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PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Published by Institut Riset dan Publikasi Indonesia
ISSN : 3024921X     EISSN : 30248043     DOI : https://doi.org/10.57152/predatecs
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering
PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science is a scientific journal published by the Institute of Research and Publication Indonesian (IRPI) or Institut Riset dan Publikasi Indonesia (IRPI). The main focus of PREDATECS Journal is Engineering, Data Technology and Computer Science. PREDATECS Journal is written in English consisting of 8 to 12 A4 pages, using Mendeley reference management and similarity/ plagiarism below 20%. Manuscript submission in PREDATECS Journal uses the Open Journal System (OJS) system using Microsoft Word format (.doc or .docx). The PREDATECS Journal review process applies a Closed System (Double Blind Reviews) with 2 reviewers for 1 article. Articles are published in open access and open to the public.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 14 Documents
 1   2 
Implementation of Naïve Bayes Classifier for Classifying Alzheimer’s Disease Using the K-Means Clustering Data Sharing Technique Wildani Putri; Delvi Hastari; Kunni Umatal Faizah; Siti Rohimah; Devy Safira
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 1 No. 1: PREDATECS July 2023
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v1i1.803

Abstract

Alzheimer's disease is a neurodegenerative disease that is very universal and characterized by memory loss and cognitive function decline which ultimately leads to dementia. In 2015, it is estimated that around million people worldwide will suffer from Alzheimer's disease or dementia. Globally, the number of Alzheimer's diseases will increase from 26.6 million in 2006 to 106.8 million cases in 2050. Due to the large number of people with Alzheimer's disease, it is necessary to classify symptoms that lead to indicators of Alzheimer's disease, so that data mining methods are used for data processing. Alzheimer's data taken from Kaggle amounted to 373 records, through the stages of data preprocessing, data sharing using the Hold-Out method and clustering with AK-Means algorithm. The data is processed using data mining techniques using NBC algorithms. Validation testing the accuracy value obtained the result that the NBC algorithm with K-Means Clustering data sharing has relatively better accuracy than the hold-Out method of 91.89%.
Implementation of C4.5 and Support Vector Machine (SVM) Algorithm for Classification of Coronary Heart Disease Muhammad Ridho Anugrah; Nola Ardelia Al-Qadr; Nanda Nazira; Nurul Ihza
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 1 No. 1: PREDATECS July 2023
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v1i1.805

Abstract

Coronary Heart Disease (CHD) is a chronic disease that is not contagious and can cause heart attacks. This makes CHD one of the diseases that cause the highest mortality globally. CHD can be caused by the main factor, namely an unhealthy lifestyle, so that in an effort to identify and deal with CHD, many studies have been conducted, one of which is the use of information technology. With so many CHD patient data, data mining can be used using. classification methods include C4.5 algorithm and Support Vector Machine (NBC). The C4.5 algorithm is a decision tree-like algorithm that groups attribute values into classes so that it resembles a tree, while SVM is an algorithm that separates data with a hyperplane. This study aims to classify the CHD dataset by comparing the C4.5 and SVM algorithms. So that the best accuracy value for this data is produced, namely the SVM algorithm of 64.51% and followed by the C4.5 algorithm of 64.30%.
Application of the Supervised Learning Algorithm for Classification of Pregnancy Risk Levels Zairy Cindy Dwinnie; Luthfia Khairani; Margareta Amalia Miranti Putri; Jeni Adhiva; Muhammad Inas Farras Tsamarah
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 1 No. 1: PREDATECS July 2023
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v1i1.806

Abstract

MMR is the number of women who die due to disorders during pregnancy or their treatment (excluding accidents, suicides, or incidental cases) during pregnancy, childbirth, and during the puerperium or 42 days after giving birth. This research aims to classify pregnancy risk datasets, namely to compare the performance of the NBC, K-NN, and SVM methods on the pregnancy risk status dataset and to find out the accuracy comparison of the algorithm results above. From the results of the analysis, it was found that of the three algorithms it resulted in a classification of pregnancy risk levels with the highest value occurring at a high level. To determine the accuracy of the data, a comparison was made between the three algorithms. Based on the confusion matrix namely Accuracy, Precision, and Recall. The results of the comparison can be concluded that the KNN algorithm provides the highest accuracy of 77.55%, NBC of 69.39%, and the lowest accuracy by SVM of 67.35%. These results state that the KNN algorithm classifies pregnancy risk level data better than the other two algorithms
Random Forest Optimization Using Particle Swarm Optimization for Diabetes Classification Pangeran Fadillah Pratama; Desvita Rahmadani; Rahma Sani Nahampun; Della Harmutika; Akhas Rahmadeyan; Muhammad Fikri Evizal
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 1 No. 1: PREDATECS July 2023
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v1i1.809

