Metode TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) adalah salah satu pendekatan yang digunakan dalam pengambilan keputusan multi-kriteria. Metode ini dikembangkan untuk membantu pengambilan keputusan dalam situasi di mana kita perlu mempertimbangkan beberapa kriteria atau faktor yang berbeda dalam proses pengambilan keputusan. Penerapan metode TOPSIS dalam penelitian ini membantu memberikan rekomendasi kepada pengguna dalam memilih smartphone gaming berdasarkan kriteria yang ada dalam pemilihan smartphone gaming. Hasil perangkingan pemilihan smartphone gaming yang mendapatkan rangking 1 yaitu POCO X3 GT dengan nilai sebesar 0,633992256, rangking 2 yaitu Realme GT Master Edition dengan nilai sebesar 0,572951301, rangking 3 yaitu Infinix Zero 20 dengan nilai sebesar 0,5, rangking 4 yaitu Infinix Note 11 Pro dengan nilai sebesar 0,366023913.

Indonesian Wooden Building Code (SNI 7973-2013) has adopted the National Design Specification (NDS) for Wood Construction since 2013. A periodic harmonization of the building-code-designated values (i.e., reference design values and adjustment factors) with the experimental data of commercial wood species is necessary. This study aimed to compare the building code’s wet service factors (C_M) with the laboratory test of some commercial wood species. Since wood is weaker when its moisture content is high, the wet service factor (C_M) must adjust the sawn lumber reference design values if the building serves in wet or aquatic environments. Four commercial wood species, namely pine (Pinus merkusii), agathis (Agathis dammara), red meranti (Shorea leprosula), and mahogany (Swietenia mahagoni), were subjected to mechanical property tests. To calculate the empirical C_M values, the mechanical properties tests were conducted on air-dry and wet wood. Instead of testing the full-sized timber, which contains the growth characteristics and defects, this study chose clear-wood specimens to resemble the boundary condition of the ceteris paribus (other things being equal). The wet (water-saturated) specimens were immersed in water for 65 days, and the test was carried out when the specimen was still immersed. The test arrangement imitated the submerged wood as the worst-case scenario of the wet environment where the construction serves, rather than green or partially immersed timber. As many as 40 specimens were tested to compare each mechanical property’s wet service factor; thus, this study reported 200 specimens’ laboratory test results. The empirical C_M values to adjust the modulus of elasticity, modulus of rupture, shear strength parallel-to-grain, tensile strength parallel-to-grain, and maximum crushing strength (C_M = 0.59, 0.76, 0.65, 0.73, and 0.67, respectively) were significantly lower than SNI 7973-2013 designated values (C_M = 0.9, 0.85, 0.97, 1, and 0.8, respectively). The empirical C_M for the compression stress perpendicular-to-grain at the proportional limit and that at the 0.04″ deformation (C_M = 0.66) were slightly lower than the designated values (C_M = 0.67), although they were not significantly different. This study resulted in lower empirical C_M values than the designated ones, which found that the building code lacked conservativeness. The lacked conservativeness is mainly attributed to the building code’s recent choices, e.g., (1) the wet service environment basis is the green timber rather than the fully water-saturated one, and (2) the ratio of near minimum (5% lower) distribution value is chosen as the C_M value rather than the average of wet timber’s mechanical property divided by the air-dry one. This study proposes changing both recent choices to alternative ones to develop more safe and reliable designated C_M values.

The purpose of this study is to look for factors, indicators, and build a model of readiness for effective and efficient implementation in order to answer the problems that arise at PT XYZ related to the readiness of Customer Relationship Management (CRM) implementation. The research method used is the method of collecting data by observing, interviewing, and distributing questionnaires to respondents using the CRM Value Chain theory as a conceptual framework and the research method used in this research is factor analysis to process data obtained from questionnaires. In this study managed to get new factors along with the constituent indicators of these factors as well as an ideal readiness model. It can be concluded that factors related to the readiness of implementing Customer Relationship Management among others are Customer Service Information, Customer Relations Value, Lack of Service and Communication.

A function from the domain (x-set) to the codomain (y-set) connects each x element to precisely one y element. Since each x-point originating from the domain corresponds to two y-points on the graph of a closed curve (i.e., circle, ellipse, superellipse, or ovoid) in a rectangular (Cartesian) diagram, it does not fulfil the function’s requirements. This non-function phenomenon obstructs the nonlinear regression application for fitting observed data resembling a closed curve; thus, it requires transforming the rectangular coordinate system into a polar coordinate system. This study discusses nonlinear regression to fit the circumference of a tree stem’s cross-section and its sapwood–heartwood transition by transforming rectangular coordinates (x, y) of the observed data points’ positions into polar coordinates (r, θ). Following a polar coordinate model, circular curve fitting fits a log’s cross-sectional shape and sapwood–heartwood transition. Ellipse models result in better goodness of fit than circular ones, while the rotated ellipse is the best-fit one. Deviation from the circular shape indicates environmental effects on vascular cambium differentiation. Foresters have good choices: (1) continuing using the circular model as the simplest one or (2) changing to the rotated ellipse model because it gives the best fit to estimate a tree stem’s cross-sectional shape; therefore, it is more reliable to determine basal area, tree volume, and tree trunk biomass. Computer modelling transforms the best-fit model’s formulas of the rotated ellipse using Python scripts provided by Wolfram engine libraries.