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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 22 Documents
Search results for , issue "Vol 5 No 5 (2021): Oktober2021" : 22 Documents clear
Implementation Word2Vec for Feature Expansion in Twitter Sentiment Analysis Naufal Adi Nugroho; Erwin Budi Setiawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 5 (2021): Oktober2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i5.3325

Abstract

Abstract Twitter is a microblog-based social media site launched on July 13, 2006. In March 2020, 476.696 tweets about the government policy in COVID-19 spread on Twitter were captured by the Institute for Development of Economics and Finance (Indef). Government policy has a standard meaning, namely a decision systematically made by the government with specific goals and objectives relating to the public interest, whether carried out directly or indirectly. Sentiment analysis analyzes people’s opinions, sentiments, evaluations, attitudes, and emotions from written language. In this decade, Sentiment Analysis is has become a trendy research area. The purpose of this paper is to focus how to implement word2vec using similarity word as a feature expansion for minimize the vocabulary mismatch in Twitter Sentiment Analysis using “word embeddings”. This research contains 11.395 tweets for a dataset, where the dataset will be used in two classifications: Support Vector Machine Algorithm and Artificial Neural Network Algorithm. The output of Word2Vec will be used for feature expansion in this research, where the algorithm of expansion will check in each row in the corpus where has a similarity vector with that word and will replace the word with the similarity of this words if the value is 0. The dataset in Feature Expansion is using 142.545 articles from Indonesian media. The result of this research is ANN is better than SVM, where the ANN without feature expansion gets 68.89 % and using feature expansion gets 72.58 %. For SVM, the final accuracy without feature expansion is 63.95 %, and using feature expansion gets 68.56 %. This research proves that feature expansion can improve the final accuracy.
Big Cats Classification Based on Body Covering Fernanda Januar Pratama; Wikky Fawwaz Al Maki; Febryanti Sthevanie
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 5 (2021): Oktober2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i5.3328

Abstract

The reduced habitat owned by an animal has a very bad impact on the survival of the animal, resulting in a continuous decrease in the number of animal populations especially in animals belonging to the big cat family such as tigers, cheetahs, jaguars, and others. To overcome the decline in the animal population, a classification model was built to classify images that focuses on the pattern of body covering possessed by animals. However, in designing an accurate classification model with an optimal level of accuracy, it is necessary to consider many aspects such as the dataset used, the number of parameters, and computation time. In this study, we propose an animal image classification model that focuses on animal body covering by combining the Pyramid Histogram of Oriented Gradient (PHOG) as the feature extraction method and the Support Vector Machine (SVM) as the classifier. Initially, the input image is processed to take the body covering pattern of the animal and converted it into a grayscale image. Then, the image is segmented by employing the median filter and the Otsu method. Therefore, the noise contained in the image can be removed and the image can be segmented. The results of the segmentation image are then extracted by using the PHOG and then proceed with the classification process by implementing the SVM. The experimental results showed that the classification model has an accuracy of 91.07%.
Analisis Kinerja Skema MPEG Surround Pada Pengkodean Audio 22 Kanal Menggunakan Bitrate 1000 - 2000 Kbps Ridwan, Ahmad; JR, Vitral; Satria, Habib; Elfitri, Ikhwana
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 5 (2021): Oktober2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i5.3372

Abstract

MPEG surround is one of the most popular multichannel audio coding standards used today. In producing quality sound, there are several parts that are influential, but those that are directly related to sound reproduction, one of which is the method of downmixing, mixing the right channels will improve the quality of the sound produced. Because this research is closely related to data computing, the Matrix Laboratory (MATLAB) application is used. There are 2 stages carried out in the MATLAB application, namely the encoding and decoding process. In the encoding process in MATLAB, each audio channel is set according to a previously designed scheme. The results of this encoding are then decoded, so that the ODG value of each tested audio channel is obtained. In his presentation, the design and analysis of several domnmixing schemes was carried out. There are 5 schemes tested and to determine the performance of each scheme, these schemes are tested at high bitrates, from the range of 1000 Kbps to 2000 Kbps. Based on the test, it was found that the effect of increasing the bitrate is directly proportional to the resulting audio quality. During testing Scheme-5 has a better level of stability among all downmixing schemes and the average percentage increase in Objective Difference Grade (ODG) for all audio samples is 9.6927 %.
Optimasi Parameter Support Vector Machine Berbasis Algoritma Firefly Pada Data Opini Film Styawati; Andi Nurkholis; Zaenal Abidin; Heni Sulistiani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 5 (2021): Oktober2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i5.3380

