Pub Date : 2023-11-06DOI: 10.32736/sisfokom.v12i3.1740
Regina Vannya, Arief Hermawan
Chicken is one of the staple foods that is widely enjoyed by all. To obtain the benefits of chicken meat, the level of freshness becomes one of the main keys. In general, the level of freshness of chicken meat is divided into two classes, namely fresh and non-fresh. The difference in the level of freshness can be seen from the color changes of each class. Spoiled chicken (chicken died yesterday) is one type of meat in the non-fresh group. The widespread sale of spoiled chicken meat among the public raises doubts about choosing chicken that is suitable and unsuitable for consumption. Therefore, chicken meat freshness classification is needed to facilitate the selection of chicken meat based on color characteristics. The use of Naive Bayes Classifier algorithm in categorizing fresh and non-fresh classes is done by calculating the probability value of each image channel input. This research was conducted to compare the Naive Bayes, decision tree, and K-NN algorithms in classifying chicken meat based on color characteristics. The results of the study showed that the Naive Bayes classifier algorithm was superior to the decision tree and K-NN algorithms with an accuracy rate of 75%, precision of 79%, and recall of 65%. It is known that 27 images were predicted correctly and 9 images were predicted incorrectly out of a total 36 data. The use of a histogram in this study aims to differentiate chicken meat images from non-meat during the testing process of the model using the Naive Bayes classifier algorithm.
{"title":"Performance Analysis of Chicken Freshness classification using Naïve Bayes, Decision Tree, and k-NN","authors":"Regina Vannya, Arief Hermawan","doi":"10.32736/sisfokom.v12i3.1740","DOIUrl":"https://doi.org/10.32736/sisfokom.v12i3.1740","url":null,"abstract":"Chicken is one of the staple foods that is widely enjoyed by all. To obtain the benefits of chicken meat, the level of freshness becomes one of the main keys. In general, the level of freshness of chicken meat is divided into two classes, namely fresh and non-fresh. The difference in the level of freshness can be seen from the color changes of each class. Spoiled chicken (chicken died yesterday) is one type of meat in the non-fresh group. The widespread sale of spoiled chicken meat among the public raises doubts about choosing chicken that is suitable and unsuitable for consumption. Therefore, chicken meat freshness classification is needed to facilitate the selection of chicken meat based on color characteristics. The use of Naive Bayes Classifier algorithm in categorizing fresh and non-fresh classes is done by calculating the probability value of each image channel input. This research was conducted to compare the Naive Bayes, decision tree, and K-NN algorithms in classifying chicken meat based on color characteristics. The results of the study showed that the Naive Bayes classifier algorithm was superior to the decision tree and K-NN algorithms with an accuracy rate of 75%, precision of 79%, and recall of 65%. It is known that 27 images were predicted correctly and 9 images were predicted incorrectly out of a total 36 data. The use of a histogram in this study aims to differentiate chicken meat images from non-meat during the testing process of the model using the Naive Bayes classifier algorithm.","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135723652","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: 10.32736/sisfokom.v12i3.1739
Afriyadi Afriyadi, Wiranto Herry Utomo
Reinforcement learning (RL) is a powerful tool for training agents to perform complex tasks. However, from time-to-time RL agents often learn to behave in unsafe or unintended ways. This is especially true during the exploration phase, when the agent is trying to learn about its environment. This research acquires safe exploration methods from the field of robotics and evaluates their effectiveness compared to other algorithms that are commonly used in complex videogame environments without safe exploration. We also propose a method for hand-crafting catastrophic states, which are states that are known to be unsafe for the agent to visit. Our results show that our method and our hand-crafted safety constraints outperform state-of-the-art algorithms on relatively certain iterations. This means that our method is able to learn to behave safely while still achieving good performance. These results have implications for the future development of human-level safe learning with combination of model-based RL using complex videogame environments. By developing safe exploration methods, we can help to ensure that RL agents can be used in a variety of real-world applications, such as self-driving cars and robotics.
