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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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Analysis of the Quality of Natural Dyes in Weaving Exposed to Sunlight Using MSE and PSNR Parameters Patrisius Batarius; Alfry Aristo Sinlae; Elisabeth F. Fahik
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i5.4339

Abstract

It is widely assumed that natural dyes in weaving degrade in quality when exposed to sunlight for an extended period. This indication is visible to the naked eye. There is currently no standard for evaluating the quality of natural dyes. The Boti tribe's weaving on Timor Island, East Nusa Tenggara Province, is one type of weaving that uses natural dyes. The dye is made from corn flour and a combination of "nobah" leaves and the bark of the "bauk ulu" tree (from the local language). White (from corn flour) and blue-black are the colors produced by dyeing the yarn. The purpose of this research is to examine the image quality of the Boti tribe's woven fabric. The parameters used were Means Square Error (MSE), Peak Signal to Noise (PSNR), and RGB values. The image of the weaving used as a reference is compared to the image of the sun-dried weaving. The image capture distance was 30 cm, and the cropped RGB image size was 423x623x3. The experimental method was used in the research. The drying time was one hour, and it was repeated every hour between 10:00 and 15:00 local time. The sun-dried images were photographed, and parameter comparisons were performed for analysis. The results demonstrated that the MSE and PSNR methods were effective in measuring the image quality of weaving dyed with natural dyes. The average value has changed by 8.42% for the R-value, 8.58% for the G value, and 9.68% for the B value. The average PSNR for RGB images is 9.44288 dB, and the MSE is 7477.52. For grayscale images, the average PSNR is 10.52 dB and the average MSE is 5832.06.
Topic Classification of Quranic Verses in English Translation Using Word Centrality Measurement Achmad Salim Aiman; Kemas Muslim Lhaksmana; Jondri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i5.4358

Abstract

Every Muslim in the world believes that the Quran is a miracle and the words of God (Kalamullah) revealed to the Prophet Muhammad SAW to be conveyed to humans. The Quran is used by humans as a guide in dealing with all problems in every aspect of life. To study the Quran, it is necessary to know what topic is being discussed in every single verse. With the help of technology, the verses of the Quran can be given topics automatically. This task is called multilabel classification where input data can be classified into one or more categories. This research aims to apply the multilabel classification to classify the topics of the Quranic verses in English translation into 10 topics using the Word Centrality measurement as the word weighting value. Then a comparison is made to the 4 classification methods, namely SVM, Naïve Bayes, KNN, and Decision Tree. The result of the centrality measurement shows that the word ‘Allah’ is the most important or the most central word of the whole document of the Quran with the scenario using stopword removal. Furthermore, the use of word centrality value as term weighting in feature extraction can improve the performance of the classification system.
Prototype of Body Temperature and Oxygen Saturation Monitoring System Using DS18B20 and MAX30100 Sensors based on IOT Izhangghani; Irmayatul Hikmah; Slamet Indriyanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i5.4385

Abstract

After people died from infections during the COVID-19 epidemic, this attracted a lot of attention. COVID-19 causes symptoms such as fever, headache, sore throat, shortness of breath, and others. Many deaths are asymptomatic, which makes the problem even bigger. Therefore, real-time system monitoring is needed. Monitoring will be carried out about measuring body temperature and oxygen saturation in patients. Monitoring body temperature is necessary because it can detect symptoms of COVID-19 in patients earlier. The concept of the Internet of Things (IoT) is to enable devices to send and receive data over an internet network. The monitoring system to be built uses NodeMCU ESP8266, DS18B20 sensor, and MAX30100 sensor. Data communication is used in the exchange of information using WiFi. Applications made using MIT App Inventor are used to view body temperature and oxygen saturation data. This system is expected to reduce the number of deaths due to COVID-19. DS18B20 sensor has a sensor accuracy of 99,73% and an average error of 0,27%. MAX30100 sensor has an accuracy rate of 99,18% and an average error of 0,82%. Delay test results show an average of 155,57 ms, and packet loss test results show an average of 0%. The result of a system that has been tested said that both sensors can read well.
Big Five Personality Assessment Using KNN method with RoBERTA Athirah Rifdha Aryani; Erwin Budi Setiawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i5.4394

