Judul: Identifikasi Freycinetia Berbasis Citra Anatomi Epidermis Daun Menggunakan k- Nearest Neighbor
Abstrak: Freycinetia identification was conducted to determine the potential values, benefits, and distribution patterns as a biodiversity in Indonesia. The purpose of this research was to compare the low frequency and high frequency in Freycinetia identification based on leaf epidermis anatomy image by k-Nearest Neighbor (k-NN). Identification of Freycinetia can be described by using morphology and anatomy characteristics. Leaf epidermis anatomy image was used in the identification process to support the morphology characteristics, especially in speciment and sample with incomplete morphology. This research analyzed ninety six data which contains four kinds of Freycinetia, namely Freycinetia angustifolia, Freycinetia imbricata, Freycinetia javanica, and Freycinetia Sumatrana. The data were transformed by Fourier transformation and filtered in frequency domain to take the low frequency of image and the high frequency of image. It was found that the accuracy of k-NN with low frequency was 90.625% and that of the high frequency was 81.25%. These accuracy values indicated that in the identification of Freycinetia based on leaf epidermis anatomy image by k-Nearest Neighbor (k-NN), the use of low frequency is better than high frequency
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Judul: Klasifikasi Freycinetia Berbasis Citra Anatomi Stomata Menggunakan K-Nearest Neighbor dan Jaringan Syaraf Tiruan
Abstrak: The classification of Freycinetia is conducted to portray the potential result and function of nutfah plasma spreader. This research is purposed to classify a variety of Freycinetia based on stomata anatomic image by K-Nearest Neighbor (K-NN) and Artificial Neural Network (ANN). The Variety of Freycinetia classification can be described by using two different ways that are morphology and anatomic characteristic. Stomata anatomic image is used to support morphology characteristic in the identification process, especially in uncompleted morphology image specimen and sample. There are two feature extraction techniques in this research. First, is to pick up the color and gray scale element in the image. The scale element has a red, green and blue color (RGB). The grayscale has an entropy, contrast, energy, homogeneity, gray scale and deviation standard. Second, is a wavelet decomposition technique which has a function for decreasing a size of the image without loosing its important element. The taken element becomes a coefficient image of w-entropy for each wavelet level. Ninety six data is used in this research, it contains of four kinds of Freycinetia. They are Freycinetia angustifolia, Freycinetia imbricata, Freycinetia javanica, and Freycinetia sumatrana. The result shows an accuracy level for K-NN according to RGB and gray scale is 86.46% and according to wavelet decomposition is 96.88%. Whereas, the accuracy level of ANN based on RGB color and gray scale element is 94.79% and according to wavelet decomposition is 9 9%. The classification result of plant variety, especially Freycinetia based on stomata image with high accuracy, will be an alternative tool to identify taxonomy type.
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Judul: Integration of Islamic Stock Markets of OIC Countries Before and During the Russia-Ukraine War Crisis
Abstrak: Pasar saham yang terintegrasi memberikan dampak negatif seperti percepatan co-movement (pergerakan bersama) yang rentan terhadap guncangan ekonomi, maupun krisis pasar global ke pasar domestik. Salah satu krisis yang terjadi pada tahun 2022 adalah perang Rusia-Ukraina yang berdampak pada pasar saham, termasuk pada saham syariah yang dipercaya lebih tahan terhadap guncangan krisis global. Penelitian ini bertujuan untuk meneliti perkembangan, integrasi dan respon lima pasar saham syariah negara OKI pada perang Rusia-Ukraina. Metode yang digunakan adalah VAR/VECM pada indeks DJIMMT25, SPSADS, SPSUAEDS, JII, DJIMT, SPBMIR, dan SPBMIU. Hasil penelitian menunjukkan pergerakan yang sama pada pasar saham UAE dan Arab Saudi, sedangkan Turki mengalami tren positif selama perang. Pasar saham syariah negara OKI selama periode perang Rusia-Ukraina terkointegrasi, sementara pasar saham Malaysia memengaruhi pasar saham syariah empat negara OKI lainnya. Hasil IRF menunjukkan terdapat efek permanen pada volatilitas pasar saham syariah negara OKI akibat guncangan yang diberikan. Namun, respon volatilitas relatif kecil karena kontribusi guncangan lebih didominasi oleh pasar saham itu sendiri, An integrated stock market has negative impacts, such as accelerating co-movement, which is vulnerable to economic shocks and global market crises on the domestic market. One of the crises in 2022 was the Russia-Ukraine war which impacted the stock market, including Sharia stocks which were believed to be more resistant to the shocks of the global crisis. This study examines the development, integration, and response of OIC countries five Islamic stock markets in the Russia-Ukraine war. The method used is VAR/VECM on the DJIMMT25, SPSADS, SPSUUAEDS, JII, DJIMT, SPBMIR, and SPBMIU indices. The results show the same movement in the UAE and Saudi Arabia stock markets, while Turkey experienced a positive trend during the war. The Islamic stock market of the OIC countries during the Russia-Ukraine war was cointegrated. In contrast, the Malaysian stock market influenced the Islamic stock markets of the other four OIC countries. The IRF results show a permanent effect on the volatility of the Islamic stock market in OKI countries due to the shocks given. However, the volatility response is relatively small because the stock market itself dominates the contribution of shocks.
Keyword: co-movement, OIC, Rusia-Ukraine war, sharia stock market integration