cover
Article Per Year (5 Year)
All
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
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 26 Documents
 1   2   3 
Search results for , issue "Vol 7 No 2 (2023): April 2023" : 26 Documents clear
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.
Analysis of Bit Rate and Distance Variation on Multiplexing System of Indoor Li-Fi Technology Using Movable LED Panel Fauza Khair; I Wayan Mustika; Anggun Fitrian Isnawati; Nur Azizah
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.v7i2.4753

Abstract

The major problem of using light fidelity (Li-Fi) technology is still limited to the line of sight (LOS) conditions, which poses a challenge to perform bandwidth efficiency to support increased bit rates, especially for indoor use. In addition, the distance between the lamp driver (transmitter) and the receiver becomes a critical discussion to determine the characteristics of propagation losses. Therefore, this study focuses on analyzing the performance of an indoor Li-Fi multiplexing system using a movable LED panel (LP) based on parameters of bit rate and distance variation on multiple-input multiple-output (MIMO) 2x2 and 4x4. The parameter analysis of signal quality included optical and electrical signal spectrum characteristics, signal-to-noise ratio (SNR), bit error rate (BER), and Q-factor parameters. Based on the results, the increase in bit rate and distance significantly increases the BER value and decreases the Q-factor value. Both the 2x2 and 4x4 mux systems can meet standards up to a bit rate of 30 Mbps at a LOS distance of 3 meters, while at a bit rate of 40 Mbps, there are no channels that meet the ITU-T standard. In addition, the quality of the signal received at a distance of 4 meters, the 2x2 mux system can only reach the standard at a bit rate of 20 Mbps for all channels. However, channel 3 and channel 4 on the 4x4 mux system model still have the BER and Q-factor values that meet the standard in the bit rate of 30 Mbps. However, the decrease in the SNR value affected by the bit rate increase and distance is insignificant. Therefore, it becomes an opportunity for further observation of the proposed multiplexing system, detection scheme, or responsivity, and signal processing on the receiver side to be reliable on the higher bit rate.
A Hybrid Method on Emotion Detection for Indonesian Tweets of COVID-19 Diana Purwitasari; Adi Surya Suwardi Ansyah; Arya Putra Kurniawan; Asiyah Nur Kholifah
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.v7i2.4816

Abstract

As a result of the COVID-19 pandemic, there have been restrictions on activities outside the home which has caused people to interact more and express their emotions through social media platforms, one of which is Twitter. Previous studies on emotion classification used only one feature extraction, namely the lexicon based or word embedding. Feature extraction using the emotion lexicon has the advantage of recognizing emotional words in a sentence while feature extraction using word embedding has the advantage of recognizing the semantic meaning. Therefore, the main contribution to this research is to use two lexicon feature extraction and word embedding to classify emotions. The classification technique used in this research is the Ensemble Voting Classifier by selecting the two best classifiers to try on both types of feature extraction. The experimental results for both types of feature extraction are the same, indicating that the best classifiers are Random Forest and SVM. Models using both types of feature extraction show increased accuracy compared to using only one feature extraction. The results of this emotional analysis can be used to determine the public's reaction to an event, product, or public policy.
Decomposing Monolithic to Microservices: Keyword Extraction and BFS Combination Method to Cluster Monolithic’s Classes Siti Rochimah; Bintang Nuralamsyah
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.v7i2.4866

Abstract

Abstract Microservices architecture is widely used because of the ease of maintaining its microservices, as opposed to encapsulating functionality in a monolithic, which may negatively impact the development process when the application continues to grow. The migration process from a monolithic architecture to microservices became necessary, but it often relies on the architect's intuition only, which may cost many resources. A method to assist developers in decomposing monolithic into microservices is proposed to address that problem. Unlike the existing methods that often rely on non-source code artifacts which may lead into inaccurate decomposition if the artifacts do not reflect the latest condition of the monolith, the proposed method relies on analyzing the application source code to produce a grouping recommendation for building microservices. By using specific keyword extraction followed by Breadth First Search traversal with certain rules, the proposed method decomposed the monolith's component into several cluster whose majority of cluster’s members have uniform business domain. Based on the experiment, the proposed method got an 0.81 accuracy mean in grouping monolithic's components with similar business domain, higher than the existing decomposition method's score. Further research is recommended to be done to increase the availability of the proposed method.
Detecting Fake News on Social Media Combined with the CNN Methods Anindika Riska Intan Fauzy; Erwin Budi Setiawan
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.v7i2.4889

