Pub Date : 2024-02-12DOI: 10.32736/sisfokom.v13i1.1943
Kirana Alyssa Putri, Dimas Febriawan, Firman Noor Hasan
Graduating on time is what every student wants to accomplish in college. Students of Prof. Dr. Hamka Muhammadiyah University are one of those who have this dream. Based on 2020 graduates data from the Tracer Study, 60% said the university had a high enough impact on improving competence. This data indicates that university needs to evaluate improvement of academic quality. Often, students have difficulty finding information about important factors that support achieving timely graduation. A prediction analysis is needed to provide information about the student's graduation study period. For this analysis, data mining is implemented using the classification function of the decision tree (C4.5) algorithm with RapidMiner tools. The methodology for implementing data mining follows the stages of Knowledge Discovery In Database (KDD), beginning with data collection, preprocessing, transformation, data mining, and evaluation. The research findings consist of visualization and decision tree rules that reveal GPA as the most influential factor in determining a student's study period.There is other information, namely, students graduated on time (less than equal to 4 years) amounted to 170 or 54.5% and students did not graduate on time (more than 4 years) amounted to 142 or 45.6%. Testing the performance of decision tree (C4.5) utilizing confusion matrix through RapidMiner tools, resulted in accuracy reaching 83.87%, with precision of 87.50% and recall of 91.18%. Provides evidence that the decision tree algorithm (C4.5) has optimal performance to provide valuable information about predicting student graduation in order to increase student enrollment with the right study period.
{"title":"Implementation of Data Mining to Predict Student Study Period with Decision Tree Algorithm (C4.5)","authors":"Kirana Alyssa Putri, Dimas Febriawan, Firman Noor Hasan","doi":"10.32736/sisfokom.v13i1.1943","DOIUrl":"https://doi.org/10.32736/sisfokom.v13i1.1943","url":null,"abstract":"Graduating on time is what every student wants to accomplish in college. Students of Prof. Dr. Hamka Muhammadiyah University are one of those who have this dream. Based on 2020 graduates data from the Tracer Study, 60% said the university had a high enough impact on improving competence. This data indicates that university needs to evaluate improvement of academic quality. Often, students have difficulty finding information about important factors that support achieving timely graduation. A prediction analysis is needed to provide information about the student's graduation study period. For this analysis, data mining is implemented using the classification function of the decision tree (C4.5) algorithm with RapidMiner tools. The methodology for implementing data mining follows the stages of Knowledge Discovery In Database (KDD), beginning with data collection, preprocessing, transformation, data mining, and evaluation. The research findings consist of visualization and decision tree rules that reveal GPA as the most influential factor in determining a student's study period.There is other information, namely, students graduated on time (less than equal to 4 years) amounted to 170 or 54.5% and students did not graduate on time (more than 4 years) amounted to 142 or 45.6%. Testing the performance of decision tree (C4.5) utilizing confusion matrix through RapidMiner tools, resulted in accuracy reaching 83.87%, with precision of 87.50% and recall of 91.18%. Provides evidence that the decision tree algorithm (C4.5) has optimal performance to provide valuable information about predicting student graduation in order to increase student enrollment with the right study period.","PeriodicalId":517030,"journal":{"name":"Jurnal Sisfokom (Sistem Informasi dan Komputer)","volume":"68 35","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139895052","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 : 2024-02-12DOI: 10.32736/sisfokom.v13i1.1958
Miftahul Jannah, Widodo Widodo, Hamidillah Ajie
The transition of learning models from face-to-face to online learning has had several impacts on student learning, reflected in their academic achievements. This study aims to determine the performance of the algorithm model using data mining classification techniques in predicting the Semester Grade Point Average (GPA) of Informatics and Computer Engineering Education students, at Universitas Negeri Jakarta during online learning. The prediction employed the Naive Bayes algorithm and the dataset obtained by collecting questionnaires from 2020 and 2021 batches. The total data obtained is 155 records with 13 (thirteen) attributes in the form of 1 (one) ID attribute including NIM, 11 (eleven) regular attributes including gender, college entrance, smartphone facilities, network conditions, preferred online applications, interest in learning, learning attitudes, learning creativity, parental support, study groups, and other activities outside of lectures during online learning, and 1 (one) the label attribute namely the Semester Grade Point Average for students in 3rd and 5th semester. The evaluation of this research involved the confusion matrix and the ROC (Receiver Operating Characteristic) curve. Confusion matrix resulted in an accuracy of 75%, precision of 28.33%, and recall of 26.43%. The ROC curve resulted in an AUC value of 0.679, indicating the category of poor classification. This study also applied the SMOTE data balancing technique, leading to a confusion matrix evaluation with 88.46% accuracy, 57.43% precision, and 52.14% recall. Furthermore, the ROC curve resulted in an AUC value of 0.809 which is categorized as a Good classification.
