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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

56 57 58 59 60

Smoke Automation and Regression Testing on a peer-to-peer lending Website with the Data-DrivenTesting Method Margaret Teacher Banjarnahor; Lucia Sri Istiyowati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 4 (2022): Agustus 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Software testing is considered as one of the most important processes in software development as it checks whether the system meets the requirements and specifications of the users. The testing process manually or automatically aims to ensure that the main features are not errors and function during development. The manual process frequently causes the publication time of a feature could not to be punctual. This study aims to create an automation script with the data-driven testing method during smoke and regression testing to ensure the quality, especially the main functions or features, can run normally and not be disturbed by the development of new features that are easily understood. One of Various decent automation tools for testing web applications is Katalon Studio which is based on the Selenium tool. The results of the research that have been carried out show that the process of automation of software testing by applying a script automation tool made with Katalon Studio which applies the data-driven testing method is very good with an achievement rate of 80,21%. The automation tools that are built are easy to use, can be learned quickly, are not too complicated and make users want to use it again.

Increased Accuracy on Image Classification of Game Rock Paper Scissors using CNN Muhammad Nur Ichsan; Nur Armita; Agus Eko Minarno; Fauzi Dwi Setiawan Sumadi; Hariyady
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 4 (2022): Agustus 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Rock Paper Scissors is one of the most popular games in the world, because of their easy and simple way to play among young and elderly people. The point of this game is to do the draw or just to find out who loses or wins. The pandemic conditions made people unable to meet face-to-face and could only play this game virtually. To carry out this activity in a virtual way, this research facilitates a model in the form of image classification to distinguish the hand gestures s in the form of rock, paper, and scissors. This classification process utilizes the Convolutional Neural Network (CNN) method. This method is one type of artificial neural network in terms of image classification. CNN uses three stages, namely convolutional layer, pooling layer, and fully connected layer. The implementation of this method for hand gesture classification in the form of rock, scissors, and paper images in this study shows an increased average accuracy towards the previous study from 97.66% to 99%.

Integration of AHP and TOPSIS Methods for Small and Medium Industries Development Decision Making Anton Yudhana; Rusdi Umar; Aldi Bastiatul Fawait Fawait
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Financial problems are one of the reasons why small and medium-sized industries (SMIs) in West Kutai have not developed optimally. Government assistance programs are one of the solutions. This program must be appropriate, so a decision-making tool is needed to help choose the right SMIs to be assisted later. The weight of the criteria was determined using the Analytical Hierarchy Process (AHP) technique, and the priority of the SMIs as the preferred proposal for the recipients of development assistance was determined using the Technique for Other Reference by Similarly to Ideal Solution (TOPSIS) approach. Labor, investment, production capacity, production value, and raw materials were used to determine the priorities of SMIs beneficiaries. Furthermore, TOPSIS prioritizes the development of alternative small and medium-sized industries with types of handicraft commodities. Integration of AHP and TOPSIS methods has been successfully used in the IKM Development Priority Determination Application, with 83.3% precision and 96.4% accuracy achieved by using a confusion matrix so that the IKM ranking can be known. The results of the study found that integration of the two methods was successfully used for Small and Medium Industries Development Decision Making.

DHF Incidence Rate Prediction Based on Spatial-Time with Random Forest Extended Features Elqi Ashok; Sri Suryani Prasetiyowati; Yuliant Sibaroni
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 4 (2022): Agustus 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

This study proposes a prediction of the classification of the spread of dengue hemorrhagic fever (DHF) with the expansion of the Random Forest (RF) feature based on spatial time. The RF classification model was developed by extending the features based on the previous 2 to 4 years. The three best RF models were obtained with an accuracy of 97%, 93%, and 93%, respectively. Meanwhile, the best kriging model was obtained with an RMSE value of 0.762 for 2022, 0.996 for 2023, and 0.953 for 2024. This model produced a prediction of the classification of dengue incidence rates (IR) with a distribution of 33% medium class and 67% high class for 2022. 2023, the medium class is predicted to decrease by 6% and cause an increase in the high class to 73%. Meanwhile, in 2024, it is predicted that there will be an increase of 10% for the medium class from 27% to 37% and the distribution of the high class is predicted to be around 63%. The contribution of this research is to provide predictive information on the classification of the spread of DHF in the Bandung area for three years with the expansion of features based on time.

Memory-based Collaborative Filtering on Twitter Using Support Vector Machine Classification Anang Furkon RIfai; Erwin Budi Setiawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Nowadays, watching films at home is one of people's entertainment. Netflix is a service provider for watching films and provides many types of film genres. However, of the many films available, it makes users confused to choose which film to watch first. The solution to the problem is a system that provides recommendations for the best films to watch based on user ratings. Twitter is still people's favorite social media to express their feelings, thoughts, and criticisms. In this system, tweets serve as input data that will be processed into data with rating values. This research implemented a recommendation system based on user ratings from tweets using collaborative filtering combined with Support Vector Machine (SVM) classification and implemented it on user-based and item-based. The test results in this study show that Collaborative Filtering gets the best RMSE value results on item-based 0.5911 and 0.8162 on user-based. The Support Vector Machine (SVM) classification algorithm using hyperparameter tuning produces item-based values with a precision of 85.03% and recall of 90.71%, while user-based values with a precision of 87.75% and recall of 88.95%.

