitas koloid pada fasa lateks dengan pengujian turbiditas dan viskositas, diketahui koloid cenderung stabil pada temperature 50?C, kecepatan aduk 200 rpm dan kadar karet kering KKK 20.

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ABSTRACTThe tendency to coagulate, which is the colloid characteristic of latex makes it difficult to be modified when hydrogenation process is running. This was caused by the latex colloid characteristic which is unstable to thermal treatment, stirring velocity, and the addition of chemical compounds. This research aims to learn the optimum process condition by maintaining the colloid stability to increase the hydrogenation degrees. Turbidity test was done to know the turbidity level of latex solution indicates the dispersed colloid inside the latex solution, while viscosity test was done as a supporting test. Based on the research result in determining the optimum condition of the colloid stability in the latex phase with the turbidity and viscosity test, colloid has the tendency to be stable at the temperature of 50oC, stirring speed of 200 rpm, and the dry rubber content of 20."

"Tanaman karet berperan sebagai komoditas penting di Indonesia karena menghasilkan karet alami yang memiliki banyak manfaat dan mampu bersaing di pasar internasional. Namun, sejak tahun 2017, produksi karet mengalami hambatan karena timbul serangan penyakit gugur daun baru yang berbeda dari penyakit terdahulu. Penyakit tersebut dapat menyebabkan gugur daun hingga 90% dan penurunan produksi lateks hingga 45%. Setelah ditelusuri, penyakit gugur daun baru ini disebabkan oleh patogen Pestalotiopsis sp. dan diberi nama penyakit gugur daun Pestalotiopsis. Sebagai penyakit baru, perlu dilakukan penelitian lebih lanjut untuk memonitor laju pertumbuhan penyakit ini. Salah satu penelitian ini adalah melakukan klasifikasi indeks atau level keparahan penyakit gugur daun Pestalotiopsis. Keparahan penyakit ini dapat dikelompokkan berdasarkan perubahan warna daun dan lesi khas yang timbul pada permukaan daun tanaman karet. Pada penelitian sebelumnya, pengukuran intensitas keparahan dilakukan dengan observasi secara langsung bercak gejala yang muncul pada daun atau pohon dalam jangka waktu tertentu. Pengamatan secara konvensional ini memerlukan tenaga yang banyak dan waktu yang cukup lama. Diperlukan suatu metode yang mampu melakukan klasifikasi level keparahan ini secara tepat dan cepat terhadap sampel daun yang berjumlah banyak. Saat ini, implementasi Artificial Intelligence (AI) melalui algoritma machine learning dapat menjadi solusi untuk menyelesaikan suatu permasalahan seperti klasifikasi multikelas secara otomatis dan efisien. Penelitian ini memanfaatkan salah satu teknik machine learning, yaitu artificial neural network berupa deep learning dengan arsitektur convolutional neural network (CNN). Dengan mempertimbangkan penelitian sebelumnya, maka penelitian ini mengajukan sebuah pengembangan dari CNN, yaitu arsitektur DenseNet121 sebagai metode untuk melakukan klasifikasi level keparahan penyakit gugur daun Pestalotiopsis menggunakan data citra daun karet. Klasifikasi level keparahan dibagi menjadi lima kelas, yaitu Level 0 (daun sehat atau tidak terinfeksi penyakit gugur daun Pestalotiopsis) dan Level 1-4 (menunjukkan tingkat keparahan penyakit gugur daun Pestalotiopsis). Pada Penelitian ini, digunakan 257 data citra daun karet yang dikumpulkan mahasiswa Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Indonesia ketika berkunjung ke Pusat Penelitian Karet Sembawa, Palembang pada tahun 2022. Data citra tersebut melalui preprocessing berupa crop dan resize agar dapat menjadi input yang diterima arsitektur.  Data dipisahkan menjadi data latih dan data uji dengan rasio 80:20. Model dilatih dengan pendekatan 5-fold cross validation sehingga pengujian dilakukan terhadap lima model berbeda. Berdasarkan simulasi, diperoleh rata-rata lima model berupa ccuracy sebesar 56,16% , precision sebesar 54,2% , recall sebesar 55,6%, skor F1 sebesar 51% , dan running time 3,110 detik.

