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All Journal Sainteks JURNAL MEDIA INFORMATIKA BUDIDARMA LLT Journal: A Journal on Language and Language Teaching Jurnal Ilmiah Matrik Jurnal Sisfokom (Sistem Informasi dan Komputer) MULTINETICS JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA JURNAL TEKNIK INFORMATIKA DAN KOMPUTER
Febriawan, Dimas
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Journal : Jurnal Sisfokom (Sistem Informasi dan Komputer)

 1 
Implementation of Data Mining to Predict Student Study Period with Decision Tree Algorithm (C4.5) Putri, Kirana Alyssa; Febriawan, Dimas; Hasan, Firman Noor
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.1943

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.
Sentiment Analysis of Society Towards the Child-free Phenomenon (Life Without Children) on Twitter Using Naïve Bayes Algorithm Nurhaliza, Siti; Febriawan, Dimas; Hasan, Firman Noor
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.1944

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%.
Implementation of Data Mining to Predict Student Study Period with Decision Tree Algorithm (C4.5) Putri, Kirana Alyssa; Febriawan, Dimas; Hasan, Firman Noor
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.1943

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.
Sentiment Analysis of Society Towards the Child-free Phenomenon (Life Without Children) on Twitter Using Naïve Bayes Algorithm Nurhaliza, Siti; Febriawan, Dimas; Hasan, Firman Noor
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.1944

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%.
Co-Authors Bahalwan, Zen Aufa Firman Noor Hasan Ikhsan, Syaif Nurul Kamayani, Mia Kurnianto, Eko Nishfahuddin, Ananda Pratama, Aditya Budi Prayogi, Jaka Putri, Kirana Alyssa Rahmi Imanda Rozak, Bahrul Silih Warni Sinduningrum, Estu Siti Nurhaliza Tian Abdul Aziz Transiska, Dwi