Pengaruh Kualitas Produk dan Harga Terhadap Keputusan Pembelian Sayur Organik di "Orgo Organic Farm" Kota Malang

“Orgo Organic Farm” melakukan bisnis sayur organik mulai dari budidaya hingga pemasaran. Ketatnya persaingan bisnis sayur organik di Malang mengharuskan pelaku bisnis memahami faktor-faktor yang mendorong keputusan konsumen melakukan pembelian. Penelitian ini bertujuan untuk menganalisis pengaruh kualitas produk dan harga terhadap keputusan pembelian di OOF. Data primer diperoleh dengan wawancara pelanggan OOF. Teknik accidental sampling digunakan untuk menentukan sampel pelanggan OOF. Data dianalisis dengan metode Partial Least Square (PLS). Hasil penelitian menunjukkan kualitas produk dan harga berpengaruh terhadap keputusan pembelian di OOF. Kesimpulan penelitian ini yaitu kualitas produk berpengaruh positif dan signifikan terhadap keputusan konsumen dengan nilai p value 0,001 atau lebih kecil dari 0,05. Nilai original sample (path coefficient) 0,336 menunjukkan arah hubungan positif dan terdapat pengaruh harga terhadap keputusan konsumen. Harga tidak signifikan terhadap keputusan konsumen dengan nilai p value 0,109 atau lebih besar dari 0,05. Nilai original sampel (path coefficient) 0,172 menunjukan arah hubungan positif. Kata kunci : Harga Sayur, Keputusan Pembelian, Kualitas Sayur, Sayur Organik.

Treated like dirt: Robust forensic and ecological inferences from soil eDNA after challenging sample storage

Biodiversity of soil microbiota is routinely assessed with environmental DNA-based methods, among which amplification and massive parallel sequencing of marker genes (eDNA metabarcoding) is the most common. Soil microbiota may for example be investigated in relation to biodiversity research or as a tool in forensic investigations. After sampling, the taxonomic composition of soil biotic communities may change. In order to minimize community changes after sampling, it is desirable to reduce biological activity, e.g. by freezing immediately after sampling. However, this may be impossible due to remoteness of study sites or, in forensic cases, where soil has been attached to a questioned item for protracted periods of time. Here we investigated the effect of storage duration and conditions on the assessment of the soil biota with eDNA metabarcoding. We extracted eDNA from freshly collected soil samples and again from the same samples after storage under contrasting temperature conditions. We used five different primer sets targeting bacteria, fungi, protists (cercozoans), general eukaryotes, and plants. For these groups, we quantified differences in richness, evenness and community composition. Subsequently, we tested whether we could correctly infer habitat type and original sample identity after storage using a large reference dataset. We found increased community composition differences with extended storage time and with higher storage temperature. However, for samples stored less than 28 days at a maximum of 20 C, changes were generally insignificant. Classification models could successfully assign most stored samples to their exact location of origin and correct habitat type even after weeks of storage. Even samples showing larger compositional changes generally retained the original sample as the best match (relative similarity). Our results show that for most biodiversity and forensic applications, storage of samples for days and even several weeks may not be a problem, if storage temperature does not exceed 20 C. Even after suboptimal storage conditions, significant patterns can be reproduced.

Peran Lingkungan Keluarga Dan Efikasi Diri Yang Memotivasi Mahasiswa Untuk Menjadi Young Entreprenuer

Tingkat pengangguran terbuka (TPT) lulusan universitas dengan rentang sarjana semakin mengkhawatirkan, hal ini banyak terjadi disebabkan bertambahnya angkatan kerja yang tidak terserap sepenuhnya oleh lapangan pekerjaan. Observasi awal yang dilakukan kepada 50 mahasiswa yang diwawancarai tentang minat dan motivasi menjadi young entrepreneur, dimana diperoleh bahwa mahasiswa yang berminat menjadi wirausaha hanya sebanyak 28%, sedangkan yang tidak berminat menjadi wirausaha sebanyak 72%. Penelitian ini dilakukan untuk melihat factor apa yang dapat memotivasi mahasiswa untuk menjadi seorang young entrepreneur dilihat dari sisi lingkungan keluarga dan efikasi diri. Penelitian menggunakan metode kuantitatif yang berjenis kausal eksperimental, dimana penelitian ini menganalisis pengaruh dan hubungan antar variabel satu dengan yang lainnya. Pertanyaan penelitian dibangun berdasarkan tiga operasional variabel, yakni Lingkungan Keluarga, Efikasi Diri, dan Motivasi menjadi seorang young entrepreneur. Penyebaran pertanyaan penelitian dilakukan secara daring, disebar kepada mahasiswa yang masih melakukan studi pada jenjang strata-1, dan diperoleh sebanyak 329 responden. Digunakan aplikasi SEM PLS pada alat pengujian dan analisis, hasil penelitian menunjukkan bahwa Lingkungan Keluarga, dan Efikasi Diri berpengaruh secara positif, signifikan terhadap motivasi menjadi young entrepreneur. Pada pengujian original sample, diperoleh nilai T Statistic variabel efikasi diri lebih besar daripada variabel lingkungan keluarga. Sehingga, hal ini menunjukkan bahwa peran variabel efikasi diri dalam memotivasi mahasiswa untuk menjadi seorang young entrepreneur lebih besar daripada peran lingkungan keluarga. Selain itu, indikator-indikator yang diujikan pada penelitian ini dapat menjadi masukan kepada para penyelenggara Pendidikan khususnya perguruan tinggi untuk lebih berperan aktif dalam memotivasi mahasiswa agar terdorong keinginan kuat didalam dirinya untuk membuka lapangan kerja ketika lulus kelak