Abstract

The Support Vector Machine (SVM) method is a method that is widely used in the classification process. The success of the classification of the SVM method depends on the soft margin coefficient C, as well as the parameter  of the kernel function. The SVM parameters are usually obtained by trial and error, but this method takes a long time because they have to try every combination of SVM parameters, therefore the purpose of this study is to find the optimal SVM parameter value based on accuracy. This study uses the Firefly Algorithm (FA) as a method for optimizing SVM parameters. The data set used in this study is data on public opinion on several films. Class labels used in data classification are positive class labels and negative class labels. The amount of data used in this study is 2179 data, with the distribution of 436 data as test data and 1743 data as training data. Based on this data, an evaluation process was carried out on the Firefly Algorithm-Support Vector Machine (FA-SVM). The results of this study indicate that the Firefly Algorithm can obtain the optimal combination of SVM parameters based on accuracy, so there is no need for trial and error to get that value. This is evidenced by the results of the FA-SVM evaluation using a value range of C=1.0-3.0 and =0.1-1.0 resulting in the highest accuracy of 87.84%. The next evaluation using a range of values ​​C=1.0-3.0 and =1.0-2.0 resulted in the highest accuracy of 87.15%.
Studi Komparasi Model Klasifikasi Berbasis Pembelajaran Mesin untuk Sistem Rekomendasi Program Studi Pratama, Ahmad Rafie; Rio Rizki Aryanto; Lizda Iswari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 5 (2021): Oktober2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i5.3392

Abstract

Selecting a major can be quite difficult for prospective college students. The choice may have an effect not only on their academic life, but also on their career path. Due to some restrictions as the impact of the COVID-19 pandemic, universities must find novel ways to reach prospective students and assist them in choosing their majors, one of which is a college major recommendation system. This system can assist prospective students in determining the most appropriate majors for them based on data from the current students. Unlike other existing systems that employ either a rule-based or fuzzy model, this study employs a machine learning approach using data from undergraduate students at Universitas Islam Indonesia. This paper aims to compare several clustering models (i.e., K-means, Agglomerative, Birch, and DBSCAN) for the purpose of categorizing current students, to which the results will be used for classification purposes using various approaches (i.e., single stage vs. multistage), algorithms (i.e., multinomial logistic regression, random forest, and support vector machine), and scenarios (i.e., with or without GPA-based label). Our findings indicate that the K-means model outperformed all other clustering models and that the single stage with random forest classification model performed the best across all scenarios.
Sentiment Classification for Film Reviews by Reducing Additional Introduced Sentiment Bias Effendi, Fery Ardiansyah; Yuliant Sibaroni
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 5 (2021): Oktober2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i5.3400

Abstract

Film business and its individual reviews cannot be separated and film review sites such as IMDb is a credible source of reviews posted in public forums. With IMDb site reviews being unstructured and bias-heavy, classification methods by reducing additional sentiment bias is needed to create a balanced classification with lower polarity bias. Elimination of additional sentiment bias will improve the model as polarity is defined by non-bias method, resulting in models correctly defined which sequences of words is either positive or negative. This research limits the dataset by 50.000 rows of randomly extracted reviews from the IMDb website using dataset preparation methods such as Preprocessing, POS-Tagging, and Word Embeddings. Then preprocessed data is used in classification methods such as ANN, SWN, and SO-Cal. This paper also used bias processing methods such as Hyperparameter Tuning and BPM, with outputs evaluated using Accuracy and PBR metrics. This research yields 77.39 % for ANN, 66.32% for BPM, 75.6% for SO-Cal, and 76.26% for Hybrid classification. Best PBR resulted in two lexicon-based methods on 0.0009 for BPM, and 0.00006 for SO-Cal. More advanced model configuration in ANN can improve the model, and much complex lexicon models will be a future in the research topic.
Perbandingan Metode TF-ABS dan TF-IDF Pada Klasifikasi Teks Helpdesk Menggunakan K-Nearest Neighbor Riza Adrianti Supono; Muhammad Azis Suprayogi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 5 (2021): Oktober2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i5.3403

Abstract

Distribution of tickets to the destination unit is a very important function in the helpdesk application, but the process of distributing tickets manually by admin officers has drawbacks, namely ticket distribution errors can occur and increase ticket completion time if the number of tickets is large. Helpdesk text classification becomes important to automatically distribute tickets to the appropriate destination units in a short time. This study was conducted to compare the performance of helpdesk text classification at the Directorate General of State Assets of the Ministry of Finance using the K-Nearest Neighbor (KNN) method with the TF-ABS and TF-IDF weighting methods. The research was conducted by collecting complaint documents, preprocessing, word weighting, feature reduction, classification, and testing. Classification using KNN with parameters n_neighbor (k) namely k=1, k=3, k=5, k=7, k=9, k=11, k=13, k=15, k=17, and k=19 to classify 10,537 helpdesk texts into 8 categories. The test uses a confusion matrix based on the accuracy value and score-f1. The test results show that the TF-ABS weighting method is better than TF-IDF with the highest accuracy value of 90.04% at 15% and k=3.
Chicken Egg Fertility Identification using FOS and BP-Neural Networks on Image Processing Saifullah, Shoffan; Andiko Putro Suryotomo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 5 (2021): Oktober2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i5.3431

