COMPARISON OF BOOK SHOPPING PATTERNS BEFORE AND DURING THE COVID-19 PANDEMIC USING THE FP-GROWTH ALGORITHM AT ZANAFA BOOKSTORES

The high number of active cases of the Corona virus (Covid-19) has a major impact on the trade sector, namely a decrease in sales turnover, causing a decrease in income by business actors and a decrease in people's purchasing power. This study aims to compare shopping patterns before and during the pandemic in Zanafa bookstores. The method used in the study is a qualitative approach related to the assessment of attitudes, opinions and behavior. In this study the attribute used is the name of the item / product, these attributes are categorized based on the shelves that there are 40 categories of bookshelves. Testing dataset using FP-Growth algorithm in tools with support value of 3% and confidence value of 10% and the pattern used is a pattern that has lift Ratio >1. Based on the results of the analysis, it was found that the rules before the pandemic pandemic many items were purchased simultaneously, that is, if the purchase of science would buy school books with the highest lift ratio of 2.9537, while during the pandemic many items were purchased simultaneously, that is, if the purchase of politics, it would buy the Qur'an with the highest lift ratio from the test results of 2.6165. This can be used by TBZ to get recommendations as promotional materials to increase profits and as a sales strategy on TBZ

IDENTIFIKASI PEMBICARA INDEPENDENT TEXT PADA DATA CLOSE-SET DENGAN MENGGUNAKAN MEL FREQUENCY CEPSTRAL COEFFICIENTS

Identifikasi pembicara adalah salah satu bagian pemrosesan sinyal digital dibidang pengolahan suara. Topik ini diangkat mengingat keterbatasan manusia dalam mengenali suara manusia yang begitu banyak ragamnya serta banyak suara yang hampir sama antara manusia satu dengan manusia lainnya. Pengenalan pembicara yang dibahas dalam penelitian ini adalah pengenalan pembicara indenpendent text pada data close-set. Dalam penelitian ini menggunakan MFCC sebagai ekstraksi ciri dan SOM sebagai pengenalan pola. Penelitian ini menggunakan 10 orang sebagai sumber suaranya. Dalam penelitian ini telah dilakukan pengujian terhadap beberapa pengaruh parameter terhadap ketepatan identifikasi pembicara, yaitu jumlah iterasi, jumlah koefesien MFCC dan jumlah data latih. Hasil pengujian memperlihatkan jumlah koefesien MFCC dan jumlah iterasi dapat meningkatkan ketepatan identifikasi pembicara dengan hasil pendeteksian pembicara tertinggi sebanyak 80% dan rata-rata akurasi 44%. Kata Kunci : Identifikasi Pembicara, MFCC, SOM

COMPARISON OF DATA MINING ALGORITHM FOR CLUSTERING PATIENT DATA HUMAN INFECTIOUS DISEASES

Tuberculosis is known as an infectious disease whose transmission through air intermediaries is caused by the germ Mycobacterium Tuberculosis. This disease has become a case that has almost spread throughout the pelalawan Regency with the number continuing to increase every year so that it is possible to be able to group the areas where this disease spreads. Grouping of tuberculosis data distribution areas using data mining methods in the form of clustering with the data used coming from the Pelalawan Regency Health Office from 2020 to 2022. The data obtained earlier will then be processed using k-medoids, k-means, and x-means algorithms. The beginning of this research was by processing data from each year using these three algorithms. Determination of the most optimal algorithm using DBI or known as the Davies Bouldin Index. The results of the processing of existing indicators are grouped into three sections, namely areas with a high, medium, and low number of cases. From the results of the study, the optimal algorithm in 2020 data is the k-medoids algorithms with a DBI value of 0,553 and in 2021 data, the most optimal algorithm is the k-means and x-means algorithm with similar DBI values of 0,582. Furthermore, the data in 2022 the most optimal algorithms are the k-means and x-means algorithms because they have the same DBI value, which is 0,510

ANALYSIS OF DIGITAL LIBRARY SERVICE QUALITY ON USER SATISFACTION USING WEBQUAL, LIBQUAL AND IPA METHODS

Universitas Pahlawan Tuanku Tambusai has used the information system Senayan Library Management System (SLiMS) version 7. SliMS is an integrated system to provide information to support operational, management and decision-making functions in libraries. However, there are still obstacles in its use, namely, the lack of tools and technology to support the implementation of the SLiMS system, the unattractive SliMS content, the OPAC service menu is less effective in searching for references in the library, and the book collection is rarely updated so it does not meet what the user needs. This study aims to measure the service quality of SLiMS from the user's perspective. This research instrument used Web Quality (WebQual), Library Quality (LibQual), and Importance Performance Analysis (IPA) methods. The results of this study resulted in a good level of system service quality but GAP was still found from perceived performance which still had a value of <0 or -0.63 and a conformity level of 78%, which meant that there were still results of user dissatisfaction with the performance provided by the service. SLiMS Hero University of Tuanku Tambusai. Quadrant A results are a top priority to be improved. the variables are: Easy to navigate (UQ3), Attractive appearance (UQ5), Latest available information (SI1), Provides detailed information (SI4), Provides up to date information (IC3), Cleanliness and beauty (LP2), Lighting and temperature settings (LP3), Guidance from the librarian (AS5)