QSAR Study of Larvicidal Phytocompounds as Anti-Aedes Aegypti by using GA-SVM Method Komang Triolascarya; Reza Rendian Septiawan; Isman Kurniawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 4 (2022): Agustus 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Aedes aegypti is one of the most dangerous mosquitoes that can cause several deadly diseases, such as dengue fever, Chikungunya, Zika, and jaundice with high mortality rate. For now, no specific drug has been found that can cure the disease caused by Aedes Aegypti. One possible solution for handling this problem is to inhibit the growth and development of Aedes aegypti larvae. This study aims to implement Genetic Algorithm-Support Vector Machine to develop Quantitative Structure-Activity Relationship model for identification larvicidal phytocompounds as anti-aedes-aegypti. Hyperparameter tuning was performed to improve the performance of the models. Based on the result, we found that the best model was developed by the RBF kernel with the value of and score are 0.64 and 0.64, respectively.

Depression Detection on Twitter Social Media Using Decision Tree Marcello Rasel Hidayatullah; Warih Maharani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 4 (2022): Agustus 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Depression is a major mood illness that causes patients to experience significant symptoms that interfere with their daily activities. As technology has developed, people now frequently express themselves through social media, especially Twitter. Twitter is a social media platform that allows users to post tweets and communicate with each other. Therefore, detecting depression based on social media can help in early treatment for sufferers before further treatment. This study created a system to detect if a person is indicating depression or not based on Depression Anxiety and Stress Scale - 42 (DASS-42) and their tweets using the Classification and Regression Tree (CART) method with TF-IDF feature extraction. The results show that the most optimal model achieved an accuracy score of 81.25% and an f1 score of 85.71%, which are higher than baseline results with an accuracy score of 62.50% and an f1 score of 66.66%. In addition, we found that there were significant effects on changing the value of the maximum features in TF-IDF and changing the maximum depth of the tree to the model performance.

Mask Detection Using Convolutional Neural Network Algorithm Rizky Amalia; Febriyanti Panjaitan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 4 (2022): Agustus 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The World Health Organizations and the Ministry of Health of the Republic of Indonesia have required the use of masks to suppress the spread of COVID-19. WHO provides guidance on how to use a good mask to cover the mouth and nose. This study aims to detect the correct use of masks using the Convolutional Neural Network. CNN is a popular Deep Learning algorithm for image data classification problems. The Mask Usage Detector is built with the help of a pre-trained MobileNetV2 model with an architecture that supports media that has minimum computations. This study will also compare the performance of three optimization methods from CNN, namely Adam, SGD, and RMSprop in detecting the use of masks. Performance will be seen from the test results by analyzing the values of accuracy, precision, and recall. The dataset used is in the form of image data of 2,029 images for 2 categories, namely "masked" and "unmasked". A total of 1,623 images were used as training data and 406 images for test data. Based on the testing process, the accuracy of each optimization is 93.84% with Adam optimization, 84.48% with SGD optimization, and 93.10% with RMSprop optimization. With the proposed model, this study obtains the performance results of the three CNN optimizations, and it is concluded that adam's optimization gives better performance results than the other two optimizations.

Improving AI Text Recognition Accuracy with Enhanced OCR For Automated Guided Vehicle Florentinus Budi Setiawan; Farrel Adriantama; Leonardus Heru Pratomo; Slamet Riyadi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

This artificial intelligence robot uses a mini-computer to operate it and uses mechanical movement like a four-wheeled vehicle with a 2WD drive system. In this article, a control strategy of the AGV robot will be shown and implemented to detect the location. This research Uses OCR (Optical Character Recognition) for the OpenCV library itself which has been enhanced/modified. This enhanced OCR is the main library used in text recognition. This research produces very accurate text detection compared to the default OCR that was previously used on the AGV robot in our university. After the process of reading this text is passed, it will produce text previously read through the camera which will then provide output in the form of text where the AGV robot is located. After the reading is validated, the AGV robot will move to the next point until it returns to its starting point. Based on hardware implementation through testing in the AGV laboratory with artificial intelligence, it can work according to the algorithm and minimize reading errors with a 95% success rate.

Hierarchical Clustering and Deep Learning for Short-Term Load Forecasting with Influenced Factors Rio Indralaksono; M. Abdul Wakhid; Novemi Uki A; Galih Hendra Wibowo; M. Abdillah; Agus Budi Rahardjo; Diana Purwitasari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 4 (2022): Agustus 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Stable and reliable electricity is one of the essential things that must be maintained by the transmission system operator (TSO). That can be achieved when the TSO is able to set the balance between demand and production. To maintain the balance between production and demand, TSO should estimate how much demand must be served. In order to do that, the next day short-term load forecasting is an essential step that TSO should be done. Generally, load forecasting can be done through conventional techniques such as least square, time series, etc. However, this method has been sought over time as the electricity demand is increasing significantly over the years. Hence, this paper proposed another approach for short-term load forecasting using Deep Neural Networks, widely known as Long Short-Term Memory (LSTM). In addition, this paper clusters historical electrical loads to obtain similar patterns into several clusters before forecasting. We also explored other influence factors in the observed days, such as weather conditions and the human activity cycle represented by holidays, in a neural network-based classification model to predict the targeted clusters of electrical loads. East Java sub-system is used as the test system to investigate the efficacy of the proposed load forecasting method. From the simulation results, it is found that the proposed method could provide a better forecast on all indicators compared to the conventional method, as indicated by MaxAPE and MAPE are around 4,91% and 2,02%, while the RMSE is 112,08 MW.

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Yuhefizar

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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