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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
FACE RECOGNITION USING DEEP NEURAL NETWORKS WITH THE COMBINATION OF DISCRETE WAVELET TRANSFORM, STATIONARY WAVELET TRANSFORM, AND DISCRETE COSINE TRANSFORM METHODS Akbar, Afrizal Laksita; Fatichah, Chastine; Saikhu, Ahmad
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.a1000

Abstract

Personal identification can be done by using face, fingerprint, palm prints, eye’s retina, or voice recognition which commonly called as biometric methods. Face recognition is the most popular and widely used among those biometric methods. However, there are some issues in the implementation of this method: lighting factor, facial expression, and attributes (chin, mustache, or wearing some accessories). In this study, we propose a combination method of Discrete Wavelet Transform and Stationary Wavelet Transform that able to improve the image quality, especially in the small-sized image. Moreover, we also use Histogram Equalization in order to correct noises such as over or under exposure, Discrete Cosine Transform in order to transform the image into frequency domain, and Deep Neural Networks in order to perform the feature extraction and classify the image. A 10-fold cross-validation method was used in this study. As the result, the proposed method showed the highest accuracy up to 92.73% compared to Histogram Equalization up to 80.73%, Discrete Wavelet Transform up to 85.85%, Stationary Wavelet Transform up to 64.27%, Discrete Cosine Transform up to 89.50%, the combination of Histogram Equalization, Discrete Wavelet Transform, and Stationary Wavelet Transform up to 69.77%, and the combination of Stationary Wavelet Transform, Discrete Wavelet Transform, and Histogram Equalization up to 77.39%.
CLOUTIDY: A CLOUD-BASED SUPPLY CHAIN MANAGEMENT SYSTEM USING SEMAR AND BLOCKCHAIN SYSTEM Zulfa, Nafa; Firdausi, Hafara; Asyrofi, Rakha
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.a937

Abstract

Supply chain management (SCM) system is an essential requirement for companies and manufacturers to collaborate in doing business. There are many techniques to manage supply chains, such as using Excel sheets and web-based applications. However, these techniques are ineffective, insecure, and prone to human error. In this paper, we propose CLOUTIDY, a cloud-based SCM system using SEMAR (Service Market) and Blockchain system. We modify JUGO architecture to develop SEMAR as a broker between users and cloud service providers. Also, we apply the Blockchain concept to store the activity log of the SCM system in a decentralized database. CLOUTIDY system can solve several common cases: service selection, resource provisioning, authentication and access control. Also, it improves the security of data by storing each activity log of the supply chain management system in the Blockchain system.
IMPLEMENTATION OF MULTILAYER PERCEPTRON FOR STUDENT FAILURE PREDICTION Haryawan, Cosmas; Sebatubun, Maria Mediatrix
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.a990

Abstract

University is one of the educational institutions and can be established by the government or the individual. At this time, Indonesia has hundreds of universities spread throughout the region. As an educational institution, university of course must be able to educate its students and issue quality graduates with the academically and non-academically qualified. In its implementation, there are many problems that should be resolved as well as possible, such as when there are students who intentionally stop or disappear before completing their education or are even unable to complete their education and issued by institution (dropout).Based on these problems, this research makes a model for predicting students who have the potential to fail or dropout during their studies using one of the data mining methods namely Multilayer Perceptron by referring to personal and academic data. The results obtained from this research are 86.9% an accuracy rate with the 54.7% sensitivity, and 95.4% specificity. This research is expected to be used to determine the need strategies to minimize the number of students who stop or dropout.
REMARKETING MEDIA ALTERNATIVES BASED ON CUSTOMER PREFERENCES Ahmadiyah, Adhatus Solichah; Aidah, Faridatul; Meutia, Navinda; Rahmadina, Denise; Lumbantobing, Daniel; Anggraini, Ratih
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.a1007

