Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) dimaksudkan sebagai media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai penelitian Rekayasa Sistem, Teknik Informatika/Teknologi Informasi, Manajemen Informatika dan Sistem Informasi. Sebagai bagian dari semangat menyebarluaskan ilmu pengetahuan hasil dari penelitian dan pemikiran untuk pengabdian pada Masyarakat luas dan sebagai sumber referensi akademisi di bidang Teknologi dan Informasi. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) menerima artikel ilmiah dengan lingkup penelitian pada: Rekayasa Perangkat Lunak Rekayasa Perangkat Keras Keamanan Informasi Rekayasa Sistem Sistem Pakar Sistem Penunjang Keputusan Data Mining Sistem Kecerdasan Buatan/Artificial Intelligent System Jaringan Komputer Teknik Komputer Pengolahan Citra Algoritma Genetik Sistem Informasi Business Intelligence and Knowledge Management Database System Big Data Internet of Things Enterprise Computing Machine Learning Topik kajian lainnya yang relevan
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"Vol 6 No 3 (2022): Juni 2022"
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<![CDATA[The Accuracy Comparison Between Word2Vec and FastText On Sentiment Analysis of Hotel Reviews ]]>
<![CDATA[Siti Khomsah]]>;
<![CDATA[Rima Dias Ramadhani]]>;
<![CDATA[Sena Wijaya]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 3 (2022): Juni 2022 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v6i3.3711
<![CDATA[Word embedding vectorization is more efficient than Bag-of-Word in word vector size. Word embedding also overcomes the loss of information related to sentence context, word order, and semantic relationships between words in sentences. Several kinds of Word Embedding are often considered for sentiment analysis, such as Word2Vec and FastText. Fast Text works on N-Gram, while Word2Vec is based on the word. This research aims to compare the accuracy of the sentiment analysis model using Word2Vec and FastText. Both models are tested in the sentiment analysis of Indonesian hotel reviews using the dataset from TripAdvisor.Word2Vec and FastText use the Skip-gram model. Both methods use the same parameters: number of features, minimum word count, number of parallel threads, and the context window size. Those vectorizers are combined by ensemble learning: Random Forest, Extra Tree, and AdaBoost. The Decision Tree is used as a baseline for measuring the performance of both models. The results showed that both FastText and Word2Vec well-to-do increase accuracy on Random Forest and Extra Tree. FastText reached higher accuracy than Word2Vec when using Extra Tree and Random Forest as classifiers. FastText leverage accuracy 8% (baseline: Decision Tree 85%), it is proofed by the accuracy of 93%, with 100 estimators.]]>
<![CDATA[Implementation of the Conversational Hybrid Design Model to Improve Usability in the FAQ ]]>
<![CDATA[Supriyanto]]>;
<![CDATA[Ika Arfiani]]>;
<![CDATA[Zain Ahmad Taufik]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 3 (2022): Juni 2022 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v6i3.3816
<![CDATA[FAQ is an important part of a system because it is used to make it easier for users to solve problems faced by users. Some FAQ systems have even started using Chatbot technology to make it easier for users. Chatbots have been widely used as a medium for services in almost all fields. Starting from marketing, service systems, education, health, culture and entertainment. Various types of chatbots have sprung up, ranging from text-based like short messaging applications to voice-based ones. However, not all forms of chatbot designs have been successfully implemented in the FAQ system. Adjustments need to be made, especially considering the persona of the user. This research provides a solution by implementing a hybrid conversational design. Hybrid conversation design is accomplished by incorporating text, voice, and buttons into the chatbot interface. Conversation activities with this hybrid interface provide keywords that users may search for in the form of buttons. The hybrid design of the FAQ Chatbot is proven to be able to improve user usability compared to full text chatbots and full text FAQs. The increase in user usability is measured using UEQ, the results of which show an increase in usability from all existing aspects. However, the implementation of this hybrid design also has the consequence that the conversation management system must have structured initial information.]]>
<![CDATA[Identification of Malaria Parasite Patterns With Gray Level Co-Occurance Matrix Algorithm (GLCM) ]]>
<![CDATA[Annas Prasetio]]>;
<![CDATA[Rika Rosnelly]]>;
<![CDATA[Wanayumini]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 3 (2022): Juni 2022 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v6i3.3850
<![CDATA[The results of the test using 5 data of malaria parasite test imagery found that image 1 has an average accuracy value of the energy of 0.55627, homogeneity average of 0.8371, PSNR of 6.1336db, and MSE of 0.24358. Image 2 has an average energy accuracy value of 0.22274, an average Homonegity of 0.98532, a PSNR of 6.1336db, and an MSE of 0.24358. Image 3 has an energy average accuracy value of 0.28735, a Homonegity average accuracy value of 0.9793, a PSNR of 6.133db, and an MSE of 0.24358. Image 4 has an energy average accuracy value of 0.32907 and an average homogeneity accuracy value of 0.97073, PSNR 6.133db, and MSE 0.24358. Image 5 has an average accuracy value of 0.74102, Homonegity average of 0.99844, PSNR of 6.133db, and MSE of 0.4358. Image 6 has an accuracy value of 0.34758 energy, an average accuracy value of homogeneity of 0.99129, a PNSR of 6.133db, and an MSE of 0.24358. Obtained the rule if the average value of energy > = 0.50 then the pattern of malaria parasites is very clear, namely Image 1 and image 5 with a pattern of malaria parasites is very clear.]]>
