Sentiment Analysis of Hotel User Review using RNN Algorithm

Sentiment analysis in user review is a growing research area at the current time. Usually, the website becomes a source of data in knowing the quality of the hotel services, and the provider can utilize the review for monitoring and evaluation. However, determining the positive or negative sentiment of a user review in unstructured textual data takes a long time. As a result, we present a model to classify positive or negative sentiment in user reviews in this article. This study suggests the RNN method in building an effective model to classify user sentiment. Based on the experiment, our model can produce accurate results in organizing hotel reviews. Furthermore, the proposed method achieved a higher evaluation metrics score with an f1-score of 91.0%.

The Design Of Augmented Reality Media Koi Fish Literacy Using Fast Corner Algorithm

Ornamental fish that are quite famous and in demand in the market is the koi fish. This fish has a relatively high economic value, and its demand is increasing. There are still many difficulties in maintaining this fish so that it can cause the growth of disease and even death in the fish. It is due to the lack of public attention in terms of literacy about koi fish. Researchers used augmented reality technology to design koi fish literacy media based on these problems using the FAST Corner algorithm. So it is hoped that it could help improve public literacy about koi fish by introducing real-time information. The Fast Corner detection algorithm is helpful to accelerate the computational time when detecting corners in real-time with the markerless Augmented Reality technique. In this technique, the marker used for object tracking has been replaced with pattern recognition or pattern recognition of an object. The study results showed that experiments using this algorithm could track targets with good and faster performance and a maximum level of accuracy.

Effective Soil Type Classification Using Convolutional Neural Network

Soil classification is a growing research area in the current era. Various studies have proposed different techniques to deal with the issues, including rule-based, statistical, and traditional learning methods. However, the plans remain drawbacks to producing an accurate classification result. Therefore, we propose a novel technique to address soil classification by implementing a deep learning algorithm to construct an effective model. Based on the experiment result, the proposed model can obtain classification results with an accuracy rate of 97% and a loss of 0.1606. Furthermore, we also received an F1-score of 98%.

Efficient Fruits Classification Using Convolutional Neural Network

Classification of fruits is a growing research topic in image processing. Various papers propose various techniques to deal with the classification of apples. However, some traditional classification methods remain drawbacks to producing an effective result with the big dataset. Inspired by deep learning in computer vision, we propose a novel learning method to construct a classification model, which can classify types of apples quickly and accurately. To conduct our experiment, we collect datasets, do preprocessing, train our model, tune parameter settings to get the highest accuracy results, then test the model using new data. Based on the experimental results, the classification model of green apples and red apples can obtain good accuracy with little loss. Therefore, the proposed model can be a promising solution to deal with apple classification.

COMPARISON OF HOP COUNT ON WIRELESS MESH NETWORK

Wireless Mesh Network (MWN) is a self-configured and self-organized network that can typically be implemented on 802.11 hardware. It consists of several nodes that make up the network backbone in a multi-story and sealed room, in contrast to building a hall or a place without bulkheads. This experiment uses an odd and even number scheme with a maximum number of routers of 8 pieces. In a sealed room, the performance of the method of installation of the number of strange Hops is better than the number of even Hops, with throughput calculation of 2665.19 KB, delay 0.25 s, data lost 0.60 %, and jitter 0.01 s and the best scheme that is with the number of Hops as much as five pieces, with the calculation of the number of throughput 7001.88 KB, delay 0.51s, data lost 0.47%, and jitter 0.002 s. In the free spaces, it can produce the better performance of the even hop count calculation scheme than the odd hop count by building throughput 16709.8 KB, delay 0.2 s, data lost 0.08 %, and jitter 0.03 s. and the best scheme that is with the number of throughput 68975,2 KB, wait for 0.0148 s, data lost 0 %, and jitter 0.0014 s. WMN performance in unshared space is more maximized than the version in a sealed area, with throughput values of 11786.82 kbps, delay of 2.08 ms, and data lost by 0.08 %, and jitter 0.03 s.it can produce the better performance of the even hop count calculation scheme than the odd hop count by producing throughput 16709.8 KB, delay 0.2 s, data lost 0.08 %, and jitter 0.03 s. and the best scheme that is with the number of throughput 68975,2 KB, wait for 0.0148 s, data lost 0 %, and jitter 0.0014 s. WMN performance in unshared space is more maximized than the version in sealed space, with throughput values of 11786.82 kbps, delay of 2.08 ms, and data lost by 0.08 %, and jitter 0.03 s. and data lost by 0.08%, and jitter 0.03s.

