Kontibusi Pendapatan Usahatani Padi Ladang Terhadap Pendapatan Petani Di Desa Taraudu Kecamatan Sahu Kabupaten Halmahera Barat

The purpose of this study was to determine and calculate the contribution of upland rice farming income to farmers' income. This research was conducted from September 2021 to November 2021. Primary data were obtained directly from selected respondents at the research location. The sampling method was carried out with a sampling census which made all members of the population as samples, the population in this study was 40 field rice farmers in Taraudu Village. The analysis used in this study is analyzed using the contribution formula and is presented in tabular form. The results of the study showed that upland rice farming contributed 10.72% to farmers' income

Kontibusi Pendapatan Usahatani Padi Ladang Terhadap Pendapatan Petani Di Desa Taraudu Kecamatan Sahu Kabupaten Halmahera Barat

The purpose of this study was to determine and calculate the contribution of upland rice farming income to farmers' income. This research was conducted from September 2021 to November 2021. Primary data were obtained directly from selected respondents at the research location. The sampling method was carried out with a sampling census which made all members of the population as samples, the population in this study was 40 field rice farmers in Taraudu Village. The analysis used in this study is analyzed using the contribution formula and is presented in tabular form. The results of the study showed that upland rice farming contributed 10.72% to farmers' income

Effect of Tuned Parameters on a LSA MCQ Answering Model

This paper presents the current state of a work in progress, whose objective is to better understand the effects of factors that significantly influence the performance of Latent Semantic Analysis (LSA). A difficult task, which consists in answering (French) biology Multiple Choice Questions, is used to test the semantic properties of the truncated singular space and to study the relative influence of main parameters. A dedicated software has been designed to fine tune the LSA semantic space for the Multiple Choice Questions task. With optimal parameters, the performances of our simple model are quite surprisingly equal or superior to those of 7th and 8th grades students. This indicates that semantic spaces were quite good despite their low dimensions and the small sizes of training data sets. Besides, we present an original entropy global weighting of answers' terms of each question of the Multiple Choice Questions which was necessary to achieve the model's success.Comment: 9 page

Pengaruh Komponen Citra Merek (Brand Image) Terhadap Loyalitas Konsumen Produk Minuman Share Tea Di Kota Manado

This study aims to determine the influence of brand image on consumer loyalty Share Tea drink in the city of Manado. In its development, bubble tea drinks into today's drinks with a variety of flavor variants that can attract consumers in all ages. Reach back the market that has declined, and to maintain the market it has gained is a challenge that Share Tea companies must face in creating consumer loyalty. This study uses data obtained from questionnaires, observations and direct interviews with the manager of Tea Share in Manado City. The analysis technique used is multiple regressions. From this research can be concluded that Components of brand image consisting of coorporate image, user image, and product image has a influence on Consumer Loyalty. The components of the brand image together have a positive influence on consumer loyalty. The coorporate image and user image individually does not significantly affect consumer loyalty, while product image individually has a significant effect on consumer loyalty

A non-intrusive movie recommendation system

Several recommendation systems have been developed to support the user in choosing an interesting movie from multimedia repositories. The widely utilized collaborative-filtering systems focus on the analysis of user profiles or user ratings of the items. However, these systems decrease their performance at the start-up phase and due to privacy issues, when a user hides most of his personal data. On the other hand, content-based recommendation systems compare movie features to suggest similar multimedia contents; these systems are based on less invasive observations, however they find some difficulties to supply tailored suggestions. In this paper, we propose a plot-based recommendation system, which is based upon an evaluation of similarity among the plot of a video that was watched by the user and a large amount of plots that is stored in a movie database. Since it is independent from the number of user ratings, it is able to propose famous and beloved movies as well as old or unheard movies/programs that are still strongly related to the content of the video the user has watched. We experimented different methodologies to compare natural language descriptions of movies (plots) and evaluated the Latent Semantic Analysis (LSA) to be the superior one in supporting the selection of similar plots. In order to increase the efficiency of LSA, different models have been experimented and in the end, a recommendation system that is able to compare about two hundred thousands movie plots in less than a minute has been developed

Meaning-focused and Quantum-inspired Information Retrieval

In recent years, quantum-based methods have promisingly integrated the traditional procedures in information retrieval (IR) and natural language processing (NLP). Inspired by our research on the identification and application of quantum structures in cognition, more specifically our work on the representation of concepts and their combinations, we put forward a 'quantum meaning based' framework for structured query retrieval in text corpora and standardized testing corpora. This scheme for IR rests on considering as basic notions, (i) 'entities of meaning', e.g., concepts and their combinations and (ii) traces of such entities of meaning, which is how documents are considered in this approach. The meaning content of these 'entities of meaning' is reconstructed by solving an 'inverse problem' in the quantum formalism, consisting of reconstructing the full states of the entities of meaning from their collapsed states identified as traces in relevant documents. The advantages with respect to traditional approaches, such as Latent Semantic Analysis (LSA), are discussed by means of concrete examples.Comment: 11 page

Entity linking of tweets based on dominant entity candidates

© 2018, Springer-Verlag GmbH Austria, part of Springer Nature. Entity linking, also known as semantic annotation, of textual content has received increasing attention. Recent works in this area have focused on entity linking on text with special characteristics such as search queries and tweets. The semantic annotation of tweets is specially proven to be challenging given the informal nature of the writing and the short length of the text. In this paper, we propose a method to perform entity linking on tweets built based on one primary hypothesis. We hypothesize that while there are formally many possible entity candidates for an ambiguous mention in a tweet, as listed on the disambiguation page of the corresponding entity on Wikipedia, there are only few entity candidates that are likely to be employed in the context of Twitter. Based on this hypothesis, we propose a method to identify such dominant entity candidates for each ambiguous mention and use them in the annotation process. Particularly, our proposed work integrates two phases (i) dominant entity candidate detection, which applies community detection methods for finding the dominant candidates of ambiguous mentions; and (ii) named entity disambiguation that links a tweet to entities in Wikipedia by only considering the identified dominant entity candidates. Our investigations show that: (1) there are only very few entity candidates for each ambiguous mention in a tweet that need to be considered when performing disambiguation. This helps us limit the candidate search space and hence noticeably reduce the entity linking time; (2) limiting the search space to only a subset of disambiguation options will not only improve entity linking execution time but will also lead to improved accuracy of the entity linking process when the main entity candidates of each mention are mined from a temporally aligned corpus. We show that our proposed method offers competitive results with the state-of-the-art methods in terms of precision and recall on widely used gold standard datasets while significantly reducing the time for processing each tweet