The Importance of Absorption Capacity and Innovation in Improving Company Performance

The purpose of this research is to analyze the importance of absorption capacity and innovation in improving company performance. The research method used in this study is a quantitative method with a descriptive analysis approach. The research population is all manufacturing companies which are included in the theoretical domain and listed on the Indonesia Stock Exchange. In this study, ten manufacturing companies listed on the Indonesia Stock Exchange were selected. For the company's performance variable, the Return on Assets (ROA) indicator is used. The type of data used in this study is secondary data. The analysis technique in this study is the outer model (convergent validity, discriminant validity and composite reliability). Based on the data and research results, it can be concluded that: 1) Absorption Capacity has an effect on Company Performance with a positive and significant influence on company performance which is indicated by the original sample estimate value of 0.922 and the T-statistic value of 11.777 which is greater than the t-value. table (1.96); and 2) Company innovation has no effect on Company Performance with the original sample estimate value of -0.23 and the T-statistic value of 0.297 which is smaller than the t-table value (1.96).

Demographic, Financial Literacy, and Financial Behavior of Women Working in Manufacturing Industry

The purpose of this study is to determine the effect of demographic factors and financial literacy on financial behavior of women working in manufacturing industry. Women are the targets of financial literacy due to their involvement in fulfilling the household needs and welfare. This research is quantitative research. Using the questionnaire on 115 respondents in this study from various manufacturing industries in Purbalingga with probability sampling as the technique. While the data analysis technique used Structural Equation Modelling (SEM) PLS 3.0. The findings show that, first, demographic factors contribute positively and significantly towards female workers’ financial behavior with original sample value 0.224 and t-value 2.420 > 1.96; second, financial literacy also contributes positively and significantly towards financial behavior with original sample value 0.256 and t-value 3.251 > 1.96. The higher the demographic factors of female workers, the better their financial management are. Likewise, students with low financial literacy tend to be able to hold back their urges to buy things and use services. Moreover, the significance of financial literacy and demographic factors has important implications for the development of policies that aim to improve financial behaviour among women working in financial education programs.

Microbial Community Succession is Associated With Corn Straw Degradation in Microbial Consortium M44 During Subculture

To systematically analyze the succession of functional microbiota that plays an important role during culture of microbial consortia M44 and its relationship with straw degradation characteristics, we determined the straw degradation ratio and activities of cellulose, hemicellulose, lignin enzyme, and VFA content of M44 in different culture periods. We also used 16S rRNA gene sequencing to analyze the change in microbial community structure in M44 and explore the differences in microbial composition in the original sample. The results showed that at 15 ℃ for 21 days, the straw degradation rate, endoglucanase activity, and filter paper enzyme activity of M44 generally decreased with increasing culture age, reaching their highest values at F1. The activities of xylanase, laccase, and lignin peroxidase, as well as VFA content, were the highest at F5, showing a single-peak curve change with first an increase and then decrease. At the phylum level, Proteobacteria, Bacteroidetes, and Firmicutes were dominant in the original samples and in different culture stages. At the genus level, Devosia and Bacillus were dominant in the original sample. During subculture, the dominant bacteria in the first generation (F1) were Pseudomonas, Flavobacterium, Brevundimonas, Achromobacter, Chryseobacterium, and Devosia. The dominant genera in the last generation (F11) were Trichococcus, Acinetobacter, Dyssgonomonas, and Rhizobium. In conclusion, we identified changes in microbial community structure occurring in M44 during subculture, as well as similarities and differences in microbial communities from the original sample.

