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The Classification Status of River Water Quality in Riau Province Using Modified K-Nearest Neighbor Algorithm with STORET Modeling and Water Pollution Index

The Classification Status of River Water Quality in Riau Pro...

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2020 Annual Conference on Science and technology research, ACOSTER 2020

作者： Mustakim Rosdina Ramadhani, Dian Afdal, M. Rahmawita, Medyantiwi Departement of Information System Puzzle Research Data Technology Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Departement of Information System Puzzle Research Data Technology Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Departement of Information System Puzzle Research Data Technology Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Departement of Information System Puzzle Research Data Technology Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Departement of Information System Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

The Department of Environment and Forestry, Pollution and Environmental Damage Control Division, has an active role in monitoring water quality in Riau Province. The rivers that are still monitored and managed are Kampar River, Siak River and Indragiri River. Division of Environment Pollution calculates river quality status manually using Microsoft Excel, this is not maximally done since this important information should be processed quickly. Division of water pollution must determine the right calculation to get the results of the water quality status. Because of many calculation formulas set by the government, the commonly used method is the STORET method and the Pollution Index. So, in overcoming the problem of classification, the researcher proposes the use of learning methods that can predict or determine the status of water quality with classification techniques on data mining that is Modified K-Nearest Neighbor (MKNN) which is a modification of K-NN. The calculation of the MKNN algorithm produced the highest accuracy of 85.10% at K = 5 using STORET result data as training data. While, using the Pollution Index data results, the highest accuracy is 76.92% at K = 1. Based on the analysis with attribute analysis, the attributes that influence the determination of river water quality are BOD, COD, NH3, Fecal Coli and Total Coli. This result can be taken into consideration by the Division of Environmental Pollution in the process of overcoming and reducing pollutant overload that exceeds quality standards. © Published under licence by IOP Publishing Ltd.

关键词： Rivers

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The Implementation of Probabilistic Neural Network Algorithm for Classification of Family Hope Program in Pekanbaru City

The Implementation of Probabilistic Neural Network Algorithm...

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2020 Annual Conference on Science and technology research, ACOSTER 2020

作者： Novita, Rice Anzir, Qumfa Mustakim Alhidayatillah, Nur Suriani, Julis Departement of Information System Puzzle Research Data Technology Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Departement of Information System Puzzle Research Data Technology Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Departement of Information System Puzzle Research Data Technology Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Departement of Community Development Faculty Dakwah and Communication Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Departement of Communication Faculty Dakwah and Communication Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

The poverty rate in Indonesia continues to increase, especially in Pekanbaru city. It was recorded in 2017 the poverty in Pekanbaru City reached 176.908 inhabitants. The poverty can be seen from the Education Factor, Economic Factor, Health Factor and Infrastructure Factor. This aim of this study is to classify beneficiary of poverty from education and health factors using the PNN Algorithm. The criteria used for the classification of education classes and health classes include elementary, junior high, high school, toddlers and pregnant women. The data used in this study were from the Harapan family program in Pekanbaru City. In clustering training data and test data, K-Means Algorithm was used. The results of the clustering are 3,543 test data and 1,520 testing data with DBI value of 0.194 and the result of calculation of Probabilistic Neural Network algorithm with accuracy value is 99.07%. In testing the algorithm using the confucion matrix method, the recall value is 99.30% and the precision value is 97.81%. The high level of health and education is an important factor in the development and progress of an area. © Published under licence by IOP Publishing Ltd.

关键词： Health

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Application of the nearest neighbor algorithm for classification of online taxibike sentiments in indonesia in the google playstore application

Application of the nearest neighbor algorithm for classifica...

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Universitas Riau International Conference on Science and Environment 2021, URICSE 2021

作者： Setyorini, Sindhy Genjang Mustakim Department of Information System Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru28293 Indonesia Puzzle Research Data Technology Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru28293 Indonesia

Google playstore is an official platform that provides applications to services, and not only that, user can perform assessments and reviews after using the Google playstore. One of the applications and services provided by Google playstore is online transportation. There's a couple of online transports available but at this time's research just scanned the official transport account of gojek and grab *** data used in this study were application review data and service review data from the two online transportations which are processed using the K-Nearest Neighbor and Improved K-Nearest Neighbor algorithms by comparing the accuracy values. An experiment was conducted to divide the data with 5 K on the K-Fold Cross-Validation. The results of the comparison of the KNN application data were that Grab had a higher accuracy of 77.77% compared to Gojek which had an accuracy value of 66.24%. In terms of the data Services using the IKNN algorithm, Grab had a higher accuracy value of 78.23% while Gojek had an accuracy value of 48.19%. Meanwhile, from the time complexity of the process, the IK-NN algorithm is better than the K-NN. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Nearest neighbor search

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Clustering of Public Opinion on Natural Disasters in Indonesia Using DBSCAN and K-Medoids Algorithms

Clustering of Public Opinion on Natural Disasters in Indones...

