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: Pemodelan Keparahan Penyakit Tanaman Karet Berbasis Citra Multispektral Drone Menggunakan Algoritme Random Forest
Abstrak: Karet menjadi salah satu komoditas ekspor yang menyumbang devisa nonmigas bagi negara. Indonesia merupakan negara dengan perkebunan karet terbesar di dunia. Pada tahun 2018 hingga 2020, produksi karet mengalami penurunan setiap tahunnya dari 3,63 juta ton menjadi 3,04 juta ton. Salah satu penyebab, yaitu penyakit Gugur Daun Karet (GDK) yang menyerang perkebunan karet pada berbagai wilayah di Indonesia. Penyakit GDK disebabkan oleh cendawan Pestalotiopsis sp. yang menyebar secara cepat melalui udara dan menyebabkan rontoknya daun karet hingga turunnya produksi getah. Oleh karena itu, penelitian ini bertujuan memodelkan keparahan penyakit tanaman karet menggunakan metode random forest melibatkan indeks NDRE dan LCI. Data penelitian yang digunakan terdiri dari citra multispektral drone dan data ground truth yang dilabeli berdasarkan citra hemispherical. Pada data ground truth, terdapat data yang overlap dengan jumlah relatif tinggi sehingga jumlahnya perlu dikurangi menggunakan prinsip densitas. Pada hasil pemodelan menggunakan random forest dengan optimasi, diperoleh rataan skor training sebesar 0.8225 atau 82.25% dengan akurasi skor testing sebesar 0.8246 atau 82.46% dan f1-score sebesar 0.8176 atau 81.76%. Kata kunci: drone, indeks vegetasi, multispektral, karet, severity, Rubber is one of the export commodities that contributes non-oil and gas foreign exchange to Indonesia. Indonesia is the country with the largest rubber plantation in the world. From 2018 to 2020, rubber production has decreased every year from 3.63 million tons to 3.04 million tons. One of the factors is the Gugur Daun Karet (GDK) disease that attacks rubber plantations in various regions in Indonesia. GDK disease is caused by the fungus Pestalotiopsis sp. which spreads rapidly through the air and causes the fall of rubber leaves to decrease latex production. Therefore, this study aims for modeling the disease severity of rubber plants using the random forest method by involving the NDRE and LCI indices. The research data used consists of multispectral drone images and ground truth data labeled based on hemispherical images. There is a relatively high amount of overlapping data in ground truth data. Therefore, the amount needs to be reduced using the density principle. The modeling results using random forest with optimization, the average training score is 0.8225 or 82.25% with a testing score accuracy of 0.8246 or 82.46% and f1-score of 0.8176 or 81.76%.
Keyword: drone, indeks vegetasi, multispektral, karet, severity

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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