Abstract

This article aims to test FOS (first-order statistical) in extracting features of embryonated eggs. This test uses the initial step of image processing to get the best input image in feature extraction. The image processing method starts from the image acquisition process, then improves with image preprocessing and segmentation. Image acquisition in this study uses the concept of egg candling in a dark place captured with a smartphone camera. The acquisition results are improved by image preprocessing using gray scaling, image enhancement (by Histogram Equalization), and segmentation of chicken egg image. The segmentation results were extracted using FOS with five parameters: mean, entropy, variance, skewness, and kurtosis. Based on the calculation of these parameters, it is graphed and shows the difference in patterns between fertile and infertile eggs. However, some eggs have a similar pattern, thus affecting the identification process. The identification process used neural networks by the backpropagation method for training and testing. The training results provide an accuracy value of 100% of all training data; however, 80% of the new test data obtained test results at testing. This test is carried out with 100 data, 50 each for training and test data. Based on the test results, which significantly affect the level of accuracy is the feature extraction method. FOS pattern in detecting the fertility of chicken eggs by BP Neural Network is still categorized as low, so it is necessary to improve methods to get maximum results.
Deteksi Sarung Samarinda Menggunakan Metode Naive Bayes Berbasis Pengolahan Citra Septiarini, Anindita; Rizqi Saputra; Andi Tejawati; Masna Wati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 5 (2021): Oktober2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i5.3435

Abstract

Samarinda sarong is one of the cultural treasures in the form of cloth from Samarinda, East Kalimantan. It has a characteristic in the form of a square motif with a unique color combination. However, several people do not know the difference between a Samarinda sarong and a non-Samarinda sarong because the Samarinda sarongs may have a similar motif or color to a non-Samarinda sarong. This study aims to develop a Samarinda sarong detection method to distinguish between the sarong of Samarinda and non-Samarinda. The detection of the Samarinda sarong was carried out based on two features: color and texture. The feature extraction of color was applied using color moments and Gray Level Co-Occurrence Matrix (GLCM) for texture. The classification was implemented using the Naive Bayes method. The dataset used consists of 250 sarong images (150 Samarinda sarong images and 100 Non-Samarinda sarong images) divided into training and test data. It was divided using percentage split and cross-validation. The test results show the implementation of the color moments, GLCM, and Naive Bayes methods using a percentage split (70%) produce the best accuracy of 0.987 compared to using cross-validation (K=10) with an accuracy of 0.984. The difference may occur because the number of training and testing data used on percentage split and cross-validation is different. Moreover, the sarong images used on training and test data were chosen randomly.
Purwarupa Sistem Deteksi COVID-19 Berbasis Website Menggunakan Algoritma Convolutional Neural Network Fadli, Ari; Ramadhani, Yogi; Aliim, Muhammad Syaiful
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 5 (2021): Oktober2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i5.3450

Abstract

COVID-19 is a disease caused by coronavirus 2 (SARS-CoV-2). This virus belongs to the group of viruses that infect the respiratory system. Furthermore, the rapid rate of spread has made several countries implement a policy of implementing a lockdown to prevent the spread of this virus. In Indonesia, the government implemented the policy of "Pemberlakuan Pembatasan Kegiatan Masyarakat (PPKM)" to suppress the spread of this virus. Based on data from the Task Force for the Acceleration of Handling COVID-19 of the Republic of Indonesia, the number of confirmed positive cases as of August 6, 2021 is 3,568,331 people with a death toll of 102,375. The existence of the COVID-19 vaccine is currently under threat, this is due to the emergence of new variants of the COVID-19 virus. The RT-PCR method as the main standard used throughout the world in detecting this virus has a fairly high specificity, which is around 95 percent, which is a manual process that can only be done by health workers. In addition, this test takes a long time and the number of testing facilities is limited. The presence of X-ray scanning machines in hospitals can be used to increase the availability of COVID-19 testing facilities. The thoracic x-ray image generated by the scanner can be used to detect the virus easily, quickly and precisely. In this study, a website-based system was designed to detect the COVID-19 virus in thoracic x-ray images using a convolutional neural network algorithm. The results obtained show that this system is able to classify chest x-ray images into three classes, namely COVID-19, Viral Pneumonia, and normal. The accuracy value obtained is 89.6% and the F1 value is 87.9%.

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