Pub Date : 2023-08-09DOI: 10.33480/inti.v18i1.4399
Frisma Handayanna
Abstract— People with low incomes are unable to obtain education and other government services. The problem of poverty faced by the government is closely related to people with low incomes who cannot meet their basic needs. The Central Bureau of Statistics describes poverty as the inability to meet basic food and non-food needs as measured by expenditure. This study aims to classify Banten province based on poverty levels, by dividing the number of poor people into high, medium, and low categories. The K-Means clustering method is very fast and easy to use in the K-Means algorithm clustering process. Where the grouping results are formed, namely group one has a moderate number of poor people in three districts/cities, Pandeglang Regency, Lebak Regency, and Tangerang Regency. The second group has the lowest population in one district/city, namely Tangerang City. The third group has the highest number of poor people in the four districts/cities, namely Serang Regency, Cilegon Regency, Serang City, and South Tangerang City. The clustering results show that the Provincial Government of Banten will give priority and special attention to poverty alleviation efforts in the district/city. This will allow for increased revenues and earnings, as well as improved livelihoods and the economy in the area. the K-Means algorithm can classify the poor based on the number of people per district or city in Banten Province.
{"title":"PENERAPAN ALGORITMA K-MEANS UNTUK KLASTERISASI PENDUDUK MISKIN DI PROVINSI BANTEN","authors":"Frisma Handayanna","doi":"10.33480/inti.v18i1.4399","DOIUrl":"https://doi.org/10.33480/inti.v18i1.4399","url":null,"abstract":"Abstract— People with low incomes are unable to obtain education and other government services. The problem of poverty faced by the government is closely related to people with low incomes who cannot meet their basic needs. The Central Bureau of Statistics describes poverty as the inability to meet basic food and non-food needs as measured by expenditure. This study aims to classify Banten province based on poverty levels, by dividing the number of poor people into high, medium, and low categories. The K-Means clustering method is very fast and easy to use in the K-Means algorithm clustering process. Where the grouping results are formed, namely group one has a moderate number of poor people in three districts/cities, Pandeglang Regency, Lebak Regency, and Tangerang Regency. The second group has the lowest population in one district/city, namely Tangerang City. The third group has the highest number of poor people in the four districts/cities, namely Serang Regency, Cilegon Regency, Serang City, and South Tangerang City. The clustering results show that the Provincial Government of Banten will give priority and special attention to poverty alleviation efforts in the district/city. This will allow for increased revenues and earnings, as well as improved livelihoods and the economy in the area. the K-Means algorithm can classify the poor based on the number of people per district or city in Banten Province.","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130454786","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 : 2023-08-07DOI: 10.33480/inti.v18i1.4288
Ibnu Akil
Abstract— The sophistication and success of machine learning in solving problems in various fields of artificial intelligence cannot be separated from the neural networks that form the basis of its algorithms. Meanwhile, the essence of a neural network lies in its activation function. However because so many activation function which are merged lately, it’s needed to search for proper activation function according to the model and it’s dataset used. In this study, the activation functions commonly used in machine learning models will be tested, namely; ReLU, GELU and SELU, for time series data in the form of stock prices. These activation functions are implemented in python and use the TensorFlow library, as well as a model developed based on the Convolutional Neural Network (CNN). From the results of this implementation, the results obtained with the CNN model, that the GELU activation function for time series data has the smallest loss value
{"title":"KOMPARASI FUNGSI AKTIVASI NEURAL NETWORK PADA DATA TIME SERIES","authors":"Ibnu Akil","doi":"10.33480/inti.v18i1.4288","DOIUrl":"https://doi.org/10.33480/inti.v18i1.4288","url":null,"abstract":"Abstract— The sophistication and success of machine learning in solving problems in various fields of artificial intelligence cannot be separated from the neural networks that form the basis of its algorithms. Meanwhile, the essence of a neural network lies in its activation function. However because so many activation function which are merged lately, it’s needed to search for proper activation function according to the model and it’s dataset used. In this study, the activation functions commonly used in machine learning models will be tested, namely; ReLU, GELU and SELU, for time series data in the form of stock prices. These activation functions are implemented in python and use the TensorFlow library, as well as a model developed based on the Convolutional Neural Network (CNN). From the results of this implementation, the results obtained with the CNN model, that the GELU activation function for time series data has the smallest loss value","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123576145","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 : 2023-08-07DOI: 10.33480/inti.v18i1.4307