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2020 Annual Conference on Science and technology research, ACOSTER 2020

作者： Mustakim Fauzi, Muhammad Zakiy Mustafa Abdullah, Assyari Rohayati Departement of Information System Puzzle Research Data Technology Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Departement of Information System Puzzle Research Data Technology Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Departement of Communication Faculty Dakwah and Communication Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Departement of Communication Faculty Dakwah and Communication Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Departement of Communication Faculty Dakwah and Communication Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

Natural disasters are disasters caused by events or series of events caused by nature such as earthquakes, tsunamis, volcanic eruptions, floods, tornadoes, and landslides. Some of these natural disasters have taken a lot of public attention, from empathy, sadness and criticism that form an opinion on social media. One of the most popular social media used by the public is Twitter. Opinions written by Twitter users are called tweets. A collection of tweets can be processed to obtain information by using data mining techniques namely Text Mining. In this study, the Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm and K-Medoids were used. The result of this study shows that DBSCAN is the best algorithm because it has the Silhouette Index (SI) validity of 0.9140 and the average execution time in RapidMiner Studio is 83.40 seconds. Meanwhile, the K-Medoids algorithm has a Silhouette Index (SI) validity of 0.2259 and an average execution time in RapidMiner Studio 849.93 seconds. The frequency of the word "earthquake"dominates for the positive category, the word "disaster"dominates the negative category, and the word "flood and earthquake"dominates the negative category. © Published under licence by IOP Publishing Ltd.

关键词： Earthquakes

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Application of Deep Learning Convolutional Neural Network for Melanoma Skin Cancer Classification

Application of Deep Learning Convolutional Neural Network fo...

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Information technology and Digital Innovation (ISITDI), International Symposium on

作者： Hamdi Syukron Mustakim Information Systems Department Puzzle Research Data and Technology (Predatech) Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

ISBN: (数字)9798350389241

ISBN: (纸本)9798350389258

Melanoma skin cancer is a global health concern due to its life-threatening nature and visual similarity to benign skin conditions. Accurate melanoma identification requires specialized expertise, which is often scarce and expensive. This scarcity highlights the critical need for accessible and cost-effective diagnostic solutions. Leveraging deep learning, an Artificial Intelligence (AI) approach rooted in computer vision, offers a promising avenue for melanoma detection. This study utilizes Convolutional Neural Network (CNN) algorithms, particularly DenseNet121 and VGG19 architecture, to develop robust melanoma classification models. Through rigorous evaluation, DenseNet121 emerges as the optimal architecture, achieving an impressive accuracy of 0.9385 and a minimal loss of 0.1497. Furthermore, we demonstrate practical implementation by deploying the DenseNet121 model in a web-based application using the Flask framework. Our findings underscore the efficacy of deep learning in melanoma detection and highlight the feasibility of translating such advancements into user-friendly diagnostic tools, addressing the urgent need for accessible healthcare solutions in melanoma management.

关键词： Deep learning Computer vision Accuracy Computational modeling Melanoma Computer architecture Classification algorithms Convolutional neural networks Medical diagnosis Artificial intelligence

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The Implementation of Fuzzy-C Means to Categorize Poverty data in Riau Province

The Implementation of Fuzzy-C Means to Categorize Poverty Da...