Abstract

Personality is the general way a person responds to and interacts with others. Personality is also often defined as the quality that distinguishes individuals. Social media was created to help people communicate remotely and easily. These personalities fall into five categories known as the Big Five personality traits, namely Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism (OCEAN). The use of K-Nearest Neighbour (KNN) is a method of classifying objects based on the training data closest to them. To overcome the data imbalance during training data, we use K-Means SMOTE (Synthetic Minority Oversampling Technique). Other features such as LIWC (Linguistic Inquiry Word Count), Information Gain, Robustly Optimized BERT Approach (RoBERTa), and hyperparameter tuning can improve the performance of the systems we build. The focus of this study is to present an analysis of Twitter user behavior that can be used to predict the personality of the Big Five Personality using the KNN method. The Important aspect to consider when using this method, namely accuracy in classifying the Big Five Personalities. The experimental results show that the accuracy of the KNN method is 72.09%, which is 95.28% gain above the specified baseline.
Network Security Analysis Simulation at the GCS in the UCAV to support the Indonesian Defense Area Bita Parga Zen; Anggi Zafia; Iwan Nofi Yono Putro
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i5.4412

Abstract

An unmanned Combat Aerial Vehicle (UCAV) is an unmanned aircraft that has a serial control communication device that can be seen directly in real-time. In carrying out UCAV flights, it requires good and stable data transmission security so that signal loss does not occur during the communication process. Researchers create a concept of a security scheme in communication at the Ground Control Station (GCS) that can be used for the use of UCAV communication at long distances, using the Quality of Service (QoS) method from OPEN VPN with parameters Throughput, Packet Loss, Delay (Latency) and Jitter can determine the reliability of a UCAV communication network. Based on the results of Quality of Service (QoS) testing with OpenVPN Autopilot UCAV objects on the ICMP protocol have the smallest packet loss value of 0%, the delay parameter is 5.2ms, the jitter parameter gets a high value of 4.68 ms higher than the TCP protocol and UDP. The TCP protocol has a relatively small packet loss value of 0.3% and ranks second to the ICMP protocol, then the delay value is 8.48 ms greater than the ICMP and UDP protocols, and the jitter parameter value is 0.0013 ms smaller than the ICMP and UDP protocols. The use of VPN OVPN is a good recommendation. Still, researchers suggest that should use not only OPEN VPN but also L2TP VPN and PPTP VPN for security at the Ground Control Station at UCAV as a comparison.
Exploring feature selection techniques on Classification Algorithms for Predicting Type 2 Diabetes at Early Stage Mila Desi Anasanti; Khairunisa Hilyati; Annisa Novtariany
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i5.4419

Abstract

Predicting early Type 2 diabetes (T2D) is critical for improved care and better T2D outcomes. An accurate and efficient T2D prediction relies on unbiased relevant features. In this study, we searched for important features to predict T2D by integrating ML-based models for feature selection and classification from 520 individuals newly diagnosed with diabetes or who will develop it. We used standard machine learning classifications, such as logistic regression (LR), Gaussian naive Bayes (NB), decision tree (DT), random forest (RF), support vector machine (SVM) with linear basis function, and k-nearest neighbors (KNN). We set out to systematically explore the viability of main feature selection representing each different technique, such as a statistical filter method (F-score), an entropy-based filter method (mutual information), an ensemble-based filter method (random forest importance), and a stochastic optimization (simultaneous perturbation feature selection and ranking (SpFSR)). We used a stratified 10-fold cross-validation technique and assessed the performance of discrimination, calibration, and clinical utility. We attained the highest accuracy of 98% using RF with the full set of features (16 features), then used RF as a classifier wrapper to select the important features. We observed a combination of SpFSR and RF as the best model with a P-value above 0.05 (P-value = 0.26), statistically attaining the same accuracy as the full features. The study's findings support the efficiency and usefulness of the suggested method for choosing the most important features of diabetic data: polyuria, gender, polydipsia, age, itching, sudden weight loss, delayed healing, and alopecia.
Aspect-Based Sentiment Analysis on Twitter Using Logistic Regression with FastText Feature Expansion Hanif Reangga Alhakiem; Erwin Budi Setiawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i5.4429

