Judul: Pengaruh Sukuk terhadap Produk Domestik Bruto, Indeks Harga Konsumen, dan Indeks Harga Produsen di Indonesia.
Abstrak: Sukuk digunakan oleh pemerintah sebagai instrumen fiskal maupun moneter. Tujuan penelitian ini adalah untuk mengetahui pengaruh penerbitan sukuk terhadap variabel makroekonomi di Indonesia selama periode Januari 2011- Desember 2017. Metode Vector Error Correction Model (VECM) digunakan dalam penelitian ini melalui analisis uji kausalitas blok, Impulse Response Function (IRF) dan Forecast Error Variance Decomposition (FEVD). Hasil penelitian menunjukkan sukuk memiliki hubungan kausalitas satu arah terhadap PDB, IHK, dan IHP. Hasil IRF menunjukkan sukuk merespon negatif guncangan PDB, cenderung merespon positif guncangan IHK, serta merespon positif guncangan IHP. Guncangan sukuk sebagian besar direspon positif oleh PDB, cenderung direspon negatif oleh IHK, dan direspon negatif oleh IHP. Kontribusi sukuk terhadap variabel makroekonomi semakin meningkat, seiring dengan periode penelitian.
Keyword: FEVD, hubungan kausalitas, indikator makroekonomi, IRF, sukuk, VECM

Judul: Analisis Pengaruh Guncangan Makroekonomi terhadap Perkembangan Sukuk Negara di Indonesia
Abstrak: Penerbitan Sukuk Negara masih berada pada kisaran 20-30 persen setiap tahunnya dari total Surat Berharga Negara yang diterbitkan serta terdapat beberapa penjualannya yang tidak mencapai target pemerintah. Sebagai negara Muslim terbesar di dunia, Indonesia hanya menyumbang 13.38 persen total penerbitan Sukuk Negara di dunia di bawah Malaysia sebesar 32.82 persen dan Arab Saudi sebesar 31.94 persen. Penelitian ini bertujuan untuk menganalisis pengaruh guncangan makroekonomi terhadap perkembangan Sukuk Negara yang berdenominasi rupiah dari tahun 2014 sampai 2019. Berdasarkan metode analisis Vector Error Correction Model (VECM) diperoleh hasil bahwa Sukuk Negara merespon positif guncangan produk domestic bruto, imbalan deposito mudharabah dan Jakarta Islamic Index (JII). Sedangkan Sukuk Negara merespon negatif inflasi, suku bunga acuan dan nilai tukar rupiah. Variabel yang memberikan kontribusi dominan dalam menjelaskan keragaman perkembangan Sukuk Negara secara berurutan adalah JII, nilai tukar rupiah dan suku bunga acuan., The issuance of Sovereign Sukuk is still in the range of 20-30 percent each year of the total Government Securities issued and there are several sales that do not reach the government’s target. As the largest Muslim country in the world, Indonesia only contributed 13.38 percent of the total issuance of Sovereign Sukuk in the world, under Malaysia at 32.82 percent and Saudi Arabia at 31.94 percent. This study aims to examines the impact of macroeconomic shocks on the development of local currency Sovereign Sukuk from 2014 to 2019. Based on the Vector Error Correction Model (VECM) analysis method, it is found that Sovereign Sukuk respond positively to the shocks of real gross domestic product, mudharabah deposits return rate, and Jakarta Islamic Index (JII). Meanwhile the Sovereign Sukuk respond negatively to the shock of inflation, policy rate, and exchange rate. The three variables that made dominant contribution in explaining the variation of development of Sovereign Sukuk were JII, exchange rate and policy rate.
Keyword: development, macroeconomic, Sovereign Sukuk, VECM

Judul: Graphology Analysis Based on Letter a and t Using Perceptron.
Abstrak: Graphology or handwriting analysis is a method of identifying, analyzing, evaluating, and understanding personality through the strokes and patterns revealed by handwriting. A person who can analyze handwriting is called a graphologist. A graphologists can be subjective in their assesments different graphologist can have different analysis of the same handwriting. Therefore, a computer technology may help to minimize the subjectivity. This research used perceptron to analyze personality based on handwriting. Perceptron is one method of neural network. This research analyze two letters to letter a and t image. Letter a are represented by 135 images, consisting of 81 images as training data and 54 images as testing data. There are 150 images used for t images, consisting of 90 images as training data and 60 images as testing data.There are 5 experiments consisting of 2 experiments for letter a and 3 experiments for letter t. The result is the character of each class letter a and t where each parameters there are 3 classes. The accuracy of letter t and letter a was 55% and 88.89% respectively.
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