Recommended System

In this study searched for the survey paper on recommendation system. Recommendation system sorts through massive amounts of data to identify interest of users and makes the information search easier. For that purpose many methods have been used. Collaborative Filtering (CF) is a method of making automatic predictions about the interests of customers by collecting information from number of other customers, for that purpose many collaborative base algorithms are used. Web Recommendation(WR) help the website visitors for easy navigation of web pages, quickly reaching their destination and to obtain relevant information. Content based filtering method(CBF) filtering is done based on customer’s interested items. In content-based filtering technique, the web pages are recommended for a user very quickly from ancient database. In that database different content of items are added that the user has used in the ancient times and/or user’s personal information and preferences.

Sistem Rekomendasi Menggunakan Algoritma Apriori Pada Aplikasi E-Commerce Toko Sudirman Sport

The System of selling and marketing products at the Sudirman Sport store which is still done conventionally causes the problem of lack of sales transactions obtained by the store because it only relies on buyers who come directly to the store. Meanwhile, many stores in Indonesia have implemented any system e-commerce in stores. The solution to this problem is to build a website-e-commerce application based for sales and marketing of products sold by the Sudirman Sport store and implement a marketing strategy, namely the System Recommendation Product attract buyers to buy products that have been offered by the Sudirman Sport store and combined with the a priori algorithm to get accuracy in the data processing process to help stores predict buyer interest in an item and then recommend it in order to attract buyers and increase product sales at the Sudirman sport store.

Movie Recommendation System

This paper represents the overview of Approaches and techniques used in Movie Recommendation system. Recommendation system is used by many companies like Netflix, Amazon, Flipkart etc. It makes the user experience better and decrease the user efforts. It plays a very vital role in our day-to-day life. It is used in recommending Movies, Articles, News, Books, Music, Videos, People (Online Dating) etc. It learns from the user past behavior and based on that behavior it recommends item to the user. Likewise, in Movie Recommendation system movie is recommended to the user on the basis of movies watched, liked, rated by the user. In year 2020, approximate 10,000 movie were launched according to IDMB data. It saves a lot of times and efforts of the user by suggesting movies according to user taste and user don’t have to select a movie from a large set of movies.

An Analysis of the Aspects Hampering Informal Sector Tax Administration: Case of the Zimbabwe Revenue Authority

The main focus of the study was to ascertain the potential of the informal sector to provide much-needed revenue for the government. It also focused on the challenges faced in informal sector revenue taxation and possible solutions thereof. The Zimbabwe revenue authority has maintained presumptive tax for the sector and subcontracting to the city of Harare for the collection of revenue from the informal sector. Despite all this, the industry still underperformed in terms of revenue raised. The study sought to find out challenges of taxing the informal sector, the potential of the informal sector, the effectiveness of the Zimbabwe revenue authority in taxing the informal sector, and possible ways of improving the taxing of this rampant sector. The study found out that there is great potential from the informal sector, but turning it into tangible gains has been elusive due to political interference, lack of proper infrastructure, unfair application of tax laws and general mistrust of the government. The study recommended that the government ought to play an active role by making sure there is the political will to make sure that players in the informal sector contribute to the focus in line with Adam Smith’s general principles which include fairness and equity. There is a need for staffing levels to be commensurate with the workloads and also the motivation of the employees. The research also recommended the adaptation of Information Communication Technology to ensure accountability and traceability of transactions in the informal sector as they move away from a cash-based system recommendation.

Movie Recommenadation System

Today the amount of information in the internet growth very rapidly and people need some instruments to find and access appropriate information. One of such tools is called recommendation system. Recommendation systems help to navigate quickly and receive necessary information. Many of us find it difficult to decide which movie to watch and so we decided to make a recommender system for us to better judge which movie we are more likely to love. In this project we are going to use Machine Learning Algorithms to recommend movies to users based on genres and user ratings. Recommendation system attempt to predict the preference or rating that a user would give to an item.

