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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<![CDATA[Implementasi Fitur Keamanan dengan JSON Web Token dan Fitur Geo-tagging pada Aplikasi Web Service Training From Home ]]>
<![CDATA[Aal Hibsy]]>;
<![CDATA[Arief Wibowo]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 4 (2020): Agustus 2020 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v4i4.1973
<![CDATA[In the Covid-19 pandemic phase, some business processes were halted, adapted, and modified to deal with the policy of social restrictions. This impact is experienced by all levels of society, including athletes who are forced to do training from home (Training From Home). Performance evaluation of athletes who do exercises from home must be able to be evaluated remotely, including in terms of presence during the exercise training program. Presence is one of the benchmarks of a person's level of performance or activity in terms of accuracy and discipline in a program of activities. Attendance activities in the form of check-in must be ensured safe and accurate, especially if there is data connectivity with the webserver. This study aims to implement security features with JSON Web Token (JWT) based on the 256 Hash algorithm. The research also implements geo-tagging features to obtain accurate coordinates based on location points. Athlete attendance data obtained by the presence of these features are then synchronized via web service using the REST architecture. All stages of implementation are then tested by the Black Box method, and the results show that JSON Web Token (JWT) is able to secure the authentication and data security process, while the Geo-tagging feature is capable of sending accurate position data. Testing the functionality of the web service shows that all features work well within 44.8 ms, while the positioning accuracy of the geo-tagging feature reaches an accuracy of 90.9%.]]>
<![CDATA[Implementasi Teknik Kriptografi CAESAR CIPHER Untuk Keamanan Data Informasi Berbasis Android ]]>
<![CDATA[Fina Triana]]>;
<![CDATA[Jon Endri]]>;
<![CDATA[Irma Salamah]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 4 (2020): Agustus 2020 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v4i4.1984
<![CDATA[Information data security is an important aspect of exchanging data and information. Data and information security can be done in various ways, including by using the cesarean cipher method in cryptographic techniques. The cesarean cipher method of cryptographic technique is a substitution coding system which is done by replacing each alphabet character with other characters along the 26 alphabet characters so that the coding only occurs in the alphabet itself without any other punctuation. This study proposes a modification to the cesarean cipher technique by adding to the number of characters used, namely 256 characters in the ASCII code. The caesar cipher application or CaesarApp uses the methodology of library study, consultation, application design and application testing. The implementation of the CaesarApp application was created using the open-source Android Studio 3.5 application. The results of tests conducted on the CaesarApp application note that the modification of the caesar cipher with 256 ASCII characters results in a secure information data security application, this application has a deficiency in reading limitations on ASCII characters, so characters cannot be read properly on android users.]]>
<![CDATA[Pengaruh Oversampling pada Klasifikasi Hipertensi dengan Algoritma Naïve Bayes, Decision Tree, dan Artificial Neural Network (ANN) ]]>
<![CDATA[Nurul Chamidah]]>;
<![CDATA[Mayanda Mega Santoni]]>;
<![CDATA[Nurhafifah Matondang]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 4 (2020): Agustus 2020 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v4i4.2015
<![CDATA[Oversampling is a technique to balance the number of data records for each class by generating data with a small number of records in a class, so that the amount is balanced with data with a class with a large number of records. Oversampling in this study is applied to hypertension dataset where hypertensive class has a small number of records when compared to the number of records for non-hypertensive classes. This study aims to evaluate the effect of oversampling on the classification of hypertension dataset consisting of hypertensive and non-hypertensive classes by utilizing the Naïve Bayes, Decision Tree, and Artificial Neural Network (ANN) as well as finding the best model of the three algorithms. Evaluation of the use of oversampling on hypertension dataset is done by processing the data by imputing missing values, oversampling, and transforming data into the same range, then using the Naïve Bayes, Decision Tree, and ANN to build classification models. By dividing 80% of data as training data to build models and 20% as validation data for testing models, we had an increase in classification performance in the form of accuracy, precision, and recall of the oversampled data when compared without oversampling. The best performance in this study resulted in the highest accuracy using ANN with 0.91, precision 0.86 and recall 0.99.]]>
<![CDATA[Perbandingan Kinerja Metode-Metode Prediksi pada Transaksi Dompet Digital di Masa Pandemi ]]>
<![CDATA[Arwin Datumaya Wahyudi Sumari]]>;
<![CDATA[Muhammad Bisri Musthafa]]>;
<![CDATA[Ngatmari]]>;
<![CDATA[Dimas Rossiawan Hendra Putra]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 4 (2020): Agustus 2020 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v4i4.2024
