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Yuhefizar
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jurnal.resti@gmail.com
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+628126777956
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Politeknik Negeri Padang, Kampus Limau Manis, Padang, Indonesia.
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INDONESIA
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
ISSN : 25800760     EISSN : 25800760     DOI : https://doi.org/10.29207/resti.v2i3.606
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
Articles 25 Documents
Search results for , issue "Vol 5 No 2 (2021): April 2021" : 25 Documents clear
Optimasi SVM Berbasis PSO pada Analisis Sentimen Wacana Pindah Ibu Kota Indonesia Arsi, Primandani; Wahyudi, Rizki; Waluyo, Retno
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i2.2698

Abstract

President Joko Widodo decided to move the capital city of the country outside Java. The relocation of the capital city is contained in the 2020-2024 National Medium-Term Development Plan. Community response to this has been mixed through national television and social media, especially Twitter. The tendency of Twitter users to respond to the government discourse can be seen with sentiment analysis. Sentiment analysis is one of the areas of Natural Language Processing (NLP) that builds systems for recognizing and extracting opinions. In this study, the Feature Selection PSO algorithm in the classification of the SVM model is proposed to improve the resulting accuracy in the sentiment analysis of moving capital cities. Experiments on the data of 1,319 tweets (457 positive sentiments and 862 negative sentiments) indicate an increase in accuracy by 2.09% from 79.06% to 81.15%, with the classification category is “Good Classification”.
Pemanfaatan RFID, Loadcell, dan Sensor Infrared Untuk Miniatur Penukaran Botol Plastik Bekas Darussalam, Darussalam; Goeritno, Arief
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i2.3048

Abstract

An embedded system has been fabricated through the use of the RFID module, load cell, and infrared sensor assisted by the Arduino module. The objectives of this study are to design an embedded system and embed applications and measure the performance of the embedded system through validation tests. The research method was carried out for the realization of a miniature structure, the integrated wiring of electronic devices, and making an application based on Arduino software. Achievements during the validation test of the embedded system were carried out through three conditions. Miniature physique as a place for the subsystems of mechanical drive, controller, and the optimization of the layout of each device is an attempt to attain the hardware handshaking through integrated wiring. Acquiring applications is an attempt to attain soft handshaking. Achievement of hardware and software handshaking is carried out during the validation test, namely checking balances, cash withdrawals, and disposing of garbage. The exchange of garbage for money, after there is a high dimensional qualification with the criteria "#3big", "#2medium", or "#1small" and according to the qualifications for the net mass of the bottle with three criteria "29 grams", "19 grams", or "10 gram”. Overall, the existence of an embedded system for exchanging plastic garbage can be operated according to the design. The miniature independent platforms can be used as a reference for the construction of independent platforms that have a large capacity and are more integrative, as an effort to safeguard the existence of plastic garbage, especially bottles used for beverage packaging.
Penerapan Convolutional Neural Network Deep Learning dalam Pendeteksian Citra Biji Jagung Kering TiaraSari, Arum; Haryatmi, Emy
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i2.3040

Abstract

Corn kernels detection can be implemented in industry area. This can be implemented in the selection and packaging the corn kernels before it is distributed. This technique can be implemented in the selection and packaging machine to detect corn kernels accurately. Corn kernel images was used before it is implemented in real-time. The objective of this research was corn kernel detection using Convolutional Neural Network (CNN) deep learning. This technique consists of 3 main stages, the first preprocessing or normalizing the input of corn kernels image data by wrapping and cropping, both modeling and training the system, and testing. The experiment used CNN method to classify images of dry corn kernels and to determine the accuracy value. This research used 20 dry corn kernels images as testing from 80 dry corn kernels images which used in training dataset. The accuracy of detection was dependent from the size of image and position when the image was taken. The accuracy is around 80% - 100% by using 7 convolutional layers and the average of accuracy for testing data was 0,90296. The convolutional layer which implemented in CNN has the strength to detect features in the input image.
Modul Front-End Sistem Informasi Geospasial Patroli Terpadu Kebakaran Hutan dan Lahan Deny Ramdhany; Imas Sukaesih Sitanggang; Ikhsan Kurniawan; Wulandari, Wulandari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i2.3045