J. Jefi, M. Fahmi, H. Hendri, Desiana Nur Kholifah, Suharjanti Suharjanti
Abstract— The use of technology in the tourism industry is becoming increasingly crucial considering changes in consumer behavior that tend to rely on digital platforms to make transactions. To analyze the effectiveness and reliability of facilitating the process of purchasing tickets online. In addition, it also includes an evaluation of the level of user satisfaction with this web-based system and its impact on increasing visits to the Cinta Bridge tourist attraction. Through surveys of tourists using a web-based ticket sales system, interviews with tour managers, and analysis of ticket transaction data documented in the system. Research participants include tourists, managers, and other related parties. The data were obtained and analyzed using statistical methods and qualitative analysis to gain a thorough understanding of the impact of the Web-Based Bridge Tourism Entrance Ticket Sales Information System. Demonstrating that managing ticket stock and arranging visit schedules more efficiently is also a positive result for the development of the technology-based tourism industry by proving the benefits of the Web-Based Bridge of Love Entrance Ticket Sales Information System. The results of this study can be the basis for the development and implementation of similar systems in other tourism destinations. In addition, the research is considered to provide valuable insights for related parties in optimizing the use of technology to improve user experience and operational efficiency in the tourism sector
{"title":"¬¬¬¬¬SISTEM INFORMASI PENJUALAN TIKET MASUK WISATA JEMBATAN CINTA BERBASIS WEB","authors":"J. Jefi, M. Fahmi, H. Hendri, Desiana Nur Kholifah, Suharjanti Suharjanti","doi":"10.33480/inti.v18i1.4307","DOIUrl":"https://doi.org/10.33480/inti.v18i1.4307","url":null,"abstract":"Abstract— The use of technology in the tourism industry is becoming increasingly crucial considering changes in consumer behavior that tend to rely on digital platforms to make transactions. To analyze the effectiveness and reliability of facilitating the process of purchasing tickets online. In addition, it also includes an evaluation of the level of user satisfaction with this web-based system and its impact on increasing visits to the Cinta Bridge tourist attraction. Through surveys of tourists using a web-based ticket sales system, interviews with tour managers, and analysis of ticket transaction data documented in the system. Research participants include tourists, managers, and other related parties. The data were obtained and analyzed using statistical methods and qualitative analysis to gain a thorough understanding of the impact of the Web-Based Bridge Tourism Entrance Ticket Sales Information System. Demonstrating that managing ticket stock and arranging visit schedules more efficiently is also a positive result for the development of the technology-based tourism industry by proving the benefits of the Web-Based Bridge of Love Entrance Ticket Sales Information System. The results of this study can be the basis for the development and implementation of similar systems in other tourism destinations. In addition, the research is considered to provide valuable insights for related parties in optimizing the use of technology to improve user experience and operational efficiency in the tourism sector","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122322227","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 : 2023-08-04DOI: 10.33480/inti.v18i1.4254
Taufik Hidayatulloh, Lestari Yusuf
Abstract—The news of the death of a man in Indonesia is in the public spotlight because doctors have difficulty treating his illness because being overweight or obese causes the organs in the body to fail to function properly. Overweight causes the body to experience several health problems, including heart defects, diabetes, and several other diseases that can attack vital organs in the body. According to data on deaths caused by obesity, there are as many as 60 per 100,000 Indonesian population, and are a very feared killer. Faster handling of recognizing our body weight is important for each individual’s health. Classification can also help overweight in a person known more quickly. In this study, the classification algorithm that will be used is the Support vector machine (SVM). With 252 data, this study will use the SVM algorithm and look for the level of accuracy of the two classification classes, namely normal and overweight. This study produces an accuracy rate of 92.11% with a ROC curve value of 0.990 which means that the classification in this study is very good.
