Pub Date : 2023-11-06DOI: 10.24843/lkjiti.2023.v14.i02.p05
Yohanes Priyo Atmojo, Dandy Pramana Hostiadi, I Made Darma Susila, Made Liandana, Gede Angga Pradipta, Putu Desiana Wulaning Ayu
Information security and threats are a concern in the cyber era. Attacks can be malicious activities. One of them is known as ARP poisoning attack activity, which attacks by falsifying a computer's identity through illegal access to retrieve confidential information in a target computer. Besides, it has also caused service deadlocks in the network. Previous studies have been introduced for the ARP Attack Detection model using rule-based and mining-based. Still, they cannot show optimal detection performance and obtain high false positive results. This paper proposed a detection model for ARP poisoning attacks using a similarity measurement approach adopting cosine similarity. The goal is to obtain measurements of host activities similar to ARP poisoning attacks. The experiment results showed that the model got an accuracy of 97.25%, recall of 96.43%, and precision of 81% with a similarity threshold value of 0.488. Comparison results with previous studies showed higher detection accuracy than previous studies and some classification methods. It shows that the model can improve intrusion detection performance and facilitate network administrators to analyze ARP poisoning attacks in computer networks.
{"title":"The Optimization of the ARP Poisoning Attack Detection Model Using a Similar Approach Based on NetFlow Analysis","authors":"Yohanes Priyo Atmojo, Dandy Pramana Hostiadi, I Made Darma Susila, Made Liandana, Gede Angga Pradipta, Putu Desiana Wulaning Ayu","doi":"10.24843/lkjiti.2023.v14.i02.p05","DOIUrl":"https://doi.org/10.24843/lkjiti.2023.v14.i02.p05","url":null,"abstract":"Information security and threats are a concern in the cyber era. Attacks can be malicious activities. One of them is known as ARP poisoning attack activity, which attacks by falsifying a computer's identity through illegal access to retrieve confidential information in a target computer. Besides, it has also caused service deadlocks in the network. Previous studies have been introduced for the ARP Attack Detection model using rule-based and mining-based. Still, they cannot show optimal detection performance and obtain high false positive results. This paper proposed a detection model for ARP poisoning attacks using a similarity measurement approach adopting cosine similarity. The goal is to obtain measurements of host activities similar to ARP poisoning attacks. The experiment results showed that the model got an accuracy of 97.25%, recall of 96.43%, and precision of 81% with a similarity threshold value of 0.488. Comparison results with previous studies showed higher detection accuracy than previous studies and some classification methods. It shows that the model can improve intrusion detection performance and facilitate network administrators to analyze ARP poisoning attacks in computer networks.
","PeriodicalId":31196,"journal":{"name":"Lontar Komputer","volume":"22 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135679164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-06DOI: 10.24843/lkjiti.2023.v14.i02.p04
Suhendro Yusuf Irianto
Coffee has substantial economic value and is a key foreign exchange source for numerous nations, including Indonesia. Moreover, it is a primary livelihood for many of the country's farmers. Recently, there have been challenges in accurately predicting the quality of coffee beans, primarily due to time, inconsistency, and imprecision issues. Consequently, this study delves into the application of region-growing segmentation and content-based image retrieval (CBIR) techniques to enhance the prediction of coffee bean quality. The proposed hybrid approach, which combines region growing and CBIR methods, aims to improve the precision for forecasting cacao bean quality. Additionally, the research introduces an automated tool that employs these hybrid techniques for quality prediction. The study conducted experiments using a dataset of 400 premium and 400 low-quality coffee beans sourced from the University of Syiah Kuala in Indonesia. The results of the experiments demonstrate a commendable precision rate of 85.4%, showcasing significant improvement compared to certain previous studies.