{"title":"Towards Human-Level Safe Reinforcement Learning in Atari Library","authors":"Afriyadi Afriyadi, Wiranto Herry Utomo","doi":"10.32736/sisfokom.v12i3.1739","DOIUrl":"https://doi.org/10.32736/sisfokom.v12i3.1739","url":null,"abstract":"Reinforcement learning (RL) is a powerful tool for training agents to perform complex tasks. However, from time-to-time RL agents often learn to behave in unsafe or unintended ways. This is especially true during the exploration phase, when the agent is trying to learn about its environment. This research acquires safe exploration methods from the field of robotics and evaluates their effectiveness compared to other algorithms that are commonly used in complex videogame environments without safe exploration. We also propose a method for hand-crafting catastrophic states, which are states that are known to be unsafe for the agent to visit. Our results show that our method and our hand-crafted safety constraints outperform state-of-the-art algorithms on relatively certain iterations. This means that our method is able to learn to behave safely while still achieving good performance. These results have implications for the future development of human-level safe learning with combination of model-based RL using complex videogame environments. By developing safe exploration methods, we can help to ensure that RL agents can be used in a variety of real-world applications, such as self-driving cars and robotics.","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135723755","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: 10.32736/sisfokom.v12i3.1750
Putri Meliana, Nurul Mutiah, Syahru Rahmayuda
DANA is a digital wallet application that has an open platform concept, meaning it can be used on different platforms but is integrated with one another. However, there were complaints that were felt by DANA application users which were conveyed in Google Playstore reviews, namely frequent errors and delays in the transaction process. This is the basis for measuring the level of acceptance of DANA application users based on the user's experience. This research model is an integration of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) model and the User Experience technology Acceptance Model (UX TAM). The data analysis technique used Partial Least Square-Structural Equation Modeling (PLS-SEM) and used SmartPLS 3 tools Data collection was carried out by randomly distributing questionnaires to 100 respondents, namely the Pontianak community with an age range of 15-40 years. Data collection was carried out by distributing questionnaires to 100 respondents, namely the Pontianak community. Of the 21 hypotheses proposed, 10 hypotheses stated there was a relationship between the two variables and the other 11 hypotheses had no relationship. The hypotheses that have an influence are Effort Expectancy on Behavior Intention, Habit on Behavior Intention, Efficiency on Effort Expectancy, Efficiency on Performance Expectancy, Output Quality on Performance Expectancy, Dependability on Habit, Stimulation on Hedonic Motivation, Output Quality on Perceived Usefulness, Dependability on Perceived Ease Of Use, and Behavioral Intention to Use Behavior. The results of the research are in the form of recommendations that are expected to improve the performance of the DANA application.