{"title":"KLASIFIKASI TIPE BERAT TUBUH MENGGUNAKAN METODE SUPPORT VECTOR MACHINE","authors":"Taufik Hidayatulloh, Lestari Yusuf","doi":"10.33480/inti.v18i1.4254","DOIUrl":"https://doi.org/10.33480/inti.v18i1.4254","url":null,"abstract":"Abstract—The news of the death of a man in Indonesia is in the public spotlight because doctors have difficulty treating his illness because being overweight or obese causes the organs in the body to fail to function properly. Overweight causes the body to experience several health problems, including heart defects, diabetes, and several other diseases that can attack vital organs in the body. According to data on deaths caused by obesity, there are as many as 60 per 100,000 Indonesian population, and are a very feared killer. Faster handling of recognizing our body weight is important for each individual’s health. Classification can also help overweight in a person known more quickly. In this study, the classification algorithm that will be used is the Support vector machine (SVM). With 252 data, this study will use the SVM algorithm and look for the level of accuracy of the two classification classes, namely normal and overweight. This study produces an accuracy rate of 92.11% with a ROC curve value of 0.990 which means that the classification in this study is very good.","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121757623","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 : 2023-08-04DOI: 10.33480/inti.v18i1.4282
Elly Indrayuni, Acmad Nurhadi
At present the development of Artificial Intelligence technology is progressing rapidly. There are many new artificial intelligence technologies available in various fields. Artificial Intelligence is an artificial intelligence program that can study data, perform processes of thinking and acting like humans. The presence of Artificial Intelligence technology has many positive impacts, especially in increasing work effectiveness and efficiency. However, AI is also a threat to human resources because slowly human work is being replaced by Artificial Intelligence. Various opinions about the development of Artificial Intelligence are widely discussed on social media such as Twitter. Sentiment analysis is a computational study to automatically categorize opinions into positive or negative categories. In this study, the Naive Bayes algorithm was used to analyze sentiment or public opinion regarding the development of Artificial Intelligence for Twitter users. The data collection method used is crawling data on Twitter. The results of the sentiment classification test for the development of Artificial Intelligence using Naive Bayes yield an accuracy value of 86.42%. Meanwhile, the results of the sentiment classification test using Naive Bayes based on Particle Swarm Optimization (PSO) increased with an accuracy value of 87.55%. Based on the results of this study, the use of PSO as an optimization technique for the Naive Bayes algorithm is proven to be the best algorithm model in sentiment analysis for the development of Artificial Intelligence for English text.
{"title":"OPTIMASI NAIVE BAYES BERBASIS PSO UNTUK ANALISA SENTIMEN PERKEMBANGAN ARTIFICIAL INTELLIGENCE DI TWITTER","authors":"Elly Indrayuni, Acmad Nurhadi","doi":"10.33480/inti.v18i1.4282","DOIUrl":"https://doi.org/10.33480/inti.v18i1.4282","url":null,"abstract":"At present the development of Artificial Intelligence technology is progressing rapidly. There are many new artificial intelligence technologies available in various fields. Artificial Intelligence is an artificial intelligence program that can study data, perform processes of thinking and acting like humans. The presence of Artificial Intelligence technology has many positive impacts, especially in increasing work effectiveness and efficiency. However, AI is also a threat to human resources because slowly human work is being replaced by Artificial Intelligence. Various opinions about the development of Artificial Intelligence are widely discussed on social media such as Twitter. Sentiment analysis is a computational study to automatically categorize opinions into positive or negative categories. In this study, the Naive Bayes algorithm was used to analyze sentiment or public opinion regarding the development of Artificial Intelligence for Twitter users. The data collection method used is crawling data on Twitter. The results of the sentiment classification test for the development of Artificial Intelligence using Naive Bayes yield an accuracy value of 86.42%. Meanwhile, the results of the sentiment classification test using Naive Bayes based on Particle Swarm Optimization (PSO) increased with an accuracy value of 87.55%. Based on the results of this study, the use of PSO as an optimization technique for the Naive Bayes algorithm is proven to be the best algorithm model in sentiment analysis for the development of Artificial Intelligence for English text.","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"35 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123514059","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 : 2023-08-03DOI: 10.33480/inti.v18i1.4230
Lestari Yusuf, Siti Masripah
Abstract— ChatGPT which is an OpenAI technology that responds to conversations between humans and machines. enabling users of all ages and backgrounds to communicate naturally in multiple languages without having prior knowledge or experience in programming or the computer world. However, a technology will always be at odds and has flaws on the human side, various assumptions about chatGPT are formed from many sides, such as in the world of education, chatGPT creates parallels for teachers and lecturers. When giving assignments, students/students can use chatGPT as material in answering assignments from teachers/lecturers. And that results in students/students not carefully reading the answers to these assignments, if that continues to happen, students/students will find it too easy to get something and then will lose interest in solving problems with their own efforts. This article aims to analyze sentiment analysis whose data is taken from Twitter using the keyword "CahtGPT OpenAI". With 2,000 data calculated using the naive Bayes algorithm and optimized using PSO, it is found that sentiment analysis for chatGPT itself has an accuracy of 69.23% with a positive class of 0.503 and a negative of 0.497 and obtains an AUC curve value of 0.68 +/- 0.55..
