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All Journal Semesta Teknika Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Informatika Jurnal Teknik Komputer AMIK BSI Jurnal Pilar Nusa Mandiri JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURNAL ILMIAH INFORMATIKA JISICOM (Journal of Information System, Infomatics and Computing) Jurnal Informatika Kaputama (JIK) Jurnal Mantik JIKA (Jurnal Informatika) Jurnal Abdimas Komunikasi dan Bahasa J-Intech (Journal of Information and Technology) Jurnal Ilmu Komputer JURNAL KESEHATAN, SAINS, DAN TEKNOLOGI (JAKASAKTI) Jurnal Pengabdian Masyarakat Bangsa Jurnal Manuhara: Pusat Penelitian Ilmu Manajemen Dan Bisnis Journal of Information Technology
Antonius Yadi Kuntoro
Universitas Bina Sarana Informatika
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences

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Journal : semesta teknika

 1 
Prediction of Employee Attendance Factors Using C4.5 Algorithm, Random Tree, Random Forest Fahlapi, Riza; Hermanto, Hermanto; Kuntoro, Antonius Yadi; Effendi, Lasman; Nitra, Ridatu Oca; Nurlela, Siti
Semesta Teknika Vol 23, No 1 (2020): MEI 2020
Publisher : Semesta Teknika

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Abstract

Research on the performance of workers based on the determination of standard working hours for absences conducted by workers in a certain period. In disciplinary supervision, workers are expected to be able to provide the best performance in the implementation of work in accordance with predetermined working hours. The measurement of the level of discipline of admission hours for placement workers is carried out every working day, continuously and continuously. Attendance monitoring already uses online attendance by using data downloaded from the online attendance provider as the main data. In addition, data collection is done by filtering employee absentee data and supporting information on the categories that cause mismatches in meeting work schedules. Mobilization of workers according to location and working hours has been regulated in company regulations allowing the placement of workers in accordance with the residence so as not to affect the desired work results the company is still within reasonable limits and can be increased. The assessment of this study as a progression factor inhibiting the company in achieving company targets. From the results of the author's analysis of the prediction of employee delay factors using three algorithms, namely the C.45 algorithm accuracy = 79.37% and AUC value = 0.646, Random Forest Algorithm accuracy = 78.58% and AUC value = 0.807 while for the Random Tree algorithm accuracy = 76.26% and the AUC value = 0.610.
Classification of Student Majors with C4.5 and Naive Bayes Algorithms (Case Study: SMAN 2 Bekasi City) Kuntoro, Antonius Yadi; Hermanto, Hermanto; Asra, Taufik; Syukmana, Ferry; Wahono, Hermanto
Semesta Teknika Vol 23, No 1 (2020): MEI 2020
Publisher : Semesta Teknika

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Abstract

School majors conducted in high school are based on interests and these have a goal to provide opportunities for learners to develop the competence of attitudes, skills competence of learners in accordance with interests, talents, and academic ability in a group of scientific subjects.In this research, the researcher uses two algorithm models that is a comparison between the C4.5 algorithm and also the Naive Bayes algorithm. In this study, the data used is the results of school entrance test data and also the data from psychological results for students who have been declared passed the entrance test school SMAN 2 Bekasi City academic year 2018/2019. By comparison of two data mining classification algorithm, can be proved with accuracy result and AUC value from each algorithm that is for Naive Bayes accuracy = 76,43% and AUC value = 0,846, while for algorithm C4.5 accuracy = 70,29% and AUC value = 0.738.
Classification of Student Majors with C4.5 and Naive Bayes Algorithms (Case Study: SMAN 2 Bekasi City) Antonius Yadi Kuntoro; Hermanto Hermanto; Taufik Asra; Ferry Syukmana; Hermanto Wahono
Semesta Teknika Vol 23, No 1 (2020): MEI 2020
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/st.v23i1.7381

Abstract

School majors conducted in high school are based on interests and these have a goal to provide opportunities for learners to develop the competence of attitudes, skills competence of learners in accordance with interests, talents, and academic ability in a group of scientific subjects.In this research, the researcher uses two algorithm models that is a comparison between the C4.5 algorithm and also the Naive Bayes algorithm. In this study, the data used is the results of school entrance test data and also the data from psychological results for students who have been declared passed the entrance test school SMAN 2 Bekasi City academic year 2018/2019. By comparison of two data mining classification algorithm, can be proved with accuracy result and AUC value from each algorithm that is for Naive Bayes accuracy = 76,43% and AUC value = 0,846, while for algorithm C4.5 accuracy = 70,29% and AUC value = 0.738.
Prediction of Employee Attendance Factors Using C4.5 Algorithm, Random Tree, Random Forest Riza Fahlapi; Hermanto Hermanto; Antonius Yadi Kuntoro; Lasman Effendi; Ridatu Oca Nitra; Siti Nurlela
Semesta Teknika Vol 23, No 1 (2020): MEI 2020
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/st.v23i1.7984

Abstract

Research on the performance of workers based on the determination of standard working hours for absences conducted by workers in a certain period. In disciplinary supervision, workers are expected to be able to provide the best performance in the implementation of work in accordance with predetermined working hours. The measurement of the level of discipline of admission hours for placement workers is carried out every working day, continuously and continuously. Attendance monitoring already uses online attendance by using data downloaded from the online attendance provider as the main data. In addition, data collection is done by filtering employee absentee data and supporting information on the categories that cause mismatches in meeting work schedules. Mobilization of workers according to location and working hours has been regulated in company regulations allowing the placement of workers in accordance with the residence so as not to affect the desired work results the company is still within reasonable limits and can be increased. The assessment of this study as a progression factor inhibiting the company in achieving company targets. From the results of the author's analysis of the prediction of employee delay factors using three algorithms, namely the C.45 algorithm accuracy = 79.37% and AUC value = 0.646, Random Forest Algorithm accuracy = 78.58% and AUC value = 0.807 while for the Random Tree algorithm accuracy = 76.26% and the AUC value = 0.610.
Co-Authors Adiputra, Mahesa Aditya, Tommy Ali Mustopa, Ali Anasanti, Mila Desi Ani Nuriska Safitri Budiarto, Gunawan Christian, Yoel Dedi Dwi Saputra Dedi Dwi Saputra Dedi Dwi Saputra Dwiza Riana Fahlapi, Riza Febrian, Dio Ferry Syukmana Hasan , Fuad Nur Hasan, Fuad Nur Hasanah, Riyan Latifahul Hermanto Hermanto Hermanto Hermanto Hermanto Hermanto Hermanto Hermanto Hermanto Hermanto Hermanto Hermanto Hermanto Wahono Hermawan, Muhamad Taufik Khiyarana Fayziyah Laia , Metodius Modianus Lasman Effendi M. Rangga Ramadhan Saelan Mahmud Syarif Mawalda Azharah Moh. Arie Hasan Muhammad Syukur Nabilatul Adzra, Salsa Nadia Septia Aisyah Nanjaya, Ahmad Fadhil Nitra, Ridatu Oca Novianti, Deny Nurajijah, Nurajijah Rachimsah, Wildan Ramadhan, Xena Hadi Ramanda, Dea Rianto, Yan Ridatu Oca Nitra Ridatu Oca Nitra Ridatu Ocanitra Riza Fahlapi Riza Fahlapi Riza Fahlapi RIZA FAHLAPI Riza Fahlapi Saputra, Dedi Dwi siti nurlela Siti Nurlela Sugiarto, Hady Sukmawati, Alfiani Sutisna, Alifan Widad Taufik Asra Usnah, Asmaul Wahono, Hermanto