Breast cancer is a prevalent form of cancer that afflicts women across all nations globally. One of the ways that can be done as a prevention to reduce elevated fatality due to breast cancer is with a detection system that can determine whether a cancer is benign or malignant. Logistic Regression and Support Vector Machine (SVM) classification algorithms are often used to detect this disease, but the use of these two algorithms often doesn’t give optimal results when applied to datasets that have many features, so additional algorithm is needed to improve classification performance by using Backward Elimination feature selection. The comparison of Logistic Regression and SVM algorithms was carried out by applying feature selection to breast cancer data to see the best model. The breast cancer dataset has 30 features and two classes, Benign and Malignant. Backward Elimination has reduced features from 30 features to 13 features, thereby increasing the performance of both classification models. The best classification was obtained by using the Backward Elimination feature selection and linear kernel SVM with an increase in accuracy value from 96.14% to 97.02%, precision from 98.06% to 99.49%, recall from 90.48% to 92.38%, and the AUC from 0.95 to 0.96.
{"title":"Backward Elimination for Feature Selection on Breast Cancer Classification Using Logistic Regression and Support Vector Machine Algorithms","authors":"Salsha Farahdiba, Dwi Kartini, Radityo Adi Nugroho, Rudy Herteno, Triando Hamonangan Saragih","doi":"10.22146/ijccs.88926","DOIUrl":"https://doi.org/10.22146/ijccs.88926","url":null,"abstract":"Breast cancer is a prevalent form of cancer that afflicts women across all nations globally. One of the ways that can be done as a prevention to reduce elevated fatality due to breast cancer is with a detection system that can determine whether a cancer is benign or malignant. Logistic Regression and Support Vector Machine (SVM) classification algorithms are often used to detect this disease, but the use of these two algorithms often doesn’t give optimal results when applied to datasets that have many features, so additional algorithm is needed to improve classification performance by using Backward Elimination feature selection. The comparison of Logistic Regression and SVM algorithms was carried out by applying feature selection to breast cancer data to see the best model. The breast cancer dataset has 30 features and two classes, Benign and Malignant. Backward Elimination has reduced features from 30 features to 13 features, thereby increasing the performance of both classification models. The best classification was obtained by using the Backward Elimination feature selection and linear kernel SVM with an increase in accuracy value from 96.14% to 97.02%, precision from 98.06% to 99.49%, recall from 90.48% to 92.38%, and the AUC from 0.95 to 0.96.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":"7 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135931035","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}
In the last decade, there have been significant developments in Image Caption Generation research to translate images into English descriptions. This task has also been conducted to produce texts in non-English, including Bahasa. However, the references in this study are still limited, so exploration opportunities are open widely. This paper presents comparative research by examining several state-of-the-art Deep Learning algorithms to extract images and generate their descriptions in Bahasa. We extracted images using three pre-trained models, namely InceptionV3, Xception, and EfficientNetV2S. In the language model, we examined four architectures: LSTM, GRU, Bidirectional LSTM, and Bidirectional GRU. The database used was Flickr8k which was translated into Bahasa. Model evaluation was conducted using BLEU and Meteor. The performance results based on the pre-trained model showed that EfficientNetV3S significantly gave the highest score among other models. On the other hand, in the language model, there was only a slight difference in model performance. However, in general, the Bidirectional GRU scored higher. We also found that step size in training affected overfitting. Larger step sizes tended to provide better generalizations. The best model was generated using EfficientNetV3S and Bidirectional GRU with step size=4096, which resulted in an average score of BLEU-1=0,5828 and Meteor=0,4520.
