Judul: Segmentasi Citra Hemispherical Menggunakan Metode Multi- Otsu untuk mengukur penutupan Kanopi Tanaman Karet (Studi  Kasus Puslit Karet Sembawa)
Abstrak: Monitoring perkebunan karet perlu dilakukan supaya penyebaran penyakit bisa dikontrol. Hemispherical photography bisa menjadi salah satu metode untuk menentukan keparahan penyakit di suatu area perkebunan tanaman karet. Tujuan dari penelitian ini adalah untuk menganalisis hubungan antara nilai canopy closure (CC) dengan tingkat keparahan penyakit dari tanaman karet. Metode penelitian ini terdiri dari pengambilan data, pembersihan dan pemilihan citra, Identifikasi keakuratan threshold, analisis hasil segmentasi, dan perbandingan CC dengan indeks lain. Identifikasi nilai threshold menghasilkan satu threshold sebagai threshold paling optimal dengan nilai F-score 0.633. Uji dengan algoritma k-nearest neighbors (k-NN) menghasilkan akurasi sebesar 0.875 untuk gambar identifikasi ahli dan 0.599 untuk semua citra. Nilai CC menunjukkan nilai tertinggi jika dibandingkan dengan nilai indeks NDRE dan LCI. Hal ini menunjukkan bahwa nilai CC bisa digunakan sebagai fitur untuk mengklasifikasikan tingkat keparahan penyakit, Monitoring rubber plantations is necessary to control the spread of diseases. Hemispherical photography can be one of the methods to determine disease severity in a rubber plantation area. The aim of this research is to analyze the relationship between canopy closure (CC) values and the level of disease severity in rubber plants. The research methodology consists of data collection, image cleaning and selection, accuracy threshold identification, segmentation result analysis, and comparison of CC with other indices. The identification of threshold values yields a single optimal threshold with an F-score value of 0.633. Testing using the k- nearest neighbors (k-NN) algorithm results in an accuracy of 0.875 for expert identification images and 0.599 for all images. CC values exhibit the highest value when compared to NDRE and LCI index values. This indicates that CC values can be used as features to classify the level of disease severity.
Keyword: segmentasi, hemispherical, remote sensing

Judul: Segmentasi dan Pengukuran Tingkat Kesehatan Tanaman Karet Berbasis Citra Satelit Sentinel-2A Menggunakan Algoritma Random Forest (Studi Kasus Perkebunan Karet Sembawa)
Abstrak: Karet merupakan salah satu komoditi perkebunan penting di Indonesia. Sayangnya produksi karet tahun 2017 sampai2019 mengalami penurunan. Salah satu penyebabnya yaitu gangguan penyakit gugur daun oleh patogen Pestalotiopsis sp. Maka dari itu perlu adanya analisis nilai indeks kesehatan tanaman karet, yang bisa dilakukan menggunakan teknologi remote sensing. Salah satunya yaitu menggunakan citra satelit Sentinel-2A dengan bantuan software Google Earth Engine yang dapat memantau suatu area dalam lingkup yang luas dan dalam periode yang panjang sehingga akan menghemat tenaga dan waktu jika dibandingkan dengan cara manual. Pengklasifikasian untuk memisahkan area perkebunan karet dengan objek lain di sekitarnya terlebih dahulu perlu dilakukan yaitu menggunakan metode klasifikasi berbasis objek dengan algoritma Simple Non-Iterative Clustering dan random forest. Akurasi terbaik dari model klasifikasi yaitu 78,04%. Di antara indeks-indeks yang dianalisis nilainya, indeks Normalized Difference Red Edge dan Leaf Chlorophyll Index dinilai paling cocok untuk dilakukan analisis. Hasil analisis memperlihatkan, penyakit gugur daun menyebabkan kondisi kesehatan tanaman karet menurun yang diperburuk oleh La Nina., Rubber is one of the important plantation commodities in Indonesia. Rubber production from 2017 to 2019 has decreased. One of the causes is the disruption of a diseases caused by the pathogen Pestalotiopsis sp. Therefore it is necessary to monitor the health of rubber plants which can be done by using remote sensing technology. One of them is using satellite imagery with the help of Google Earth Engine software. Classification to separate the rubber area from various objects around it needs to be done first. One of the methods is object-based classification method using Simple Non-Iterative Clustering and Random Forest algorithm. The best model has 78,04% accuracy. Among all the indices, Normalized Difference Red Edge dan Leaf Chlorophyll Index are considered the most suitable for analysis. The results show that Pestalotiopsis sp leaf fall disease causes the health condition of rubber plants to decline, which is exacerbated by La Nina.
Keyword: Google Earth Engine, Klasifikasi Berbasis Objek, Karet, Remote Sensing

Judul: Dynamic New Keynesian Model for Unemployment and Inflation Rate.
Abstrak: This paper deals with a scientific analysis of the equilibrium level of hiring, employment and unemployment. The basic model being used is a standard new Keynesian model, which states that utility function depends on consumptions and working hours. Utility maximization refers to a standard labour supply equation, with the marginal cost’s dynamics on new Keynesian model simply reflects movements in real wages and productivity. Furthermore, trade-off between output and inflation stabilization can be considered using the new Keynesian Phillips curve. Dynamic general equilibrium model is a model that can be used to study interactions between market imperfections and nominal rigidities, which is capable of overcoming the main shortcoming of the standard new Keynesian model. In the general equilibrium model there are three groups of the economy agents, namely households, firms and monetary authority. The results of this research science that bargaining processes between firms and workers will yield optimal wage. Increase in labour will cause wages to fall, but the unemployment rate can be reduced. An increase in the firm’s monopoly power will shift the labour demand curve downward, which will reduce the number of workers that firm is willing to hire for any given bargained wage.
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