Respon Penetasan Telur Ikan Mas (Cyprinus carpio) pada Tingkatan Suhu yang Berbeda

ABSTRACT: The present study aimed to determine the effect of different temperature levels on the hatching rate of common carp eggs. This study consisted of 12 experimental units, in which 4 treatments with 3 replications were applied, namely A (control temperature 22-29 ° C), B (24 ° C), C (temperature 28 ° C), and D (temperature 32 ° C). Temperature, pH, DO and hatching rate was measured for data collection. Analysis of variance (ANOVA) with a confidence level of 95% was performed for statistical analysis. Water quality data were descriptively described. The results showed that the highest number of hatching rate was obtained in treatment D (32 ° C) of 290 (99%), C (28 ° C) of 276 (94%), treatment B (24 ° C) of 251 individuals, and the lowest was observed in treatment A (22-29 ° C) of 242 individuals (83%). Statistically, the temperature of 22-32 ° C did not affect the hatching rate of common carp eggs. ABSTRAK: Penelitian ini bertujuan untuk mengetahui pengaruh tingkatan suhu yang berbeda terhadap daya tetas telur ikan mas. Penelitian ini terdiri dari 12 satuan percobaan, yang mana terdapat 4 perlakuan dengan 3 ulangan yakni A (kontrol suhu 22-29°C), B (24°C), C (suhu 28°C) dan D (suhu 32°C). Parameter uji meliputi suhu, pH, DO dan daya tetas telur (hatching rate). Data yang diperoleh dianalisis menggunakan analisis ragam (ANOVA) dengan tingkat kepercayaan 95% dan jika menunjukkan pengaruh nyata akan dilanjutkan dengan uji Beda Nyata Terkecil (BNT). Data kualitas air dijelaskan secara deskriptif sesuai kelayakan hidup ikan mas. Hasil penelitian menunjukkan bahwa jumlah telur yang menetas terbanyak diperoleh pada perlakuan D (32°C) sebesar 290 ekor (99%), perlakuan C (28°C) sebesar 276 ekor (94%), perlakuan B (24°C) sebesar 251 ekor dan terendah pada perlakuan A (22-29°C) sebesar 242 ekor (83%). Hasil uji statistik menunjukkan bahwa suhu 22-32°C tidak berpengaruh terhadap daya tetas telur Ikan Mas

Visualization for Recommendation Explainability: A Survey and New Perspectives

Providing system-generated explanations for recommendations represents an important step towards transparent and trustworthy recommender systems. Explainable recommender systems provide a human-understandable rationale for their outputs. Over the last two decades, explainable recommendation has attracted much attention in the recommender systems research community. This paper aims to provide a comprehensive review of research efforts on visual explanation in recommender systems. More concretely, we systematically review the literature on explanations in recommender systems based on four dimensions, namely explanation goal, explanation scope, explanation style, and explanation format. Recognizing the importance of visualization, we approach the recommender system literature from the angle of explanatory visualizations, that is using visualizations as a display style of explanation. As a result, we derive a set of guidelines that might be constructive for designing explanatory visualizations in recommender systems and identify perspectives for future work in this field. The aim of this review is to help recommendation researchers and practitioners better understand the potential of visually explainable recommendation research and to support them in the systematic design of visual explanations in current and future recommender systems.Comment: Updated version Nov. 2023, 36 page

OpenLAP: a user-centered open learning analytics platform

During the last few years, Learning Analytics (LA) has gained the interest of researchers in the field of Technology Enhanced Learning (TEL). Generally, LA deals with the development of methods that harness educational data sets to support the learning process. It shares a movement from data to analysis to action to learning. Recently, the demand for self-organized, networked, and lifelong learning opportunities has increased. Therefore, there is a need to provide an understanding of how different learners learn in these open learning settings and how learners, educators, institutions, and researchers can best support this process. Moreover, this openness should be reflected in the conceptualization and development of innovative LA approaches in order to achieve more effective learning experiences. Open Learning Analytics (OLA) is an emerging research field that has the potential to deal with these challenges in open learning environments. However, the concrete solutions and implementations that can deliver an effective and efficient OLA are still lacking. Most solutions currently available does not continuously involve end-users in the LA process and follow design patterns which make it difficult to adopt new user requirements. Furthermore, the available implementations are designed and developed for specific scenarios, which address the requirements of a specific set of stakeholders by relying on a predefined set of questions and indicators. These limitations restrict the scope of such solutions and implementations in the context of OLA targeting various stakeholders with different needs. The aim of this dissertation is to introduce personalization in the LA process by investigating the design of an effective user-centered Open Learning Analytics Platform (OpenLAP) and providing its conceptual, implementation, and evaluation details. OpenLAP provides a user-friendly interface that supports an interactive, exploratory, and real-time user experience to allow the end-users to dynamically define new indicators that meet their goals. Moreover, OpenLAP is designed to be modular and extensible allowing easy integration of new data sources, analytics methods, and visualization techniques at runtime to adopt the new requirements of multiple stakeholders and deliver an ecosystem for OLA. The main contributions of this dissertation include (1) a comprehensive analysis of the currently available LA tools and solutions with respect to their support for openness and personalization, (2) a theoretically sound design of a user-centered OpenLAP based on the requirements gathered from the empirical analysis of the literature, (3) a concrete implementation of OpenLAP providing an interface to self-define the indicators and an extensible mechanism to easily integrated new data sources, analytics methods, and visualization techniques, and (4) a thorough evaluation of OpenLAP in a pilot study at RWTH Aachen University to assess it in terms of usability, usefulness, extensibility, and modularity

The PERLA Framework: Blending Personalization and Learning Analytics

Personalization is crucial for achieving smart learning environments in different lifelong learning contexts. There is a need to shift from one-size-fits-all systems to personalized learning environments that give control to the learners. Recently, learning analytics (LA) is opening up new opportunities for promoting personalization by providing insights and understanding into how learners learn and supporting customized learning experiences that meet their goals and needs. This paper discusses the Personalization and Learning Analytics (PERLA) framework which represents the convergence of personalization and learning analytics and provides a theoretical foundation for effective analytics-enhanced personalized learning. The main aim of the PERLA framework is to guide the systematic design and development of effective indicators for personalized learning