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Indonesian Journal of Artificial Intelligence and Data Mining
Published by Universitas Islam Negeri Sultan Syarif Kasim Riau
ISSN : 26143372     EISSN : 26146150     DOI : -
Core Subject : Science,
Computer Science & IT
Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM) is an electronic periodical publication published by Puzzle Research Data Technology (Predatech) Faculty of Science and Technology UIN Sultan Syarif Kasim Riau, Indonesia. IJAIDM provides online media to publish scientific articles from research in the field of Artificial Intelligence and Data Mining. IJAIDM will be published 2 (two) times a year, in March and September, each edition contains 7 (seven) articles. Articles may be written in English or Indonesia.
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Articles 7 Documents
 1 
Search results for , issue "Vol 3, No 1 (2020): March 2020" : 7 Documents clear
Internet of Things (IOT) Development for The Chicken Coop Temperature and Humidity Monitoring System Based on Fuzzy Jamaluddin Husein; Oktaf Brillian Kharisma
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 1 (2020): March 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i1.9294

Abstract

The high increase in broiler each year there are several factors that must be considered, one of them is the temperature and humidity of the cage. The brooding cage space of 10 ekor/1. If more then that couse broiler is not optimal. The temperatur needed is 29°C-35°C and humidity 60%-70% for brooding. In general control of temperature and humidity, unable to maintain cage needs. Then it is necessary to do automatic control using the fuzzy logic method and the concept of Internet of Things (IoT). Base on the testing result the system has been able to maintain the set point of temperature and humidity and get broiler growth not same, The best stability value during the test is condition without DOC produces no overshoot temperature parameters and steady state error 0,48,  no overshoot and steady state for humidity. On the condition with DOC produces overshoot temperature 0,06  and steady state error 0,15, and overshoot 0,1 and steady state 0,4 for humidity. While on the IoT concept has been able to display the temperature and humidity values on the cage
Ant Colony Optimization for Traveling Tourism Problem on Timor Island East Nusa Tenggara Yampi R Kaesmetan; Marlinda Vasty Overbeek
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 1 (2020): March 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i1.9274

Abstract

Timor island consists of five districts and one city, namely Kupang District, South Central Timor District, North Central Timor, Belu District, Malaka District, and Kupang City. On the Timor island, it has natural tourist destinations, culinary tours, cultural and historical attractions most on the island of Timor. The Ant Colony Optimization (ACO) Algorithm is very unique compared to the other nearby search algorithm, this algorithm adopted because of Ant Colony who were looking for food from the nest to food sources by leaving a footprint called Pheromone. Mapping system algorithm using ant, tourist sites can show the shortest route between two points is desired. Ants algorithm proved to be applied in determining the optimum route, but still has the disadvantage of dependence on the parameter value is not maximized. From the test results based on parameters of the cycle and the number of ants affects the simulation time, for ant algorithm parameters. From the test results based on the parameters, α and β affects, number of node, the simulation time and the shortest distance varying toward the destination even if the starting location and ending on the same location.
Optimzation of Interval Fuzzy Time Series With Particle Swarm Optimization for Prediction Air Quality on Pekanbaru Fitri Insani (Scopus ID: 57190404820); Ade Puspita Sari
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 1 (2020): March 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i1.9298

Abstract

Kota Pekanbaru memiliki jumlah penduduk terbanyak di provinsi Riau yaitu 1.046.566 penduduk dengan jumlah kendaraan bermotor 105.941 unit. Badan Lingkungan Hidup menyatakan bahwa kota Pekanbaru memiliki kualitas udara yang tercemar yang disebabkan oleh kebakaran hutan dan lahan serta emisi gas buang kendaraam bermotor. Dengan adanya kondisi tersebut, kota Pekanbaru menggunakan alat pemantau udara yaitu Air Quality Monitoring System (AQMS) dengan penyampaian informasi kualitas udara melalui papan display ISPU. Penelitian ini bertujuan untuk memprediksi kualitas udara esok hari di kota Pekanbaru dengan menggunakan metode Fuzzy Time Series yang di optimasi menggunakan Particle Swarm Optimization. Tingkat akurasi prediksi diukur dengan menggunakan Mean Absolute Percentage Error (MAPE) dengan menghitung selisih antara data aktual dan hasil prediksi. Adapun data masukan yang digunakan yaitu 729 data dengan 5 parameter pengukur kualitas udara yaitu PM10, SO2, CO, O3 dan NO2. Hasil keluaran berupa angka prediksi untuk masing-masing parameter pengukur kualitas udara. Hasil pegujian metode FTS-PSO menunjukkan nilai MAPE sebesar 18,3583%. Parameter PSO terbaik yang digunakan adalah jumlah partikel 10, maksimal iterasi 25 dan bobot inersia 0,6. Dari hasil pengujian dapat disimpulkan bahwa prediksi kualitas udara menggunakan FTS-PSO bernilai cukup akurat.
Sentiment Analysis Using Twitter Data Regarding BPJS Cost Increase and Its Effect on Health Sector Stock Prices Evita Dyah Wardhani; Satria Kurnia Areka; Arya Wahyu Nugroho; Ayufi Reyza Zakaria; Arya Damar Prakasa; Rani Nooraeni
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 1 (2020): March 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i1.8245