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Rubber plant is an essential commodity in Indonesia since natural rubbers from this plant are very beneficial and have high international market potential. Unfortunately, since 2017, a new leaf fall disease has caused massive decline of the rubber production. This disease leads to at most 90% leaf fall percentage and production decline as high as 45%. Subsequently, researchers found that this new leaf fall disease is caused by Pestalotiopsis sp., thus, the name of this disease is Pestalotiopsis leaf fall disease. Studies must be conducted to further investigate the growth and pattern of this new leaf fall disesase. One of these studies is to classify the intensity of the Pestalotiopsis leaf fall disease.The intensity can be measure by observing distinct symptoms and lesion frequency that would appear on the rubber plant’s leave surface. In earlier works, intensity are measured by conventionally taking notes of the symptomps that appear on the leaves or trees and these methods was done on timely basis. These traditional approaches takes a lot of time and requires a handful of people. Hence, there must be new methods to classify this disease’s intensity with less time and resource when the amount of leaf samples increase. Recent studies implement Artificial Intelligence (AI) by using machine learning to solve classification problems efficiently. This study takes a technique of machine learning, that is, deep learning convolutional neural network (CNN) architectures. By comparing previous researches, we propose the architecture DenseNet121 to implement CNN in multiclass classification problem by using leaf image data. The classification consists of five classes, which are the intensity of the Pestalotiopsis leaf fall disease from level 0 to level 4. Level 0 corresponds to healthy leaves or leaves with other diseases whereas Level 1-4 refer to leaves with the intensity of lesion and discoloration caused by Pestalotiopsis leaf fall disease. This study uses 257 image data that was taken by students of the Math and Science Faculty from Universitas Indonesia when they visited Rubber Research Center, Sembawa in 2022. The data is split into train and test data with 80:20 ratio. Models are trained with 5-fold cross validation approach so the that each model will be trained and tested towards 5 folds of data. Then, five different models are tested by evaluating their predictions to the test data. The result of this simulation shows the average performance from five models, they are an accuracy of 56,16%, a precision of 54,2%, a recall of 55,6%, an F1-score of 51% , and an average running time of 3,110 seconds."

"Rubber clones of Indonesian Rubber Research (IRR) 200 series have been produced from intensive breeding program started in 1985. Some clones showed superior characteristics such as high latex yielding, vigorous, and disease resistant. This study aimed to test their performances in a field trial conducted since 1999 at Sungei Putih Experimental Garden, North Sumatra. The experiment was designed in a randomized block, using twelve IRR clones as treatment and PB 260 clone as control, three replications. Planting distance was 5 m x 4 m and plot size was 10 rows x 50 trees. Observations were made on girth size of the 2, 3 and 4 year-old trees, dry rubber yield following the 1/2S d/2 and 1/2S d/3.ET2.5% tapping systems, bark thickness, rings number and diameter of latex vessels, as well as leaf fall diseases intensities of 3-5 year-old trees. The results showed four IRR 200 clones, i.e. IRR 207, IRR 208, IRR 211 and IRR 220 produced high latex. Using the 1/2S d/2 tapping system, three clones with highest dry rubber yield were obtained, namely IRR 208 (49.8 g tree-1 tapping-1 or g t-1 t-1), IRR 211 (48.8 g t-1 t-1) and IRR 220 (52.0 g t-1 t-1), whereas that using the 1/2S d/3. ET2.5% tapping system, their yields were 63.2 g, 64.3 g, and 66.2 g t-1 t-1, respectively. At four year-old, these clones had girth size of 41.4-51.0 cm, girth increment 9.7-11.6 cm year-1, bark thickness 6.3-7.2 mm, latex vessel rings number 6.8-7.0, and diameter of latex vessels 23.75-26.57 mm. All of the clones were moderately resistant to Colletotrichum, Oidium and Corynespora leaf fall diseases. This study suggests that IRR 207, IRR 208, IRR 211 and IRR 220 rubber clones are suitable for commercial stake holders and the recommended tapping system is 1/2S d/3.ET2.5%."