<![CDATA[Content Based VGG16 Image Extraction Recommendation ]]>
<![CDATA[Arif Laksito]]>;
<![CDATA[Muhammad Royyan Saputra]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 3 (2022): Juni 2022 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v6i3.3909
<![CDATA[Data transfer across numerous platforms has increased dramatically due to the enormous number of visitors or users of the present e-commerce platform. With the rise of increasingly massive data, consumers are finding it challenging to obtain the right data. The recommendation engine may be used to make it simpler to find information that is relevant to the user's needs. Clothing, gadgets, autos, furniture, and other e-commerce items rely on product visualization to entice shoppers. There are millions of images in these items. Displaying the information sought by clients based on visual data is a difficult challenge to address. One strategy that is simple to use in a recommendation system is content-based filtering. This approach will eventually make suggestions to consumers based on previously accessible goods or product descriptions. Content-based filtering works by searching for similarities based on the properties of a product item. User interactions with a product will be recorded and analyzed in order to recommend certain similarities to users. Text-based datasets are used in the majority of content-based filtering studies. In this study, however, we attempt to leverage a dataset received from Kaggle in the form of images of futsal shoes. Then, VGG16 architecture is used to extract the image dataset. The top 5 most relevant item rankings are generated by this recommendation method using cosine similarity. In addition, the NDCG (Normalized Discounted Cumulative Gain) approach is used to assess the results of the suggestions. The NDCG was evaluated in ten test scenarios, with an average NDCG value of 0.855, indicating that the system delivers a reasonable performance suggestion.]]>
<![CDATA[Educational Data Mining Using Cluster Analysis Methods and Decision Trees based on Log Mining ]]>
<![CDATA[Safira Nuri Safitri]]>;
<![CDATA[Haryono Setiadi]]>;
<![CDATA[Esti Suryani]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 3 (2022): Juni 2022 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v6i3.3935
<![CDATA[Educational Data Mining (EDM) often appears to be applied in big data processing in the education sector. One of the educational data that can be further processed with EDM is activity log data from an e-learning system used in teaching and learning activities. The log activity can be further processed more specifically by using log mining. The purpose of this study was to process log data from the Sebelas Maret University Online Learning System (SPADA UNS) to determine student learning behavior patterns and their relationship to the final results obtained. The data mining method applied in this research is cluster analysis with the K-means Clustering and Decision Tree algorithms. The clustering process is used to find groups of students who have similar learning patterns. While the decision tree is used to model the results of the clustering in order to enable the analysis and decision-making processes. Processing of 11,139 SPADA UNS log data resulted in 3 clusters with a Davies Bouldin Index (DBI) value of 0.229. The results of these three clusters are modeled by using a Decision Tree. The decision tree model in cluster 0 represents a group of students who have a low tendency of learning behavior patterns with the highest frequency of access to course viewing activities obtained accuracy of 74.42% . In cluster 1, which contains groups of students with high learning behavior patterns, have a high frequency of access to viewing discussion activities obtained accuracy of 76.47%. While cluster 2 is a group of students who have a pattern of learning behavior that is having a high frequency of access to the activity of sending assignments obtained accuracy of 90.00%.]]>
<![CDATA[bahasa inggris ]]>
<![CDATA[Muhammad Iqbal Izzul Haq]]>;
<![CDATA[Aniati Murni Arymurthy]]>;
<![CDATA[Irham Muhammad Fadhil]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 3 (2022): Juni 2022 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v6i3.3993
<![CDATA[Class imbalance is a serious problem that disrupts the process of semantic segmentation of satellite imagery in urban areas in Earth remote sensing. Due to the large objects dominating the segmentation process, small object are consequently limited, so solutions based on optimizing overall accuracy are often unsatisfactory. Due to the class imbalance of semantic segmentation in Earth remote sensing images in urban areas, we developed the concept of Down-Sampling Block (DownBlock) to obtain contextual information and Up-Sampling Block (UpBlock) to restore the original resolution. We proposed an end-to-end deep convolutional neural network (DenseU-Net) architecture for pixel-wise urban remote sensing image segmentation. this method to segmentation the small object in satellite imagery.The accuracy of the small object class in this study was further improved using our proposed method. This study used data from the Massachusetts Buildings dataset using Dense U-Net method and obtained an overall accuracy of 84.34%.]]>
<![CDATA[A Security Framework for Secure Host-to-Host Environments ]]>
<![CDATA[Togu Sianturi]]>;
<![CDATA[Kalamullah Ramli]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 3 (2022): Juni 2022 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v6i3.4018
