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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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Cattle Weight Estimation Using Linear Regression and Random Forest Regressor Anjar Setiawan; Ema Utami; Dhani Ariatmanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5494

Abstract

The global cattle farming industry has benefits as a food source, livelihood, economic contribution, land environmental restoration, and energy source. The importance of predicting cow weight for farmers is to monitor animal development. Meanwhile, for traders, knowing the animal's weight makes it easier to calculate the price of the animal meat they buy. The authors propose estimating cattle weighting linear regression and random forest regression. Linear regression can interpret the linear relationship between dependent and independent variables, and random forest regression can generalize the data well. The data set used in this study consisted of ten variables: live body weight, withers height, sacrum height, chest depth, chest width, maclocks width, hip joint width, oblique body length, oblique back length and chest circumference. Find the model that produces the smallest MAE value. The results show that the linear regression algorithm can produce estimated weight values for cattle with the best performance. This model produces a mean absolute error (MAE) of 0.35 kg, a mean absolute percentage error (MAPE) of 0.07%, a root mean square error (RMSE) of 0.5 kg, and an R² of 0.99. Each variable has excellent correlation performance results and contributes to computer vision and machine learning.
Forecasting the Magnitude Category Based on The Flores Sea Earthquake Jufriansah, Adi; Khusnani, Azmi; Saputra, Sabarudin; Suwandi Wahab, Dedi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5495

Abstract

Earthquakes are a phenomenon that is still a mystery in terms of predicting events, one of which is the magnitude. As technology develops, there are many algorithms that can be used as approaches in earthquake forecasting. In the context of magnitude forecasting, the application of GaussianNB, Random Forest, and SVM has the potential to reveal these patterns and relationships in the data. With the six main phases of this research, namely data acquisition, data pre-processing, feature selection, model training, forecast result evaluation, and performance analysis, this study is expected to contribute to the development of more accurate and effective earthquake forecasting methods. From these results we first obtain the result that the GaussianNB model has a relatively simple and fast method in training its model. However, the weakness lies in the assumption of a Gaussian distribution, which may not always suit the complex and diverse characteristics of earthquake data. Second, Random Forest, this method can increase accuracy and overcome the overfitting problem that occurs when forecasting magnitudes. In contrast to GaussianNB, it tends to result in models with greater complexity and requires more time to compute. The third option is SVM, which has both benefits and drawbacks that must be taken into account. The capacity of SVM to separate data that has both linear and nonlinear separation is one of its key advantages; nevertheless, the main drawback is that it is sensitive to hyperparameter adjustments.
Analysis and Development of Eight Deep Learning Architectures for the Classification of Mushrooms Lia Farokhah; Suastika Yulia Riska
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5498

Abstract

One food item that is easy to find in nature is the mushroom. In terms of form and characteristics, mushrooms are similar. Arranging mushrooms into groups so that poisonous and non-poisonous ones can be separated is important. Real-time analysis of mushrooms is still not used very often. Previous studies focused primarily on performance and accuracy, ignoring architectural computing and a significant amount of data preprocessing. The data set used is more laboratory-conditioned. This will impede the process of widespread implementation. The study suggests changes to eight current architectures: Modified DenseNet201, DenseNet121, VGG16, VGG19, ResNet50, InceptionNetV3, MobileNet, and EfficientNet B1. The development of this architecture took place within the areas of classification and hyperparameter learning. In contrast to the other eight architectures, the MobileNet architecture exhibits the lowest computational performance and highest accuracy, according to the comparison results. When the confusion matrix is used for evaluation, an accuracy of 82.7% is achieved. Modified MobileNet has the best speed because it keeps a lower computation architecture and cuts down on unnecessary preprocessing. This means that many people can use smartphones with more realistic data conditions to make it work.
MobileNetV3-based Handwritten Chinese Recognition Towards the Effectiveness of Learning Hanzi Liang, Suwarno; Tony Tan; Jonathan, Jonathan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5505