<![CDATA[A pandemic situation such as Covid-19 which is still ongoing has given significant impacts to various sectors such as education, economy, tourism, and social which is in turn impacting the community at a national scale. On the other hand, the pandemic situation has also brought a positive impact on companies engaged in finance that utilizes information technology, namely digital wallets, a company that runs a market place in the digital world. In an effort to anticipate a dynamic market place, the company needs to predict the movement of transactions from time to time by building a model and performain the simulation to such model. Based on this problem, this paper presents simulations on the prediction models based on methods namely, naïve, Single Moving Average (SMA), Exponential Moving Average (EMA), combined SMA-naive methods, combined EMA-naive methods, as well as did the comparison of the best performance of every model by using Mean Absolute Percentage Error (MAPE) measurement. From the results of comparison, it is concluded that exponential moving average method delivers the best performance as prediction tool with MAPE of 23,4%.]]>
<![CDATA[Model Text-Preprocessing Komentar Youtube Dalam Bahasa Indonesia ]]>
<![CDATA[Siti Khomsah]]>;
<![CDATA[Agus Sasmito Aribowo]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 4 (2020): Agustus 2020 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v4i4.2035
<![CDATA[YouTube is the most widely used in Indonesia, and it’s reaching 88% of internet users in Indonesia. YouTube’s comments in Indonesian languages produced by users has increased massively, and we can use those datasets to elaborate on the polarization of public opinion on government policies. The main challenge in opinion analysis is preprocessing, especially normalize noise like stop words and slang words. This research aims to contrive several preprocessing model for processing the YouTube commentary dataset, then seeing the effect for the accuracy of the sentiment analysis. The types of preprocessing used include Indonesian text processing standards, deleting stop words and subjects or objects, and changing slang according to the Indonesian Dictionary (KBBI). Four preprocessing scenarios are designed to see the impact of each type of preprocessing toward the accuracy of the model. The investigation uses two features, unigram and combination of unigram-bigram. Count-Vectorizer and TF-IDF-Vectorizer are used to extract valuable features. The experimentation shows the use of unigram better than a combination of unigram and bigram features. The transformation of the slang word to standart word raises the accuracy of the model. Removing the stop words also contributes to increasing accuracy. In conclusion, the combination of preprocessing, which consists of standard preprocessing, stop-words removal, converting of Indonesian slang to common word based on Indonesian Dictionary (KBBI), raises accuracy to almost 3.5% on unigram feature.]]>
<![CDATA[Sistem Deteksi Hoax pada Twitter dengan Metode Klasifikasi Feed-Forward dan Back-Propagation Neural Networks ]]>
<![CDATA[Crisanadenta Wintang Kencana]]>;
<![CDATA[Erwin Budi Setiawan]]>;
<![CDATA[Isman Kurniawan]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 4 (2020): Agustus 2020 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v4i4.2038
<![CDATA[Social media is one of the ways to connect every individual in the world. It also used by irresponsible people to spread a hoax. Hoax is false news that is made as if it is true. It may cause anxiety and panic in society. It can affect the social and political conditions. This era, the most popular social media is Twitter. It is a place for sharing information and users around the world can share and receive news in short messages or called tweet. Hoax detection gained significant interest in the last decade. Existing hoax detection methods are based on either news-content or social-context using user-based features. In this study, we present a hoax detection based on FF & BP neural networks. In the developing of it, we used two vectorization methods, TF-IDF and Word2Vec. Our model is designed to automatically learn features for hoax news classification through several hidden layers built into the neural network. The neural network is actually using the ability of the human brain that is able to provide stimulation, process, and output. It works by the neuron to process every information that enters, then is processed through a network connection, and will continue learning to produce abilities to do classification. Our proposed model would be helpful to provide a better solution for hoax detection. Data collection obtained through crawling used Twitter API and retrieve data according to the keywords and hashtags. The neural networks highest accuracy obtained using TF-IDF by 78.76%. We also found that data quality affects the performance.]]>
<![CDATA[Aplikasi Kombinasi Heuristik dalam Kerangka Hyper-Heuristic untuk Permasalahan Penjadwalan Ujian ]]>
<![CDATA[Gabriella Icasia]]>;
<![CDATA[Raras Tyasnurita]]>;
<![CDATA[Etria Sepwardhani Purba]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 4 (2020): Agustus 2020 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v4i4.2066