Mining Pareto-Optimal Counterfactual Antecedents With A Branch-And-Bound Model-Agnostic Algorithm

Mining counterfactual antecedents became a valuable tool to discover knowledge and explain machine learning models. It consists of generating synthetic samples from an original sample to achieve the desired outcome in a machine learning model thus helping to understand the prediction. An insightful methodology would explore a broader set of counterfactual antecedents to reveal multiple possibilities while operating on any classifier. Thus, we create a tree-based search that requires monotonicity from the objective functions (a.k.a. cost functions); it allows pruning branches that will not improve the objective functions. Since monotonicity is only required for the objective function, this method can be used for any family of classifiers (e.g., linear models, neural networks, decision trees). However, additional classifier properties speed up the tree-search when it foresees branches that will not result in feasible actions. Moreover, the proposed optimization generates a diverse set of Pareto-optimal counterfactual antecedents by relying on multi-objective concepts. The results show an algorithm with working guarantees that enumerates a wide range of counterfactual antecedents. It helps the decision-maker understand the machine learning decision and finds alternatives to achieve the desired outcome. The user can inspect these multiple counterfactual antecedents to find the most suitable one and have a broader understanding of the prediction.

Generalized Algorithm for Finding Outliers in a Regression Model

One of the actively developing areas of modern computational problems is data analysis. The studied data have a different structure, which causes certain difficulties in the process of smoothing and analysis. This fact entails the need to search for new universal algorithms for data processing and create computer programs that analyze data of various nature. Today, a widely used method of data processing is regression modeling. It is used in problems of pattern recognition, classification, dimensionality reduction, and many others. The literature describes various methods of constructing regression models, the basis of which is the optimization of a certain indicator — the quality functional. A very important requirement for the quality of such models is the absence of outliers (outliers) in the data. This article discusses a method for examining a sample for outliers. The obtained algorithm can be applied to regression models estimated by the most common methods (least squares method, least modulus method). The mathematical basis of this procedure is the Legendre transformation, which provides computational accuracy in computer implementation. The adequacy of the obtained algorithm was investigated on a number of test samples. All tests were positive in terms of emissions. The MatLab system is used to develop a set of programs, which allows the building of various regression models and evaluation of the original sample for sharply distinguished observations.

Analysis of Role of Digital Marketing to Improve Brand Image through Brand Equity at PT Pegadaian (Persero) Labuhan Deli Sub-Branch, Medan

Digital marketing is a marketing process that utilizes online channels (internet) to introduce, educate, branding and also establish communication with customers and the form of online channels can be in the form of websites or applications on mobile phones. The formulation of the problem in this study are: (1) Does Digital Marketing affect the formation of Brand Image at PT Pegadaian (Persero) Labuhan Deli Sub-Branch, Medan; (2) Does Digital Marketing affect the establishment of Brand Equity at PT Pegadaian (Persero) Sub Branch Labuhan Deli, Medan; (3) Does brand equity affect the formation of brand image in PT Pegadaian (Persero) Sub Branch Labuhan Deli, Medan; (4) Does Digital Marketing affect the formation of Brand Image through Brand Equity at PT Pegadaian (Persero) Sub Branch Labuhan Deli, Medan. The type of research used in this study is a descriptive method with a correlational type using a quantitative approach, using two types of data collection, namely secondary data and primary data by taking a sample of 130 respondents, which is carried out by testing the validity and. reliability test, and using data analysis methods, namely descriptive analysis and Structural Equation Model - Partial Least Square. The results show that (1) Digital Marketing has a positive and significant effect on Brand Image, in the sense that Digital Marketing at PT Pegadaian (Persero) Labuhan Deli Sub-Branch has a good system so that the company's brand image can be understood and remembered by the public or customers who transact using pawn services; (2) Digital Marketing has a positive and significant effect on Brand Equity, in the sense that Digital Marketing at PT Pegadaian (Persero) Labuhan Deli Sub-Branch has a good system so that the company's brand equity can be accepted and easily remembered by the public or customers who transact using pawn services based on marketing and financial activities; (3) partially the Brand Equity variable has a positive and significant influence on Brand Image, in the sense that if customers are interested in the digital Pegadaian brand at PT Pegadaian (Persero) Labuhan Deli Sub-Branch, the brand image of the product will also increase; (4) the digital marketing variable (X) has a simultaneous effect on brand image (Y) through brand equity (Z), where the original sample value (Original Sample) is 0.244, the t-statistic value is 4.955 and the p-values is 0.000, then the brand equity variable (Z) is able to mediate the relationship of digital marketing variable (X) to brand image (Y). Keywords: Digital Marketing, Brand Image, Brand Equity.

Research on Classification Method of Network Resources Based on Modified SVM Algorithm

According to the traditional classification method of network capital resources, there are some problems such as low efficiency, low recall rate, and low precision rate of information. Therefore, this paper proposes a new classification method of network capital resources based on SVM algorithm. Firstly, the original sample data are analyzed by principal component analysis to realize the design of resource classification process. Then, the dimension reduction of network resources data is realized by word segmentation and denoising. Thirdly, the reduced sample data are trained by the SVM classifier, and the best parameters of the reduced data are obtained by the grid search method. Lastly, the search range of SVM classifier parameters based on the original sample data is reset, so as to quickly obtain the best SVM classifier parameters of the original sample data and realize the classification. The experimental results show that this method can improve the recall and precision of network resource information and shorten the classification time of network resources.