{"title":"User Acceptance Analysis Dana Application E-Wallet Using UTAUT 2 and UX TAM","authors":"Putri Meliana, Nurul Mutiah, Syahru Rahmayuda","doi":"10.32736/sisfokom.v12i3.1750","DOIUrl":"https://doi.org/10.32736/sisfokom.v12i3.1750","url":null,"abstract":"DANA is a digital wallet application that has an open platform concept, meaning it can be used on different platforms but is integrated with one another. However, there were complaints that were felt by DANA application users which were conveyed in Google Playstore reviews, namely frequent errors and delays in the transaction process. This is the basis for measuring the level of acceptance of DANA application users based on the user's experience. This research model is an integration of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) model and the User Experience technology Acceptance Model (UX TAM). The data analysis technique used Partial Least Square-Structural Equation Modeling (PLS-SEM) and used SmartPLS 3 tools Data collection was carried out by randomly distributing questionnaires to 100 respondents, namely the Pontianak community with an age range of 15-40 years. Data collection was carried out by distributing questionnaires to 100 respondents, namely the Pontianak community. Of the 21 hypotheses proposed, 10 hypotheses stated there was a relationship between the two variables and the other 11 hypotheses had no relationship. The hypotheses that have an influence are Effort Expectancy on Behavior Intention, Habit on Behavior Intention, Efficiency on Effort Expectancy, Efficiency on Performance Expectancy, Output Quality on Performance Expectancy, Dependability on Habit, Stimulation on Hedonic Motivation, Output Quality on Perceived Usefulness, Dependability on Perceived Ease Of Use, and Behavioral Intention to Use Behavior. The results of the research are in the form of recommendations that are expected to improve the performance of the DANA application.","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135723758","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: 10.32736/sisfokom.v12i3.1723
Faridatul Warda, Fathorazi Nur Fajri, Abu Tholib
Every year, the Faculty of Engineering at Nurul Jadid University forms a committee to manage the process of students' final projects from the title selection stage to the final examination process until graduation. The process of selecting the final project title is still done manually, namely by checking the titles one by one, which takes a long time and allows errors because there is a lot of data to check, so human errors can also occur. Therefore, this research proposes to use the Bidirectional Long Short Term Memory (BiLSTM) method to classify the final project title based on its grade category. Several experiments were conducted to generate the most appropriate labels. The first experiment produced 4 labels and the second experiment produced 2 labels. From the results of several experiments, it was concluded that the second experiment had the best accuracy results with the 'good enough' and 'good' classes. The oversampling technique was then applied to overcome overlapping data, and the turning process was then performed on several parameters that could re-optimize the previous accuracy result of 75.24% to 91.15%. With a configuration of 10 random state parameters, using 64 batch sizes and 50 epochs. In addition, model adjustments were made to the hidden layer by adding a dropout layer and relu activation.
{"title":"Classification of Final Project Titles Using Bidirectional Long Short Term Memory at the Faculty of Engineering Nurul Jadid University","authors":"Faridatul Warda, Fathorazi Nur Fajri, Abu Tholib","doi":"10.32736/sisfokom.v12i3.1723","DOIUrl":"https://doi.org/10.32736/sisfokom.v12i3.1723","url":null,"abstract":"Every year, the Faculty of Engineering at Nurul Jadid University forms a committee to manage the process of students' final projects from the title selection stage to the final examination process until graduation. The process of selecting the final project title is still done manually, namely by checking the titles one by one, which takes a long time and allows errors because there is a lot of data to check, so human errors can also occur. Therefore, this research proposes to use the Bidirectional Long Short Term Memory (BiLSTM) method to classify the final project title based on its grade category. Several experiments were conducted to generate the most appropriate labels. The first experiment produced 4 labels and the second experiment produced 2 labels. From the results of several experiments, it was concluded that the second experiment had the best accuracy results with the 'good enough' and 'good' classes. The oversampling technique was then applied to overcome overlapping data, and the turning process was then performed on several parameters that could re-optimize the previous accuracy result of 75.24% to 91.15%. With a configuration of 10 random state parameters, using 64 batch sizes and 50 epochs. In addition, model adjustments were made to the hidden layer by adding a dropout layer and relu activation.","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135775246","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: 10.32736/sisfokom.v12i3.1738
Lilik Sumaryanti, Syaiful Nugraha, Lusia Lamalewa
Roads are infrastructure for organizing transportation, which are places for traffic to flow both for people and goods to reach destinations safely, securely, comfortably, quickly, smoothly, orderly, and efficiently, especially roads in residential areas. Setting priorities for the road improvement program is the responsibility of the Public Housing, Settlement Areas, and Land Affairs Office, which handles technical planning, development, arrangement, supervision, and control of development in residential areas. Recommendations for road proposals for the currently running improvement program, based on an assessment of their physical condition, are carried out by experts. This prioritization certainly takes a long time because experts have to compare the physical conditions of the roads one by one to make a decision. A decision support system is specifically designed for the decision-making process that can be applied in various aspects of the decision-making field. Recommendations for alternative roads in the road improvement program were analyzed using the SMART method to find alternatives with the highest preference value and the advantage that they can be used for all weighting techniques. Accuracy testing shows that the priority recommendation output presented by the application has an accuracy rate of 80%. This value is obtained by comparing the results of recommendations from experts.