{"title":"Exploring Pre-Trained Model and Language Model for Translating Image to Bahasa","authors":"Ade Nurhopipah, Jali Suhaman, Anan Widianto","doi":"10.22146/ijccs.76389","DOIUrl":"https://doi.org/10.22146/ijccs.76389","url":null,"abstract":"In the last decade, there have been significant developments in Image Caption Generation research to translate images into English descriptions. This task has also been conducted to produce texts in non-English, including Bahasa. However, the references in this study are still limited, so exploration opportunities are open widely. This paper presents comparative research by examining several state-of-the-art Deep Learning algorithms to extract images and generate their descriptions in Bahasa. We extracted images using three pre-trained models, namely InceptionV3, Xception, and EfficientNetV2S. In the language model, we examined four architectures: LSTM, GRU, Bidirectional LSTM, and Bidirectional GRU. The database used was Flickr8k which was translated into Bahasa. Model evaluation was conducted using BLEU and Meteor. The performance results based on the pre-trained model showed that EfficientNetV3S significantly gave the highest score among other models. On the other hand, in the language model, there was only a slight difference in model performance. However, in general, the Bidirectional GRU scored higher. We also found that step size in training affected overfitting. Larger step sizes tended to provide better generalizations. The best model was generated using EfficientNetV3S and Bidirectional GRU with step size=4096, which resulted in an average score of BLEU-1=0,5828 and Meteor=0,4520.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":"7 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135931303","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}
Putu Sugiartawan, Yusril Eka Saputra, Agus Qomaruddin Munir
The retail company PT Terang Abadi Raya has a solid commitment to supporting distributors of LED lights and electrical equipment who have joined them, helping to spread their products widely in various regions. To face increasingly intense market competition, it is essential to produce high-quality products to win the competition and meet consumer demands. To achieve this, efficient production planning is necessary. The Convolutional Long Short-Term Memory (C-LSTM) method is used in this study to forecast product sales at PT Terang Abadi Raya. The research results show that C-LSTM has the potential to predict sales effectively. Evaluation is conducted using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The calculations reveal that the smallest values are obtained at epoch 10, with an MAE of 0.1051 and a MAPE of 22% in the testing data. For the cable data, the smallest values are found at epoch 100, with an MAE of 0.0602 and a MAPE of 44% in the testing data. The Long Short-Term Memory (LSTM) method with ten neurons produces the most minor errors during training.
{"title":"Convolutional Long Short-Term Memory (C-LSTM) For Multi Product Prediction","authors":"Putu Sugiartawan, Yusril Eka Saputra, Agus Qomaruddin Munir","doi":"10.22146/ijccs.90149","DOIUrl":"https://doi.org/10.22146/ijccs.90149","url":null,"abstract":"The retail company PT Terang Abadi Raya has a solid commitment to supporting distributors of LED lights and electrical equipment who have joined them, helping to spread their products widely in various regions. To face increasingly intense market competition, it is essential to produce high-quality products to win the competition and meet consumer demands. To achieve this, efficient production planning is necessary. The Convolutional Long Short-Term Memory (C-LSTM) method is used in this study to forecast product sales at PT Terang Abadi Raya. The research results show that C-LSTM has the potential to predict sales effectively. Evaluation is conducted using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The calculations reveal that the smallest values are obtained at epoch 10, with an MAE of 0.1051 and a MAPE of 22% in the testing data. For the cable data, the smallest values are found at epoch 100, with an MAE of 0.0602 and a MAPE of 44% in the testing data. The Long Short-Term Memory (LSTM) method with ten neurons produces the most minor errors during training.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":"285 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135930513","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}
Pratiwi Hendro Wahyudiono, Ahmad Syafruddin Indrapriyatna, Ismail Yusuf Panessai, Nurus Sabah, Achmad Yani, Abdi Manaf, Nur Iksan
Currently the problem of Optical Distribution Point (ODP) infrastructure is important in fiber to the home (FTTH) network access because ODP infrastructure development is no longer dependent on demand, so placing ODP manually without a systematic method can cause an increase in the value of optical fiber attenuation. on the length of the cable and cause the cable distribution to be irregular. This study aims to optimize the placement of ODP devices in PT BCV's FTTH network by using the Traveling Salesman Problem (TSP) scheme with the genetic algorithm (GA) approach and using hybrid GA, testing is carried out using Matlab software. Testing with development using Hybrid GA gets the best path with a fitness value of 28.6457 and a computation time of 89.93 seconds.
