JUTI: Jurnal Ilmiah Teknologi Informasi

Journal Information
ISSN / EISSN : 1412-6389 / 2406-8535
Total articles ≅ 337
Current Coverage
DOAJ
Archived in
SHERPA/ROMEO
Filter:

Latest articles in this journal

Dino Budi Prakoso, Royyana Muslim Ijtihadie, Tohari Ahmad
JUTI: Jurnal Ilmiah Teknologi Informasi, Volume 19; https://doi.org/10.12962/j24068535.v19i1.a1009

Abstract:
In the technology world especially in the field of current network of Autonomous Systems connectivity (AS) is indispensable. Especially against the dynamic routing protocols that are often used compared to static routing protocols. In supporting this current network, it takes efficient and effective routing protocols capable of covering a sizable scale. Software Defined Network (SDN) is a technological innovation in the network world that has a separate Control Plane and Data Plane that makes it easy to configure on the Control Plane side. Control Plane is the focal point on a process of bottleneck in SDN architecture. Performance is a critical issue in large-scale network implementations because of the large demand load occurring in the Control Plane by generating low throughput value. This research will be conducted testing on the Hybrid network of SDN by using OSPF routing protocol, based on the Fibbing architecture implemented on the system network Hybrid SDN also able to assist in improving performance, but there are constraints when sending flooding which is used as a fake node forming. Many nodes are not skipped as distribution lines in the formation of a fake node, in which case it will certainly affect the value of throughput to be unstable and decrease. This can be overcome by using the Isolation Domain method to manage the LSA Type-5 flooding efficiency.
Junaidi Junaidi, Amirullah Andi Bramantya, Bintang Satya Pradipta
JUTI: Jurnal Ilmiah Teknologi Informasi, Volume 19; https://doi.org/10.12962/j24068535.v19i1.a962

Abstract:
Context – Internet of Things (IoT) interrelates computing devices, machines, animals, or people and things that use the power of internet usage to utilize data to be much more usable. Food is one of the mandatory human needs to survive, and most of it is produced by agriculture. Using IoT in agriculture needs appropriate software architecture that plays a prominent role in optimizing the gain. Objective and Method – Implementing a solution in a specific field requires a particular condition that belongs to it. The objectives of this research study are to classify the state of the art IoT solution in the software architecture domain perspective. We have used the Evidence- Based Software Engineering (EBSE) and have 24 selected existing studies related to software architecture and IoT solutions to map to the software architecture needed on IoT solutions in agriculture. Result and Implications – The results of this study are the classification of various IoT software architecture solutions in agriculture. The highlighted field, especially in the areas of cloud, big data, integration, and artificial intelligence/machine learning. We mapped the agriculture taxonomy classification with IoT software architecture. For future work, we recommend enhancing the classification and mapping field to the utilization of drones in agriculture since drones can reach a vast area that is very fit for fertilizing, spraying, or even capturing crop images with live cameras to identify leaf disease.
Akbar Pandu Segara, Royyana Muslim Ijtihadie, Tohari Ahmad
JUTI: Jurnal Ilmiah Teknologi Informasi, Volume 19; https://doi.org/10.12962/j24068535.v19i1.a1011

Abstract:
Software Defined Network is a network architecture with a new paradigm which consists of a control plane that is placed separately from the data plane. All forms of computer network behavior are controlled by the control plane. Meanwhile the data plane consisting of a router or switch becomes a device for packet forwarding. With a centralized control plane model, SDN is very vulnerable to congestion because of the one-to-many communication model. There are several mechanisms for congestion control on SDNs, one of which is modifying packets by reducing the size of packets sent. But this is considered less effective because the time required will be longer because the number of packets sent is less. This requires that network administrators must be able to configure a network with certain routing protocols and algorithms. Johnson's algorithm is used in determining the route for packet forwarding, with the nature of the all-pair shortest path that can be applied to SDN to determine through which route the packet will be forwarded by comparing all nodes that are on the network. The results of the Johnson algorithm's latency and throughput with the comparison algorithm show good results and the comparison of the Johnson algorithm's trial results is still superior. The response time results of the Johnson algorithm when first performing a route search are faster than the conventional OSPF algorithm due to the characteristics of the all pair shortest path algorithm which determines the shortest route by comparing all pairs of nodes on the network.
Rizky Januar Akbar, Nurul Fajrin Ariyani, Adistya Azhar, Andika Andra
JUTI: Jurnal Ilmiah Teknologi Informasi, Volume 19; https://doi.org/10.12962/j24068535.v19i1.a1022