{"title":"SENTIMEN ANALISIS CHATGPT DENGAN ALGORITMA NAÏVE BAYES DAN OPTIMASI PSO","authors":"Lestari Yusuf, Siti Masripah","doi":"10.33480/inti.v18i1.4230","DOIUrl":"https://doi.org/10.33480/inti.v18i1.4230","url":null,"abstract":"Abstract— ChatGPT which is an OpenAI technology that responds to conversations between humans and machines. enabling users of all ages and backgrounds to communicate naturally in multiple languages without having prior knowledge or experience in programming or the computer world. However, a technology will always be at odds and has flaws on the human side, various assumptions about chatGPT are formed from many sides, such as in the world of education, chatGPT creates parallels for teachers and lecturers. When giving assignments, students/students can use chatGPT as material in answering assignments from teachers/lecturers. And that results in students/students not carefully reading the answers to these assignments, if that continues to happen, students/students will find it too easy to get something and then will lose interest in solving problems with their own efforts. This article aims to analyze sentiment analysis whose data is taken from Twitter using the keyword \"CahtGPT OpenAI\". With 2,000 data calculated using the naive Bayes algorithm and optimized using PSO, it is found that sentiment analysis for chatGPT itself has an accuracy of 69.23% with a positive class of 0.503 and a negative of 0.497 and obtains an AUC curve value of 0.68 +/- 0.55..","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124063172","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 : 2023-08-02DOI: 10.33480/inti.v18i1.4286
Hesti Ratna Setyaningrum, M. R. Muttaqin, Mochzen Gito Resmi
Abstract— Nowadays, awareness of the importance of cultural heritage is decreasing among the public, especially among youth who will become leaders and inherit the culture of their region. Law Number 11 of 2010 stipulates the importance of protecting and preserving cultural heritage because it has significant value in history, science, education, religion and culture. Therefore, the existence of cultural heritage must be considered and maintained properly according to applicable regulations. There are several criteria for assessing buildings that will be used as cultural heritage according to Law number 11 of 2010 in Chapter III, article 5, cultural heritage criteria, namely the age of the building, historical value, cultural value and architectural value. This study aims to create a system that can determine the feasibility of a building as a cultural heritage in a precise and accurate way (case study DISPORAPARBUD Purwakarta). In this study, the Simple Multi Attribute Rating Technique (SMART) method was used in the Decision Support System (SPK). The results of this study are to produce recommendations for buildings that are worthy of being cultural heritage in accordance with predetermined criteria, namely the Normal School building with a value of 1 by occupying the first rank, which will then be recommended to the Purwakarta DISPORAPARBUD
{"title":"PENENTUAN KELAYAKAN BANGUNAN CAGAR BUDAYA MENGGUNAKAN METODE SIMPLE MULTI ATTRIBUTE RATING TECHNIQUE (SMART)","authors":"Hesti Ratna Setyaningrum, M. R. Muttaqin, Mochzen Gito Resmi","doi":"10.33480/inti.v18i1.4286","DOIUrl":"https://doi.org/10.33480/inti.v18i1.4286","url":null,"abstract":"Abstract— Nowadays, awareness of the importance of cultural heritage is decreasing among the public, especially among youth who will become leaders and inherit the culture of their region. Law Number 11 of 2010 stipulates the importance of protecting and preserving cultural heritage because it has significant value in history, science, education, religion and culture. Therefore, the existence of cultural heritage must be considered and maintained properly according to applicable regulations. There are several criteria for assessing buildings that will be used as cultural heritage according to Law number 11 of 2010 in Chapter III, article 5, cultural heritage criteria, namely the age of the building, historical value, cultural value and architectural value. This study aims to create a system that can determine the feasibility of a building as a cultural heritage in a precise and accurate way (case study DISPORAPARBUD Purwakarta). In this study, the Simple Multi Attribute Rating Technique (SMART) method was used in the Decision Support System (SPK). The results of this study are to produce recommendations for buildings that are worthy of being cultural heritage in accordance with predetermined criteria, namely the Normal School building with a value of 1 by occupying the first rank, which will then be recommended to the Purwakarta DISPORAPARBUD","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132174612","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 : 2023-08-02DOI: 10.33480/inti.v18i1.4266
Andi Nurkholis, Yusra Fernando, Faris Arkans Ans