{"title":"Prediction of Grade Point Average (GPA) for Students at Informatics and Computer Engineering Education – Universitas Negeri Jakarta during Online Learning Using Naive Bayes Algorithm","authors":"Miftahul Jannah, Widodo Widodo, Hamidillah Ajie","doi":"10.32736/sisfokom.v13i1.1958","DOIUrl":"https://doi.org/10.32736/sisfokom.v13i1.1958","url":null,"abstract":"The transition of learning models from face-to-face to online learning has had several impacts on student learning, reflected in their academic achievements. This study aims to determine the performance of the algorithm model using data mining classification techniques in predicting the Semester Grade Point Average (GPA) of Informatics and Computer Engineering Education students, at Universitas Negeri Jakarta during online learning. The prediction employed the Naive Bayes algorithm and the dataset obtained by collecting questionnaires from 2020 and 2021 batches. The total data obtained is 155 records with 13 (thirteen) attributes in the form of 1 (one) ID attribute including NIM, 11 (eleven) regular attributes including gender, college entrance, smartphone facilities, network conditions, preferred online applications, interest in learning, learning attitudes, learning creativity, parental support, study groups, and other activities outside of lectures during online learning, and 1 (one) the label attribute namely the Semester Grade Point Average for students in 3rd and 5th semester. The evaluation of this research involved the confusion matrix and the ROC (Receiver Operating Characteristic) curve. Confusion matrix resulted in an accuracy of 75%, precision of 28.33%, and recall of 26.43%. The ROC curve resulted in an AUC value of 0.679, indicating the category of poor classification. This study also applied the SMOTE data balancing technique, leading to a confusion matrix evaluation with 88.46% accuracy, 57.43% precision, and 52.14% recall. Furthermore, the ROC curve resulted in an AUC value of 0.809 which is categorized as a Good classification.","PeriodicalId":517030,"journal":{"name":"Jurnal Sisfokom (Sistem Informasi dan Komputer)","volume":"61 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139895100","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 : 2024-02-12DOI: 10.32736/sisfokom.v13i1.1944
Siti Nurhaliza, Dimas Febriawan, Firman Noor Hasan
The difference in societal perspective regarding personal well-being and understanding life choices is genuinely diverse. Lately, there is a prevalent thought where individuals believe that personal well-being can be achieved by choosing to live without children. Most of them prefer to prioritize their careers, education, or other activities that they believe can bring greater happiness and well-being to their lives. This topic has become a frequently discussed subject in almost every region of Indonesia, especially in urban areas. Not only facing negative stigma, the choice to live a life without children in Indonesia also carries positive connotations. Views on child-free in Indonesia are highly diverse, considering the many differences in social environments and each individual’s personal experiences. In this research, the Naïve Bayes algorithm is used as a sentiment classifier in the form of textual data collected through Twitter using the Rapid Miner. The data collection period spanned from May 3rd to May 10th, 2023. The research aims to analyze and present data regarding public sentiment towards the child-free phenomenon in Indonesia. The results of this research reveal the presence of 320 positive sentiments and 180 negative sentiments, with the accuracy value of the Naïve Bayes algorithm in conducting sentiment analysis on the child-free phenomenon reached 95.00%.