Optimizing Placement of Field Experience Program: An Integration of MOORA and Rule-Based Decision Making

The lack of optimality in the Field Experience Program (FEP) placement has affected universities’ educational services to the stakeholders. Bringing together the stakeholders’ needs, university capacities, and participants’ willingness to quality and quantity is not easy. This study tries to optimize the placement of FEP by considering the interests of multiple perspectives through the application of Multi-Objective Optimization on the Basic of Ratio Analysis (MOORA) and Rule-Based methods in the form of a decision-making model. MOORA ranked the students based on the FEP committee’s perspective and other criteria, such as micro-teaching grades, final GPAs, study programs, number of credits, and student addresses. Meanwhile, the school perspective was ordered based on its accreditations, levels, types, facilities, and performances. To achieve the optimal recommendation of FEP placement, the integration of MOORA and Rule-based intertwined the requirement of such perspectives. A prototype of the system recommendation is then acquired to simplify the decision-making model. As adjudications, a survey from twenty stakeholders evidenced around 86.92% of system user acceptances. The confusion matrix testing defines the accuracy of this method reaches 78.33%. This paper reveals that the recommendation model has been successfully increasing the effectiveness of decision making in FEP placement under the needs and expectations of the entire stakeholders.

Implementasi Metode Topsis pada Sistem Penunjang Keputusan Pemilihan Wedding Venue

To prepare for the wedding, it takes energy and enough time for the program to work as it wishes. The dream of having a beautiful and exciting wedding is every couple's dream. Surabaya was one of the towns with the heavy population. Surabaya was one of the towns with the heavy population. Such conditions are problematic because if couples who are going to celebrate the wedding don't have enough land to host a wedding venue in Surabaya. Gathering information regarding a wedding venue takes a long time. On the question of obtaining it, a wedding venue customization system was developed to make it easier for the tenants to select the appropriate venue. One algorithm that can be used to make recommendations with multiple criteria and alternatives is Technique For Order of Preference by Similarity to Ideal Solusition (TOPSIS). TOPSIS is an algorithm with the highest alternative proceeds to have the shortest distance from the ideal positive solution and the farthest from the negative ideal solution to the criteria used by wedding venue elections to include rent costs, the sum of housing, facilities and the exact distance from the tenants' location to the building. To design this recommendation system, SDLC development methods are used that have grooves of grooves at every step made. Results obtained through two testing stages are functional and non-functional testing. At functional testing were black box testing techniques and 100% obtained results. Non-functional testing is done to shift from system recommendation output to manual calculating output. Results derived from non-functional testing by 100%.

COMPARISON MODELS OF MACHINE LEARNING FOR MOVIE RECOMMENDATION SYSTEMS

The trend of the Internet makes the presentation of the right content for the right user inevitable. To this end, recommendation systems are used in areas such as music, books, movies, travel planning, e-commerce, education, and more. One of the most popular recommendation systems in the world is Netflix, which generated record profits during quarantine in the first quartile of 2020. The systematic approach of recommendations is based on the history of user selections, likes and reviews, each of which is interpreted to predict future user selections. This article provides a meaningful analysis of various recommendation systems, such as content-based, collaborative filtering and popularity. We reviewed 7 articles published from 2005 to 2019 to discuss issues related to existing models. The purpose of this article is to compare machine learning algorithms in the Surprise library for a recommendation system. Recommendation system has been implemented and quality has been evaluated using the MAE and RMSE metrics.

COMPARISON MODELS OF MACHINE LEARNING FOR MOVIE RECOMMENDATION SYSTEMS

The trend of the Internet makes the presentation of the right content for the right user inevitable. To this end, recommendation systems are used in areas such as music, books, movies, travel planning, e-commerce, education, and more. One of the most popular recommendation systems in the world is Netflix, which generated record profits during quarantine in the first quartile of 2020. The systematic approach of recommendations is based on the history of user selections, likes and reviews, each of which is interpreted to predict future user selections. This article provides a meaningful analysis of various recommendation systems, such as content-based, collaborative filtering and popularity. We reviewed 7 articles published from 2005 to 2019 to discuss issues related to existing models. The purpose of this article is to compare machine learning algorithms in the Surprise library for a recommendation system. Recommendation system has been implemented and quality has been evaluated using the MAE and RMSE metrics.