Abstract

Writing Mandarin characters is considered the most challenging component for beginners due to the rules and character formations. This paper explores the potential of a machine learning-based digital learning tool for writing Mandarin characters. It also conducts a comparative study between MobileNetV2 and MobileNetV3, exploring different configurations. The research follows the Multimedia Development Life Cycle (MDLC) method to create both the application and machine learning models. Participants from higher education institutions that offer Mandarin courses in Batam, Indonesia, were involved in a User Acceptance Test (UAT). Data was gathered through questionnaires and analyzed using the System Usability Scale (SUS) methods. The results show positive user acceptance, with an SUS score of 77.92%, indicating a high level of acceptability. MobileNetV3Small was also preferred for recognizing the user’s handwriting, due to comparable accuracy size, rapid inference time, and smallest model size. While the application was well-received, several participants provided constructive feedback, suggesting potential improvements.
Artificial Neural Network-Based Prediction Model Back Propagation on Blood Demand and Blood Supply Tedy, Frengky; Batarius, Patrisius; Samane, Ign. Pricher A. N.; Sinlae, Alfry Aristo Jansen
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5508

Abstract

The balance between blood demand and supply at the Indonesian Red Cross Blood Transfusion Unit (UTD-PMI) is crucial. This condition must be maintained to reduce unused or expired blood supplies. Despite the situation in UTD-PMI, where the blood supply exceeds demand, there is still a shortage of blood when needed by patients. This research aims to model the prediction of blood demand and supply for each blood type using the Back Propagation artificial neural network approach. Data from the last 3 years, from 2020 to 2022, were utilized in this research process. There are three stages in this research process. The first stage involves the training process, using data from January 2020 to December 2021. The testing process utilizes data from January 2021 to December 2022. The prediction process involves displaying the forecasted data for the next 12 months from January to December 2023. The accuracy of the calculations is assessed using the mean square error (MSE). Ultimately, the research results present the prediction model for the four types of blood with respect to the demand and supply. These findings can serve as a reference to regulate future blood donation activities carried out by the UTD-PMI.
Predicting Smart Office Electricity Consumption in Response to Weather Conditions Using Deep Learning Wahyuzi, Zikri; Ahmad Luthfi; Dhomas Hatta Fudholi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5530

Abstract

This study investigates the intricate relationship between electricity consumption in smart office environments, temporal elements such as time, and external factors such as weather conditions. Using a data set that encompasses electrical consumption statistics, temporal data, and weather conditions, the research employs preprocessing, visualization, and feature engineering techniques. The predictive model for electric energy usage is constructed using deep learning architectures, including Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), and Bidirectional Gated Recurrent Unit (Bi-GRU). Evaluation metrics reveal that the LSTM model outperforms others, achieving minimal Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The study acknowledges the limitations of the data set, particularly when comparing electricity usage during work hours and outside working hours in a residential context. Future research aims to address these limitations, considering detailed meteorological data, missing data imputation, and real-time applications for broader applicability. The ultimate goal is to develop a predictive model that serves as a valuable tool for improving energy management in smart office settings, optimizing electricity usage, and contributing to long-term firm profitability.
Rotation Double Random Forest Algorithm to Predict The Food Insecurity Status of Households Rais; Agus Mohamad Soleh; Budi Susetyo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5540

Abstract

The ensemble tree method has been proven to handle classification problems well. The strength of the ensemble tree technique lies in the diversity and independence between each tree. Increasing the diversity of mutually independent decision trees improves the performance of the model. Various studies propose the development of ensemble tree-based models by forming algorithms that create decision trees that are formed independently of each other and have various inputs. These include random forest (RF), rotation forest (RoF), double random forest (DRF), and the latest is rotation double random forest (RoDRF). RoDRF rotates or transforms data with the intent of producing better diversity among the learning base. RoDRF applies the concept of variable rotation to trees based on the DRF algorithm. Random rotations or transformations on different feature subspaces produce different projections, leading to better generalization or prediction performance. This research aims to compare the performance of RoDRF with the RF, RoF, and DRF models on unbalanced data in cases of food insecurity. Class imbalance will be handled with two methods, namely EasyEnsemble and SMOTE-NC. The research results show that the DRF's model with EasyEnsemble techniques produces a model with the best performance among several algorithms tested. Although the resulting precision is 0.62274 and the AUC value is 0.68501, the model can predict each class equally. All algorithms with EasyEnsemble treatment have average AUC values significantly different from each other based on statistical test results. This research also used SHAP to explain variables that significantly contribute to the household's food insecurity status model.
Indonesian Crude Oil Price (ICP) Prediction Using Support Vector Regression Algorithm Des Suryani; Fadhila, Mutia
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5551