{"title":"Sentiment Analysis of Society Towards the Child-free Phenomenon (Life Without Children) on Twitter Using Naïve Bayes Algorithm","authors":"Siti Nurhaliza, Dimas Febriawan, Firman Noor Hasan","doi":"10.32736/sisfokom.v13i1.1944","DOIUrl":"https://doi.org/10.32736/sisfokom.v13i1.1944","url":null,"abstract":"The difference in societal perspective regarding personal well-being and understanding life choices is genuinely diverse. Lately, there is a prevalent thought where individuals believe that personal well-being can be achieved by choosing to live without children. Most of them prefer to prioritize their careers, education, or other activities that they believe can bring greater happiness and well-being to their lives. This topic has become a frequently discussed subject in almost every region of Indonesia, especially in urban areas. Not only facing negative stigma, the choice to live a life without children in Indonesia also carries positive connotations. Views on child-free in Indonesia are highly diverse, considering the many differences in social environments and each individual’s personal experiences. In this research, the Naïve Bayes algorithm is used as a sentiment classifier in the form of textual data collected through Twitter using the Rapid Miner. The data collection period spanned from May 3rd to May 10th, 2023. The research aims to analyze and present data regarding public sentiment towards the child-free phenomenon in Indonesia. The results of this research reveal the presence of 320 positive sentiments and 180 negative sentiments, with the accuracy value of the Naïve Bayes algorithm in conducting sentiment analysis on the child-free phenomenon reached 95.00%.","PeriodicalId":517030,"journal":{"name":"Jurnal Sisfokom (Sistem Informasi dan Komputer)","volume":"61 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139895102","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 : 2024-02-12DOI: 10.32736/sisfokom.v13i1.1849
Novia Dewi, Jan Everhard Riwurohi
The consumption of electrical energy for the community every year has increased including the electricity consumption of PLN UP3 Cengkareng customers. Therefore, PLN UP3 Cengkareng must supply electricity to customers in all categories such as Social Category, Household Category, Business Category, Industry Category and Government Category. With customer needs that continue to increase, it is necessary to forecast future electricity needs, so that PLN UP3 Cengkareng can provide the required electrical power. For this reason, it is necessary to predict the electricity demand. This research was conducted to forecast the electricity demand of UP3 Cengkareng by using the Deep Learning Model Long Short-Term Memory (LSTM). The data set used in this study was taken from the PLN UP3 Cengkareng information system, for 10 years, the period from 2012 to 2021. The data used is divided into 2 categories, namely 70% training data and 30% testing data. The results obtained from this prediction are 96,689, with an average neuron value of 32 and an epoch value of 10.
{"title":"FORECASTING THE ELECTRICITY CONSUMPTIONS OF PLN UP3 CENGKARENG USING DEEP LEARNING","authors":"Novia Dewi, Jan Everhard Riwurohi","doi":"10.32736/sisfokom.v13i1.1849","DOIUrl":"https://doi.org/10.32736/sisfokom.v13i1.1849","url":null,"abstract":"The consumption of electrical energy for the community every year has increased including the electricity consumption of PLN UP3 Cengkareng customers. Therefore, PLN UP3 Cengkareng must supply electricity to customers in all categories such as Social Category, Household Category, Business Category, Industry Category and Government Category. With customer needs that continue to increase, it is necessary to forecast future electricity needs, so that PLN UP3 Cengkareng can provide the required electrical power. For this reason, it is necessary to predict the electricity demand. This research was conducted to forecast the electricity demand of UP3 Cengkareng by using the Deep Learning Model Long Short-Term Memory (LSTM). The data set used in this study was taken from the PLN UP3 Cengkareng information system, for 10 years, the period from 2012 to 2021. The data used is divided into 2 categories, namely 70% training data and 30% testing data. The results obtained from this prediction are 96,689, with an average neuron value of 32 and an epoch value of 10.","PeriodicalId":517030,"journal":{"name":"Jurnal Sisfokom (Sistem Informasi dan Komputer)","volume":"74 26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139895221","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 : 2024-02-12DOI: 10.32736/sisfokom.v13i1.1949
Yohanes Setiawan, Oktavia Ayu Permata, M. P. Yuda
Thalassemia is an inherited blood disease which lacks hemoglobin, the protein that is carrying oxygen to the body. The severe one is called Thalassemia Major which needs special care about blood transfusion. The use of rule-based method to create an inference as the first diagnosis of Thalassemia Major is not effective as rules have to be achieved from long interview with the medical personnel. This research aims to create a model based on decision tree for first detection of Thalassemia Major. The dataset is obtained by interview of Thalassemia symptoms and primary data of medical records from a hospital. Classical decision tree models used are ID3, C4.5 and CART. The models are evaluated by Train-Test Split consists of 70% training and 30% testing data and k-Fold Validation for checking model’s overfitting or underfitting. The output of this research is a final tree model from the best performance of decision tree models. The final result shows that C4.5 has the best performance with accuracy 100% and not overfitting or underfitting. Also, C4.5 performs feature selections to its tree modeling to simplify the inference. In brief, decision tree based modeling is effective to be used as first detection of Thalassemia Major by interview symptoms with generating automatic rules from its tree model.