Abstract

Remarketing is a powerful tool for marketers to offer products over and over to existing customers or potential customers. By using remarketing, the marketers target to further down their sales funnel. As in traditional marketing, most online marketers find it challenging to deliver the best way of advertising their products according to what customers need or like. This research aims to achieve the right promotional media alternatives based on customer preferences. A clustering method was used to perform behavior segmentation on sales data. Then, customer reviews on the purchased products collected from online platforms were analyzed to obtain customer preferences. Finally, customer preference was mapped to some suitable promotion media. The experiment result showed that pipelining sales data and product reviews could obtain definite and distinct promotional media based on customer preference. Overall, this research may help online marketers bundle specific remarketing content into promotional media that matches to customer favorites.
GECOM: GREEN COMMUNICATION CONCEPTS FOR ENERGY EFFICIENCY IN WIRELESS MULTIMEDIA SENSOR NETWORK Diputra, Muhammad Ihsan; Megantara, Ahmad Akbar; Safitri, Pima Hani; Purwanto, Didik
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.a942

Abstract

Wireless multimedia sensor network (WMSN) is one of broad wide application for developing a smart city. Each node in the WMSN has some primary components: sensor, microcontroller, wireless radio, and battery. The components of WMSN are used for sensing, computing, communicating between nodes, and flexibility of placement. However, the WMSN technology has some weakness, i.e. enormous power consumption when sending a media with a large size such as image, audio, and video files. Research had been conducted to reduce power consumption, such as file compression or power consumption management, in the process of sending data. We propose Green Communication (GeCom), which combines power control management and file compression methods to reduce the energy consumption. The power control management method controls data transmission. If the current data has high similarity with the previous one, then the data will not be sent. The compression method compresses massive data such as images before sending the data. We used the low energy image compression algorithm algorithm to compress the data for its ability to maintain the quality of images while producing a significant compression ratio. This method successfully reduced energy usage by 2% to 17% for each data.   
DELINEATION OF ECG FEATURE EXTRACTION USING MULTIRESOLUTION ANALYSIS FRAMEWORK Hikmah, Nada Fitrieyatul; Arifin, Achmad; Sardjono, Tri Arief
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.a992

Abstract

ECG signals have very features time-varying morphology, distinguished as P wave, QRS complex, and T wave. Delineation in ECG signal processing is an important step used to identify critical points that mark the interval and amplitude locations in the features of each wave morphology. The results of ECG signal delineation can be used by clinicians to associate the pattern of delineation point results with morphological classes, besides delineation also produces temporal parameter values of ECG signals. The delineation process includes detecting the onset and offset of QRS complex, P and T waves that represented as pulse width, and also the detection of the peak from each wave feature. The previous study had applied bandpass filters to reduce amplitude of P and T waves, then the signal was passed through non-linear transformations such as derivatives or square to enhance QRS complex. However, the spectrum bandwidth of QRS complex from different patients or same patient may be different, so the previous method was less effective for the morphological variations in ECG signals. This study developed delineation from the ECG feature extraction based on multiresolution analysis with discrete wavelet transform. The mother wavelet used was a quadratic spline function with compact support. Finally, determination of R, T, and P wave peaks were shown by zero crossing of the wavelet transform signals, while the onset and offset were generated from modulus maxima and modulus minima. Results show the proposed method was able to detect QRS complex with sensitivity of 97.05% and precision of 95.92%, T wave detection with sensitivity of 99.79% and precision of 96.46%, P wave detection with sensitivity of 56.69% and precision of 57.78%. The implementation in real time analysis of time-varying ECG morphology will be addressed in the future research.
DEVELOPMENT OF LOAD BALANCING MECHANISMS IN SDN DATA PLANE FAT TREE USING MODIFIED DIJKSTRA’S ALGORITHM Rangkuty, Muhammad Fattahilah; Ijtihadie, Royyana Muslim; Ahmad, Tohari
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.a1008