{"title":"Refining Content-Based Segmentation for Prediction of Coffee Bean Quality","authors":"Suhendro Yusuf Irianto","doi":"10.24843/lkjiti.2023.v14.i02.p04","DOIUrl":"https://doi.org/10.24843/lkjiti.2023.v14.i02.p04","url":null,"abstract":"Coffee has substantial economic value and is a key foreign exchange source for numerous nations, including Indonesia. Moreover, it is a primary livelihood for many of the country's farmers. Recently, there have been challenges in accurately predicting the quality of coffee beans, primarily due to time, inconsistency, and imprecision issues. Consequently, this study delves into the application of region-growing segmentation and content-based image retrieval (CBIR) techniques to enhance the prediction of coffee bean quality. The proposed hybrid approach, which combines region growing and CBIR methods, aims to improve the precision for forecasting cacao bean quality. Additionally, the research introduces an automated tool that employs these hybrid techniques for quality prediction. The study conducted experiments using a dataset of 400 premium and 400 low-quality coffee beans sourced from the University of Syiah Kuala in Indonesia. The results of the experiments demonstrate a commendable precision rate of 85.4%, showcasing significant improvement compared to certain previous studies.","PeriodicalId":31196,"journal":{"name":"Lontar Komputer","volume":"25 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135679310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-04DOI: 10.24843/lkjiti.2023.v14.i02.p03
Shilta Inda Qurroti A'yun Achmadi, Anjela Faye M. Basco, Oka Sudana, Ni Kadek Dwi Rusjayanthi
Subak is a customary law society with socio-agrarian-religious characteristics, consisting of a group of farmers who manage the irrigation of rice fields or paddies. The agricultural irrigation system in Bali or Subak was officially recognized as one of the world's cultural heritages in 2012 by UNESCO. Many Subak data still rely on traditional recording systems, making it crucial to have a digital transformation using an information system platform capable of collecting data on Subak and providing related information about Subak in the Bali Province. To address this problem, a mobile-based Geographic Information System (GIS) that encompasses information about Subak is developed. The data collection method used in this research includes literature studies and questionnaires. The result obtained from this research is an Android mobile application tested using Black Box Testing. All tests were successfully met according to the testing criteria that have been established.
{"title":"Digital Transformation Of Subak Management In Bali Through GIS Implementation","authors":"Shilta Inda Qurroti A'yun Achmadi, Anjela Faye M. Basco, Oka Sudana, Ni Kadek Dwi Rusjayanthi","doi":"10.24843/lkjiti.2023.v14.i02.p03","DOIUrl":"https://doi.org/10.24843/lkjiti.2023.v14.i02.p03","url":null,"abstract":"Subak is a customary law society with socio-agrarian-religious characteristics, consisting of a group of farmers who manage the irrigation of rice fields or paddies. The agricultural irrigation system in Bali or Subak was officially recognized as one of the world's cultural heritages in 2012 by UNESCO. Many Subak data still rely on traditional recording systems, making it crucial to have a digital transformation using an information system platform capable of collecting data on Subak and providing related information about Subak in the Bali Province. To address this problem, a mobile-based Geographic Information System (GIS) that encompasses information about Subak is developed. The data collection method used in this research includes literature studies and questionnaires. The result obtained from this research is an Android mobile application tested using Black Box Testing. All tests were successfully met according to the testing criteria that have been established.
","PeriodicalId":31196,"journal":{"name":"Lontar Komputer","volume":"29 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135775337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dopamine levels fall due to brain nerve cell destruction, producing Parkinson's symptoms. Humans with this illness experience central nervous system damage, which lowers the quality of life. This disease is not deadly, but when people's quality of life decreases, they cannot perform daily activities as people do. Even in one case, this disease can cause death indirectly. Contrast support vector machines (SVM) and naive Bayesian approaches with and without fast correlation-based filter (FCBF) feature selection, this study attempts to determine the optimum model to detect Parkinson's disease categorization. In this study, datasets from the UCI Machine Learning Repository are used. The results showed that SVM with FCBF achieved the highest accuracy among all the models tested. SVM with FCBF provides an accuracy of 86.1538%, sensitivity of 93.8775%, and specificity of 62.5000%. Both methods, SVM and Naive Bayes, have improved in performance due to FCBF, with SVM showing a more significant increase in accuracy. This research contributed to helping paramedics determine if a patient has Parkinson's disease or not using characteristics obtained from data, such as movement, sound, or other pertinent factors.