{"title":"Priority Recommendations for Residential Road Improvement Using the SMART Analysis Method","authors":"Lilik Sumaryanti, Syaiful Nugraha, Lusia Lamalewa","doi":"10.32736/sisfokom.v12i3.1738","DOIUrl":"https://doi.org/10.32736/sisfokom.v12i3.1738","url":null,"abstract":"Roads are infrastructure for organizing transportation, which are places for traffic to flow both for people and goods to reach destinations safely, securely, comfortably, quickly, smoothly, orderly, and efficiently, especially roads in residential areas. Setting priorities for the road improvement program is the responsibility of the Public Housing, Settlement Areas, and Land Affairs Office, which handles technical planning, development, arrangement, supervision, and control of development in residential areas. Recommendations for road proposals for the currently running improvement program, based on an assessment of their physical condition, are carried out by experts. This prioritization certainly takes a long time because experts have to compare the physical conditions of the roads one by one to make a decision. A decision support system is specifically designed for the decision-making process that can be applied in various aspects of the decision-making field. Recommendations for alternative roads in the road improvement program were analyzed using the SMART method to find alternatives with the highest preference value and the advantage that they can be used for all weighting techniques. Accuracy testing shows that the priority recommendation output presented by the application has an accuracy rate of 80%. This value is obtained by comparing the results of recommendations from experts.","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135775245","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: 10.32736/sisfokom.v12i3.1731
Ine Widyaningrum Mustama Putri, Rusdah Rusdah, Lis Suryadi, Dian Anubhakti
Higher Education is a level of education after secondary education which includes diploma programs, undergraduate programs, master programs, doctoral programs, professional programs, and specialist programs organized based on the culture of the Indonesian nation. Student graduation is one of the important factors to improve university accreditation. Students who graduate above 5 years and the number of students who drop out are important indicators in determining accreditation which then causes the difficulty of accrediting a college to rise. This research aims as an early warning for students who graduate on time and graduate late from the Faculty of Information Technology, Institute of Science and Business Atma Luhur using the C4.5 decision tree algorithm by implementing the Cross-Industry Standard Process for Data Mining (CRISP- DM) method. The initial data of this research amounted to 1,015 which was taken through a query in the database of the Atma Luhur Institute of Science and Business. However, the data that will be used becomes 694 after preprocessing due to the large number of record contents that do not have a graduation year, with a total of 641 graduates graduating on time and 53 graduates graduating late. Based on the application of the model using the C4.5 decision tree algorithm and the Confusion Matrix method, the accuracy is 93.94%, Recall is 98.59%, and Precision is 95.03%. So it can be concluded that the C4.5 decision tree algorithm is the most effective algorithm for predicting student graduation, because it has a high level of accuracy.
