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Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

RESTI
Website

Published by Ikatan Ahli Informatika Indonesia

ISSN : 25800760 EISSN : 25800760 DOI : https://doi.org/10.29207/resti.v2i3.606

Core Subject : Science, Engineering,

Computer Science & IT Engineering

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

Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)

Articles 1,046 Documents

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Analisis Sentimen Pemindahan Ibu Kota Negara dengan Feature Selection Algoritma Naive Bayes dan Support Vector Machine Faried Zamachsari; Gabriel Vangeran Saragih; Susafa'ati; Windu Gata
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 3 (2020): Juni 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The decision to move Indonesia's capital city to East Kalimantan received mixed responses on social media. When the poverty rate is still high and the country's finances are difficult to be a factor in disapproval of the relocation of the national capital. Twitter as one of the popular social media, is used by the public to express these opinions. How is the tendency of community responses related to the move of the National Capital and how to do public opinion sentiment analysis related to the move of the National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine to get the highest accuracy value is the goal in this study. Sentiment analysis data will take from public opinion using Indonesian from Twitter social media tweets in a crawling manner. Search words used are #IbuKotaBaru and #PindahIbuKota. The stages of the research consisted of collecting data through social media Twitter, polarity, preprocessing consisting of the process of transform case, cleansing, tokenizing, filtering and stemming. The use of feature selection to increase the accuracy value will then enter the ratio that has been determined to be used by data testing and training. The next step is the comparison between the Support Vector Machine and Naive Bayes methods to determine which method is more accurate. In the data period above it was found 24.26% positive sentiment 75.74% negative sentiment related to the move of a new capital city. Accuracy results using Rapid Miner software, the best accuracy value of Naive Bayes with Feature Selection is at a ratio of 9:1 with an accuracy of 88.24% while the best accuracy results Support Vector Machine with Feature Selection is at a ratio of 5:5 with an accuracy of 78.77%.

Implementasi Web Service pada Perusahaan Logistik menggunakan JSON Web Token dan Algoritma Kriptografi RC4 Mochammad Rizky Royani; Arief Wibowo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 3 (2020): Juni 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The development of e-commerce in Indonesia in the last five years has significantly increased the growth for logistics service companies. The Indonesian Logistics and Forwarders Association (ALFI) has predicted the growth potential of the logistics business in Indonesia to reach more than 30% by 2020. One of the efforts of logistics business companies to improve services in the logistics services business competition is to implement web service technology on mobile platforms, to easy access to services for customers. This research aims to build a web service with a RESTful approach. The REST architecture has limitations in the form of no authentication mechanism, so users can access and modify data. To improve its services, JSON Web Token (JWT) technology is needed in the authentication process and security of access rights. In terms of data storage and transmission security, a cryptographic algorithm is also needed to encrypt and maintain confidentiality in the database. RC4 algorithm is a cryptographic algorithm that is famous for its speed in the encoding process. RC4 encryption results are processed with the Base64 Algorithm so that encrypted messages can be stored in a database. The combination of the RC4 method with the Base64 method has strengthened aspects of database security. This research resulted in a prototype application that was built with a combination of web service methods, JWT and cryptographic techniques. The test results show that the web service application at the logistics service company that was created can run well with relatively fast access time, which is an average of 176 ms. With this access time, the process of managing data and information becomes more efficient because before making this application the process of handling a transaction takes up to 20 minutes.

Bagaimana IoT Dapat diManfaatkan untuk Melatih Keterampilan Motorik Kasar Melalui Permainan Hopscotch? Irvan Naufali Rahmanto; Novian Anggis Suwastika; Rahmat Yasirandi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 3 (2020): Juni 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Motor development is the result of changes caused by physical growth, muscle strengthening, and the ability to interact with the environment. There are two types of motor development, namely gross motor and fine motor. The best age for a child for motor development is 0 to 8 years. At the age of 4 to 6 years mostly of children's gross motor activities related to balance and coordination. Child’s development of gross motor can be achieved by stimulating using games. Hopscotch is type of game that implements balance and coordination skills that support the development of gross motor skills. In Indonesia, children aged 4 years to 6 years have started to enter the Early Childhood Education and Kindergarten level. When the child is at school, parents cannot provide motor stimulation and must wait for the child's motor development reports submitted by the teachers. In this study we implemented system to stimulate the development of gross motor balance and coordination in children aged 4 to 6 years using hopscotch game integrated with Internet of Things (IoT) technology. IoT provides the ability to read, record, and evaluate children's activities and publish their results online for parents to access. This system is evaluated based on the system's functionality and performance parameters. From the test results found that the functionality of the system runs 100% by the specified function. The system performance test results from the sensor readings are under 1 second and the accuracy of the assessment activity of the first test variation of the foot position in the middle of 68.75%, and the foot position at the edge of 81.25% with the program delay setting from the node to the IoT platform an average of 1 second.

