Low-momentum effective interaction in the three-dimensional approach

The formulation of the low-momentum effective interaction in the model space Lee-Suzuki and the renormalization group methods is implemented in the three-dimensional approach. In this approach the low-momentum effective interaction V_{low k} has been formulated as a function of the magnitude of momentum vectors and the angle between them. As an application the spin-isospin independent Malfliet-Tjon potential has been used into the model space Lee-Suzuki method and it has been shown that the low-momentum effective interaction V_{low k} reproduces the same two-body observables obtained by the bare potential V_{NN}.Comment: 15 pages, 5 eps figure

ANALISIS PENGARUH KUALITAS PELAYANAN TERHADAP KEPUTUSAN PEMBELIAN KONSUMEN ONLINE SHOP SHOPEE

This study aims to determine whether the service quality factors consisting of Efficiency, Fulfillment, System availability, Privacy, Responsiveness, Compensation, and Contact have partial and simultaneous effect on purchasing decisions. This type of research is quantitative research. Data collection techniques using questionnaires. The technique of taking respondents using Judgment Sampling (Assessment) with a total of 100 consumer respondents who have made purchases at the online shop shoppe in the last 6 months. Data analysis using Descriptive Statistics and Linear Regression. The results of the study show that Efficiency, Fulfillment, System availability, Privacy, Responsiveness, Compensation, and Contact simultaneously and partially have an influence on Shopee Online Shop Consumer purchasing decisions. This means that the better Efficiency, Fulfillment, System availability, Privacy, Responsiveness, Compensation, and Contact will improve purchasing decisions

Eksperimen Seleksi Fitur Pada Parameter Proyek Untuk Software Effort Estimation dengan K-Nearest Neighbor

Software Effort Estimation adalah proses estimasi biaya perangkat lunak sebagai suatu proses penting dalam melakukan proyek perangkat lunak. Berbagai penelitian terdahulu telah melakukan estimasi usaha perangkat lunak dengan berbagai metode, baik metode machine learning  maupun non machine learning. Penelitian ini mengadakan set eksperimen seleksi atribut pada parameter proyek menggunakan teknik k-nearest neighbours sebagai estimasinya dengan melakukan seleksi atribut menggunakan information gain dan mutual information serta bagaimana menemukan  parameter proyek yang paling representif pada software effort estimation. Dataset software estimation effort yang digunakan pada eksperimen adalah  yakni albrecht, china, kemerer dan mizayaki94 yang dapat diperoleh dari repositori data khusus Software Effort Estimation melalui url http://openscience.us/repo/effort/. Selanjutnya peneliti melakukan pembangunan aplikasi seleksi atribut untuk menyeleksi parameter proyek. Sistem ini menghasilkan dataset arff yang telah diseleksi. Aplikasi ini dibangun dengan bahasa java menggunakan IDE Netbean. Kemudian dataset yang telah di-generate merupakan parameter hasil seleksi yang akan dibandingkan pada saat melakukan Software Effort Estimation menggunakan tool WEKA . Seleksi Fitur berhasil menurunkan nilai error estimasi (yang diwakilkan oleh nilai RAE dan RMSE). Artinya bahwa semakin rendah nilai error (RAE dan RMSE) maka semakin akurat nilai estimasi yang dihasilkan. Estimasi semakin baik setelah di lakukan seleksi fitur baik menggunakan information gain maupun mutual information. Dari nilai error yang dihasilkan maka dapat disimpulkan bahwa dataset yang dihasilkan seleksi fitur dengan metode information gain lebih baik dibanding mutual information namun, perbedaan keduanya tidak terlalu signifikan

EVALUASI SIMPANGAN ANTAR LANTAI (INTER STORY DRIFT) PADA GEDUNG BERTINGKAT DENGAN METODE RIWAYAT WAKTU

Aceh salah satu provinsi paling barat yang berada pada zona tektonik yang sangat aktif karena ada sembilan lempeng kecil lainnya yang membentuk jalur komplek selain tiga lempeng besar dunia. Peristiwa gempa yang terjadi di Aceh secara horizontal dan vertikal terjadi pada 26 Desember 2004 sebesar 9,2 Mw, 11 April 2012 dengan 8,6 Mw, 23 januari 2013 dengan 6,1 Mw dan Desember 2016 yang menyebabkan kerusakan serius pada bangunan dengan berbagai tipe pola keruntuhan. Ini merupakan faktor terpenting untuk evaluasi kerusakan bangunan dan desain seismik jika terjadi gempa. Simpangan antar lantai (inter story drift) menjadi faktor utama kerusakan bangunan akibat gempa. Nilai simpangan pada bangunan juga sebagai acuan untuk menentukan tingkat kerusakan pada suatu bangunan akibat gempa berdasarkan perencanaan berbasis kinerja. Penelitian ini bertujuan untuk menganalisis simpangan antar lantai (inter story drift) pada bangunan bertingkat untuk mengetahu tingkat kerusakan bangunan akibat gempa. Hal ini bertujuan untuk mengantisipasi atau melakukan upaya mitigasi pada saat terjadi gempa sehingga menguruangi kerugian materian dan korban jiwa. Hasil penelitian menunjukkan simpangan antar lantai maksimum sebesar 0.086 m akibat riwayat gempa Irpinia pada lantai 2 dalam arah X. Simpangan antar lantai maksimum arah Y sebesar 0,056 akibat gempa Irpinia. Nilai simpangan antar lantai maksimum memenuhi persyaratan SNI-1726-2019 tentang Tata Cara Perencaan Ketahanan Gempa Untuk Struktur Banguanan Geudng dan Non Gedung yaitu 0,548 m. Sehingga Gedung DPRA Kota Banda Aceh aman terhadap kegagalan simpangan antar lantai.<img 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

A Spin-Isospin Dependent 3N Scattering Formalism in a 3D Faddeev Scheme

We have introduced a spin-isospin dependent three-dimensional approach for formulation of the three-nucleon scattering. Faddeev equation is expressed in terms of vector Jacobi momenta and spin-isospin quantum numbers of each nucleon. Our formalism is based on connecting the transition amplitude $T$ to momentum-helicity representations of the two-body $t$-matrix and the deuteron wave function. Finally the expressions for nucleon-deuteron elastic scattering and full breakup process amplitudes are presented.Comment: 17 page

Senjata tradisional Lampung

Tujuan Proyek Penelitian Pengkajian dan Pembinaan Nilai-Nilai Budaya adalah menggali nilai-nilai luhur budaya bangsa dalam rangka memperkuat penghayatan dan pengamalan Pancasila demi tercapainya ketahanan nasional di bidang sosial budaya. Untuk mencapai tujuan itu, diperlukan penyebarluasan buku-buku yang memuat berbagai macam aspek kebudayaan daerah. Pencetakan naskah yang berjudul, Senjata Tradisional Lampung, adalah usaha untuk mencapai tujuan di atas

A formulation without partial wave decomposition for scattering of spin-1/2 and spin-0 particles

A new technique has been developed to calculate scattering of spin-1/2 and spin-0 particles. The so called momentum-helicity basis states are constructed from the helicity and the momentum states, which are not expanded in the angular momentum states. Thus, all angular momentum states are taken into account. Compared with the partial-wave approach this technique will then give more benefit especially in calculations for higher energies. Taking as input a simple spin-orbit potential, the Lippman-Schwinger equations for the T-matrix elements are solved and some observables are calculated.Comment: 4 pages, 1 figur