{"title":"Comparing Support Vector Machine and Naïve Bayes Methods with A Selection of Fast Correlation Based Filter Features in Detecting Parkinson's Disease","authors":"Yuniar Farida, Nurissaidah Ulinnuha, Silvia Kartika Sari, Latifatun Nadya Desinaini","doi":"10.24843/lkjiti.2023.v14.i02.p02","DOIUrl":"https://doi.org/10.24843/lkjiti.2023.v14.i02.p02","url":null,"abstract":"Dopamine levels fall due to brain nerve cell destruction, producing Parkinson's symptoms. Humans with this illness experience central nervous system damage, which lowers the quality of life. This disease is not deadly, but when people's quality of life decreases, they cannot perform daily activities as people do. Even in one case, this disease can cause death indirectly. Contrast support vector machines (SVM) and naive Bayesian approaches with and without fast correlation-based filter (FCBF) feature selection, this study attempts to determine the optimum model to detect Parkinson's disease categorization. In this study, datasets from the UCI Machine Learning Repository are used. The results showed that SVM with FCBF achieved the highest accuracy among all the models tested. SVM with FCBF provides an accuracy of 86.1538%, sensitivity of 93.8775%, and specificity of 62.5000%. Both methods, SVM and Naive Bayes, have improved in performance due to FCBF, with SVM showing a more significant increase in accuracy. This research contributed to helping paramedics determine if a patient has Parkinson's disease or not using characteristics obtained from data, such as movement, sound, or other pertinent factors.
","PeriodicalId":31196,"journal":{"name":"Lontar Komputer","volume":"29 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135775336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-30DOI: 10.24843/lkjiti.2023.v14.i01.p02
I Putu Juni Adi Widianata, Nori Wilantika
Data on the number of aircraft passengers is essential to airport managers and the government's policies. The policy relates to improving the facilities and capacity of airports and other affected sectors, such as the transportation and tourism industries. A policy taken will be better if the data used is very close to the time of policy decision-making. Therefore, a technique is needed to forecast very close to the current condition of the number of aircraft passengers, namely nowcasting. One of the data sources that can be used for nowcasting is Google Trends data. In this study, the identification of relevant keywords used for nowcasting, the formation of nowcasting models, and the search for the best model for nowcasting the number of aircraft passengers was carried out. The nowcasting methods used are SARIMAX and multilayer perceptron. In this study, five relevant keywords were generated for domestic departures and two for international departures. In the nowcasting modeling, the best model for nowcasting domestic departures is produced, namely the multilayer perceptron with MAPE and MAE values of 11.194% and 28.048 respectively, while for departures Internationally, the best model was produced, namely SARIMAX with MAPE and MAE values of 8,641% and 50,205 respectively.
{"title":"Nowcasting the Number of Airplane Passengers at Ngurah Rai Airport Using Google Trends Data","authors":"I Putu Juni Adi Widianata, Nori Wilantika","doi":"10.24843/lkjiti.2023.v14.i01.p02","DOIUrl":"https://doi.org/10.24843/lkjiti.2023.v14.i01.p02","url":null,"abstract":"Data on the number of aircraft passengers is essential to airport managers and the government's policies. The policy relates to improving the facilities and capacity of airports and other affected sectors, such as the transportation and tourism industries. A policy taken will be better if the data used is very close to the time of policy decision-making. Therefore, a technique is needed to forecast very close to the current condition of the number of aircraft passengers, namely nowcasting. One of the data sources that can be used for nowcasting is Google Trends data. In this study, the identification of relevant keywords used for nowcasting, the formation of nowcasting models, and the search for the best model for nowcasting the number of aircraft passengers was carried out. The nowcasting methods used are SARIMAX and multilayer perceptron. In this study, five relevant keywords were generated for domestic departures and two for international departures. In the nowcasting modeling, the best model for nowcasting domestic departures is produced, namely the multilayer perceptron with MAPE and MAE values of 11.194% and 28.048 respectively, while for departures Internationally, the best model was produced, namely SARIMAX with MAPE and MAE values of 8,641% and 50,205 respectively.","PeriodicalId":31196,"journal":{"name":"Lontar Komputer","volume":"38 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136106931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-30DOI: 10.24843/lkjiti.2023.v14.i01.p06
Ayu Wirdiani, I Ketut Gede Darma Putra, Made Sudarma, Rukmi Sari Hartati, Lennia Savitri Azzahra Lofiana
Face recognition is one of the most popular methods currently used for biometric systems. The selection of a suitable method greatly affects the reliability of the biometrics system. This research will use Deep learning to improve the reliability of the biometric system and will compare it with the SVM method. The Deep Learning method will be adopted using the Siamese Network with the YoloV5 detection method as a real-time face detector. There are two stages in this research: the registration process and the recognition process. The registration process is image acquisition using YoloV5. The image result will be saved in the storage folder, and the preprocessing and training process will use the Siamese Network. The face feature model will be stored in the database. The recognition process is the same as the registration, but the feature extraction result will be embedded and compared with the already trained models. The accuracy rate using the Siamese model was 94%.