{"title":"Optimizing ODP Device Placement on FTTH Network Using Genetic Algorithms","authors":"Pratiwi Hendro Wahyudiono, Ahmad Syafruddin Indrapriyatna, Ismail Yusuf Panessai, Nurus Sabah, Achmad Yani, Abdi Manaf, Nur Iksan","doi":"10.22146/ijccs.84358","DOIUrl":"https://doi.org/10.22146/ijccs.84358","url":null,"abstract":"Currently the problem of Optical Distribution Point (ODP) infrastructure is important in fiber to the home (FTTH) network access because ODP infrastructure development is no longer dependent on demand, so placing ODP manually without a systematic method can cause an increase in the value of optical fiber attenuation. on the length of the cable and cause the cable distribution to be irregular. This study aims to optimize the placement of ODP devices in PT BCV's FTTH network by using the Traveling Salesman Problem (TSP) scheme with the genetic algorithm (GA) approach and using hybrid GA, testing is carried out using Matlab software. Testing with development using Hybrid GA gets the best path with a fitness value of 28.6457 and a computation time of 89.93 seconds.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":"123 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135931028","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}
Automated essay scoring (AES) is used to evaluate and assessment student essays are written based on the questions given. However, there are difficulties in conducting automatic assessments carried out by the system, these difficulties occur due to typing errors (typos), the use of regional languages , or incorrect punctuation. These errors make the assessment less consistent and accurate. Based on the dataset analysis that has been carried out, there is an imbalance between the number of right and wrong answers, so a technique is needed to overcome the data imbalance. Based on the literature, to overcome these problems, the Random Forest and AdaBoost classification algorithms can be used to improve the consistency of classification accuracy and the SMOTE method to overcome data imbalances.The Random Forest method using SMOTE can achieve an F1 measure of 99%, which means that the hybrid method can overcome the problem of imbalanced datasets that are limited to AES. The AdaBoost model with SMOTE produces the highest F1 measure reaching 99% of the entire dataset. The structure of the dataset is something that also affects the performance of the model. So the best model obtained in this study is the Random Forest model with SMOTE.
{"title":"ESSAY ANSWER CLASSIFICATION WITH SMOTE RANDOM FOREST AND ADABOOST IN AUTOMATED ESSAY SCORING","authors":"Wilia Satria, Mardhani Riasetiawan","doi":"10.22146/ijccs.82548","DOIUrl":"https://doi.org/10.22146/ijccs.82548","url":null,"abstract":"Automated essay scoring (AES) is used to evaluate and assessment student essays are written based on the questions given. However, there are difficulties in conducting automatic assessments carried out by the system, these difficulties occur due to typing errors (typos), the use of regional languages , or incorrect punctuation. These errors make the assessment less consistent and accurate. Based on the dataset analysis that has been carried out, there is an imbalance between the number of right and wrong answers, so a technique is needed to overcome the data imbalance. Based on the literature, to overcome these problems, the Random Forest and AdaBoost classification algorithms can be used to improve the consistency of classification accuracy and the SMOTE method to overcome data imbalances.The Random Forest method using SMOTE can achieve an F1 measure of 99%, which means that the hybrid method can overcome the problem of imbalanced datasets that are limited to AES. The AdaBoost model with SMOTE produces the highest F1 measure reaching 99% of the entire dataset. The structure of the dataset is something that also affects the performance of the model. So the best model obtained in this study is the Random Forest model with SMOTE.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":"120 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135931036","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}
Lack of information related to maternal health care during pregnancy and post-pregnancy, especially in rural areas, results in many cases of pregnancy complications. Risk analysis for pregnant women is really needed as a reference in handling pregnant women so that the risk to pregnant women can be minimized. To analyze the risk of pregnant women can use data mining techniques by classifying the risk of pregnant women. This study proposes to classify Maternal Health Risk using the Naive Bayes method with three models, namely Gaussian, Multinomial, and Bournolli. The data used is the health data of pregnant women based on risk intensity which is grouped into three classes, namely low, mid and high risk. while the attributes are Age, Systolic Blood Pressure as SystolicBP, Diastolic BP as DiastolicBP, Blood Sugar as BS, Body Temperature as BodyTemp, and HeartRate. The results show that among the three Naïve Bayes models that have the best performance are the Multinomial and Bournolli with an accuracy of 84.8% while the Gaussian produces an accuracy of 82.6%.
