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Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
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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Character Recognition of Handwriting of Javanese Character Image using Information Gain Based on the Comparison of Classification Method Irham Ferdiansyah Katili; Mochamad Arief Soeleman; Ricardus Anggi Pramunendar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4488

Abstract

Indonesia is a country rich in a variety of regional cultures. Regional airspace needs to be preserved so as not to become extinct. One of them is the local culture of Central Java Province, namely Javanese Character. In this modern era, globalization is growing in every country. The impact of globalization is increasingly widespread and developing in society. One effect of globalization is local people prefer foreign language skills to learn local languages. This study, applies the method of character recognition using a new combination workflow that contains Local Binary Pattern (LBP) and Information Gain. Then compare Support Vector Machine (SVM), k-Nearest Neighbor and Naïve Bayes. The LBP method is used to obtain an image's texture or shape characteristics. Information Gain is used for the feature selection algorithm, whereas SVM, k-Nearest Neighbor and Naïve ayes is used for the classification method. From previous research, the information gain method succeeded in increasing the accuracy by 2%. This research compares the SVM classification with another classification method, and the result shows that our proposed can improve classification performance. The best accuracy result using SVM classification gets 87,86%, at ten folds and cell size 64x64.
Identification of Color and Texture of Ripe Passion Fruit with Perceptron Neural Network Method Siswanto; Riefky N. Sungkar; M. Anif; Basuki Hari Prasetyo; Subandi; Ari Saputro; Buana Suhurdin Putra
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4612

Abstract

Research using artificial neural network methods has been developed as a tool that can help human tasks, one of which is for passion fruit UMKM entrepreneurs. The problem so far that has been faced by UMKM entrepreneurs of passion fruit is that it is difficult to identify ripe passion fruit with sweet and sour taste, because there are 6 colors of passion fruit and the color of passion fruit skin is visually slightly different, as well as the texture of maturity. The main purpose of this study was to identify the color structure and texture of the ripeness of passion fruit, in order to recognize the color and texture of the ripeness of passion fruit which is good for processing into syrup, jam, jelly, juice, passion fruit juice powder by entrepreneurs of UMKM of passion fruit. This study empirically tested the color and texture of the ripeness of 10 passion fruit using the perceptron artificial neural network learning method. The data is obtained from an image that will be entered into the program. The results of the identification process using the perceptron artificial neural network from the tests that have been carried out previously, the highest calculation results obtained with the best results using a learning rate of 0.8 and 500 epoch iterations and producing an accuracy of 80%.
Image Classification of Vegetable Quality using Support Vector Machine based on Convolutional Neural Network Hanny Nurrani; Andi Kurniawan Nugroho; Sri Heranurweni
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4715

Abstract

As part of an effort to develop intelligent agriculture, new methods for enhancing the quality of vegetables are being continually developed. In recent years, the Convolutional Neural Network (CNN) has shown to be the most successful and extensively used approach for identifying the quality of pre-trained vegetables. However, this method is time-consuming due to the scarcity of truly large, significant datasets. Using a pre-trained CNN model as a feature extractor is a straightforward method for utilizing CNNs' capabilities without investing time in training. While, Support Vector Machine (SVM excels at processing data with tiny dimensions and significantly larger instances. SVM more accurately classifies the flatten/vector feature supplied by the CNN fully connected layer with small dimensions. In addition, implementing Data Augmentation (DA) and Weighted Class (WC) for data variety and class imbalance reduction can improve CNN-SVM performance. The research results show highest accuracy during training always achieves 100% across all experimental options. With an average accuracy of 69.66% in the testing process and 92.51% in the prediction process for all data, the experimental findings demonstrate that CNN-SVM outperforms CNN in terms of accuracy performance in all possible experiments, with or without WC and or DA approach.
Comparison of Sentiment Analysis Methods Based on Accuracy Value Case Study: Twitter Mentions of Academic Article Muhamad Fahmi Fakhrezi; Adian Fatchur Rochim; Dinar Mutiara Kusomo Nugraheni
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4767

