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JUTI: Jurnal Ilmiah Teknologi Informasi
Published by Institut Teknologi Sepuluh Nopember Surabaya
ISSN : 24068535     EISSN : 14126389     DOI : http://dx.doi.org/10.12962/j24068535
Core Subject : Health, Science, Education,
Computer Science & IT Dentistry Education Library & Information Science
JUTI (Jurnal Ilmiah Teknologi Informasi) is a scientific journal managed by Department of Informatics, ITS.
Arjuna Subject : -
Articles 15 Documents
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Search results for , issue "Vol. 18, No. 2, July 2020" : 15 Documents clear
PREDICT URBAN AIR POLLUTION IN SURABAYA USING RECURRENT NEURAL NETWORK – LONG SHORT TERM MEMORY Muh. Anas Faishol; Endroyono Endroyono; Astria Nur Irfansyah
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 18, No. 2, July 2020
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v18i2.a988

Abstract

Air is one of the primary needs of living things. If the condition of air is polluted, then the lives of humans and other living things will be disrupted. So it is needed to perform special handling to maintain air quality. One way to facilitate the prevention of air pollution is to make air pollutionforecasting by utilizing past data. Through the Environmental Office, the Surabaya City Government has monitored air quality in Surabaya every 30 minutes for various air quality parameters including CO, NO, NO2, NOx, PM10, SO2 and meteorological data such as wind direction, wind direction, wind speed, wind speed, global radiation, humidity, and air temperature. These data are very useful to build a prediction model for the forecast of air pollution in the future. With the large amount and variance of data generated from monitoring air quality in Surabaya city, a qualified algorithm is needed to process it. One algorithm that can be used is Recurrent Neural Network - Long Short Term Memory (RNN-LSTM). RNN-LSTM is built for sequential data processing such as time-series data. In this study, several analyses are performed. There are trend analysis, correlation analysis of pollutant values to meteorological data, and predictions of carbon monoxide pollutants using the Recurrent Neural Network - LSTM in the city of Surabaya correlated with meteorological data. The results of this study indicate that the best prediction model using RNN-LSTM with RMSE calculation gets an error of 1,880 with the number of hidden layer 2 and epoch 50 scenarios. The predicted results built can be used as a reference in determining the policy of the city government to deal with air pollution going forward.
CONTINUOUS MULTIQUERIES K-DOMINANT SKYLINE ON ROAD NETWORK Syukron Rifail Muttaqi; Bagus Jati Santoso
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 18, No. 2, July 2020
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v18i2.a999

Abstract

The increasing use of mobile devices makes spatial data worthy of consideration. To get maximum results, users often look for the best from a collection of objects. Among the algorithms that can be used is the skyline query. The algorithm looks for all objects that are not dominated by other objects in all of its attributes. However, data that has many attributes makes the query output a lot of objects so it is less useful for the user. k-dominant skyline queries can be a solution to reduce the output. Among the challenges is the use of skyline queries with spatial data and the many user preferences in finding the best object. This study proposes IKSR: the k-dominant skyline query algorithm that works in a road network environment and can process many queries that have the same subspace in one processing. This algorithm combines queries that operate on the same subspace and set of objects with different k values by computing from the smallest to the largest k. Optimization occurs when some data for larger k are precomputed when calculating the result for the smallest k so the Voronoi cell computing is not repeated. Testing is done by comparing with the naïve algorithm without precomputation. IKSR algorithm can speed up computing time two to three times compared to naïve algorithm.
ENHANCEMENT OF DECISION TREE METHOD BASED ON HIERARCHICAL CLUSTERING AND DISPERSION RATIO Dimas Ari Setyawan; Chastine Fatichah
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 18, No. 2, July 2020
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v18i2.a1005

Abstract

The classification process using a decision tree is a classification method that has a feature selection process. Decision tree classifications using information gain have a disadvantage when the dataset has unique attributes for each imbalanced class record and distribution. The data used for decision tree classification has 2 types, numerical and nominal. The numerical data type is carried out a discretization process so that it gets data intervals. Weaknesses in the information gain method can be reduced by using a dispersion ratio method that does not depend on the class distribution, but on the frequency distribution. Numeric type data will be dis-criticized using the hierarchical clustering method to obtain a balanced data cluster. The data used in this study were taken from the UCI machine learning repository, which has two types of numeric and nominal data. There are two stages in this research namely, first the numeric type data will be discretized using hierarchical clustering with 3 methods, namely single link, complete link, and average link. Second, the results of discretization will be merged again then the formation of trees with splitting attributes using dispersion ratio and evaluated with cross-validation k-fold 7. The results obtained show that the discretization of data with hierarchical clustering can increase predictions by 14.6% compared with data without discretization. The attribute splitting process with the dispersion ratio of the data resulting from the discretization of hierarchical clustering can increase the prediction by 6.51%.
IMPLEMENTATION OF BLUETOOTH LOW ENERGY TECHNOLOGY AND TRILATERATION METHOD FOR INDOOR ROUTE SEARCH Bahri Rizaldi; Doni Setio Pambudi; Taufiqotul Bariyah
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 18, No. 2, July 2020
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v18i2.a897

Abstract

Currently, route search is made easier by the presence of a Global Positioning System (GPS) technology that can be used by using the Maps application on a smartphone. By using the Maps application, people can find out their current location and can find a route to their desired destination. But the level of GPS accuracy will decrease if the user is in a building or in a closed room. This is caused by the satellite signals being sent that are not able to penetrate thick walls or concrete so that the search for routes using GPS is limited to the search for routes outside the building or outdoors. In this research, Bluetooth Low Energy and trilateration are used to determine the location in a room or building and Dijkstra's algorithm for finding the shortest route to the destination location. The proposed method has a location determination error of 0.728 meters with a distance between the user and the beacon less than 10 meters to get a stable signal.
EFFECTIVENESS STUDIES OF THE LEARNING BASIC MATHEMATICAL OPERATIONS ON USERS USING EDUCATION GAMES WITH ESCALATING DIFFICULTY LEVEL IN SEVERAL TYPES OF GAMES Ari Mahardika Ahmad Nafis; Darlis Herumurti
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 18, No. 2, July 2020
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v18i2.a989

Abstract

Mathematic is basic but fundamental knowledge, but in fact many students do not have the motivation to learn it because they think mathematic is boring. Therefore, an innovation is needed to motivate students, one of them is by using an educational game. Racing, shooting and fighting games are the most popular types of games in 2019 according to InvisionCommunity. Shooting game is a genre that used a lot in the educational games for learning math, while racing game and fighting game are not used much for educational games. This research aims to develop and measure the effectiveness of the games from these three genre of games as a means of learning elementary arithmetic at the elementary school level. The effectiveness of an educational game can be observed from the increment in learning outcomes obtained after conducting an experiment. We can know the most effective type of game in this experiment by compare the improvement in learning outcomes after playing all three games. The comparative analysis will be carried out using ANOVA. In this research, we used data from 60 participant with elementary level of education between grade 1 to 3. The results were obtained by calculating the difference in the participants' initial scores obtained from before playing the game and participants’ final scores obtained after playing the educational game. The results show that educational racing games have the highest increase of 6.3% compared to shooter games with 3% increase or fighting games with increase of 4.3%.

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