{"title":"Prediction of Graduation for Students at the ISB Atma Luhur Faculty of Information Technology Using the C4.5 Algorithm","authors":"Ine Widyaningrum Mustama Putri, Rusdah Rusdah, Lis Suryadi, Dian Anubhakti","doi":"10.32736/sisfokom.v12i3.1731","DOIUrl":"https://doi.org/10.32736/sisfokom.v12i3.1731","url":null,"abstract":"Higher Education is a level of education after secondary education which includes diploma programs, undergraduate programs, master programs, doctoral programs, professional programs, and specialist programs organized based on the culture of the Indonesian nation. Student graduation is one of the important factors to improve university accreditation. Students who graduate above 5 years and the number of students who drop out are important indicators in determining accreditation which then causes the difficulty of accrediting a college to rise. This research aims as an early warning for students who graduate on time and graduate late from the Faculty of Information Technology, Institute of Science and Business Atma Luhur using the C4.5 decision tree algorithm by implementing the Cross-Industry Standard Process for Data Mining (CRISP- DM) method. The initial data of this research amounted to 1,015 which was taken through a query in the database of the Atma Luhur Institute of Science and Business. However, the data that will be used becomes 694 after preprocessing due to the large number of record contents that do not have a graduation year, with a total of 641 graduates graduating on time and 53 graduates graduating late. Based on the application of the model using the C4.5 decision tree algorithm and the Confusion Matrix method, the accuracy is 93.94%, Recall is 98.59%, and Precision is 95.03%. So it can be concluded that the C4.5 decision tree algorithm is the most effective algorithm for predicting student graduation, because it has a high level of accuracy.","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135775386","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: 10.32736/sisfokom.v12i3.1716
Anistya Rosyida, Theopilus Bayu Sasongko
Alzheimer's disease is a degenerative disease associated with memory loss, communication difficulties, mental health, thinking skills, and other psychological disorders that affect a person's daily activities. Alzheimer's disease is a disease that causes disability for people aged 70 years and over and is the seventh highest contributor to death in the world. However, until now there has not been found an effective treatment to cure Alzheimer's disease. Thus, early detection of Alzheimer's disease is very important so that sufferers of Alzheimer's disease can immediately receive intensive medical care so as to reduce the death rate from Alzheimer's disease. One method that can be used to detect Alzheimer's disease is by utilizing a machine learning algorithm model. The machine learning model in this study was carried out using the Decision Tree C4.5 algorithm classification method based on Binary Particle Swarm Optimization (BPSO). The C4.5 Decision Tree algorithm is used to classify Alzheimer's disease, while the BPSO algorithm is used to perform feature selection. By performing feature selection with the BPSO algorithm, the results show that the BPSO algorithm can improve accuracy and can increase the performance of the C4.5 algorithm in the Alzheimer's disease classification process. The results of the accuracy of the C4.5 algorithm using the BPSO feature selection are greater, namely 98.2% compared to the C4.5 algorithm without BPSO feature selection, which is only 96.4%.
{"title":"Early Detection of Alzheimer's Disease with the C4.5 Algorithm Based on BPSO (Binary Particle Swarm Optimization)","authors":"Anistya Rosyida, Theopilus Bayu Sasongko","doi":"10.32736/sisfokom.v12i3.1716","DOIUrl":"https://doi.org/10.32736/sisfokom.v12i3.1716","url":null,"abstract":"Alzheimer's disease is a degenerative disease associated with memory loss, communication difficulties, mental health, thinking skills, and other psychological disorders that affect a person's daily activities. Alzheimer's disease is a disease that causes disability for people aged 70 years and over and is the seventh highest contributor to death in the world. However, until now there has not been found an effective treatment to cure Alzheimer's disease. Thus, early detection of Alzheimer's disease is very important so that sufferers of Alzheimer's disease can immediately receive intensive medical care so as to reduce the death rate from Alzheimer's disease. One method that can be used to detect Alzheimer's disease is by utilizing a machine learning algorithm model. The machine learning model in this study was carried out using the Decision Tree C4.5 algorithm classification method based on Binary Particle Swarm Optimization (BPSO). The C4.5 Decision Tree algorithm is used to classify Alzheimer's disease, while the BPSO algorithm is used to perform feature selection. By performing feature selection with the BPSO algorithm, the results show that the BPSO algorithm can improve accuracy and can increase the performance of the C4.5 algorithm in the Alzheimer's disease classification process. The results of the accuracy of the C4.5 algorithm using the BPSO feature selection are greater, namely 98.2% compared to the C4.5 algorithm without BPSO feature selection, which is only 96.4%.","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135775244","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: 10.32736/sisfokom.v12i3.1721