Abstract

The assessment of academic articles is based on the number of citations, but the number only is not enough. So now there is Altmetric which can measure the impact of academic articles from the number of citations and using social media, usually Twitter. Still, the number of mentions on Twitter is not enough because the expressions of the sentences vary. Mentions must be classified according to neutral, positive, and negative criteria. Sentiment analysis is performed on tweets to measure social media volume and attention related to research findings from academic articles. There are many sentiment analysis methods, so this study aims to compare sentiment analysis methods using Decision Tree, K-NN, Naïve Bayes, and Random Forest to get the most suitable methods. The evaluation method in this study uses the Confusion Matrix by searching for Accuracy, Precision, and Recall values. The results show that the most suitable sentiment analysis method is Naïve Bayes by obtaining the highest classification suitability value of the other methods, which has an actual positive sentiment value of neutral 2056, positive 1200, and negative 1292. In addition, Naïve Bayes gets the highest accuracy score of 95, 45%.
Covid-19 Fake News Detection on Twitter Based on Author Credibility Using Information Gain and KNN Methods Nanda Ihwani Saputri; Yuliant Sibaroni; Sri Suryani Prasetiyowati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4871

Abstract

Twitter is one of the social media that is used as a tool to share various kinds of information about various kinds of things that are of concern to social media users. One of the information shared is information about COVID-19, which is known that the COVID-19 pandemic is currently spreading throughout the world at a very alarming rate. COVID-19 is an infectious disease caused by SARS-COV-2. The World Health Organization (WHO) claims that the spread of COVID-19 is supported by the spread of false/fake news. So to find out the truth of the news, a COVID-19 fake news detector is needed so that users don't fall for the hoaxes circulating. This study aims to classify COVID-19 news on Twitter based on author credibility. Credibility in question is a person's perception of the validity of information and is a multidimensional concept that is used as a means of receiving information to assess the source of communication. The method used in this research is Information Gain and KNN. KNN (K-Nearest Neighbor) is a supervised learning algorithm that works by classifying a set of data based on classified training data. Information Gain is used to ranking the most influential attributes, and KNN is used to classify data based on learning data taken from the nearest neighbors. The research consists of 6 main stages, namely data collection (crawling data), data preprocessing, feature extraction, feature selection, data split into training data and testing data, KNN stage, and data evaluation stage. The research carried out succeeded in obtaining an accuracy value of 91%, a correlation value between credibility and hoax of 0.115, and a p-value <0.005.
Implementation of Naïve bayes Method for Predictor Prevalence Level for Malnutrition Toddlers in Magelang City Endah Ratna Arumi; Sumarno Adi Subrata; Anisa Rahmawati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 2 (2023): April 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i2.4438

Abstract

Nutritional status is an important factor in assessing the growth and development rate of babies and toddlers. Cases of malnutrition are increasing, especially in magelang city. Because nutritional problems (Malnutrition) can affect the health of toddlers. Therefore, this study aims to predict the level of prevalence of malnutrition with the Naïve Bayes method. This research uses an observational design, a single center study at the Magelang City Office, using the Naïve bayes method which is used as an application of time series data, and is most widely used for prediction, especially in data sets that have many categorical or nominal type attributes. The Naïve bayes method is used to predict such cases of malnutrition. The results of this study show that the Naïve Bayes method succeeded in predicting the magnitude of cases of malnourished toddlers in Magelang City with an accuracy percentage of 75% due to the very minimal amount of training data, and the areas that have the most malnutrition are in three areas, namely Magersari, North Tidar and Panjang.
TAARA Method for Processing on the Network Forensics in the Event of an ARP Spoofing Attack Agus Wijayanto; Imam Riadi; Yudi Prayudi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 2 (2023): April 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i2.4589

Abstract

According to reports in 2021 by Kaspersky, requests for investigations into suspicious network activity, such as ARP Spoofing, which can result in sophisticated attacks, reached up to 22%. Several difficulties with examining network systems have been overcome thanks to network forensic investigations. This study aims to perform a network forensic analysis of ARP spoofing attacks using Wireshark forensic tools and Network Miner with a sniffer design process to capture traffic on the router side. In order to gather reliable evidence, this study employs the TAARA method as a network forensic investigation process. Based on the research conducted, it can be demonstrated that an attack took place from eight PCAP files. The information that was gathered, such as the IP address and MAC address of the attacker, the IP address and MAC address of the target, and the date and time of the attack are examples of evidence information that was gathered. This study also shows that network forensic operations can use the Wireshark forensic tool to obtain more detailed data.
Forensic Analysis of Faces on Low-Quality Images using Detection and Recognition Methods Verry Noval Kristanto; Imam Riadi; Yudi Prayudi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 2 (2023): April 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i2.4630