Abstract

Diabetes mellitus is a chronic degenerative disease caused by a lack of insulin production in the pancreas or the body's ability to use insulin less effectively. According to a report by the World Health Organization (WHO), 4% of the total deaths in the world are caused by diabetes. The International Diabetes Federation (IDF) notes that in 2013 there has been an increase in diabetes sufferers. Indonesia is the seventh place with the largest number of cases of diabetes mellitus. In this study, the method used to classify diabetes is using a random forest algorithm with Particle Swarm Optimization (PSO) optimization. This study resulted in an accuracy of the random forest classification algorithm of 78.2% and 82.1 using PSO optimization with an increase in value of 3.9%. It can be concluded that PSO optimization can provide a better increase in classification accuracy values when compared to the random forest algorithm without PSO optimization
Sentiment Analysis of Towards Electric Cars using Naive Bayes Classifier and Support Vector Machine Algorithm Suryani Suryani; Muhammad Fauzi Fayyad; Daffa Takratama Savra; Viki Kurniawan; Baihaqi Hilmi Estanto
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 1 No. 1: PREDATECS July 2023
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v1i1.814

Abstract

The use of non-renewable energy sources causes a reduction in fossil fuel resources, and greenhouse gas emissions. Based on the 2020 Climate Transparency Report, G20 member countries are trying to minimize gas emissions according to the target of the Nationally Determined Contribution (NDC), that the transportation sector contributes 27% of air pollution. The solution to reduce greenhouse gas emissions is to start using electric cars. The change from conventional transportation to electric transportation is expected to reduce carbon emissions and dependency on fossil fuels. However, the transition from conventional transportation to electric transportation raises pros and cons for the people of Indonesia. Social media Twitter is a forum for sharing opinions. Twitter users can express opinions on a matter. This study uses the sentiment analysis method to determine public opinion on the use of electric cars in Indonesia. Sentiment classification was performed using the NBC and SVM Algorithms. The results of this study indicate the use of two different algorithms, namely the Naive Bayes Classifier and SVM with the highest accuracy in Naive Bayes with k = 2 and k = 9 is 88%, while the highest accuracy in SVM with k = 9 and k = 10 is 90%. Thus, SVM has better capabilities than Naive Bayes in this study.
Implementation of Support Vector Machine and Random Forest for Heart Failure Disease Classification Astriana Rahmah; Nurhafiza Sepriyanti; Muhammad Hafis Zikri; Isnani Ambarani; Muhammad Yusuf bin Shahar
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 1 No. 1: PREDATECS July 2023
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v1i1.816

Abstract

Heart failure is a life-threatening disease and its management should be considered a global public health priority. The use of data mining in data processing operations to identify existing patterns and identify the information stored in them. In this study, researchers classify using two algorithms for comparison of algorithms, namely Random Forest (RF) and Support Vector Machine (SVM). The purpose of this study is to find patterns in finding the best accuracy for the 2 algorithms. The results of this study obtained an accuracy of 81.51%. with a Hold Out of 60 : 40% on the SVM algorithm, while an accuracy of 83.33 % with a Hold Out of 9 0 : 1 0% on the R F algorithm . From these results it can be seen that the highest accuracy value is obtained at RF making the best algorithm compared to the SVM algorithm.
Comparative Study on NACA-9405, NACA-9503 and NACA-9506 Airfoil Profiled Blade Open-Channel Flow Cross-Flow Turbine Aji Putro Prakoso; Wirawan Piseno; Damawidjaya Bisono; Deny Bayu Saefudin; Ahmad Fudholi; Fikri Nur Rohman
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 1 No. 1: PREDATECS July 2023
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v1i1.833