{"title":"Real-time Face Recognition System Using Deep Learning Method","authors":"Ayu Wirdiani, I Ketut Gede Darma Putra, Made Sudarma, Rukmi Sari Hartati, Lennia Savitri Azzahra Lofiana","doi":"10.24843/lkjiti.2023.v14.i01.p06","DOIUrl":"https://doi.org/10.24843/lkjiti.2023.v14.i01.p06","url":null,"abstract":"Face recognition is one of the most popular methods currently used for biometric systems. The selection of a suitable method greatly affects the reliability of the biometrics system. This research will use Deep learning to improve the reliability of the biometric system and will compare it with the SVM method. The Deep Learning method will be adopted using the Siamese Network with the YoloV5 detection method as a real-time face detector. There are two stages in this research: the registration process and the recognition process. The registration process is image acquisition using YoloV5. The image result will be saved in the storage folder, and the preprocessing and training process will use the Siamese Network. The face feature model will be stored in the database. The recognition process is the same as the registration, but the feature extraction result will be embedded and compared with the already trained models. The accuracy rate using the Siamese model was 94%.
","PeriodicalId":31196,"journal":{"name":"Lontar Komputer","volume":"18 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136106933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-30DOI: 10.24843/lkjiti.2023.v14.i01.p05
Oka Sudana, I Made Suwija Putra, Pradita Dewi
The university's integrated registration management system is a system to facilitate the registration of prospective new students at University X. A good registration system should be able to provide accurate, and relevant information to improve the quality of the information system. The quality improving can be done with business process analysis. In this research business process analysis is done using business process improvement methods (BPI) up to phase 3, namely streamlining. The data to be analyzed is obtained from the results of questionnaires distributed to stakeholders. Determination of quality factor indicators on questionnaire questions using the McCall framework. The questionnaire results showed a business process that is categorized as critical, namely Study Program Transfer with an average value of 80%, Quality factors categorized as critical are Correctness 80% and Integrity 51% and Scholarship Application Management with an average value of quality factor 78%, quality factors categorized critically are Correctness 80% and Integrity 53%. Recommendations for business process improvement in the form of draft Standard Operating Procedures (SOP) also flowchart using streamlining with bureaucracy elimination and upgrading simplification tools in the process of moving management program and management of waiver submissions.