{"title":"Decision Tree based Data Modelling for First Detection of Thalassemia Major","authors":"Yohanes Setiawan, Oktavia Ayu Permata, M. P. Yuda","doi":"10.32736/sisfokom.v13i1.1949","DOIUrl":"https://doi.org/10.32736/sisfokom.v13i1.1949","url":null,"abstract":"Thalassemia is an inherited blood disease which lacks hemoglobin, the protein that is carrying oxygen to the body. The severe one is called Thalassemia Major which needs special care about blood transfusion. The use of rule-based method to create an inference as the first diagnosis of Thalassemia Major is not effective as rules have to be achieved from long interview with the medical personnel. This research aims to create a model based on decision tree for first detection of Thalassemia Major. The dataset is obtained by interview of Thalassemia symptoms and primary data of medical records from a hospital. Classical decision tree models used are ID3, C4.5 and CART. The models are evaluated by Train-Test Split consists of 70% training and 30% testing data and k-Fold Validation for checking model’s overfitting or underfitting. The output of this research is a final tree model from the best performance of decision tree models. The final result shows that C4.5 has the best performance with accuracy 100% and not overfitting or underfitting. Also, C4.5 performs feature selections to its tree modeling to simplify the inference. In brief, decision tree based modeling is effective to be used as first detection of Thalassemia Major by interview symptoms with generating automatic rules from its tree model.","PeriodicalId":517030,"journal":{"name":"Jurnal Sisfokom (Sistem Informasi dan Komputer)","volume":"65 48","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139895057","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 : 2024-02-12DOI: 10.32736/sisfokom.v13i1.1812
J. Fadila, Sayyed Aamir Hassan, Maulana Hilmi Arkan, Danendra Farrel Bhagawanta Indaru, Senator Marcielio Cheviray Diano
This research addresses the challenge of effectively conveying the historical narrative of Islam's spread through 3D animation. The primary objective is to explore the potential of staging techniques in enhancing message delivery and audience engagement. Staging, a method that emphasizes scene element arrangement, character placement, and camera perspective, is pivotal for a clear and impactful narrative. Through this technique, elements are organized to guide viewers' attention and emphasize the intended message. This study demonstrates that careful alignment of characters, backgrounds, and camera angles, combined with visual symbolism representing Islamic values, can significantly enhance the narrative's depth and viewer comprehension. Experiments reveal that strategic staging not only strengthens the storyline but also boosts audience understanding. The research underscores the importance of staging in 3D animation, especially for intricate narratives like the spread of Islam. It offers insights into the advantages of staging over traditional methods, emphasizing its role in narrative comprehension, deeper meaning conveyance, and audience engagement.
{"title":"Enhancing Historical Narrative through Application of Staging Techniques in 3D Animation “How Islam Spread Around the World”","authors":"J. Fadila, Sayyed Aamir Hassan, Maulana Hilmi Arkan, Danendra Farrel Bhagawanta Indaru, Senator Marcielio Cheviray Diano","doi":"10.32736/sisfokom.v13i1.1812","DOIUrl":"https://doi.org/10.32736/sisfokom.v13i1.1812","url":null,"abstract":"This research addresses the challenge of effectively conveying the historical narrative of Islam's spread through 3D animation. The primary objective is to explore the potential of staging techniques in enhancing message delivery and audience engagement. Staging, a method that emphasizes scene element arrangement, character placement, and camera perspective, is pivotal for a clear and impactful narrative. Through this technique, elements are organized to guide viewers' attention and emphasize the intended message. This study demonstrates that careful alignment of characters, backgrounds, and camera angles, combined with visual symbolism representing Islamic values, can significantly enhance the narrative's depth and viewer comprehension. Experiments reveal that strategic staging not only strengthens the storyline but also boosts audience understanding. The research underscores the importance of staging in 3D animation, especially for intricate narratives like the spread of Islam. It offers insights into the advantages of staging over traditional methods, emphasizing its role in narrative comprehension, deeper meaning conveyance, and audience engagement.","PeriodicalId":517030,"journal":{"name":"Jurnal Sisfokom (Sistem Informasi dan Komputer)","volume":"140 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139894860","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 : 2024-02-12DOI: 10.32736/sisfokom.v13i1.1950
Zaqi Kurniawan, Rizka Tiaharyadini
The Covid-19 pandemic significantly changed education with social distancing and changes in the learning environment. In this study, one strong reason for the significance of the research is the urgency of changes in students' learning styles during the Covid-19 pandemic. Investigating differences in learning styles before and during the pandemic not only provides deep insight into students' adaptation to these changes, but also provides a foundation for the development of more inclusive and adaptive learning strategies in the future. This study aims to analyze the effect of the Covid-19 pandemic on students' learning styles in an educational context, focusing on the comparison of two classification methods, Naïve Bayes and Decision Tree. The study was conducted by collecting data on students' learning styles before and during the Covid-19 pandemic, using various relevant indicators. The data was obtained based on school survey results and online platforms, involving student characteristics and learning preferences. The data was then analyzed using Naïve Bayes and Decision Tree classification methods to identify significant changes in students' learning styles. The results showed the prediction accuracy of learning style changes with Naïve Bayes 68.75% and Decision Tree 87.50%. Recommendations for educators and education policy makers are to develop inclusive and adaptive learning strategies to meet diverse learning preferences.