Abstract

News about the increase in BPJS that will increase 2x gives a variety of responses in the community. One of the social media that people use in responding is Twitter. This research is used to see people's sentiment on Twitter about BPJS tariff policies. In addition, the impact of this sentiment will also be seen on the price of health shares. The analysis used is descriptive analysis and inference analysis. Descriptive analysis is used to look at the general picture of community sentiment and inference analysis is used to see the impact of community sentiment on the price of health stocks, namely Indo Farma and Kimia Farma. The results of this study indicate that public sentiment towards rising BPJS is dominated by negative sentiment. And for the level of tendency that has been processed through binary logistic regression analysis shows that negative sentiment will make Kimia Farma shares will go down while positive sentiment will make Kimia Farma shares will go up. As for IndoFarma shares, positive and negative sentiments from IndoFarma shares will tend to fall.
Comparative Study of Mamdani-type and Sugeno-type Fuzzy Inference Systems for Coupled Water Tank Halim Mudia
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 1 (2020): March 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i1.9309

Abstract

The level and flow control in tanks are the heart of all chemical engineering system. The control of liquid level in tanks and flow between tanks is a basic problem in the process industries. Many times the liquids will be processed by chemical or mixing treatment in the tanks, but always the level of fluid in the tanks must be controlled and the flow between tanks must be regulated in presence of non-linearity. Threfore, in this paper will use fuzzy inference systems to control of  level 2 are developed using Mamdani-type and Sugeno-type fuzzy models. The outcome obtained by two fuzzy inference systems is evaluated. This paper summarizes the essential variation among the Mamdani-type and Sugeno-type fuzzy inference systems with setpoint of level is 10 centimeter. Matlab fuzzy logic toolbox is used for the simulation of both the models. This also confirms which one is a superior choice of the two fuzzy inference systems to control of level 2 in tank 2. The results show madani-type fuzzy inference system is superior as compared to sugeno-type fuzzy inference system.
Sentiment Analysis Of Cyberbullying On Twitter Using SentiStrength Ulfa Khaira; Ragil Johanda; Pradita Eko Prasetyo Utomo; Tri Suratno
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 1 (2020): March 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i1.9145

Abstract

Cyberbullying is a form of bullying that takes place across virtually every social media platform. Twitter is a form of social media that allows users to exchange information. Bullying has been a growing problem on Twitter over the past few years. Sentiment analysis is done to identify the element of bullying in a tweet. Sentiments are divided into 3 classes, namely Bullying, Non-Bullying and neutral. There are three steps to classify cyberbullying i.e. collection of data set, preprocessing data, and classification process. This research used sentiStrength, an algorithm which uses a lexicon based approach. This SentiStrength lexicon contains the weight of its sentiment strength. The assessment results from 454 tweets data obtained 161 tweet non-bullying (35.4%), 87 tweet neutral (19.1%), and 206 tweet bullying (45.4%). This research produces an accuracy value of 60.5%.
Classifications Using Artificial Neural Network Method In Protecting Credit Fitness Elin Panca Saputra; Indriyanti Indriyanti; Supriatiningsih Supriatiningsih
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 1 (2020): March 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i1.9442

Abstract

Classification is information that has the closest relationship with data, we make a prediction in providing customer eligibility to get a loan from a financial service institution. In this study, we use the Artificial Neural Network (NN) method in combination with the Particle Swarm Optimization method. It is known that the method has excellent generalizations to solve a problem in increasing accuracy. However, some of the attributes in the data can reduce accuracy and increase the complexity of the Artificial Neural Network (ANN) algorithm. Therefore, attribute selection is very necessary, the attribute selection method used in this study is the Particle swarm optimization (PSO) method. This method can be used for proper attribute selection in determining lending to customers, therefore the Particle Swarm Optimization (PSO) method can increase the value of higher accuracy weights in determining attribute selection.

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