Abstract

Facial recognition is an essential aspect of conducting criminal action investigations. Captured images from the camera or the recording video can reveal the perpetrator's identity if their faces are deliberately or accidentally captured. However, many of these digital imagery results display the results of image quality that is not good when seen by the human eye. Hence, the facial recognition process becomes more complex and takes longer. This research aims to analyze face recognition on a low-quality image with noise, blur and brightness problem to help digital forensic investigator do an investigation in recognizing faces that the human eye can’t do. The Viola-Jones algorithm method has several processes such as the Haar feature, integral image, adaboost, and cascade classifier for detecting a face in an image. Detected face will be passed to the next process for recognition call Fisher’s Linear Discriminant (FLD), Local Binary Pattern’s (LBP) and Principal Component analysis (PCA). The software's facial recognition feature shows one of the images in the database that the program suspects has the same face as the analyzed face image. In conclusion, from the analysis we determined that LBP approach is the best among the other recognition methods for blur and brightness problem, bet found PCA method is the best for recognize face in noise problem. The software's facial recognition feature shows one of the images in the database that the program suspects has the same face as the analyzed face image. The position of the face object in the image, whether or not there is an additional object that was not previously included in the image in the dataset, as well as the brightness level of an image and the color of the face's skin, all affect the accuracy rates.
Improving the Accuracy of C4.5 Algorithm with Chi-Square Method on Pure Tea Classification Using Electronic Nose Mula Agung Barata; Edi Noersasongko; Purwanto; Moch Arief Soeleman
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 2 (2023): April 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i2.4687

Abstract

Tea is one of the plantation products within the Ministry of Agriculture of the Republic of Indonesia, which plays an essential role as a mainstay commodity that boosts the Indonesian economy. Each type of tea has different properties, and the aroma of each type of tea can measure the quality of the tea. The human sense of smell is still very limited in classifying pure types of tea. Therefore, a device is needed to help measure the aroma of tea from an electronic nose. The devices attached to several gas sensors help humans take data from the smell of pure tea and calculate the value of each type of tea to test datasets with data mining algorithms. This study uses the C4.5 algorithm as a classification method with advantages over noise data, missing values, and handling variables with discrete and continuous types. Meanwhile, Chi-square is used to perform attribute severing in the data preprocessing process to increase the accuracy of dataset testing. Testing a pure tea dataset with four whole attributes, namely CO2, CO, H2, and CH4, using the C4.5 algorithm resulted in an accuracy of 93.65% and an increase in the accuracy performance of the C4.5 algorithm by 94.27% with dataset testing using Chi-Square feature selection with the two highest value attributes.
Tajweed-YOLO: Object Detection Method for Tajweed by Applying HSV Color Model Augmentation on Mushaf Images Anisa Nur Azizah; Chastine Fatichah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 2 (2023): April 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i2.4739

Abstract

Tajweed is a basic knowledge of learning to read the Al-Qur’an correctly. Tajweed has many laws grouped into several parts so that only some people can memorize and implement Tajweed properly. Therefore, it is necessary to have an automatic detection system to facilitate the recognition of Tajweed, which can be used daily. This study presents Tajweed-YOLO, which applies the HSV color augmentation model to detect Tajweed objects in Mushaf images using YOLO. The contribution to this study was to compare the three versions of You Only Look Once (YOLO), i.e., YOLOv5, YOLOv6, and YOLOv7, and usage of the HSV color model augmentation to improve Tajweed detection performance. Comparing the three YOLO versions aims to solve problems in detecting small objects and recognizing various forms of Mushaf writing fonts in Tajweed detection. Meanwhile, the HSV color model aims to recognize Tajweed objects in various Mushaf and handle minority class problems. In this study, we collected four different Al-Qur’an mushaf with 10 Tajweed classes. The augmentation process can increase the detection performance by up to 85% compared to without augmentation 6th Class (Mad Jaiz Munfashil) using YOLOv6. The comparison of three YOLO versions concluded that YOLOv7 was better than YOLOv5 and YOLOv6, seen in data with augmentation and without augmentation. The evaluation results of mAP0.5 on 17 test data on the YOLOv7, YOLOv6, and YOLOv5 models are 80%, 69%, and 71%, respectively. These results prove that this research model’s results are suitable for the real-time detection of Tajweed.

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