<![CDATA[Data security is an infrastructure designed to protect and secure data from unauthorized access, data manipulation, malfunction, destruction, and inappropriate data disclosure. Currently, organizations widely use data transfer to validate and verify data using different media particularly in host-to-host connections. This research focuses on data exchanged (end-to-end communication) using Multi Protocol Label Switching (MPLS), metro ethernet, and Software Defined Wide Area Network (SD-WAN) network architecture with third parties. This research aims to develop a design and analysis framework for verifying data transferred from one host to another in ABC organization by applicable security standards that are appropriate and follow its needs to help the organization. Furthermore, the analysis result is used as materials for drafting a cybersecurity framework through the three standards ISO/EIC 27001:2013, NIST SP800-161, and ITU-T X.805. The methodology used in this study is the comparative analysis of three frameworks, requirement analysis, and content analysis to develop a framework. The framework proposed of eight security dimensions, five threats, and providing mitigation is expected to enhance the security system of data exchange on host-to-host connections in ABC organization.]]>
<![CDATA[Implementation of CNN-MLP and CNN-LSTM for MitM Attack Detection System ]]>
<![CDATA[Hartina Hiromi Satyanegara]]>;
<![CDATA[Kalamullah Ramli]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 3 (2022): Juni 2022 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v6i3.4035
<![CDATA[Man in the Middle (MitM) is one of the attack techniques conducted for eavesdropping on data transitions or conversations between users in some systems secretly. It has a sizeable impact because it could make the attackers will do another attack, such as website or system deface or phishing. Deep Learning could be able to predict various data well. Hence, in this study, we would like to present the approach to detect MitM attacks and process its data, by implementing hybrid deep learning methods. We used 2 (two) combinations of the Deep Learning methods, which are CNN-MLP and CNN-LSTM. We also used various Feature Scaling methods before building the model and will determine the better hybrid deep learning methods for detecting MitM attack, as well as the feature selection methods that could generate the highest accuracy. Kitsune Network Attack Dataset (ARP MitM Ettercap) is the dataset used in this study. The results prove that CNN-MLP has better results than CNN-LSTM on average, which has the accuracy rate respectively at 99.74%, 99.67%, and 99.57%, and using Standard Scaler has the highest accuracy (99.74%) among other scenarios.]]>
<![CDATA[The Formula Study in Determining the Best Number of Neurons in Neural Network Backpropagation Architecture with Three Hidden Layers ]]>
<![CDATA[Syaharuddin Syaharuddin]]>;
<![CDATA[Fatmawati Fatmawati]]>;
<![CDATA[Herry Suprajitno]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 3 (2022): Juni 2022 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v6i3.4049
<![CDATA[The researchers conducted data simulation experiments, but they did so unstructured in determining the number of neurons in the hidden layer in the Artificial Neural Network Back-Propagation architecture. The researchers also used a general architecture consisting of one hidden layer. Researchers are still producing minimal research that discusses how to determine the number of neurons when using hidden layers. This article examines the results of experiments by conducting training and testing data using seven recommended formulas including the Hecht-Nelson, Marchandani-Cao, Lawrence & Fredrickson, Berry-Linoff, Boger-Guterman, JingTao-Chew, and Lawrence & Fredrickson modifications. We use rainfall data and temperature data with a 10-day type for the last 10 years (2012-2021) sourced from Lombok International Airport Station, Indonesia. The training and testing data used showed the results that in determining the number of neurons on the hidden-1 screen, it was more appropriate to use the Hecht-Nelson formula and the Lawrence & Fredricson formula which is more suitable for use in the 2nd & 3rd hidden layer. The resulting research was able to provide an accuracy rate of up to 97.79% (temperature data) and 99.94% (rainfall data) with an architecture of 36-73-37-19-1.]]>
<![CDATA[Interdependency and Priority of Critical Infrastructure Information (Case Study: Indonesia Payment System) ]]>
<![CDATA[Arini Muhafidzah]]>;
<![CDATA[Kalamullah Ramli]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 3 (2022): Juni 2022 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v6i3.4051
<![CDATA[The sturdy and reliable payment system is one of the most important systems in the digitalization era, especially in the pandemic COVID-19 period. As part of Critical Infrastructure Information (CII), a strategy to protect the payment system is needed to reduce risks that may arise. But the author's best knowledge, in Indonesia, no reference describes the interdependency of the CII sector that could be used as input on strategy making for reducing the risk on all CII sectors which are influencing the payment system. This research uses the Fuzzy-based DANP (FDANP) Framework based on a multi-expert perspective on inter-sector influences to identify the interdependency and priority of the CII sector with a case study on the payment system. The contribution of this research is to provide information about the interdependency and priority of the CII sector. The findings of this research show that 5 sectors that have an influence on other sectors with a case study of the payment system and the information and communication technology sector, the energy and mineral resources sector, and the financial sector are the three major sectors that must be paid more attention to because they have an impact on many sectors.]]>
