Judul: Analysis of Factors Affecting People's Intention  to Buy Electric Cars in Jabodetabek
Abstrak: Penggunaan kendaraan bermotor di Indonesia selalu mengalami peningkatan dari tahun ke tahun. Ini menyebabkan pencemaran udara yang semakin tinggi. Penggunaan mobil listrik dinilai dapat mengurangi pencemaran udara. Namun, penggunaan mobil listrik di Indonesia masih mengalami hambatan. Penelitian ini bertujuan untuk menganalisis faktor yang menjadi pengaruh masyarakat untuk membeli mobil listrik di Jabodetabek melalui integrasi TAM dan TPB yang diperluas dengan menambahkan peubah Price Value, Perceived Risk, Environmental Awareness, dan Infrastructure Barrier. Responden pada penelitian ini sebanyak 200 responden. Teknik penarikan sampel dilakukan menggunakan non probability sampling dengan metode purposive sampling. Teknik analisis data yang digunakan adalah analisis deskriptif dan SEM PLS. Hasil dari penelitian ini menunjukkan perceived usefulness dan perceived ease of use memiliki pengaruh positif terhadap attitude. Peubah attitude, peceived behavioral control, dan price value memiliki pengaruh positif terhadap intention to buy electric car, sedangkan subjective norm, perceived risk, environmental awareness, dan infrastructure barier tidak berpengaruh signifikan terhadap intention to buy electric car., The use of motorized vehicles in Indonesia always increased from year to year. This caused higher air pollution. The use of electric cars was considered to reduce air pollution. However, the use of electric cars in Indonesia still experienced obstacles. This study aimed to analyze the factors that influenced people to buy electric cars in Jabodetabek through the integration of TAM and TPB, which were expanded by adding variables Price Value, Perceived Risk, Environmental Awareness, and Infrastructure Barrier. Respondents in this study were 200 respondents. The sampling technique was carried out using non-probability sampling with purposive sampling method. The data analysis technique used was descriptive analysis and SEM PLS. The results of this study indicated that perceived usefulness and perceived ease of use had a positive influence on attitude. The variables attitude, perceived behavioral control, and price value had a positive influence on the intention to buy electric cars, while subjective norm, perceived risk, environmental awareness, and infrastructure barriers had no significant effect on the intention to buy electric cars.
Keyword: electric car adoption, C-TAM-TPB, purchase intention, ground transportation, adopsi mobil listrik, niat pembelian, transportasi darat

Judul: The Potential Use of Electric-Based Private Vehicles as an Effort to Control Carbon Dioxide Emissions in Bogor City
Abstrak: Pemanasan global dan perubahan iklim yang dihadapi masyarakat dunia merupakan akibat dari meningkatnya konsentrasi Gas Rumah Kaca (GRK) di atmosfer. Salah satu penyumbang terbesar emisi GRK ialah sektor transportasi, khususnya di wilayah perkotaan yang memiliki aktivitas transportasi yang sangat tinggi. Upaya yang dapat dilakukan untuk menurunkan emisi GRK dari sektor ini diantaranya ialah dengan mengganti kendaraan berbahan bakar minyak dengan kendaraan listrik. Penelitian ini bertujuan menganalisis minat masyarakat menggunakan kendaraan listrik dan menghitung potensi penurunan emisi GRK dari penggunaan kendaraan listrik. Pengumpulan data tentang minat masyarakat dilakukan melalui wawancara dengan menggunakan kuesioner dan dianalisis dengan model regresi logistik. Perhitungan emisi GRK menggunakan metode (Intergovernmental Panel on Climate Change (IPCC) Tier 2. Hasil penelitian menunjukkan bahwa minat untuk beralih ke kendaraan listrik dipengaruhi secara nyata oleh usia dan besar dukungan pemerintah terhadap penggunaan kendaraan listrik. Minat yang tinggi untuk pindah ke kendaraan listrik adalah dari kelompok masyarakat berusia muda, dan ketersediaan infrastruktur pendukung seperti kemudahan akses tempat pengisian listrik dengan daya yang besar dan subsidi pemerintah yaitu keringanan pajak kendaraan. Saat ini rata-rata emisi karbon dioksida yang dihasilkan oleh kendaraan bermotor roda dua sebesar 12,40 kg/minggu, sementara mobil 38,87 kg/minggu. Potensi penurunan emisi dari penggunaan kendaraan listrik di Kota Bogor dapat mencapai 65%., Global warming and climate change faced by global community is caused by the increase of Greenhouse Gases (GHG) in the atmosphere. One of the most significant contributors to GHG emissions is the transportation sector, especially in urban areas with high transportation activities. One of the efforts to reduce GHG emissions from this sector is to replace oil-fueled vehicles with electric vehicles. This study aims to analyze public interest in using electric vehicles and to calculate the potential emission reduction from the use of electric vehicles. Data on public interest is collected through interviews using questionnaires and analyzed using logistic regression. The calculation of GHG emissions used the Intergovernmental Panel on Climate Change (IPCC) Tier 2 method. The results showed that the public interest to move to electric vehicles is significantly influenced by age and government supports. The high interest to move to electric vehicles is from young people, and availability of supporting infrastructure such as easy access to electric charging points with large power and government subsidies, namely vehicle tax breaks. At present, average carbon dioxide emissions produced by motor vehicles amounted to 12,40 kg/week, while cars were 38,87 kg/week. The potential emission reduction from the use of electric vehicles in Bogor City can reach 65%.