Footstep Recognition Using Mel Frequency Cepstral Coefficients and Artificial Neural Network Thasya Nurul Wulandari Siagian; Hilal Hudan Nuha; Rahmat Yasirandi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 3 (2020): Juni 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Footstep recognition is relatively new biometrics and based on the learning of footsteps signals captured from people walking on the sensing area. The footstep signals classification process for security systems still has a low level of accuracy. Therefore, we need a classification system that has a high accuracy for security systems. Most systems are generally developed using geometric and holistic features but still provide high error rates. In this research, a new system is proposed by using the Mel Frequency Cepstral Coefficients (MFCCs) feature extraction, because it has a good linear frequency as a copycat of the human hearing system and Artificial Neural Network (ANN) as a classification algorithm because it has a good level of accuracy with a dataset of 500 recording footsteps. The classification results show that the proposed system can achieve the highest accuracy of validation loss value 57.3, Accuracy testing 92.0%, loss value 193.8, and accuracy training 100%, the accuracy results are an evaluation of the system in improving the foot signal recognition system for security systems in the smart home environment.

Implementasi Fitur Keamanan dengan JSON Web Token dan Fitur Geo-tagging pada Aplikasi Web Service Training From Home Aal Hibsy; Arief Wibowo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 4 (2020): Agustus 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

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%.

Implementasi Teknik Kriptografi CAESAR CIPHER Untuk Keamanan Data Informasi Berbasis Android Fina Triana; Jon Endri; Irma Salamah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 4 (2020): Agustus 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

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.

Pengaruh Oversampling pada Klasifikasi Hipertensi dengan Algoritma Naïve Bayes, Decision Tree, dan Artificial Neural Network (ANN) Nurul Chamidah; Mayanda Mega Santoni; Nurhafifah Matondang
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 4 (2020): Agustus 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

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.

Perbandingan Kinerja Metode-Metode Prediksi pada Transaksi Dompet Digital di Masa Pandemi Arwin Datumaya Wahyudi Sumari; Muhammad Bisri Musthafa; Ngatmari; Dimas Rossiawan Hendra Putra
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 4 (2020): Agustus 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

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%.

Implementasi Deteksi Rumor pada Twitter Menggunakan Metode Klasifikasi SVM Annisa Rahmaniar Dwi Pratiwi; Erwin Budi Setiawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 5 (2020): Oktober 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Twitter is one of the popular social network sites, that was first launched in 2006. This service allows users to spread real-time information. However, the information obtained is not always based on facts and sometimes deliberately used to spread rumors that cause fear to the public. So detection efforts are needed to overcome and prevent the spread of rumors on Twitter. Much research regarding the detection of rumors but is limited to English and Chinese. In this study, the authors built a system to detect Indonesian-language rumors based on the implementation of the SVM classification and feature selection using the TF-IDF weighting. Data collection was conducted in November 2019 to February 2020 using crawling methods by keywords and manual labeling process. Research data used topics around government and trending with 47,449 records and features combination based on users and tweets. Stages of research include the process of collecting data on the Twitter social networking site which is then carried out preprocessing consists of case-folding, URL removal, normalization, stopwords removal, and stemming. The next stage is feature selection, N-Gram modeling, classification, and evaluation using a confusion matrix. Based on the results of the study, the system gets good performance in the test scenario using 10% of testing data and unigram features with the highest accuracy value of 78.71%. As for features twitter that affected the detection of rumors covering the number of following, the number of like and mention.

Model Text-Preprocessing Komentar Youtube Dalam Bahasa Indonesia Siti Khomsah; Agus Sasmito Aribowo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 4 (2020): Agustus 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

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.

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Yuhefizar

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Politeknik Negeri Padang, Kampus Limau Manis, Padang, Indonesia.

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Yuhefizar

Contact Email jurnal.resti@gmail.com

Phone +628126777956

Journal Mail Official ephi.lintau@gmail.com

Editorial Address Politeknik Negeri Padang, Kampus Limau Manis, Padang, Indonesia.

Location , INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Yuhefizar

Contact Email
jurnal.resti@gmail.com

Phone
+628126777956

Journal Mail Official
ephi.lintau@gmail.com

Editorial Address
Politeknik Negeri Padang, Kampus Limau Manis, Padang, Indonesia.

Location
,

INDONESIA