A Comparative Study of Student Satisfaction Levels on Online Learning Using K-NN and Naïve Bayes

The outbreak of the Covid-19 pandemic in Indonesia led to restrictions on human social activities to minimize transmission. Teaching-learning is also affected when students must stay home and follow distance learning based on Government Regulation Number 21 of 2020, the Large-Scale Social Restrictions (PSBB) policy, issued on March 31, 2020. This has led to the emergence of learning support applications such as Zoom, Google Classroom, Google Meet, E-Learning, and many more. However, this new learning culture requires adaptation for effective implementation. During the adaptation process, researchers want to measure the level of student satisfaction and find out the best algorithm for classifying the level of student satisfaction. This measurement uses two data mining algorithms, K-Nearest Neighbour (K-NN) and Naïve Bayes, and the Islamic State University of Sultan Syarif Kasim Riau students as the research object. Different algorithms have varying strengths and weaknesses in handling specific data types and classification tasks. By comparing both algorithms, we can assess their generalization capabilities. A model that performs well on training data but fails to generalize to unseen data may not be as effective as a more robust algorithm that exhibits better generalization performance. K-NN classification with a value of k = 3 gets good results. Based on the study results, the conclusion is that K-NN is more optimal in classifying student satisfaction levels than Naïve Bayes with an accuracy ratio of 85% : 80%, precision of 85% : 84%, and recall of 99% : 93%

A Model to Predict The Live Bodyweight of Livestock Using Back-Propagation Algorithm

Cattle is the most popular livestock in Indonesia. Assessments of the live bodyweight of cattle can be conducted through weighing or predicting. Weighing is an accurate method, but it is not efficient due to the prices of scales that most traditional farmers cannot afford. Prediction is a more affordable technique however occurrences of error remains high. To deal with this issue this research has created a model predicting the live bodyweight of cattle through Back-Propagation algorithm. There are four morphometric variables examined in this study: (1) body length; (2) withers height; (3) chest girth; and (4) hip width. Based on comparative results with conventional prediction methods, Schoorl Indonesia and Schoorl Denmark, showed that the method offered has a lower error. Rate of error is 60.54% lower than Schoorl Denmark and 53.95% lower than Schoorl Indonesia

Model for Estimating Waste Generation in Pekanbaru Using Backpropagation Algorithm

Waste generation in Pekanbaru City cannot be managed optimally. Based on 2020 data, less than 50% of the waste that reaches the Final Disposal Site (TPA) reaches. To overcome this problem, this study aims to create an estimation model that can estimate the amount of waste generated each year. So that it can help the authorities to implement various policies to control waste generation. The estimation model is created using the backpropagation algorithm. The attributes used are those related to population and waste generation. Based on the results of experiments conducted using RapidMiner, the best network architecture model is the 6-6-1 model, namely six nodes in the input layer, six nodes in the hidden layer, and one node in the output layer. The six nodes in the input layer refer to the number of attributes used. The activation function used is binary sigmoid. The RMSE value generated from the best model is very low, namely 0.0181. So it can be concluded that this model can be used to estimate the generation of solid waste in Pekanbaru Cit

Identification of paddy leaf diseases based on texture analysis of Blobs and color segmentation

There are three types of paddy leaf diseases that have similar symptoms, making it difficult for farmers to identify them, namely blast, brown-spot, and narrow brown-spot. This study aims to identification paddy plant diseases based on texture analysis of Blobs and color segmentation. Blobs analysis is used to get the number of objects, area and perimeter. Color segmentation is used to find out some color parameters of paddy leaf disease such as the color of the lesion boundary, the color of the spot of the lesion, and the color of the paddy leaf lesion. To get the best results, four methods have been chosen to obtained the threshold value, Otsu threshold value, variable threshold value, local threshold value and global threshold value. The best accuracy of the four methods using threshold variables is 90.7%. The results of this study indicate that the method used has been very satisfactory in identifying paddy plant disease