Keyword: transportasi, IPCC Tier 2, emisi kendaraan, regresi logistik, analisis skenario

Judul: The Construction of Greedy SVP LLL Algorithm
Abstrak: A lattice is a set of all integer linear combination of a set of linearly independent vectors in ℝ𝑛���. The independent vectors are called bases of lattice. Any lattice can be generated from many bases, and these bases have the same cardinality. In the lattice, the most fundamental and renowned problem is the Shortest Vector Problem (SVP). SVP is a tracking problem of the shortest nonzero vector in a lattice with equivalent bases. In two dimensions, SVP problem has resolved exactly by Gauss’ algorithm. When the lattice dimension is higher than two, A.K. Lenstra, H.W. Lenstra and L. Lovasz gave a LLL algorithm to compute approximation of nonzero shortest vector in the lattice. Reduced bases solution obtained from LLL algorithm still be an approximation and has polynomial running time of arbitrary dimension which large enough. In 1994, Schnoor and Euchner modified this LLL algorithm as a new variant which later was named deep insertion LLL algorithm. This algorithm is a modified version of the LLL algorithm to the terms of the exchange step to improve the accuracy of output of LLL reduced bases and applying it to the subset sum problem. The purpose of this research are: (1) Reconstruct the LLL algorithm and deep insertion LLL algorithm using a geometric approach. (2) Construct a greedy SVP LLL algorithm. (3) Implement the three algorithms in a symbolic programming language, and evaluate them experimentally. A bases ℬ=[𝐛���1,𝐛���2,…,𝐛���𝑛���] in ℝ𝑚��� is called reduced LLL with parameter 𝛿��� if satisfies: (a) |𝜇���𝑗���𝑖���|≤12, for every integer 𝑖���,𝑗��� with 1≤𝑖���<𝑗���<𝑛���. (b)𝛿���‖𝜋���𝑗���(𝐛���𝑗���)‖2≤‖𝜋���𝑗���(𝐛���𝑗���+1)‖2, for 𝑗���=1,2,…,𝑛���−1,where 𝛿��� is a reduced parameter of real numbers with 14<𝛿���<1. The first requirement is the reduced basis 𝛿��� must “nearly orthogonal” and in its computation case, this requirement can be reached out by using the Gram-Schmidt’s orthogonalization. While the second requirement is called exchange step, or used to called as Lovasz condition. By using both of these requirements, then they establish the LLL algorithm. Then, Schnoor and Euchner modify the LLL algorithm based on the requirements of the exchange step called the deep insertion LLL algorithm, where the exchange step is based on the comparison of the projected vector in orthogonal complement [𝐛���1,𝐛���2,…,𝐛���𝑘���−1]⊥ after j-th reduction. To construct greedy SVP LLL algorithm, the requirements of the exchange step is based on purely by comparing norm of lattice 𝐛���𝑗��� with norm lattice 𝐛���𝑖��� for 𝑖���=1,2,3…,𝑗���−1. The process of insertion reduced vector after the exchange step also has done greedily. Then, we calculate the number of arithmetic operation in greedy SVP LLL algorithm for further analysis. The result of our exprimentation for three algorithms on the certain matrix size shows that the running time of the three algorithms increases propotional to the size of the matrix. The result also shows that by using 𝛿���=34 for the LLL algorithm and the deep insertion LLL algorithm, and the greedy SVP LLL algorithm which is a new variant made by using no parameter of 𝛿���, outperform of the other of two previous algorithm in terms of speed with the same output.
Keyword: LLL algorithm, LLL deep insertion algorithm, greedy SVP LLL algorithm