Implementation of CNN for Plant Leaf Classification

Many deep learning-based approaches for plant leaf stress identification have been proposed in the literature, but there are only a few partial efforts to summarize various contributions. This study aims to build a classification model to enable people or traditional medicine experts to detect medicinal plants by using a scanning camera. This Android-based application implements the Java programming language and labels using the Python programming language to build deep learning applications. The study aims to construct a deep learning model for image classification for plant leaves that can help people determine the types of medicinal plants based on android. This research can help the public recognize five types of medicinal plants, including spinach Duri, Javanese ginseng, Dadap Serep, and Moringa. In this study, the accuracy is 0.86, precision 0.22, f-1 score 0.23, while recall is 0.2375.

Modern Web Semantic Application For Selling Fish

Betta fish is famous as a fighter fish with a difference between betta fish with other types of fishes. Many people are interested in buying the Betta fish with a beautiful tail and attractive color with a giant belly. Thus, the paper aims to build a Semantic Web with API of delivery services. We design a system to enable and increase the betta fish sales of the Yogyakarta Community, Indonesia. We construct an application with REST of a web semantics to retrieve various data, including places and shipping cost of items to enable buyers to estimate fish prices and shipping costs.

Design and Build a Seminar Management Information System to Manage 2019 Indonesian Qualitative Seminar & Workshop (SLKI)

Matana University collaborated with Indonesia Qualitative Researcher Association (IQRA) held a 2019 Indonesian Qualitative Seminar & Workshop (SLKI) 2019. The organizer required a seminar management information system (SIM) to run this event smoothly. However, seminar SIM was not available at that time, and needed to be built according to the needs of 2019 SLKI. The methodology to build seminar SIM is study literature system development with prototyping model which has the stages of user requirement, system / sub system prototyping, prototype evaluation, prototype improvement, system testing, system implementation, system maintenance. SIM 2019 SLKI consists of 2 (two) main applications, namely a web-based data management application and mobile based attendance application. QR Code was used for the attendance of participants. This seminar SIM successfully has been built and implemented in this event. This seminar SIM helped the participants and the committee to share data and information that can be done anytime and anywhere as long they are connected to the internet. In the future, it is necessary to develop several features such as article management, QR code generator, automatic certificate generation, digital payment, and more complete reporting & webinar implementation.

IMPLEMENTATION OF THE COMSTOCK METHOD FOR ANDROID BASED PATIENT LEFTOVERS FOOD MANAGEMENT

Nowadays, nutritionists should count manually to know how much nutrition that patients get as the development of technology, human work easier likewise in counting the patient's food waste so it will be more efficient and not waste a lot of time. This research aims to build an application that can facilitate nutritionists in the calculating amount of food waste by using the Comstock method. The system is designed to use Unified Modeling Language (UML) and programming language Hypertext Preprocessor (PHP) and Hyper Markup Language (HTML). The result of this research is in the form of application which could be used by nutritionists at hospital calculate the amount of food waste of patients using the android-based Comstock method at the hospital using the Comstock method.

Use Of Customer Relationship Management Application For Credit Union

Cooperatives are legal entities based on the principle of kinship, which consists of all members of natural or legal persons for the welfare of their members. Where most people want to open a business, it has to have a lot of capital to keep the business from running or growing. Kabanjahe The majority of the population opens a shop to meet their daily needs. The existence of a savings and loan unit can help people in the Kabanjahe region to open a business to improve the economy of people in Kabanjahe. Therefore, we need a system that is very helpful in the cooperative's transaction process. The Customer Relationship Management (CRM) application can support the notification process and simplify the cooperative payment process. This research can help cooperatives and cooperative members to conduct transactions and help members with notification of the funds spent and the date of the last payment of the paper.