Abstract

To prevent and handle forest and land and forest fire (karhutla), the Ministry of Environment and Forestry assembled a patrol team that conducts a daily task to observe directly to the hotspot location as an indication for land fire. Currently, the patrol team reported the investigation result into a group chat. This method consumed many storage spaces and not suitable for formal reporting. This study aims to develop a front-end module for a web GIS application that visualizes the patrol team's daily report. The application has its data recapitulation method and able to create a formal report. The data used in this study are a set of the report that collected in 2016 by Sumatera and Kalimantan patrol team. The steps to build this application include communication, integrate with the API from the back-end system, developing functional needs, software testing, and the last is software release. The application was build using HTML and CSS for its interface and Javascript and API from the back-end module for its content management. The system uses Google Maps services and library to support the functionalities of the application. The unit testing method's test result shows that the module runs well and can afford all of the required functionality. In addition, the system testing result that the ratio between actual error and expected error is equal to 1. This result indicates the functions of the system are working properly according to the use cases of the system.
Algoritma Fungsi Perlatihan pada Machine Learning berbasis ANN untuk Peramalan Fenomena Bencana Wanto, Anjar; Defit, Sarjon; Perdana Windarto, Agus
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i2.3031

Abstract

Research has been carried out with several training functions using standard backpropagation methods, One-Step Secant (OSS), and Bayesian regulation. The purpose of this study was to (i) analyze the Performance accuracy (Performance) of the standard backpropagation method and (ii) optimize the training function with the One-Step Secant (OSS) and Bayesian regulation methods to obtain comparison results of the three methods in the search for the best results implementation of disaster phenomenon forecasting data. The research method is based on quantitative methods with times-series data on disaster phenomena in Indonesia over the last ten years (2011-2020) which were analyzed using two network architecture models, namely 4-8-1 and 4-10-1. The results showed that the 4-8-1 architectural model with the Bayesian regulation training function method was able to optimize quite well through accelerating training time and resulted in a low MSE measurement, although not the lowest with an epoch value of 197 iterations and a Performance of 0.0148480766. The lowest epoch value is generated by the OSS method, but it Performs poorly. The best Performance is produced by the standard backpropagation method with the traingd training function, but the training process for achieving convergence is also too long. In general, it can be concluded that the 4-8-1 architectural model with Bayesian regulation can be used to predict (predict) the phenomenon of natural disasters in Indonesia because the training time to achieve convergence is not too long and Performs exceptionally well.
Metode PCQ dan Queue Tree untuk Implementasi Manajemen Bandwidth Berbasis Mikrotik Christanto, Febrian Wahyu; Daru, April Firman; Kurniawan, Arif
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i2.3026

Abstract

Bandwidth problems are the most frequently encountered problems in sharing information traffic or internet access on computer networks. The factor that causes slow internet access is the large number of devices connected to the internet that are not matched by the availability of sufficient bandwidth and the lack of available bandwidth. This can be accommodated with bandwidth management methods. Bandwidth management in Mikotik uses several bandwidth sharing methods such as PCQ (Per Connection Queue), Queue Tree, and HTB (Hierchichal Token Bucket). This research uses 2 (two) bandwidth management methods, namely PCQ and Queue Tree because these methods can divide bandwidth automatically according to the number of active users and are more effective in sharing bandwidth based on Mikrotik. PCQ is a method aimed at optimizing QoS for large-scale internet networks where all queues are the same for all sub-streams, while Queue Tree is a method designed to carry out more complex queuing tasks for network traffic. The purpose of this research is to optimize the limited internet bandwidth so that it can be accessed by all users in the Local Area Network and automate the queue of devices connected to the network according to user needs so as to produce a more stable computer network performance using the network development method, namely NDLC. The results of the tests were carried out 10 times using a bandwidth of 10 Mbps which resulted in an average jitter of 1.64 ms, ping 36.8 ms, 2 Mbps throughput, and 0.1% packet loss so that the QoS of internet access was categorized as satisfactory. It is hoped that from this research the company will be able to save on internet access expenses by maximizing a small bandwidth without having to increase the existing bandwidth.
Evaluasi Usability Sistem Informasi Akademik Dosen Menggunakan User Experience Questionnaire dan Heuristic Walkthrough Sari, Yuslena; Arafah, Maulidia; Novitasari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i2.3022

