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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 2 (2020): Spetember 2020" : 7 Documents clear
Data Train Reduction on Data Image With K Support Vector Nearest Neighbor (Case Study : Maize Leaf Image) Marlinda Vasty Overbeek; Yampi R Kaesmetan
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 2 (2020): Spetember 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i2.10451

Abstract

In this study, we applied the K Support Vector Nearest Neighbor algorithm to reduce data train on data image. The data image that we used is the maize leaves image infected with fungi and healthy maize leave. The aim of data train reduction in this study is to get faster and more accurate prediction results. This because by using the K Support Vector Nearest Neighbor algorithm, a support vector that is formed from the algorithm really characterize the objective function of the problem. The accuracy obtained from this study is 0.20 or 20% mean error for the value of nearest neighbor K  = 3 and using K Nearest Neighbor as a model construction algorithm. The error value is smaller than when we compared to the construction of the model without performing data train reduction. The error value if not doing any reduction is 0.209 or 20.9%. Whereas in terms of time efficiency, working with the K Support Vector Nearest algorithm is 24 seconds faster than without performing data train reduction 
An Optimization Model for Teaching Assignment based on Lecturer’s Capability using Linear Programming Imam Eko Wicaksono; I Wayan Wiprayoga Wisesa
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 2 (2020): Spetember 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i2.9705

Abstract

In the campus, the arrangement of teaching assignment for the lecturers have been the porblem encounterd by the management on the beginning of each semester. This process including assigning a class with suitable lecturer while adjusting the appropriate load for the lecturer. Such problem is non-trivial and can be considered as a linear system model. In this article, we try to solve the problem of teaching assignment using optimization model. We tried to maximize the capability of lecturers on particular subject while also considering their loads. Using branch and bound algorithm, the optimal solution were found and the problem are well solved.
Comparison Of Data Mining In E-Learning Learning Based On Log Aktivity On PSO-Based Nural Network Algorithms With PSO-Based SVM Elin Panca Saputra; Supriatiningsih Supriatiningsih; Indriyanti Indriyanti
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 2 (2020): Spetember 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i2.10519

Abstract

The purpose of this research is to find a higher or better level of accuracy, we make a comparison between the Neural Network method based on Particle Swarm Optimization and the Particle Swarm Optimization-based support vector machine method, from evaluation on e-learning based learning systems is very important to determine the level. accuracy in learning.. In addition, the purpose of this study is to find the attributes of the highest predictive results of student learning who follow the e-learning learning system. The data we use are 641 users which are taken from the log of student learning activities from the LMS. The logs we use are Gender, Excercise, Forum, Chat, Diskusi, Upload An Assgmnt, Message, Excercise Quiz, dan Total Log. All logs will be recorded in the LMS. The data used in this study, the results of the tests we conducted, the results obtained using the PSO-based Neural Network (NN) method obtained an accuracy value of 97.35%, and the results of the AUC value were 98.60%. Then we did the second trial using the PSO-based support vectore machine (SVM) method to get an accuracy value of 88.47% and an AUC value of 93.80%. Then the conclusion is that using the neural network method is higher than using the spport vector machine method with an accuracy difference of 8.88% while the AUC accuracy value is a difference of 4.8%.
Radial Basis Function Neural Network Control for Coupled Water Tank Halim Mudia
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 2 (2020): Spetember 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i2.10002

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. Therefore, in this paper will use neural network based on radial basis function (RBF) to control of  level 2 in the tank 2 with the setpoint of 10 centimeters and can follow the setpoint changes to 8 centimeters given in 225 seconds. The results show that neural netwotk based on radial basis function can follow setpoint given with steady state error is 0 cm, overshoot is 0%, rising time is 48 seconds, settling time is 52 seconds and can follow setpoint changes in 51 seconds.
Clustering Productivity of Rice in Karawang Regency Using the Fuzzy C-Means Method Suna Mulyani; Betha Nurina Sari; Azhari Ali Ridha
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 2 (2020): Spetember 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i2.10415

Abstract

Rice is a major food commodity that has a strategic role in the development of community nutrition, agriculture and the economy in Indonesia. Karawang Regency is known as a city of rice barns which is one of the largest rice producing and supplying regions in the province of West Java and even Indonesia. The importance of rice as a staple food in Karawang Regency needs to ensure rice productivity remains stable. Data Mining is a data mining technique that produces an output in the form of knowledge. The purpose of this study is to classify the productivity of rice plants so as to know the area of high rice productivity in Karawang Regency. The data used in this study were 180 data from 30 districts. Data grouping will use the Fuzzy C-Means (FCM) algorithm which is a data clustering technique where the existence of each data point in a cluster is determined by the degree of membership. With Silhouette Coefficient evaluation techniques the results of clustering obtained in 2010, 2011, 2013, 2014 and 2015 show that the results of grouping have a good structure that is above 0.5. Only in 2012 showed that the grouping results had a weak structure of 0.49.
Local Binary Pattern and Learning Vector Quantization for Classification of Principal Line of Palm-Hand Suwanto Sanjaya; Ulfah Adzkia; Lestari Handayani; Febi Yanto
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 2 (2020): Spetember 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i2.10236

Abstract

Biometrics such as DNA, face, fingerprints, and iris still had disadvantages. The principal line of palm-hand biometric was expected to cover the weakness of the other biometric. This research was used dataset amounted to 150 images of palms-hand of the left-hand side. The dataset sourced 15 people who captured 10 times. The cropping technique that has used is the Region of Interest (ROI). Local Binary Pattern (LBP) was used to feature extraction. The feature extraction consists of the five parameters statistical. They were mean, variance, skewness, kurtosis, and entropy. Learning Vector Quantization (LVQ) was used to train the weight to produce optimal weight. The Confusion matrix method was used to evaluate the accuracy of the classification. The experiment was used the learning rates 0.01; 0.05; 0.1; 0.5; and 0.7. Based on testing and the experimental results, the highest accuracy obtained was on the learning rate value 0.5 which achieve 80%. In future work, we can explore with added the second-order statistics feature for better result.
IoT-based Architecture for Automatic Detection of Fall Incident using Accelerometer Data I Wayan Wiprayoga Wisesa; Genggam Mahardika
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 2 (2020): Spetember 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i2.9686

Abstract

Fall is an unintentional incident that could happened in our daily life. For the elderly, fatal fall incident might increase the risk of death. There is a need to quickly do the first aid after fall incident occur. IoT based architecture made it possible to monitor fall incident remotely. The monitoring device records the activity and object movement using tri-axial accelerometer sensor attached to user’s waist. The system implemented simple thresholding technique based on total acceleration recorded over time. Various scenarios were performed in order to test the system including normal daily activities and fall incident. Using sensitivity and specificity measurement to evaluate the system, the proposed system achieved the value of 98% and 96% respectively.

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