Abstract

Several agricultural areas in Indonesia are classified as underdeveloped rural areas, and challenging to acquire electricity from the primary grid. On the other hand, some pico hydropower potentials can be found in agriculture, especially at the irrigation dam spillway flows. However, no such technology can convert the energy inside the water open-channel flow through dam spillways into electricity. Crossflow turbines (CFT) are expected to utilize dam spillways’ power due to their ability to convert the kinetic energy of water. Prior studies found that airfoil profile could slightly increase CFT efficiency and affect the interaction between water and turbine. Using planar two-dimensional computational fluid dynamics (CFD) numerical simulation analysis, the current study investigated the energy conversion phenomenon inside different open-channel CFT blade profiles. This study simulates CFT working at 3 meters of total head and 0.04 m3/s of water discharge put into the dam spillway’s downstream. The turbine blade profiled with the National Advisory Committee of Aeronautics (NACA) standard airfoil numbered 9405, 9503, and 9506 are being compared and investigated in the present study. CFD numerical analysis results show that the forward direction NACA airfoil profiled blades deliver better efficiency than the reversed one. These findings contradict the prior study’s results which tested airfoil profiled CFT working as usual with nozzle. This phenomenon indicated differences in the energy transfer process between open channel CFT and the ordinary CFT with a nozzle. Furthermore, current work finds that forward NACA-9503 CFT has a higher efficiency than other tested airfoil profiles, with 57.05% efficiency. In addition, the present study finds that the NACA-95XX airfoil has a more suitable chamber curve with the original CFT blade’s curve than the NACA-94XX airfoil by velocity triangle analysis. Then, the NACA-9503 profile is thinner than the NACA-9506 profile, which eases water flowing through the turbine blades.
Implementation of Association Rules Algorithm to Identify Popular Topping Combinations in Orders Rizki Aulia Putra; Margareta Amalia Miranti Putri; Sri Maharani Sinaga; Sania Fitri Octavia; Raihan Catur Rachman
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 1 No. 2: PREDATECS January 2024
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v1i2.863

Abstract

Association rule is a data mining technique to find associative rules between a combination of items. This research aims to apply association rules algorithm in identifying popular topping combinations in food orders. This application aims to help restaurant owners or food businesses understand their customers' preferences and optimize their menu offerings. Data obtained from kaggle, the association rules algorithm is applied to this dataset to identify patterns or combinations of toppings that often appear together in orders. The results of this study show toppings with chocolate as a popular item in orders. These findings can provide valuable insights for food business owners in structuring their menus and determining attractive offers for customers. This study also applied a comparison between the apriori, fp- growth and eclat algorithms, with the result that the best item transaction rule was found: a combination of dill & unicorn toppings with chocolate with 60% confidence. Overall, the application of eclat algorithm in this study provides the best performance with higher execution speed, thus providing insight into customer preferences regarding topping combinations in food orders. Despite the shortcomings of the data form from this study, it is expected to help business owners in optimizing their offerings, increasing customer satisfaction, and improving their business performance.
Performance Comparison Between Artificial Neural Network, Recurrent Neural Network and Long Short-Term Memory for Prediction of Extreme Climate Change Nanda Try Luchia; Ena Tasia; Indah Ramadhani; Akhas Rahmadeyan; Raudiatul Zahra
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 1 No. 2: PREDATECS January 2024
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v1i2.864

Abstract

Extreme climate change is the most common problem in Indonesia. Extreme climate change for months can cause various natural disasters. Therefore, it is necessary to make predictions about climate change that will occur in order to avoid the risk of future conflicts. This study uses the Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) algorithms by comparing the performance of the three using Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) evaluations. The results of this study indicate that RNN is better at predicting temperature in Indonesia compared to ANN and LSTM. This is evidenced by the MAPE value generated by the RNN which is smaller than the ANN and LSTM, which is 1.852 %, the RMSE value is 1,870, and the MSE value is 3,497.
Application of Convolutional Neural Network ResNet-50 V2 on Image Classification of Rice Plant Disease Delvi Hastari; Salsa Winanda; Aditya Rezky Pratama; Nana Nurhaliza; Ella Silvana Ginting
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 1 No. 2: PREDATECS January 2024
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v1i2.865

Abstract

Rice is the most important crop in global food security and socioeconomic stability. A part of the world's population makes rice a food requirement but the problem is found that all rice varieties suffer from several diseases and pests. Therefore, it is necessary to ensure the quality of healthy and proper rice growth by detecting diseases present in rice plants and treatment of affected plants. In this study, the Convolutional Neural Network (CNN) algorithm was applied in classifying diseases on the leaves of rice plants by experimenting with several parameters and architecture to get the best accuracy. This study was conducted image classification of rice plant disease using CNN architecture ResNet-50V2 with data using preprocessing Augmentation. The test was conducted with three optimizers such as SGD, Adam, and RMSprop by combining various parameters, namely epoch, batch size, learning rate, and SGD and RMSprop optimizers. Division of image data with 70:30 ratio of training data and test data; 80:20; 90:10. From these results, it was found that Adam was the best optimizer in the 80:20 data division in this study with an accuracy level of 0.9992, followed by the SGD optimizer with an accuracy level of 0.9983, while the RMSProp optimizer was ranked third with an accuracy level of 0.9978.

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