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INDONESIA
IJoICT (International Journal on Information and Communication Technology)
Published by Universitas Telkom
ISSN : -     EISSN : 23565462     DOI : -
Core Subject : Science,
Computer Science & IT
International Journal on Information and Communication Technology (IJoICT) is a peer-reviewed journal in the field of computing that published twice a year; scheduled in December and June.
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "Vol. 4 No. 2 (2018): December 2018" : 5 Documents clear
Weather Forecasting in Bandung Regency based on FP-Growth Algorithm Farida Nur Khasanah; Fhira Nhita
International Journal on Information and Communication Technology (IJoICT) Vol. 4 No. 2 (2018): December 2018
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/IJOICT.2018.42.203

Abstract

Weather change is one of the things that can affect people around the world in doing activities, including in Indonesia. The area of Indonesia, especially in Bandung regency has a high intensity of rainfall, compared with other regions. The people of Bandung Regency mostly have livelihoods in the fields of industry and agriculture, both of which are closely related to the effects of weather. Weather prediction is used for reference, so the future of society can prepare all possible weather before the move. One method of data mining used to predict weather is the association rule method. In this method there is Frequent Pattern Growth (FP-Growth) algorithm, this algorithm is used to determine the pattern of linkage between attribute weather with rainfall. The result of the FP-Growth algorithm is an association rule, the result of the algorithm rules is then used as reference for data entry in the classification process, where the process is done to get the forecast based on the rainfall category to obtain maximum accuracy. The highest performance result of FP-Growth from the result of rules based on its confidence value is 92%.
Price Prediction of Chili Commodities in Bandung Regency Using Bayesian Network Putri Nuvaisiyah; Fhira Nhita; Deni Saepudin
International Journal on Information and Communication Technology (IJoICT) Vol. 4 No. 2 (2018): December 2018
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/IJOICT.2018.42.204

Abstract

Chili is one of the agricultural commodities consumed by Indonesian people. Market data in recent years show that chili prices tend to fluctuate as supply and demand changes. One of the impacts of chili price changes for farmers is the production cost is higher than the selling price. In addition to supply and demand changes, the weather is also indicated as a factor of price changes due to the weather being considered by farmers to grow chili. Price prediction is needed to determine the condition of chili prices in the future to help farmers in making decisions to plant at the right time. One method that can be used to make prediction is Data Mining classification method. In this paper, Bayesian network algorithm was used as Data Mining classification method to predict the price of chili commodity in Bandung Regency based on weather information and classified the price into economic class and not economic class. The result shows that the prediction model obtained by the Bayesian Network gives a system’s performance for precision and recall that is 1 and 0.94 respectively with average accuracy of 85.5% in classifying the price.
Increasing Feature Selection Accuracy through Recursive Method in Intrusion Detection System Andreas Jonathan Silaban; Satria Mandala; Erwid Mustofa Jadied
International Journal on Information and Communication Technology (IJoICT) Vol. 4 No. 2 (2018): December 2018
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/IJOICT.2018.42.216

Abstract

Artificial intelligence semi supervised-based network intrusion detection system detects and identifies various types of attacks on network data using several steps, such as: data preprocessing, feature extraction, and classification. In this detection, the feature extraction is used for identifying features of attacks from the data; meanwhile the classification is applied for determining the type of attacks. Increasing the network data directly causes slow response time and low accuracy of the IDS. This research studies the implementation of wrapped-based and several classification algorithms to shorten the time of detection and increase accuracy. The wrapper is expected to select the best features of attacks in order to shorten the detection time while increasing the accuracy of detection. In line with this goal, this research also studies the effect of parameters used in the classification algorithms of the IDS. The experiment results show that wrapper is 81.275%. The result is higher than the method without wrapping which is 46.027%.
Wind Wave Prediction by using Autoregressive Integrated Moving Average model : Case Study in Jakarta Bay Didit Adytia; Alif Rizal Yonanta; Nugrahinggil Subasita
International Journal on Information and Communication Technology (IJoICT) Vol. 4 No. 2 (2018): December 2018
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/IJOICT.2018.42.300

Abstract

Prediction of wind wave is highly needed to support safe navigation, especially for ship. Besides that, loading and unloading activities in a harbour, as well as for design purpose of coastal and offshore structures, data of prediction of wave height are needed. Based on its nature, the wind wave has random behaviour that is highly depending on behaviour of wind as the main driving force. In this paper, we propose a prediction method for wind wave by using Autoregressive Integrated Moving Average or ARIMA. To obtain historical data of wind wave, we perform  wave simulation by using a phase-averaged wave model SWAN (Simulating Wave Near Shore).  From the simulation, time series of wind wave is obtained. The prediction of wind wave is performed to calculate forecast of 24  hours ahead. Here, we perform wind wave prediction in a location in Jakarta Bay, Indonesia. We perform several combination of ARIMA model to obtain best fit model for wind wave prediction in the location in Jakarta Bay. Results of prediction show that ARIMA model give an accurate prediction especially for short term prediction.
Image Spoofing Detection Using Local Binary Pattern and Local Binary Pattern Variance Indra Bayu Kusuma; Arida Kartika; Tjokorda Agung Budi W; Kurniawan Nur Ramadhani; Febryanti Sthevanie
International Journal on Information and Communication Technology (IJoICT) Vol. 4 No. 2 (2018): December 2018
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/IJOICT.2018.42.134

Abstract

Particularly in the field of biometric security using human face has been widely implemented in the real world. Currently the human face is one of the guidelines in the security system. Nowadays the challenge is how to detect data falsification; such an attack is called spoofing. Spoofing occurs when someone is trying to pretend to be someone else by falsifying the original data and then that person may gain illegal access and benefit him. For example one can falsify the face recognition system using photographs, video, masks or 3D models. In this paper image spoofing human face detection using texture analysis on input image is proposed. Texture analysis used in this paper is the Local Binary Pattern (LBP) and Local Binary Pattern Variance (LBPV). To classified input as original or spoof K-Nearest Neighbor (KNN) used. Experiment used 5761 spoofs and 3362 original from NUAA Imposter dataset. The experimental result yielded a best success rate of 87.22% in term of accuracy with configuration of the system using LBPV and histogram equalization with ratio 𝑅 = 7 and 𝑃 = 8.

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