Abstract— In digitalization era, internet is at the center of all lines of community activity, just like the field of work. Currently, many platforms provide job vacancies, especially for freelancers. To obtain this information, users usually need to open several websites to find information about suitable job vacancies. Web scraping offers solution to overcome these problems. Based on research that has been done, the BeautifulSoup and Selenium libraries will be used to collect data. To search for data, vector space model method is used to find the level of data similarity between the query and the document. In exploring data, the average near-perfect recall value is 98%, while the average precision value is 56%. This is because data search uses three parameters, so the possibility of retrieving irrelevant data is more significant if the document contains a word in the user's query, even though the context does not match. Utilizing the Streamlit framework in Python can display the data processing results and help users navigate the web scraping process, data processing, and data search. This study aims to implement the web scraping method to retrieve data from freelance websites: Freelance, Project, and Sribulancer. By applying the vector space model method, users can search data from several websites without opening freelance websites one by one. Using data visualization in the form of a web application using the Streamlit framework, the web scraping results can also be processed to be presented in a more helpful form and save the user's time
{"title":"METODE VECTOR SPACE MODEL UNTUK WEB SCRAPING PADA WEBSITE FREELANCE","authors":"Andi Nurkholis, Yusra Fernando, Faris Arkans Ans","doi":"10.33480/inti.v18i1.4266","DOIUrl":"https://doi.org/10.33480/inti.v18i1.4266","url":null,"abstract":"Abstract— In digitalization era, internet is at the center of all lines of community activity, just like the field of work. Currently, many platforms provide job vacancies, especially for freelancers. To obtain this information, users usually need to open several websites to find information about suitable job vacancies. Web scraping offers solution to overcome these problems. Based on research that has been done, the BeautifulSoup and Selenium libraries will be used to collect data. To search for data, vector space model method is used to find the level of data similarity between the query and the document. In exploring data, the average near-perfect recall value is 98%, while the average precision value is 56%. This is because data search uses three parameters, so the possibility of retrieving irrelevant data is more significant if the document contains a word in the user's query, even though the context does not match. Utilizing the Streamlit framework in Python can display the data processing results and help users navigate the web scraping process, data processing, and data search. This study aims to implement the web scraping method to retrieve data from freelance websites: Freelance, Project, and Sribulancer. By applying the vector space model method, users can search data from several websites without opening freelance websites one by one. Using data visualization in the form of a web application using the Streamlit framework, the web scraping results can also be processed to be presented in a more helpful form and save the user's time","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123758625","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 : 2023-08-02DOI: 10.33480/inti.v18i1.4314
Cucu Kardila, M. R. Muttaqin, Mochzen Gito Resmi
Abstract—The decreasing sheep population has raised serious concerns regarding its impact on both the livestock industry and export opportunities. One of the main factors contributing to this decline is the prevalence of diseases among sheep. These illnesses present a significant problem as they can lead to reduced meat production, animal fatalities, and economic losses. The limited knowledge among farmers regarding these diseases and sheep care makes it challenging to diagnose and treat the conditions effectively. To address this issue and aid farmers in easily diagnosing diseases, a web-based expert system utilizing the fuzzy Mamdani method was developed. The selection of the fuzzy Mamdani method was based on its ability to handle uncertainty in disease diagnosis, providing reasonably accurate results by evaluating symptoms, determining disease severity, and recommending appropriate treatments. Through the fuzzy Mamdani method and the web-based platform, this system offers convenient access for farmers to diagnose diseases in their sheep online. According to the analysis results, reproductive health disorders are the primary cause of the decline in the sheep population. Consequently, the expert system for diagnosing sheep diseases serves as an alternative for early prevention and suitable treatment. System testing indicates an accuracy rate of 80%, signifying the system's capability to provide reasonably accurate diagnoses. The main goal of this research is to support the livestock and fisheries department in Purwakarta in diagnosing sheep diseases, preventing epidemic outbreaks, and implementing proper measures to mitigate the negative impacts on the livestock industry while promoting sustainable growth of the sheep population