Debby Erce Sondakh, Raissa C Maringka, Ferlien P Ayorbaba, Joanne S. C. B. T. Mangi, Stenly Richard Pungus
As consumer transaction preferences shifted from analog to digital, banks were compelled to develop digital transactions in the form of mobile banking. Users of mobile banking provide feedback regarding the application's usability. The opinions of users can be emotive. Emotions influence what a person emits or applies. Emotions are the behavioral response of a person when he is happy or unhappy. Thus, the manifestation of a person's emotions, whether in the form of facial expressions, verbal communication, written text, or judgment, can be used as a source of information to aid in decision making. The objective of this study is to apply emotion mining to the analysis of user evaluations of the BRImo application, one of the three most popular platforms in Indonesia as of August 2022, with a total of 800,000 reviews on the Play Store. Emotion Mining can be used to analyze the four categories of emotions expressed by users in the comments section: happy, angry, sad, and afraid. According to BRImo user evaluations, the decision tree algorithm is used to categorize happy, sad, afraid, and angry feelings. Using a decision tree to manage large data category sets is effective. The obtained dataset included 2959 happy classes, 2196 sad classes, 387 angry classes, and 81 scared classes. According to the findings of the analysis, a significant number of users of the BRImo application express positive sentiments in their evaluations, which are indicative of happy emotions. The Decision Tree algorithm yields results with a performance specification of 84.5%, sensitivity of 85.5%, and precision of 84.4%.
{"title":"Emotion Mining User Review of the BRImo Mobile Banking Application Using the Decision Tree Algorithm","authors":"Debby Erce Sondakh, Raissa C Maringka, Ferlien P Ayorbaba, Joanne S. C. B. T. Mangi, Stenly Richard Pungus","doi":"10.32736/sisfokom.v12i3.1721","DOIUrl":"https://doi.org/10.32736/sisfokom.v12i3.1721","url":null,"abstract":"As consumer transaction preferences shifted from analog to digital, banks were compelled to develop digital transactions in the form of mobile banking. Users of mobile banking provide feedback regarding the application's usability. The opinions of users can be emotive. Emotions influence what a person emits or applies. Emotions are the behavioral response of a person when he is happy or unhappy. Thus, the manifestation of a person's emotions, whether in the form of facial expressions, verbal communication, written text, or judgment, can be used as a source of information to aid in decision making. The objective of this study is to apply emotion mining to the analysis of user evaluations of the BRImo application, one of the three most popular platforms in Indonesia as of August 2022, with a total of 800,000 reviews on the Play Store. Emotion Mining can be used to analyze the four categories of emotions expressed by users in the comments section: happy, angry, sad, and afraid. According to BRImo user evaluations, the decision tree algorithm is used to categorize happy, sad, afraid, and angry feelings. Using a decision tree to manage large data category sets is effective. The obtained dataset included 2959 happy classes, 2196 sad classes, 387 angry classes, and 81 scared classes. According to the findings of the analysis, a significant number of users of the BRImo application express positive sentiments in their evaluations, which are indicative of happy emotions. The Decision Tree algorithm yields results with a performance specification of 84.5%, sensitivity of 85.5%, and precision of 84.4%.","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135775387","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 : 2023-11-03DOI: 10.32736/sisfokom.v12i3.1693
Susanto Susanto, M. Agus Syamsul Arifin, Harma Oktafia Lingga Wijaya
The use of IoT devices has grown rapidly, leading to an increase in cyber attacks that pose greater security and privacy threats than ever before. One such threat is botnet attacks on IoT devices. An IoT botnet is a group of Internet-connected IoT devices infected with malware and remotely controlled by an attacker. Machine learning techniques can be employed to detect botnet attacks. The use of machine learning-based detection methods has been shown to be effective in identifying cyber attacks. The performance of the detection system in machine learning can be improved by utilizing data reduction methods. The data reduction process in classification is used to overcome the problem of scalability and computation resources in the IoT. This paper proposes a detection system using the Autoencoder reduction method and the Decision tree classification method. The test results demonstrate that the Deep Autoencoder algorithm can reduce data and memory usage from 1.62 GB to 75.9 MB, while also improving the performance of decision tree classification, resulting in a high level of accuracy up to 100%. The Autoencoder approach in conjunction with the Decision Tree exhibits superior capabilities compared to previous studies.