{"title":"Impact of The Covid-19 Pandemic on Student Learning Styles: Naïve Bayes and Decision Tree Classification in Education","authors":"Zaqi Kurniawan, Rizka Tiaharyadini","doi":"10.32736/sisfokom.v13i1.1950","DOIUrl":"https://doi.org/10.32736/sisfokom.v13i1.1950","url":null,"abstract":"The Covid-19 pandemic significantly changed education with social distancing and changes in the learning environment. In this study, one strong reason for the significance of the research is the urgency of changes in students' learning styles during the Covid-19 pandemic. Investigating differences in learning styles before and during the pandemic not only provides deep insight into students' adaptation to these changes, but also provides a foundation for the development of more inclusive and adaptive learning strategies in the future. This study aims to analyze the effect of the Covid-19 pandemic on students' learning styles in an educational context, focusing on the comparison of two classification methods, Naïve Bayes and Decision Tree. The study was conducted by collecting data on students' learning styles before and during the Covid-19 pandemic, using various relevant indicators. The data was obtained based on school survey results and online platforms, involving student characteristics and learning preferences. The data was then analyzed using Naïve Bayes and Decision Tree classification methods to identify significant changes in students' learning styles. The results showed the prediction accuracy of learning style changes with Naïve Bayes 68.75% and Decision Tree 87.50%. Recommendations for educators and education policy makers are to develop inclusive and adaptive learning strategies to meet diverse learning preferences. ","PeriodicalId":517030,"journal":{"name":"Jurnal Sisfokom (Sistem Informasi dan Komputer)","volume":"230 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139895209","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 : 2024-02-12DOI: 10.32736/sisfokom.v13i1.2021
L. Zahrotun, Yosyadi Rizkika Amanatullah, Utaminingsih Linarti, Anna Hendry Soleliza Jones
The high number of migrants in the city of Yogyakarta has resulted in increased opportunities for Micro, Small and Medium Enterprises (MSMEs) in Culinary and Handicrafts. The large amount of data collected by the Cooperative Office, which reached thousands, caused inas to have difficulties in determining what training was needed by MSMEs and also difficulties in choosing which MSMEs would receive training held by the Cooperative Office. In addition, the Yogyakarta Cooperatives and UMKM Office had difficulties in selecting which UMKM needed to receive these trainings. Grouping can be used as a strategy in selecting MSMEs and determining training according to their individual needs. The purpose of this study was to group SMEs using the Agglomerative Hierarchical Clustering Single Linkage method and its application to provide recommendations for MSME groups to the Yogyakarta Cooperative and MSME Office. The results of the recommendations for the number of groups can be used in providing implementation, design, and evaluation of the development and empowerment of MSME data in the City of Yogyakarta. This study uses the Agglomerative Hierarchical Clustering Single Linkage method. The stages in this research are Load Data, Cleaning Data, Data Selection, Transformation Data, Clustering Process with AHC single linkage, Silhouette Coefficient, and Knowledge Representation. This research resulted in 2 group recommendations from a total of 1336 Culinary MSME data and 3 group recommendations from a total of 145 Handicraft MSME data. The results of the silhouette score test in the Culinary Sector are included in the strong structure category with a value of 0.79 and the Crafts Sector is included in the Medium Structure category with a value of 0.615. From the number of these groups, recommendations were obtained for improving a service in increasing MSMEs, especially those with a turnover of less than 10 million, marketing purposes within the Yogyakarta area, and not having financial assistance from the government. The high number of immigrants in the city of Yogyakarta has resulted in increased opportunities for Micro, Small and Medium Enterprises (MSMEs) in the Culinary and Crafts sector. The large number of MSMEs creates increasingly higher competitiveness. Apart from that, the large amount of data collected by the Department of Cooperatives and MSMEs, which reaches thousands, causes the Department to have difficulties in efforts to improve and empower these MSMEs. Grouping is one method that can be used as a strategy in mapping MSMEs, especially in efforts to improve and empower MSMEs through training conducted by the Department. The aim of this research is to group MSMEs using the Agglomerative Hierarchical Clustering (AHC) method in an effort to achieve strategies for improving and empowering MSMEs. The focus of this research is[a1] MSMEs in the craft sector and MSMEs in the culinary sector. The results of this research provide 2 group recommendations