{"title":"SISTEM PAKAR DIAGNOSA PENYAKIT PADA DOMBA DENGAN MENGGUNAKAN METODE FUZZY MAMDANI","authors":"Cucu Kardila, M. R. Muttaqin, Mochzen Gito Resmi","doi":"10.33480/inti.v18i1.4314","DOIUrl":"https://doi.org/10.33480/inti.v18i1.4314","url":null,"abstract":"Abstract—The decreasing sheep population has raised serious concerns regarding its impact on both the livestock industry and export opportunities. One of the main factors contributing to this decline is the prevalence of diseases among sheep. These illnesses present a significant problem as they can lead to reduced meat production, animal fatalities, and economic losses. The limited knowledge among farmers regarding these diseases and sheep care makes it challenging to diagnose and treat the conditions effectively. To address this issue and aid farmers in easily diagnosing diseases, a web-based expert system utilizing the fuzzy Mamdani method was developed. The selection of the fuzzy Mamdani method was based on its ability to handle uncertainty in disease diagnosis, providing reasonably accurate results by evaluating symptoms, determining disease severity, and recommending appropriate treatments. Through the fuzzy Mamdani method and the web-based platform, this system offers convenient access for farmers to diagnose diseases in their sheep online. According to the analysis results, reproductive health disorders are the primary cause of the decline in the sheep population. Consequently, the expert system for diagnosing sheep diseases serves as an alternative for early prevention and suitable treatment. System testing indicates an accuracy rate of 80%, signifying the system's capability to provide reasonably accurate diagnoses. The main goal of this research is to support the livestock and fisheries department in Purwakarta in diagnosing sheep diseases, preventing epidemic outbreaks, and implementing proper measures to mitigate the negative impacts on the livestock industry while promoting sustainable growth of the sheep population","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124958582","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 : 2023-08-02DOI: 10.33480/inti.v18i1.4175
Henny Leidiyana
Abstrak— Sentiment analysis can be used to glean information about user opinions and identify social or political trends. There have been many studies on sentiment analysis using machine learning or lexicon-based methods that have been quite impressive. However, machine learning models often have difficulty generalizing to new data due to various reasons, such as overfitting and limited training data. These models are also prone to bias and variance, which negatively affect the accuracy of their predictions. This study discusses the application of the ensemble stacking method in sentiment analysis with the topic of the takeover of Afghanistan by the Taliban. By monitoring social media, the author uses a dataset in the form of comments on YouTube news channels related to the topic raised. Several studies have shown how the ensemble stacking method predicts better than the single model. The research was carried out by creating a sentiment classification model with logistic regression machine learning algorithms, SVM, KNN, and CART then the ensemble stacking classifier formed by the base learner of the four algorithms. As a result, for a single classifier, the highest average accuracy is the logistic regression algorithm of 74.6 percent. The four algorithms are compiled and predicted by logistic regression, and the stacking ensemble classifier that is applied produces better accuracy than the stand-alone classifier, which is 75.3 percent
{"title":"ENSEMBLE STACKING DALAM ANALISA SENTIMEN REAKSI VETERAN MILITER AS TERHADAP PENGAMBILALIHAN AFGHANISTAN OLEH TALIBAN","authors":"Henny Leidiyana","doi":"10.33480/inti.v18i1.4175","DOIUrl":"https://doi.org/10.33480/inti.v18i1.4175","url":null,"abstract":"Abstrak— Sentiment analysis can be used to glean information about user opinions and identify social or political trends. There have been many studies on sentiment analysis using machine learning or lexicon-based methods that have been quite impressive. However, machine learning models often have difficulty generalizing to new data due to various reasons, such as overfitting and limited training data. These models are also prone to bias and variance, which negatively affect the accuracy of their predictions. This study discusses the application of the ensemble stacking method in sentiment analysis with the topic of the takeover of Afghanistan by the Taliban. By monitoring social media, the author uses a dataset in the form of comments on YouTube news channels related to the topic raised. Several studies have shown how the ensemble stacking method predicts better than the single model. The research was carried out by creating a sentiment classification model with logistic regression machine learning algorithms, SVM, KNN, and CART then the ensemble stacking classifier formed by the base learner of the four algorithms. As a result, for a single classifier, the highest average accuracy is the logistic regression algorithm of 74.6 percent. The four algorithms are compiled and predicted by logistic regression, and the stacking ensemble classifier that is applied produces better accuracy than the stand-alone classifier, which is 75.3 percent","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"316 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116135940","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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