{"title":"Classification Of Maternal Health Risk Using Three Models Naive Bayes Method","authors":"Nurul Fathanah Mustamin, Firman Aziz, Firmansyah Firmansyah, Pertiwi Ishak","doi":"10.22146/ijccs.84242","DOIUrl":"https://doi.org/10.22146/ijccs.84242","url":null,"abstract":"Lack of information related to maternal health care during pregnancy and post-pregnancy, especially in rural areas, results in many cases of pregnancy complications. Risk analysis for pregnant women is really needed as a reference in handling pregnant women so that the risk to pregnant women can be minimized. To analyze the risk of pregnant women can use data mining techniques by classifying the risk of pregnant women. This study proposes to classify Maternal Health Risk using the Naive Bayes method with three models, namely Gaussian, Multinomial, and Bournolli. The data used is the health data of pregnant women based on risk intensity which is grouped into three classes, namely low, mid and high risk. while the attributes are Age, Systolic Blood Pressure as SystolicBP, Diastolic BP as DiastolicBP, Blood Sugar as BS, Body Temperature as BodyTemp, and HeartRate. The results show that among the three Naïve Bayes models that have the best performance are the Multinomial and Bournolli with an accuracy of 84.8% while the Gaussian produces an accuracy of 82.6%.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135931302","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}
The formation of quality human resources cannot be separated from food, as nutritional intake affects human performance and health. As time increases, the number of residents increases to increase food needs. The ability of a region to meet food needs in its territory is different from other regions. This study aims to classify regions in East Java Province based on food security and determine areas with the best and lowest food security. The method used is the Fuzzy C-Means (FCM) and TOPSIS methods.This research uses criteria based on the Food Security Index compiled by the Food Security Agency. The results of regional clustering using FCM selected the best cluster using three clusters for all requirements, except in food utilization in the city using five clusters. Furthermore, from the clustering results, clustering and cluster members use TOPSIS and produce Magetan regency and Madiun city as areas with the highest food security. At the same time, the lowest food securities are Probolinggo regency and Kediri city.
{"title":"Evaluation of Food Security Area of East Java Province Using Fuzzy C-Means (FCM) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)","authors":"Yuniar Farida, Ghina Salsabila Firdaus, Ahmad Teguh Wibowo, Silvia Kartika Sari, Latifatun Nadya Desinaini","doi":"10.22146/ijccs.82297","DOIUrl":"https://doi.org/10.22146/ijccs.82297","url":null,"abstract":"The formation of quality human resources cannot be separated from food, as nutritional intake affects human performance and health. As time increases, the number of residents increases to increase food needs. The ability of a region to meet food needs in its territory is different from other regions. This study aims to classify regions in East Java Province based on food security and determine areas with the best and lowest food security. The method used is the Fuzzy C-Means (FCM) and TOPSIS methods.This research uses criteria based on the Food Security Index compiled by the Food Security Agency. The results of regional clustering using FCM selected the best cluster using three clusters for all requirements, except in food utilization in the city using five clusters. Furthermore, from the clustering results, clustering and cluster members use TOPSIS and produce Magetan regency and Madiun city as areas with the highest food security. At the same time, the lowest food securities are Probolinggo regency and Kediri city.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135931298","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}
The emergence of Covid-19 in December 2019 has disrupted life worldwide, including Indonesia. The government has made various efforts to control the pandemic, one of which is the development of an application called PeduliLindungi. This app aims to be a reliable tool for the government and the entire community during the pandemic. As a new regulation, the use of PeduliLindungi has prompted numerous reviews assessing its quality and performance. With the app's emergence and growth, various topics have emerged and become trending among the public. These topics were identified through user reviews of the PeduliLindungi app, using the Latent Dirichlet Allocation (LDA) algorithm. The data, consisting of 15,522 reviews, was collected from the Google Play Store and underwent pre-processing, including dictionary and corpus creation, determining the number of topics, and modeling with LDA. The resulting topic modeling process generated the ten most prominent topics. The outcomes were visualized using word clouds and topic distribution graphs, representing the most discussed aspects of the PeduliLindungi app among users. These topics are considered diverse since each issue has no relation or similarity to one another.