Abstract

Social media has recently been widely used by users, especially Indonesians, as a place to express themselves in sentences, pictures, sounds, or videos. Twitter is one of the social media favored by people of diverse ages. Twitter is a social media that provides features like social media in general. However, Twitter has a unique feature where users can send or read text messages limited to only a few characters. Therefore, user tweets with topics related to a particular product can be utilized by companies to become input in the development of these products. This research was conducted using tweet data on the topic of Telkomsel, which is divided into two aspects, namely signal and service. Aspect-based sentiment analysis of Telkomsel was carried out using Logistic Regression with FastText feature expansion to reduce vocabulary mismatch in tweets so that the classification stage can be performed optimally. In addition, the Synthetic Minority Oversampling Technique (SMOTE) sampling method was applied to overcome data imbalance. The test results prove that feature expansion can improve F1-Score values for signal and service aspects. For the signal aspect, F1-Score increased by 3.33% from the baseline with a value of 96.48%. While for the service aspect, F1-Score increased by 12.91% from the baseline with a value of 95.57%.
Optimization Ground Glass Opacities (GGO) Detection Using Multipixel Interpolation Techniques Musli Yanto; Yogi Wiyandra
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i5.4433

Abstract

Ground Glass Opacities (GGO) are a picture of abnormal lung conditions characterized by white or gray areas. This picture of GGO in the lungs could previously be detected based on the results of medical examinations such as Computerized Tomography (CT scan) and Magnetic Resonance Imaging (MRI) images of patients suffering from Covid-19. However, from the results of the examination, it can be seen that the CT scan and MRI images still have a noise level that is too high, causing difficulties in describing the distribution pattern of the GGO itself. The purpose of this study was to optimize the detection of GGO on MRI images using the Multipixel Interpolation technique. The detection process adopts several stages including image preprocessing, edge detection process, and gradient morphological segmentation. Image preprocessing is done to remove noise and improve the MRI input image. The edge detection process is carried out to detect lung organs automatically using the Canny method which is optimized with the multipixel interpolation technique. The final stage of the research is the segmentation process using a gradient morphology technique to see the spread of GGO in patients with Covid-19 contained in the MRI image. The results of this study present an overview of the GGO pattern with fairly good results. The results of the GGO pattern description will also measure the level of spread to see the severity of pneumonia. Based on the results presented, this research is useful as an alternative solution in the process of diagnosis and treatment of Covid-19 patients.
Intermittent Demand Forecasting Using LSTM With Single and Multiple Aggregation Fityan Azizi; Wahyu Catur Wibowo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i5.4435

Abstract

Intermittent demand data is data with infrequent demand with varying number of demand sizes. Intermittent demand forecasting is useful for providing inventory control decisions. It is very important to produce accurate forecasts. Based on previous research, deep learning models, especially MLP and RNN-based architectures, have not been able to provide better intermittent data forecasting results compared to traditional methods. This research will focus on analyzing the results of intermittent data forecasting using deep learning with several levels of aggregation and a combination of several levels of aggregation. In this research, the LSTM model is implemented into two traditional models that use aggregation techniques and are specifically used for intermittent data forecasting, namely ADIDA and MAPA. The result, based on tests on the six predetermined data, the LSTM model with aggregation and disaggregation is able to provide better test results than the LSTM model without aggregation and disaggregation.
Identify the Color and Shape of Eggplant Using Back Propagation Method Siswanto; Yuhefizar; M. Anif; Basuki Hari Prasetyo; Ari Saputro
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i5.4453

Abstract

Currently, artificial neural networks are being developed as a tool that can help with human tasks. The main purpose of this study is to identify the structure of an eggplant, and to distinguish the type of eggplant. This study empirically tested the shape and color of several eggplants using the back propagation neural network learning method. The data is obtained from an image that will be entered into the program. The data used in the identification process are two photos containing two types of eggplant, the first eggplant is green and round and the next eggplant is purple and oval. The results of the identification process using this backpropagation from the tests that have been carried out previously, the highest calculation results obtained with the best results using a learning rate of 0.7 and epoch iterations of 500 and producing an accuracy of 73.33%.

Page 60 of 105 | Total Record : 1046

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