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2020 Annual Conference on Science and technology research, ACOSTER 2020

作者： Mustakim Saputa, Hadi Eka Mutasir Nuryanti Salmiah Departement of Information System Puzzle Research Data Technology Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Departement of Information System Puzzle Research Data Technology Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Departement of Family Law Faculty of Syari'ah and Law Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Departement of Syari'ah Economic Faculty of Syari'ah and Law Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Departement of Economic Education Faculty of Tarbiyah and Education Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

Riau Province was the 13th in line as the poverty contributor in Indonesia. It was because of the fund from government was not properly distributed. Therefore, an identification system to distribute the poverty funding was needed in order to reach the citizens in poverty. Based on the available parameter, it could accelerate the eradication of poverty. Poverty caused by various factors, i.e. educational background, living environment, job and health. One of the solutions to distribute the fund properly was by categorizing the poverty data. This research used Fuzzy Clustering Means (FCM) method because every area has the possibility to become part of each cluster with different membership level between score 0 and 1. The recommended parameter was w = 2, Max_iter = 100, ϵ = 0.1, P0 = 10.000.0000, t = 1, C = 3 with Partition Coefficient Index (PCI) validity score 0.47. The web built based-system uses programming language PHP with Framework YII 2 Advanced. All clustered data was analyzed based on poverty variable level that was dominant in range of regency and District. © Published under licence by IOP Publishing Ltd.

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Variable Selection to Determine Majors of Student using K-Nearest Neighbor and Naïve Bayes Classifier Algorithm 1

Variable Selection to Determine Majors of Student using K-Ne...

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1st Workshop on Environmental Science, Society, and technology, WESTECH 2018

作者： Mustakim Hastarimasuci, Reysa Papilo, Petir Zarkasih Zaitun Nazir, Alwis Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru28293 Indonesia Faculty of Education and Teching Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru28293 Indonesia Puzzle Research Data Technology Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru28293 Indonesia

Appropriate student's major placement in high school can help students to better improve their academic achievement. There are many variables which must be considered to determine the student's majors, such as: Gender, Interests, Intelligence Quotient (IQ);Four subjects in Junior High School (JHS), average junior high school grades, matriculation score of four subjects, and average rate of matriculation. The number of variables used in the selection, causes some weaknesses among them, such as the complexity of variable, the inefficiency of variable and the existence of some variables which is only as an addition without having a significant contribution. This study aims to reduce the number of these variables so it will become easier to analyze and to be applied. The Process of reduction was done by combining experiments with Predefined attributes. A total of ten combinations were attempted using K-Nearest Neighbor (K-NN) and Naïve Bayes Classifier (NBC) which then was measured by Confusion Matrix accuracy. The experimental result showed that the combination of variables which produce the best accuracy were the 9th and 10th experiment with variable matriculation, interest, and IQ, and an accuracy of 96.77% from K-NN also 98.38% from NBC. By combining both algorithms, 99.87% of maximum accuracy was obtained from those three variables. New information which can be extracted from this research is that there are only three important variables to determine major placement in Senior High School, Average Scores of Matriculation, Interests and IQ followed by four supporting variables such as the scores of Mathematic, Physics, English and Economics in Matriculation. © Published under licence by IOP Publishing Ltd.

关键词： Students

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data Sharing Technique Modeling for Naive Bayes Classifier for Eligibility Classification of Recipient Students in the Smart Indonesia Program 2

Data Sharing Technique Modeling for Naive Bayes Classifier f...

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2nd International Conference on Advance and Scientific Innovation, ICASI 2019

作者： Mustakim Syahidatul Helma, Siti Ramadhani, Ulya Achmad Daengs, G.S. Novita, Rice Nuryanti Fitriatien, Sri Rahmawati Departement of Information System Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Puzzle Research Data Technology Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Faculity of Economic Universitas 45 Surabaya Surabaya Indonesia Departement of Syariah Economic Faculty of Syariah and Law Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Universitas PGRI Adi Buana Surabaya Surabaya Indonesia

The objective of Smart Indonesia Program (Program Indonesia Pintar: PIP) is to help school-aged people from poor/vulnerable/priority families to continue to receive education services to graduate from secondary education, both through formal and non-formal education channels. In its implementation, there are still many fraudulent in the proces of nominating proposal PIP funds and there are still many prospective students who should not receive PIP because they do not meet the technical guidelines provided by the Ministry of Education and Culture to determine the eligibility of prospective recipients of PIP funds can be done by schools and stakeholders, one of them by using classification techniques. One algorithm that is widely used in classification is the Naive Bayes Classifier (NBC) algorithm. In this study three data sharing techniques were used, namely Hold Out 70% training data and 30% testing data, K-Means Clustering, and also 10 Fold Cross Validation. Determination of the best data sharing technique will be determined by looking at the value of Accuracy, Precision, and Recall and also the value of Area Under Curve (AUC) which is illustrated by the Receiver Operating Characteristic (ROC) curve so that the NBC algorithm is generated with 10 Fold Cross Validation has a very good classification level with the values of accuracy, precision, and recall respectively at 97.40%;100%;and 76.14%. © Published under licence by IOP Publishing Ltd.