{"title":"IoT Botnet Detection Using Autoencoders and Decision Trees","authors":"Susanto Susanto, M. Agus Syamsul Arifin, Harma Oktafia Lingga Wijaya","doi":"10.32736/sisfokom.v12i3.1693","DOIUrl":"https://doi.org/10.32736/sisfokom.v12i3.1693","url":null,"abstract":"The use of IoT devices has grown rapidly, leading to an increase in cyber attacks that pose greater security and privacy threats than ever before. One such threat is botnet attacks on IoT devices. An IoT botnet is a group of Internet-connected IoT devices infected with malware and remotely controlled by an attacker. Machine learning techniques can be employed to detect botnet attacks. The use of machine learning-based detection methods has been shown to be effective in identifying cyber attacks. The performance of the detection system in machine learning can be improved by utilizing data reduction methods. The data reduction process in classification is used to overcome the problem of scalability and computation resources in the IoT. This paper proposes a detection system using the Autoencoder reduction method and the Decision tree classification method. The test results demonstrate that the Deep Autoencoder algorithm can reduce data and memory usage from 1.62 GB to 75.9 MB, while also improving the performance of decision tree classification, resulting in a high level of accuracy up to 100%. The Autoencoder approach in conjunction with the Decision Tree exhibits superior capabilities compared to previous studies.","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135874916","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 : 2023-11-03DOI: 10.32736/sisfokom.v12i3.1699
Phie Chyan, Tri Saptadi
Digital images can experience various disturbances in acquisition and storage, one of which is a disturbance indicated by damage to certain areas of the image field and causes the loss of some of the information represented by the image. One of the ways to restore an image experiencing disturbances like this is with image completion technology. Image completion is an image restoration technology capable of filling in or completing missing or corrupted parts of an image. Various methods have been developed for this image completion, starting from those based on basic image processing to the latest relying on artificial intelligence algorithms. This study aims to develop and implement an image completion model based on deep learning with the transfer learning method from the completion.net architecture. Using the Facesrub training dataset consisting of a collection of unique facial photos allows the model to understand facial attributes better. Compared to conventional image completion based on image patches, the method developed in this study can perform image filling in image gaps with more realistic results. Based on visual tests conducted on respondents, the results obtained enable respondents to understand all the information represented by the restored image, similar to the original image.
{"title":"Image Restoration Using Deep Learning Based Image Completion","authors":"Phie Chyan, Tri Saptadi","doi":"10.32736/sisfokom.v12i3.1699","DOIUrl":"https://doi.org/10.32736/sisfokom.v12i3.1699","url":null,"abstract":"Digital images can experience various disturbances in acquisition and storage, one of which is a disturbance indicated by damage to certain areas of the image field and causes the loss of some of the information represented by the image. One of the ways to restore an image experiencing disturbances like this is with image completion technology. Image completion is an image restoration technology capable of filling in or completing missing or corrupted parts of an image. Various methods have been developed for this image completion, starting from those based on basic image processing to the latest relying on artificial intelligence algorithms. This study aims to develop and implement an image completion model based on deep learning with the transfer learning method from the completion.net architecture. Using the Facesrub training dataset consisting of a collection of unique facial photos allows the model to understand facial attributes better. Compared to conventional image completion based on image patches, the method developed in this study can perform image filling in image gaps with more realistic results. Based on visual tests conducted on respondents, the results obtained enable respondents to understand all the information represented by the restored image, similar to the original image.","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135874917","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：