Abstract

Social media platforms are created to facilitate human social life as technology develops. Twitter is one of the most popular and frequently used social media for exchanging information. This social media platform disseminates real-time and complete information. Unfortunately, there are not a few tweets that contain false information or are often referred to as hoaxes. Those hoaxes that existed on Twitter are very troubling for society. Fake news or hoaxes can cause misunderstandings in receiving information. Therefore, this research aimed at developing a system that can detect hoaxes on Twitter to anticipate their spread, which can be detrimental to related parties. The system being developed uses a deep learning approach with the Convolutional Neural Network (CNN), Term Frequency-Inverse Document Frequency (TF-IDF), Bidirectional Encoder Representations from Transformers (BERT), and Global Vectors (GloVe). The results of this study display the fake news detected by the system using the CNN method with baseline, BERT, and GloVe. The data have been adjusted to the keywords related to fake news and spread on online media, such as Hoax or Not from Detik.com, CekFakta from Kompas.com, etc. The results show the highest accuracy of 98.57% using CNN with a split ratio of 90:10, baseline unigram-bigram, BERT, and Top10 corpus tweet+IndoNews with an increase of 4.7%.
Image Transformation With Lung Image Thresholding and Segmentation Method Sahat Sonang Sitanggang; Yuhandri Yuhandri; Adil Setiawan
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.v7i2.4321

Abstract

Image transformation is important to obtain and find certain information about an image that was not previously known, such as pixels, geometry, size, and color. Following this, this research aims to analyze image transformation in producing better values using threshold and segmentation methods. The segmentation process is carried out based on two color models, namely hue saturation value (HSV) and red green blue (RGB). The image data used in this study was the x-ray image of the lungs from www.fk.unair.ac.id. which is processed using the Matlab 2021a application to help the analysis process. on the results of the image segmentation analysis carried out in this case, the greater the HSV and RGB threshold values used in the image data, the better and clearer the segmentation of the detected image results. In other words, the size of the thresholding value generated greatly affects the quality, brightness, size, and color of the resulting image. The best lung X-ray image segmentation results were obtained when using the threshold values HSV = 0.9 and RGB = 9.

Page 1 of 3 | Total Record : 26

 1   2   3 

Filter by Year

2023 2023


Reset
Filter By Issues
All Issue Vol 9 No 6 (2025): December 2025 Vol 9 No 5 (2025): October 2025 Vol 9 No 4 (2025): August 2025 Vol 9 No 3 (2025): June 2025 Vol 9 No 2 (2025): April 2025 Vol 9 No 1 (2025): February 2025 Vol 8 No 6 (2024): December 2024 Vol 8 No 5 (2024): October 2024 Vol 8 No 4 (2024): August 2024 Vol 8 No 3 (2024): June 2024 Vol 8 No 2 (2024): April 2024 Vol 8 No 1 (2024): February 2024 Vol 7 No 6 (2023): December 2023 Vol 7 No 5 (2023): October 2023 Vol 7 No 4 (2023): August 2023 Vol 7 No 3 (2023): Juni 2023 Vol 7 No 2 (2023): April 2023 Vol 7 No 1 (2023): February 2023 Vol 6 No 6 (2022): Desember 2022 Vol 6 No 5 (2022): Oktober 2022 Vol 6 No 4 (2022): Agustus 2022 Vol 6 No 3 (2022): Juni 2022 Vol 6 No 2 (2022): April 2022 Vol 6 No 1 (2022): Februari 2022 Vol 5 No 6 (2021): Desember 2021 Vol 5 No 5 (2021): Oktober2021 Vol 5 No 4 (2021): Agustus 2021 Vol 5 No 3 (2021): Juni 2021 Vol 5 No 2 (2021): April 2021 Vol 5 No 1 (2021): Februari 2021 Vol 4 No 6 (2020): Desember 2020 Vol 4 No 5 (2020): Oktober 2020 Vol 4 No 4 (2020): Agustus 2020 Vol 4 No 3 (2020): Juni 2020 Vol 4 No 2 (2020): April 2020 Vol 4 No 1 (2020): Februari 2020 Vol 3 No 3 (2019): Desember 2019 Vol 3 No 2 (2019): Agustus 2019 Vol 3 No 1 (2019): April 2019 Vol 2 No 3 (2018): Desember 2018 Vol 2 No 2 (2018): Agustus 2018 Vol 2 No 1 (2018): April 2018 Vol 1 No 3 (2017): Desember 2017 Vol 1 No 2 (2017): Agustus 2017 Vol 1 No 1 (2017): April 2017 More Issue