{"title":"Business Process Analysis with Business Process Improvement Method Case Study: University Integrated Registration Management System","authors":"Oka Sudana, I Made Suwija Putra, Pradita Dewi","doi":"10.24843/lkjiti.2023.v14.i01.p05","DOIUrl":"https://doi.org/10.24843/lkjiti.2023.v14.i01.p05","url":null,"abstract":"The university's integrated registration management system is a system to facilitate the registration of prospective new students at University X. A good registration system should be able to provide accurate, and relevant information to improve the quality of the information system. The quality improving can be done with business process analysis. In this research business process analysis is done using business process improvement methods (BPI) up to phase 3, namely streamlining. The data to be analyzed is obtained from the results of questionnaires distributed to stakeholders. Determination of quality factor indicators on questionnaire questions using the McCall framework. The questionnaire results showed a business process that is categorized as critical, namely Study Program Transfer with an average value of 80%, Quality factors categorized as critical are Correctness 80% and Integrity 51% and Scholarship Application Management with an average value of quality factor 78%, quality factors categorized critically are Correctness 80% and Integrity 53%. Recommendations for business process improvement in the form of draft Standard Operating Procedures (SOP) also flowchart using streamlining with bureaucracy elimination and upgrading simplification tools in the process of moving management program and management of waiver submissions.","PeriodicalId":31196,"journal":{"name":"Lontar Komputer","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136106932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-28DOI: 10.24843/lkjiti.2023.v14.i01.p03
Alesia Arum Frederika, I Putu Agung Bayupati, Wira Buana
Data mining can be used for businesses with large amounts of data. One of the data mining techniques is Associative Classification. It is a new strategy in data processing that combines association and classification techniques to build a classification model. This research used an associative classification technique on sales transaction data of Frozen Food Stores, which had sales transaction data on their business activities. It would be used in sales strategies to find items often purchased by class customers, namely, members and general. This research aimed to classify based on association rules using the CBA (Classification based Association) algorithm on sales transaction data. The application used the R programming language that business owners could use. The results of the rules obtained from the trial had the value of support, confidence, coverage, and lift ratio, which were the best value levels of a rule. The results of the rules that had the highest lift ratio value from all the data that have been inputted can be used as a reference to be implemented in sales strategies in knowing consumer needs.
数据挖掘可用于具有大量数据的业务。其中一种数据挖掘技术是关联分类。将关联技术和分类技术相结合，建立分类模型是一种新的数据处理策略。本研究使用关联分类技术对冷冻食品店的销售交易数据进行分类，冷冻食品店的经营活动中有销售交易数据。它将用于销售策略中，以查找类客户(即会员和普通客户)经常购买的物品。本研究旨在利用CBA (Classification based association)算法对销售交易数据进行基于关联规则的分类。该应用程序使用了企业所有者可以使用的R编程语言。试验获得的规则结果具有支持值、置信度值、覆盖率值和提升比值，这是规则的最佳值水平。在所有已输入的数据中，升降机比值最高的规则的结果可以作为参考，在了解消费者需求的销售策略中实施。
{"title":"Associative Classification with Classification Based Association (CBA) Algorithm on Transaction Data with Rshiny","authors":"Alesia Arum Frederika, I Putu Agung Bayupati, Wira Buana","doi":"10.24843/lkjiti.2023.v14.i01.p03","DOIUrl":"https://doi.org/10.24843/lkjiti.2023.v14.i01.p03","url":null,"abstract":"Data mining can be used for businesses with large amounts of data. One of the data mining techniques is Associative Classification. It is a new strategy in data processing that combines association and classification techniques to build a classification model. This research used an associative classification technique on sales transaction data of Frozen Food Stores, which had sales transaction data on their business activities. It would be used in sales strategies to find items often purchased by class customers, namely, members and general. This research aimed to classify based on association rules using the CBA (Classification based Association) algorithm on sales transaction data. The application used the R programming language that business owners could use. The results of the rules obtained from the trial had the value of support, confidence, coverage, and lift ratio, which were the best value levels of a rule. The results of the rules that had the highest lift ratio value from all the data that have been inputted can be used as a reference to be implemented in sales strategies in knowing consumer needs.
","PeriodicalId":31196,"journal":{"name":"Lontar Komputer","volume":"173 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136233379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-27DOI: 10.24843/lkjiti.2023.v14.i01.p04
I Made Sukarsa, Ni Kadek Dwi Rusjayanthi, Made Srinitha Millinia Utami, Ni Wayan Wisswani
Traffic accidents are still significant contributors to a fairly high death. Denpasar’s resort police record every traffic accident in the form of a daily report. The stored data can generate valuable information to improve policies and propagate better traffic practices. This research utilizes the classification technique with the XGBoost, random forest algorithm, and SMOTE method. The study shows that the SMOTE technique can increase the model's accuracy. Using the classification method with the two algorithms produces factors that affect the severity of traffic accident victims with feature importance. The feature importance obtained using the XGBoost model by counting the weight value for testing using the original dataset, the dataset for the type of two-wheeled vehicle, and the dataset of the kind of vehicle other than two-wheeled indicate that the variables influencing the severity of victims in road accidents are the time of accident between 00.00-06.00, the type of vehicle motorcycle, the type of opponent vehicle truck and pickup car, the age of the driver between 16-25, sub-district road status and front – side type of accident.