Abstract

SDN is a computer network approach that allows network administrators to manage network services through the abstraction of functionality at a higher level, by separating systems that make decisions about where traffic is sent (control plane), then forwarding traffic to the chosen destination (data plane). SDN can have problems with network congestion, high latency, and decreased throughput due to unbalanced traffic allocation on available links, so a load-balancing load method is needed. This technique divides the entire load evenly on each component of the network on the path or path that connects the data plane and S-D (Source Destination) host. The Least Loaded Path (LLP) of our proposed concept, which is a Dijkstra development, selects the best path by finding the shortest path and the smallest traffic load, the smallest traffic load (minimum cost) obtained from the sum of tx and rx data in the switchport data plane involved in the test, this result which will then be determined as the best path in the load balancing process.
GCRFP - PAGE REPLACEMENT FOR SOLID STATE DRIVE USING GHOST-CACHE Suadi, Wahyu; Djanali, Supeno; Wibisono, Waskitho; Anggoro, Radityo; Shiddiqi, Ary Mazharuddin
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.a986

Abstract

State Drive (SSD) is an alternative to data storage that is popular today, widely used as a media cache to speed up data access to the hard disk (HDD). This paper proposes page replacement technique on SSD cache that used frequency and recency parameter, alternately. The algorithm is selected adaptively based on trace input. This method helps to overcome changes in access patterns while minimizing the number of write processes to SSD. The proposed algorithm can choose a replacement technique that suits the user access pattern so that it can bring a better hit rate. The proposed algorithm is also integrated with the ghost-cache mechanism so that the reduction in the number of writing processes to SSD is significant. The experiment runs using a real dataset, describing trace of data read, and data write taken from real usage. The trial shows that the proposed algorithm can give good results compared to other similar algorithms.
APPLIED MACHINE LEARNING IN LOAD BALANCING Junaidi, Junaidi; Wibowo, Prasetyo; Yuniasri, Dini; Damayanti, Putri; Shiddiqi, Ary Mazharuddin; Pratomo, Baskoro Adi
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.a940

Abstract

A common way to maintain the quality of service on systems that are growing rapidly is by increasing server specifications or by adding servers. The utility of servers can be balanced with the presence of a load balancer to manage server loads. In this paper, we propose a machine learning algorithm that utilizes server resources CPU and memory to forecast the future of resources server loads. We identify the timespan of forecasting should be long enough to avoid dispatcher's lack of information server distribution at runtime. Additionally, server profile pulling, forecasting server resources, and dispatching should be asynchronous with the request listener of the load balancer to minimize response delay. For production use, we recommend that the load balancer should have friendly user interface to make it easier to be configured, such as adding resources of servers as parameter criteria. We also recommended from beginning to start to save the log data server resources because the more data to process, the more accurate prediction of server load will be.
FACIAL INPAINTING IN UNALIGNED FACE IMAGES USING GENERATIVE ADVERSARIAL NETWORK WITH FEATURE RECONSTRUCTION LOSS Maulana, Avin; Fatichah, Chastine; Suciati, Nanik
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.a1004

Abstract

Facial inpainting or face restoration is a process to reconstruct some missing region on face images such that the inpainting results still can be seen as a realistic and original image without any missing region, in such a way that the observer could not realize whether the inpainting result is a generated or original image. Some of previous researches have done inpainting using generative network, such as Generative Adversarial Network. However, some problems may arise when inpainting algorithm have been done on unaligned face. The inpainting result show spatial inconsistency between the reconstructed region and its adjacent pixel, and the algorithm fail to reconstruct some area of face. Therefore, an improvement method in facial inpainting based on deep-learning is proposed to reduce the effect of the stated problem before, using GAN with additional loss from feature reconstruction and two discriminators. Feature reconstruction loss is a loss obtained by using pretrained network VGG-Net, Evaluation of the result shows that additional loss from feature reconstruction loss and two type of discriminators may help to increase visual quality of inpainting result, with higher PSNR and SSIM than previous result.

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