Abstract:
There is an impersonation (login as) feature in several applications that can be used by system administrators who have special privileges. This feature can be utilized by development and maintenance teams that have administrator rights to reproduce errors or bugs, to check specific features in applications according to the specific users’ login sessions. Beside its benefits, there is a security vulnerability that allows administrators to abuse the rights. They can access users’ private data or execute some activities inside the system without account or resource owners’ consents.This research proposes an impersonation method on authorization server using Client-Initiated Back-channel Authentication (CIBA) protocol. This method prevents impersonation without account or resource owners’ consent. The application will ask users’ authentication and permission via authentication device possessed by resource owners before the administrator performs impersonation. By utilizing authentication device, the impersonation feature should be preceded by users’ consent and there is no direct interaction needed between the administrator and resource owners to prove the users’ identities. The result shows that the implementation of CIBA protocol can be used to complement the impersonation method and can also run on the authorization server that uses OAuth 2.0 and OpenID Connect 1.0 protocols. The system testing is done by adopting FAPI CIBA conformance testing.
Syavira Tiara Zulkarnain, Nanik Suciati
JUTI: Jurnal Ilmiah Teknologi Informasi, Volume 19; https://doi.org/10.12962/j24068535.v19i1.a1031

Abstract:
Facial expression recognition (FER) on images with illumination variation and noises is a challenging problem in the computer vision field. We solve this using deep learning approaches that have been successfully applied in various fields, especially in uncontrolled input conditions. We apply a sequence of processes including face detection, normalization, augmentation, and texture representation, to develop FER based on Convolutional Neural Network (CNN). The combination of TanTriggs normalization technique and Adaptive Gaussian Transformation Method is used to reduce light variation. The number of images is augmented using a geometric augmentation technique to prevent overfitting due to lack of training data. We propose a representation of Modified Local Ternary Pattern (Modified LTP) texture image that is more discriminating and less sensitive to noise by combining the upper and lower parts of the original LTP using the logical AND operation followed by average calculation. The Modified LTP texture images are then used to train a CNN-based classification model. Experiments on the KDEF dataset show that the proposed approach provides a promising result with an accuracy of 81.15%.
Adenuar Purnomo, Handayani Tjandrasa
JUTI: Jurnal Ilmiah Teknologi Informasi, Volume 19; https://doi.org/10.12962/j24068535.v19i1.a1023

Abstract:
Deep learning is commonly used to solve problems such as biomedical problems and many other problems. The most common architecture used to solve those problems is Convolutional Neural Network (CNN) architecture. However, CNN may be prone to overfitting, and the convergence may be slow. One of the methods to overcome the overfitting is batch normalization (BN). BN is commonly used after the convolutional layer. In this study, we proposed a further usage of BN in CNN architecture. BN is not only used after the convolutional layer but also used after the fully connected layer. The proposed architecture is tested to detect types of seizures based on EEG signals. The data used are several sessions of recording signals from many patients. Each recording session produces a recorded EEG signal. EEG signal in each session is first passed through a bandpass filter. Then 26 relevant channels are taken, cut every 2 seconds to be labeled the type of epileptic seizure. The truncated signal is concatenated with the truncated signal from other sessions, divided into two datasets, a large dataset, and a small dataset. Each dataset has four types of seizures. Each dataset is equalized using the undersampling technique. Each dataset is then divided into test and train data to be tested using the proposed architecture. The results show the proposed architecture achieves 46.54% accuracy for the large dataset and 93.33% accuracy for the small dataset. In future studies, the batch normalization parameter will be further investigated to reduce overfitting.
Muhammad Ihsan Diputra, Ahmad Akbar Megantara, Pima Hani Safitri, Didik Purwanto
JUTI: Jurnal Ilmiah Teknologi Informasi, Volume 18; https://doi.org/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.
Cosmas Haryawan, Maria Mediatrix Sebatubun
JUTI: Jurnal Ilmiah Teknologi Informasi, Volume 18; https://doi.org/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.
Syukron Rifail Muttaqi, Bagus Jati Santoso
JUTI: Jurnal Ilmiah Teknologi Informasi, Volume 18; https://doi.org/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.
Avin Maulana, Chastine Fatichah, Nanik Suciati
JUTI: Jurnal Ilmiah Teknologi Informasi, Volume 18; https://doi.org/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.
Back to Top Top