{"title":"The Use of XGBoost Algorithm to Analyse the Severity of Traffic Accident Victims","authors":"I Made Sukarsa, Ni Kadek Dwi Rusjayanthi, Made Srinitha Millinia Utami, Ni Wayan Wisswani","doi":"10.24843/lkjiti.2023.v14.i01.p04","DOIUrl":"https://doi.org/10.24843/lkjiti.2023.v14.i01.p04","url":null,"abstract":"Traffic accidents are still significant contributors to a fairly high death. Denpasar’s resort police record every traffic accident in the form of a daily report. The stored data can generate valuable information to improve policies and propagate better traffic practices. This research utilizes the classification technique with the XGBoost, random forest algorithm, and SMOTE method. The study shows that the SMOTE technique can increase the model's accuracy. Using the classification method with the two algorithms produces factors that affect the severity of traffic accident victims with feature importance. The feature importance obtained using the XGBoost model by counting the weight value for testing using the original dataset, the dataset for the type of two-wheeled vehicle, and the dataset of the kind of vehicle other than two-wheeled indicate that the variables influencing the severity of victims in road accidents are the time of accident between 00.00-06.00, the type of vehicle motorcycle, the type of opponent vehicle truck and pickup car, the age of the driver between 16-25, sub-district road status and front – side type of accident.","PeriodicalId":31196,"journal":{"name":"Lontar Komputer","volume":"56 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136319000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-30DOI: 10.24843/lkjiti.2023.v14.i02.p01
Mardhiya Hayaty, Timur Haryo Mahissanular
The agricultural sector in Indonesia is still faced with low agrarian production caused by pests and diseases. Therefore, agricultural land that is still vulnerable to pests but can detect the development of pest attacks must be designed. This study uses the PlantVillage dataset. The dataset will go through the preprocessing stage for dimension adjustment, and then the result will be used for building the network. The results are evaluated using a confusion matrix and showed that the convolutional neural network performs well in image processing and obtains architectural optimization in its field. The method we propose is an Efficient Network by selecting the correct input size. Implementing an Efficient Network in the convolutional neural network architecture increases its F1-score to 93%, indicating that Efficient Network has a higher F1-Score than the baseline convolution neural network. Implementing this network architecture can quickly increase the CNN baseline to a more varied target resource while maintaining the efficiency of the resulting model.
{"title":"Detecting Pests and Diseases in Plants Using Efficient Network","authors":"Mardhiya Hayaty, Timur Haryo Mahissanular","doi":"10.24843/lkjiti.2023.v14.i02.p01","DOIUrl":"https://doi.org/10.24843/lkjiti.2023.v14.i02.p01","url":null,"abstract":"The agricultural sector in Indonesia is still faced with low agrarian production caused by pests and diseases. Therefore, agricultural land that is still vulnerable to pests but can detect the development of pest attacks must be designed. This study uses the PlantVillage dataset. The dataset will go through the preprocessing stage for dimension adjustment, and then the result will be used for building the network. The results are evaluated using a confusion matrix and showed that the convolutional neural network performs well in image processing and obtains architectural optimization in its field. The method we propose is an Efficient Network by selecting the correct input size. Implementing an Efficient Network in the convolutional neural network architecture increases its F1-score to 93%, indicating that Efficient Network has a higher F1-Score than the baseline convolution neural network. Implementing this network architecture can quickly increase the CNN baseline to a more varied target resource while maintaining the efficiency of the resulting model.","PeriodicalId":31196,"journal":{"name":"Lontar Komputer","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136241766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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