{"title":"Strategy for improving and empowering MSMEs through grouping using the AHC method","authors":"L. Zahrotun, Yosyadi Rizkika Amanatullah, Utaminingsih Linarti, Anna Hendry Soleliza Jones","doi":"10.32736/sisfokom.v13i1.2021","DOIUrl":"https://doi.org/10.32736/sisfokom.v13i1.2021","url":null,"abstract":"The high number of migrants in the city of Yogyakarta has resulted in increased opportunities for Micro, Small and Medium Enterprises (MSMEs) in Culinary and Handicrafts. The large amount of data collected by the Cooperative Office, which reached thousands, caused inas to have difficulties in determining what training was needed by MSMEs and also difficulties in choosing which MSMEs would receive training held by the Cooperative Office. In addition, the Yogyakarta Cooperatives and UMKM Office had difficulties in selecting which UMKM needed to receive these trainings. Grouping can be used as a strategy in selecting MSMEs and determining training according to their individual needs. The purpose of this study was to group SMEs using the Agglomerative Hierarchical Clustering Single Linkage method and its application to provide recommendations for MSME groups to the Yogyakarta Cooperative and MSME Office. The results of the recommendations for the number of groups can be used in providing implementation, design, and evaluation of the development and empowerment of MSME data in the City of Yogyakarta. This study uses the Agglomerative Hierarchical Clustering Single Linkage method. The stages in this research are Load Data, Cleaning Data, Data Selection, Transformation Data, Clustering Process with AHC single linkage, Silhouette Coefficient, and Knowledge Representation. This research resulted in 2 group recommendations from a total of 1336 Culinary MSME data and 3 group recommendations from a total of 145 Handicraft MSME data. The results of the silhouette score test in the Culinary Sector are included in the strong structure category with a value of 0.79 and the Crafts Sector is included in the Medium Structure category with a value of 0.615. From the number of these groups, recommendations were obtained for improving a service in increasing MSMEs, especially those with a turnover of less than 10 million, marketing purposes within the Yogyakarta area, and not having financial assistance from the government. The high number of immigrants in the city of Yogyakarta has resulted in increased opportunities for Micro, Small and Medium Enterprises (MSMEs) in the Culinary and Crafts sector. The large number of MSMEs creates increasingly higher competitiveness. Apart from that, the large amount of data collected by the Department of Cooperatives and MSMEs, which reaches thousands, causes the Department to have difficulties in efforts to improve and empower these MSMEs. Grouping is one method that can be used as a strategy in mapping MSMEs, especially in efforts to improve and empower MSMEs through training conducted by the Department. The aim of this research is to group MSMEs using the Agglomerative Hierarchical Clustering (AHC) method in an effort to achieve strategies for improving and empowering MSMEs. The focus of this research is[a1] MSMEs in the craft sector and MSMEs in the culinary sector. The results of this research provide 2 group recommendations ","PeriodicalId":517030,"journal":{"name":"Jurnal Sisfokom (Sistem Informasi dan Komputer)","volume":"71 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139895049","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 : 2024-02-12DOI: 10.32736/sisfokom.v13i1.1916
Khuswatun Hasanah
The current COVID-19 pandemic has greatly changed the order of consumption and the Indonesian economy. During the health crisis that hit Indonesia, the e-commerce sector experienced very rapid development because of changes in consumer behavior that are looking for safe and comfortable shopping alternatives. During the COVID-19 pandemic, Shopee became the number 1 online shopping site in Indonesia. However, this cannot be used as a standard for user satisfaction. User satisfaction can only be measured from comments by Shopee application users through the comments and rating features provided by the Google Play Store. Therefore, to be able to find out public opinion about Shopee, a sentiment analysis of the Shopee application will be carried out which can later be used by management to develop even better applications. In this study, the dataset taken is the rating and reviews of Shopee application users on the Google Play Store using the Multinomial Naïve Bayes method, Random Forest Classifier, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Extra Trees Classifier. This study uses 1000 comment and rating data which are processed using the Python language. The results of this study indicate that the method that has the highest level of accuracy is the Support Vector Machine algorithm with an accuracy of 88%, Extra Trees Classifier with an accuracy of 86%, Logistic Regression with an accuracy of 85%, Random Forest Classifier with an accuracy of 85%, K- Nearest Neighbors with an accuracy of 83%, and the last is Multinomial Naïve Bayes with an accuracy of 78%.