2019年12月新冠肺炎的出现扰乱了包括印度尼西亚在内的世界各地的生活。政府为控制疫情做出了各种努力，其中之一是开发一款名为PeduliLindungi的应用程序。该应用程序旨在成为疫情期间政府和整个社会的可靠工具。作为一种新制剂，PeduliLindungi的使用引发了许多评价其质量和性能的评论。随着这款应用的出现和发展，各种话题层出不穷，并成为公众关注的热点。这些主题是通过PeduliLindungi应用程序的用户评论确定的，使用潜在狄利克雷分配(LDA)算法。数据由15522条评论组成，从Google Play Store收集，并进行了预处理，包括字典和语料库创建，确定主题数量以及使用LDA建模。最终的主题建模过程生成了十个最突出的主题。结果使用词云和主题分布图进行可视化，代表了PeduliLindungi应用程序在用户中讨论最多的方面。这些主题被认为是多样化的，因为每个问题彼此之间没有关系或相似之处。
{"title":"Identify Reviews of Pedulilindungi Applications using Topic Modeling with Latent Dirichlet Allocation Method","authors":"Layli Hardiyanti, Dina Anggraini, Ana Kurniawati","doi":"10.22146/ijccs.86025","DOIUrl":"https://doi.org/10.22146/ijccs.86025","url":null,"abstract":"The emergence of Covid-19 in December 2019 has disrupted life worldwide, including Indonesia. The government has made various efforts to control the pandemic, one of which is the development of an application called PeduliLindungi. This app aims to be a reliable tool for the government and the entire community during the pandemic. As a new regulation, the use of PeduliLindungi has prompted numerous reviews assessing its quality and performance. With the app's emergence and growth, various topics have emerged and become trending among the public. These topics were identified through user reviews of the PeduliLindungi app, using the Latent Dirichlet Allocation (LDA) algorithm. The data, consisting of 15,522 reviews, was collected from the Google Play Store and underwent pre-processing, including dictionary and corpus creation, determining the number of topics, and modeling with LDA. The resulting topic modeling process generated the ten most prominent topics. The outcomes were visualized using word clouds and topic distribution graphs, representing the most discussed aspects of the PeduliLindungi app among users. These topics are considered diverse since each issue has no relation or similarity to one another.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135930505","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}
Muhammad Auzan, Dzikri Rahadian Fudholi, Paulus Josianlie P, M Ridho Fuadin
In the distribution sector, logistic package experience activities, such as transport, distribution, storage, packaging, and handling. Even though those processes have reasonable operational procedures, sometimes the package experience mishandling. The mishandling is hard to identify because many packages run simultaneously, and not all processes are monitored. An Inertial Measurement Unit (IMU) is installed inside a package to collect three acceleration and rotation data. The data is then labeled manually into four classes: correct handling, vertical fall, and thrown and rotating fall. Then, using cross-validation, ten classifiers were used to generate a model to classify the logistic package status and evaluate the accuracy score. It is hard to differentiate between free-fall and thrown. The classification only uses the accelerometer data to minimize the running time. The correct handling classification gives a good result because the data pattern has few variations. However, the thrown, free-fall and rotating data give a lower result because the pattern resembles each other. The average accuracy of the ten classifications is 78.15, with a mean deviation of 4.31. The best classifier for this research is the Gaussian Process, with a mean accuracy of 94.4 % and a deviation of 3.5 %.