关键词： Students

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DBSCAN algorithm: Twitter text clustering of trend topic pilkada pekanbaru 1

DBSCAN algorithm: Twitter text clustering of trend topic pil...

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1st Workshop on Environmental Science, Society, and technology, WESTECH 2018

作者： Mustakim Nurul Gayatri Indah, Reza Novita, Rice Kharisma, Oktaf Brillian Vebrianto, Rian Sanjaya, Suwanto Hasbullah Andriani, Tuti Sari, Wardani Purnama Novita, Yulia Rahim, Robbi Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru28293 Indonesia Puzzle Research Data Technology Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru28293 Indonesia Faculty of Education and Teaching Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru28293 Indonesia Faculty of Ushuluddin Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru28293 Indonesia Informatic Management Sekolah Tinggi Ilmu Manajemen SUKMA Medan Indonesia

Social media is one of the most common sources used to communicate, such as Twitter. Every tweet on Twitter contains data such as text which when collected can be processed into information. data processed from Twitter tweet will create a trend which can be used for information such as in education, economics, politics, etc. This then created the concept of text mining. Text mining techniques are needed to find an interesting pattern in search of trends based on Twitter text with topics related to Pilkada Pekanbaru 2017. This research is intended to cluster Twitter text data using Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm. This research was conducted with several experiments using different Eps and MinPts parameters for 2,184 text data which has been through several stages, such as cleaning, duplication removal, pre-processing like stemming and stopwords. Based on the highest average of Silhouette Index, Eps 0.1 and MinPts 10 with SI = 0.413 were chosen as paramaters, thus forming 31 clusters. According to the frequency of word occurrences in the cluster, the highest are "kpu", followed by "firdaus", "kota", "pasang", and "ayat". As can be seen that the candidate pairs most often appear on cluster results are Firdaus-Ayat, and based on the results of Pilkada 2017, Firdaus-Ayat was chosen as Mayor and Vice Mayor of Pekanbaru. © Published under licence by IOP Publishing Ltd.

关键词： Social networking (online)

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Market Basket Analysis Using Apriori and FP-Growth for Analysis Consumer Expenditure Patterns at Berkah Mart in Pekanbaru Riau

Market Basket Analysis Using Apriori and FP-Growth for Analy...

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2018 Workshop on Multidisciplinary and Its Applications: Applied Mathematics, Computer Science, Information Systems, and Information technology, WMA-Mathcomtech 2018

作者： Mustakim Herianda, Della Maulina Ilham, Ahmad Daeng Gs, Achmad Laumal, Folkes E. Kurniasih, Nuning Iskandar, Akbar Manulangga, Gloria Indra Iswara, Ida Bagus Ary Rahim, Robbi Puzzle Research Data Technology UIN Sultan Syarif Kasim Riau Indonesia Department of Information System UIN Sultan Syarif Kasim Riau Indonesia Informatics Department Universitas Muhammadiyah Semarang Semarang Indonesia Department of Management University of 45 Surabaya Surabaya Indonesia Department of Electrical Engineering Politeknik Negeri Kupang Kupang Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Informatics Engineering STMIK AKBA Makassar Indonesia Informatics Department STMIK STIKOM Indonesia Denpasar Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Kubang Gajah Malaysia

Berkah Mart is one of the new minimarkets in Pekanbaru city which began to develop as a retail business to meet the needs of the community. In his operation, Berkah Mart still uses the traditional store management perception that the placement of product layout on sale shelves has not been through sales analysis. Existing products have not been reviewed from the consumer's point of view, whereas the marketing economics concept provides a variety of product and consumer management methods for business management in Berkah Mart, including market bucket analysis techniques and various itemset search algorithms. In this research, Market Basket Basket Analysis with FP-Growth algorithm is proposed to determine the layout and planning of goods availability. The application of FP-Growth algorithm proved to be useful in generating many and informative association rules to find out the consumer spending pattern at Berkah Mart in Pekanbaru. Moreover, from the rules of the customer association can be segmented separately to meet the specific needs of customers with cost-effective by using some special promotions for the general group. The experimental results show that FP-Growth algorithm can analyze quickly and efficiently informing consumer shopping pattern at Berkah Mart and can increase Berkah Mart revenue. © Published under licence by IOP Publishing Ltd.

关键词： Sales

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