目前的 COVID-19 大流行极大地改变了消费秩序和印尼经济。在印尼遭遇健康危机期间，由于消费者行为的改变，他们开始寻求安全、舒适的购物选择，电子商务行业经历了非常快速的发展。在 COVID-19 大流行期间，Shopee 成为印尼第一大在线购物网站。然而，这并不能作为用户满意度的标准。用户满意度只能通过 Google Play 商店提供的评论和评级功能，从 Shopee 应用程序用户的评论中进行衡量。因此，为了了解公众对 Shopee 的看法，我们将对 Shopee 应用程序进行情感分析，以便管理层日后开发出更好的应用程序。本研究使用多项式奈夫贝叶法、随机森林分类器、逻辑回归、支持向量机、K-近邻和 Extra Trees 分类器对 Google Play 商店中 Shopee 应用程序用户的评分和评论进行数据集分析。本研究使用 Python 语言处理了 1000 条评论和评分数据。研究结果表明，准确率最高的方法是支持向量机算法（准确率为 88%）、额外树分类器（准确率为 86%）、逻辑回归（准确率为 85%）、随机森林分类器（准确率为 85%）、K-近邻（准确率为 83%），最后是多项式奈维贝叶斯（准确率为 78%）。
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Pub Date : 2024-02-12DOI: 10.32736/sisfokom.v13i1.1777
Thomas Aquino Berno Doduk, Heri Supriyanto, Mohammad Al Hafidz, Muhammad Septama Prasetya, M. A. Karyawan
Productive Micro Business Assistance (BPUM) is a government policy. This assistance has been carried out since the Covid-19 Pandemic in Indonesia. The Mojokerto city government conducts a selection of MSMEs which is expected to avoid errors in determining MSME assistance. Therefore, a decision support system is needed that is developed using the Weighted Product method to make it easier and faster to determine MSMEs that are eligible to receive assistance. The stages of system development start from problem analysis, data collection, analysis of method application, and system development. Based on the calculation of the resulting S vector, the largest value is 0.10568 and the smallest value is 0.05886 from 9382 MSME data. The last calculation is the V vector value which produces recommendations in the form of data ranking that can be used by the Mojokerto City Diskopukmperindag to determine which MSMEs are entitled to receive assistance. The results of the selected alternatives are in accordance with the ranking with the largest value of 0.10568 and the smallest value of 0.05886. Providing recommendations by the decision support system to policy makers can be based on the largest relative preference value owned by MSMEs.
生产性微型企业援助（BPUM）是一项政府政策。自印度尼西亚 Covid-19 大流行以来，该援助一直在实施。Mojokerto 市政府对微型企业和中小型企业进行筛选，以避免在确定微型企业和中小型企业援助时出现错误。因此，需要使用加权乘积法开发一个决策支持系统，以便更方便快捷地确定哪些中小微企业有资格获得援助。系统开发的各个阶段包括问题分析、数据收集、方法应用分析和系统开发。根据计算得出的 S 向量，9382 个中小微企业数据中最大值为 0.10568，最小值为 0.05886。最后计算的是 V 向量值，它以数据排序的形式产生建议，可供 Mojokerto 市 Diskopukmperindag 用来确定哪些中小微企业有权获得援助。所选替代方案的结果与排名一致，最大值为 0.10568，最小值为 0.05886。决策支持系统可根据中小微企业拥有的最大相对偏好值向决策者提供建议。
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