{"title":"Classification Methods Performance On Logistic Package State Recognition","authors":"Muhammad Auzan, Dzikri Rahadian Fudholi, Paulus Josianlie P, M Ridho Fuadin","doi":"10.22146/ijccs.82697","DOIUrl":"https://doi.org/10.22146/ijccs.82697","url":null,"abstract":"In the distribution sector, logistic package experience activities, such as transport, distribution, storage, packaging, and handling. Even though those processes have reasonable operational procedures, sometimes the package experience mishandling. The mishandling is hard to identify because many packages run simultaneously, and not all processes are monitored. An Inertial Measurement Unit (IMU) is installed inside a package to collect three acceleration and rotation data. The data is then labeled manually into four classes: correct handling, vertical fall, and thrown and rotating fall. Then, using cross-validation, ten classifiers were used to generate a model to classify the logistic package status and evaluate the accuracy score. It is hard to differentiate between free-fall and thrown. The classification only uses the accelerometer data to minimize the running time. The correct handling classification gives a good result because the data pattern has few variations. However, the thrown, free-fall and rotating data give a lower result because the pattern resembles each other. The average accuracy of the ten classifications is 78.15, with a mean deviation of 4.31. The best classifier for this research is the Gaussian Process, with a mean accuracy of 94.4 % and a deviation of 3.5 %.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":"6 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135931300","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}
Ade Silvia Handayani, Nur Hopipah, Mohammad Fadhli
Telecommunications have an important role in facilitating communication and information exchange, especially in emergency situations such as natural disasters and unexpected community events. Implementing mobile technology is a promising solution to improve the response and handling of in-kind problems. Mobile technology allows the public to quickly report incidents of disaster or security issues through applications or short message services. The implementation of mobile technology facilitates real-time communication between the community and Babinsa. The public can send reports quickly, convey important information, and share photos or videos as evidence of events. Babinsa, on the other hand, can respond more efficiently to these reports and take necessary actions based on the information received. Apart from that, mobile technology also supports two-way communication between Babinsa and the community. The public can also get the latest information about emergencies, efforts handling, or evacuation via app or direct message notification. Thus, the implementation of mobile technology can make a significant contribution to improving communication, response, and handling of disasters and community events.
{"title":"The Implementation of Mobile Technology in The Process of Reporting Disasters and Events","authors":"Ade Silvia Handayani, Nur Hopipah, Mohammad Fadhli","doi":"10.22146/ijccs.87660","DOIUrl":"https://doi.org/10.22146/ijccs.87660","url":null,"abstract":"Telecommunications have an important role in facilitating communication and information exchange, especially in emergency situations such as natural disasters and unexpected community events. Implementing mobile technology is a promising solution to improve the response and handling of in-kind problems. Mobile technology allows the public to quickly report incidents of disaster or security issues through applications or short message services. The implementation of mobile technology facilitates real-time communication between the community and Babinsa. The public can send reports quickly, convey important information, and share photos or videos as evidence of events. Babinsa, on the other hand, can respond more efficiently to these reports and take necessary actions based on the information received. Apart from that, mobile technology also supports two-way communication between Babinsa and the community. The public can also get the latest information about emergencies, efforts handling, or evacuation via app or direct message notification. Thus, the implementation of mobile technology can make a significant contribution to improving communication, response, and handling of disasters and community events.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":"6 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135931301","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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