Abstract

University of Lambung Mangkurat (ULM) has an academic information system that can facilitate access to information and provide quality academic services. One of the ULM academic information system services is the Portal Akademik Dosen ULM. Since its release in 2016, there has been no research regarding the evaluation of the Portal Akademik Dosen ULM. Most of users feel that the interface of the Portal Akademik Dosen ULM needs to be improved to make it more attractive, and needs improvements in several features. In connection with the important function of the Portal Akademik Dosen ULM, an evaluation is needed to assess the success of implementing the information system. This study aims to evaluate the usability of the Portal Akademik Dosen ULM. The methods used are the User Experience Questionnaire (UEQ) and Heuristic Walkthrough. Based on the results of the UEQ questionnaire from 56 respondents, it was found that the aspects of attractiveness, perspicuity, efficiency, dependability, and stimulation got positive values, while the novelty aspect got a neutral value. For the evaluation results with Heuristic Walkthrough method which involved 4 evaluators, found 33 problems at the Cognitive Walkthrough stage, and as many as 21 problems at the Heuristic Evaluation stage with an average severity rating of 13.4. The overall results of the evaluation showed that the Portal Akademik Dosen ULM needs to be improved on novelty items and need to be improved to reduce the number of problems.
Penerapan Algoritma Support Vector Machine Untuk Model Prediksi Kelulusan Mahasiswa Tepat Waktu Haryatmi, Emy; Pramita Hervianti, Sheila
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i2.3007

Abstract

A University can have many student data in their database because many students did not graduate on time. Data mining technique can be used to process student data to predict student graduation on time. Support Vector Machine (SVM) algorithm is one of data mining techniques. Objectives of this research was implementation of SVM algorithm to model the prediction of student graduation on time in private university in Indonesia. This research was conducted using CRISP-DM (Cross Industry Standard Process for Data Mining) method. There are five steps in that method such as understanding business to predict student graduation in time which is not available, data understanding by choosing the right attribute for the next step, data preparation includes cleaning the null data and transforming data into category which has been specified, modeling was used by implementing data training and data testing on SVM algorithm and evaluation to validate and measure the accuracy of the model. The result of this research shown that accuracy value of data testing was 94,4% using 90% data training and 10% data testing. This concluded SVM algorithm can be used to model the prediction of student graduation on time.
Perbandingan Support Vector Machine dan Modified Balanced Random Forest dalam Deteksi Pasien Penyakit Diabetes Purbolaksono, Mahendra Dwifebri; Irvan Tantowi, Muhammad; Imam Hidayat, Adnan; Adiwijaya, Adiwijaya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i2.3008

Abstract

Diabetes (diabetes) was a metabolic disorder caused by high levels of sugar in the blood caused by disorders of the pancreas and insulin. According to data from the Ministry of Health of the Republic of Indonesia, Diabetes was the third-largest cause of death in Indonesia with a percentage of 6.7%. The high rate of death from diabetes encouraged this study, with the aim of early detection. This research used a Machine Learning approach to classify the data. In this paper, a comparison of Support Vector Machine (SVM) and Modified Balanced Random Forest (MBRF) was discussed for classifying diabetes patient data. Both methods were chosen because it was proven in previous studies to get high accuracy, so that the two methods are compared to find the best classification model. Several preprocessing methods were used to prepare the data for the classification process. The entire combination of preprocessing steps will be carried out on the two classification methods to produce the same dataset. The evaluation was carried out using the Confusion Matrix method. Based on the experimental results in the process of testing the system being built, the maximum performance results were 87.94% using SVM and 97.8% using MBRF.
Penerapan Metode Localization Tampering dan Hashing untuk Deteksi Rekayasa Video Digital Alfian, Alfiansyah Imanda Putra; Umar, Rusydi; Fadlil, Abdul
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i2.3015

Abstract

The development of digital video technology which is increasingly advanced makes digital video engineering crimes prone to occur. The change in digital video has changed information communication, and it is easy to use in digital crime. One way to solve this digital crime case is to use the NIST (National Institute of Standards and Technology) method for video forensics. The initial stage is carried out by collecting data and carrying out the process of extracting the collected results. A local hash and noise algorithm can then be used to analyze the resulting results, which will detect any digital video interference or manipulation at each video frame, and perform hash analysis to detect the authenticity of the video. In digital video engineering, histogram analysis can be performed by calculating the histogram value metric, which is used to compare the histogram values ​​of the original video and video noise and make graphical comparisons. The results of the difference in frame analysis show that the results of the video show that the 2nd to 7th frames experience an attack while the histogram calculation of the original video centroid value and video tampering results in different values ​​in the third frame, namely with a value of 124.318 and the 7th frame of the video experiencing a difference in the value of 105,966 videos. tampering and 107,456 in the original video. Hash analysis on video tampering results in an invalid SHA-1 hash, this can prove that the video has been manipulated.

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