<![CDATA[Examination Timetabling Problem is one of the optimization and combinatorial problems. It is proved to be a non-deterministic polynomial (NP)-hard problem. On a large scale of data, the examination timetabling problem becomes a complex problem and takes time if it solved manually. Therefore, heuristics exist to provide reasonable enough solutions and meet the constraints of the problem. In this study, a real-world dataset of Examination Timetabling (Toronto dataset) is solved using a Hill-Climbing and Tabu Search algorithm. Different from the approach in the literature, Tabu Search is a meta-heuristic method, but we implemented a Tabu Search within the hyper-heuristic framework. The main objective of this study is to provide a better understanding of the application of Hill-Climbing and Tabu Search in hyper-heuristics to solve timetabling problems. The results of the experiments show that Hill-Climbing and Tabu Search succeeded in automating the timetabling process by reducing the penalty 18-65% from the initial solution. Besides, we tested the algorithms within 10,000-100,000 iterations, and the results were compared with a previous study. Most of the solutions generated from this experiment are better compared to the previous study that also used Tabu Search algorithm.]]>
<![CDATA[Pengembangan Aplikasi Virtual Reality dengan Model ADDIE untuk Calon Tenaga Pendidik Anak dengan Autisme ]]>
<![CDATA[Dhomas Hatta Fudholi]]>;
<![CDATA[Rahadian Kurniawan]]>;
<![CDATA[Dimas Panji Eka Jalaputra]]>;
<![CDATA[Izzati Muhimmah]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 4 (2020): Agustus 2020 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v4i4.2092
<![CDATA[Knowledge is needed for children with special needs to support their quality of life. This is a challenge for prospective educators / prospective teachers. A deeper knowledge is needed to really understand children with special needs. This research is carried out to develop a skill simulator application for autistic child’s prospective educator using Virtual Reality technology. This application will be used as a teaching medium which incorporates motion sensor tools. The sensors will make the virtual application looks realistic. The application was developed using the ADDIE method (Analysis, Design, Development, Implementation and Evaluation). The application development begins with discovering the characteristic of autistic children. This is done to formulate the learning materials. The knowledge base of the autistic children was obtained from the Sekolah Luar Biasa (SLB). By using the obtained knowledge, storyboard was designed and implemented. The developed application has been evaluated by 16 prospective child educators with autism and two professional experts. In general, the application can help prospective educators understand the characteristics of children with autism. Moreover, it provides a safe and pleasant teaching skill practice for the prospective educators.]]>
<![CDATA[Analisis Recovery Bukti Digital Skype berbasis Smartphone Android Menggunakan Framework NIST ]]>
<![CDATA[Anton Yudhana]]>;
<![CDATA[Abdul Fadlil]]>;
<![CDATA[Muhammad Rizki Setyawan]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 4 (2020): Agustus 2020 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v4i4.2093
<![CDATA[Cybercrime is an activity utilizing electronic devices and network technology as tools or media to commit crimes. One of them uses the Skype application that is installed on the smartphone. In finding evidence from a cybercrime case, a forensic activity known as digital forensic must be carried out. This study aims to recover digital evidence that has been erased using the NIST framework and forensic tools such as Oxygen and Belkasoft. The results of digital evidence recovery from smartphone Samsung J2 in the removal scenario via the application manager, the Oxygen tool cannot recover deleted data and the percentage of success using Belkasoft is 26%. While the results of data recovery with the manual removal method the percentage of success using Oxygen was 63% and Belkasoft was 44%. Digital evidence recovery results from smartphones Andromax A on the erase scenario through the application manager, Oxygen and Belkasoft tools cannot recover deleted data. While manual removal of Oxygen by 61% and Belkasoft cannot restore data. It can be concluded the results of data recovery from both smartphones that are used according to the erasure method through the application manager, Belkasoft has better performance than Oxygen, and data recovery according to the method of erasing manually, Oxygen has better performance than Belkasoft.]]>
<![CDATA[Penentuan Lokasi Industri Menggunakan Metode WASPAS Dengan Data Spasial Sebagai Data Kriteria ]]>
<![CDATA[Agusta Praba Ristadi Pinem]]>;
<![CDATA[Siti Asmiatun]]>;
<![CDATA[Astrid Novita Putri]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 4 (2020): Agustus 2020 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v4i4.2094
<![CDATA[Today, the development of the use of spatial data is not only used for information geographic or transportation. But also can be used for site selection with integrating decision support system methods. Generated information can help in making decisions and meet the expected aspects. One method that can be used to support the decision making process is the Weighted Aggregated Sum Product Assessment (WASPAS). WASPAS is included in Multi Criteria Decision Making which can produce selected information from the data or criteria used. This study uses the WASPAS method as a determinant of strategic industrial locations by spatial data collection. In determining strategic industrial locations, WASPAS uses several different criteria and weights for each criterion. The WASPAS method can produce precise information related to the determination of strategic industrial locations. The results of the Spearman Rating trial with data on industrial locations in the city of Semarang show a strong conformity, as seen from the resulting compatibility value of 1.0. The results obtained from this study are the establishment of a system model that supports the decision to determine the location of the industry using the WASPAS method.]]>