Abstract

Indonesian crude oil prices (ICP) experience fluctuating movements, influenced by several factors and other conditions that make ICP prices difficult to predict. ICP price prediction can be done with the Support Vector Regression (SVR) method. The information utilized originates from the Ministry of Energy and Mineral Resources' official website, specifically focusing on crude oil pricing data for six primary types of crude oil: SLC, Attaka, Duri, Belida, Banyu and SC. The data applied covers the time frame from January 2018 to August 2023. The forecast of the ICP relies on the date Brent variable and the Alpha factor through the use of support vector regression (SVR. In the case of a linear kernel, the parameters (epsilon) and C (cost) are determined using the Grid Search algorithm. In the Dated-Brent variable, the best parameter value is obtained with the value of C = 100 and  = 1 while for the Alpha variable, the best parameter value for the SLC crude oil type is C= 0.01 and  = 0.01, SC value C = 10 and  = 1, Banyu value C = 100 and  = 0.1, Banyu value C = 100 and  = 0.1, Belida value C = 0.01 and  = 0.1, Attaka value C = 0.1 and  = 0.01 and Duri value C = 1 and  = 1. The Alpha value of the main crude oil type is the Duri crude oil type with the lowest RMSE value of 0.9651. The MAPE value for SC crude oil type = 19.55% and Duri = 19.46% is in the good category. The R2 value for Banyu crude oil = 0.60610, SC = 0.42717 and Duri = 0.50421 is in the good category and the MAPE value for Dated-Brent of 49.73% is included in the fair category.
The Image Extraction Using the HSV Method to Determine the Maturity Level of Palm Oil Fruit with the k-nearest Neighbor Algorithm Mohammad Yazdi Pusadan; Indah Safitri; Wirdayanti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5558

Abstract

The oil palm is one of the monocot oil-producing plants in Indonesia. Sorting errors in oil palm fruit are caused by a sorter error when distinguishing the color of ripe and immature oil palm fruit. In addition to inefficient time, the area of oil palm plantations is also a factor that causes the sorter to make mistakes in sorting. This study aims to produce a system that can classify the maturity of oil palms based on feature extraction of characteristics of the hue, saturation and value (HSV) color features. The HSV method is used to produce color characteristics from the image of the oil palm fruit. Classification of oil palm fruit maturity is classified using the K-Nearest Neighbor (KNN) algorithm with a dataset of 400 oil palm fruit image data with a data sharing ratio of 70% training data and 30% test data. 280 image data were used as training data, divided into 140 image data of ripe oil palm fruit, 140 image data of immature oil palm fruit and 120 image data of oil palm used as test data which is divided into 60 image data of ripe oil palm and 45 unripe palm oil. Based on the result of tests that have been carried out using a confusion matrix with varied k values, namely, 5 and 7, the average precision is 94.16%.
A Comparative Study of HTTP and MQTT for IoT Applications in Hydroponics Nugraha, Irvan Rizki; Putra, Widhy Hayuhardhika Nugraha; Setiawan, Eko
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5561

Abstract

Hydroponics is based on nutrients in water. It must be regularly monitored to prevent plant defects. The Internet of Things has become a solution for remote hydroponic monitoring and is currently being tested on the Yuan Hidroponik Kelompok Wanita Tani (KWT). This system will send data every minute, and each data has a possibility of loss in transmission. There is a chance that this system will be implemented in other hydroponic organizations. As more devices are involved, it will affect server resources. This research will compare Message Queue Telemetry Transport (MQTT) and Hypertext Transfer Protocol (HTTP) as popular protocols used in IoT. A test with increasing clients shows that at 50 clients HTTP needs 87% CPU, while MQTT needs 22.63% CPU. A test with increasing payload shows that at 10,000 payload HTTP needs 94% CPU while MQTT needs 28.35% CPU. A test with fixed clients and payloads shows that HTTP has a CPU limit based on the clients involved. A transfer time test shows that HTTP needs 177.344 seconds while MQTT needs 3.24 seconds. An acceptance rate is calculated by incrementing the count for every incoming payload. It shows that HTTP can receive 30,000 payloads, unlike MQTT which can only receive 1680 payloads before losses.

Page 81 of 105 | Total Record : 1046

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