"Saat ini, Indonesia menempati peringkat kedua sebagai produsen karet terbesar di dunia, menyumbang sekitar 29,8% dari kebutuhan global. Namun, produksi karet di Indonesia mengalami penurunan dari tahun ke tahun, salah satu faktornya adalah serangan penyakit gugur daun yang disebabkan oleh jamur Pestalotiopsis sp. Pada tahun 2021, luas perkebunan karet yang terkena penyakit mencapai 30.328,84 hektar dan tanaman yang terinfeksi oleh penyakit tersebut mengalami penurunan produksi lateks hingga 30%. Penyakit ini menyerang daun dengan gejala pembentukan bercak berukuran 0,5-2 cm yang menyebabkan nekrosis dan gugur. Penklasifikasian tingkat keparahan penyakit Pestalotiopsis sp. secara morfologi melalui pengamatan jumlah bintik dan warna pada daun karet membutuhkan waktu dan tenaga besar, terutama karena luasnya perkebunan yang terinfeksi. Oleh karena itu, penggunaan metode machine learning diusulkan untuk mengurangi waktu dan usaha yang dibutuhkan dalam menklasifikasi penyakit gugur daun akibat jamur Pestalotiopsis sp. Pada penelitian ini, model machine learning digunakan untuk mengklasifikasi 5 kelas tingkat keparahan penyakit Pestalotiopsis sp. yaitu tingkat 0 (sehat), tingkat 1 (terinfeksi ringan), tingkat 2 (terinfeksi sedang), tingkat 3 (terinfeksi parah), dan tingkat 4 (terinfeksi sangat parah). Dataset yang digunakan adalah citra daun tanaman karet yang diperoleh dari Pusat Penelitian Karet Sembawa. Model machine learning menerima input data citra daun tanaman karet, lalu citra disegmentasi menggunakan k-mean clustering. Data yang telah tersegmentasi kemudian diekstraksi dengan fitur warna hue, saturation, dan value (HSV) dan fitur jumlah bintik dengan metode contour detection menggunakan Suzuki’s contour algorithm. Selanjutnya, fitur-fitur ini diklasifikasikan menggunakan Support Vector Machine (SVM) tipe one vs rest multiclass classification dan Grid Search Cross Validation dengan 5 fold untuk menemukan hyperparameter terbaik untuk SVM. Hyperparameter terbaik adalah kernel radial basis function dengan C=100. Berdasarkan hasil percobaan sebanyak 5 kali, diperoleh kesimpulan bahwa model dengan akurasi tertinggi adalah model yang menggunakan fitur warna dan jumlah bintik dengan nilai rata-rata akurasi sebesar 81,86% dan nilai rata-rata Cohen’s kappa statistic sebesar 0,77 yang artinya model mampu mengklasifikasi data citra daun tanaman karet dengan cukup baik.

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Currently, Indonesia ranks as the second largest rubber producer in the world, contributing about 29.8% of global demand. However, rubber production in Indonesia has decreased from year to year, one of the factors is the attack of leaf fall disease caused by the fungus Pestalotiopsi sp. In 2021, the area of rubber plantations affected by the disease reached 30,328.84 hectares with infected plants have a 30% decrease in latex production. The disease attacks the leaves with symptoms of spot formation measuring 0.5-2 cm which causes necrosis and fall. Detecting the severity of Pestalotiopsis sp. morphologically through the observation of the number of spots and colors on rubber leaves requires a lot of time and energy, especially due to the large area of infected plantations. Therefore, the use of machine learning methods is proposed to reduce the time and effort required in classifying leaf fall disease caused by the fungus Pestalotiopsis sp. In this study, a machine learning model is used to classify 5 classes of Pestalotiopsis sp. disease severity, namely level 0 (healthy), level 1 (mild infected), level 2 (moderate infected), level 3 (severe infected), and level 4 (very severe infected).  The dataset used is an image of rubber plant leaves obtained from the Sembawa Rubber Research Center. The machine learning model received input data of rubber plant leaf images, then the image is segmented using k-mean clustering. The segmented data will then be extracted with hue, saturation, and value (HSV) color features and the number of spots feature with the contour detection method using Suzuki’s contour algorithm.  In this study, the performance evaluation used is accuracy and Cohen's kappa statistic. Furthermore, these features are classified using Support Vector Machine (SVM) type one vs rest multiclass classification and Grid Search Cross Validation with 5 folds to find the best hyperparameter for SVM. The best hyperparameter is the radial basis function kernel with C=100. Based on the results of 5 experiments, it is concluded that the model with the highest accuracy is a model that uses color and the number of spots features with an average accuracy value of 81.86% and an average Cohen's kappa statistic value of 0.77, which means that the model is able to classify rubber plant leaf image data quite well."