Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Semesta Teknika Jurnal Informatika Kaputama (JIK)
Ferry Syukmana
Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
ANALISA SENTIMEN VAKSINASI COVID-19 DENGAN METODE SUPPORT VECTOR MACHINE DAN NAÏVE BAYES BERBASIS TEKNIK SMOTE Riza Fahlapi; Hermanto Hermanto; Taufik Asra; Antonius Yadi Kuntoro; Ridatu Ocanitra; Lasman Effendi; Ferry Syukmana
Jurnal Informatika Kaputama (JIK) Vol 6 No 1 (2022): Volume 6, Nomor 1, Januari 2022
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jik.v6i1.136

Abstract

Keadaan dan tantangan pandemi pada awal tahun 2021 dengan telah ditemukannya vaksin atas virus Covid-19 tentunya diperlukan percepatan dalam pemberian vaksin kepada seluruh umat manusia di seluruh dunia. Di Indonesia, Pemerintah menggalakan program vaksinasi masal kepada seluruh Warga Negara Indonesia dengan melakukan percepatan vaksinasi di seluruh wilayah Indonesia sampai dengan saat ini. Berdasarkan hal tersebut diatas dipandang perlu melakukan analisa sentimen. Media sosial twitter dipilih sebagai salah satu sarana dalam analisas sentiman ini. Terdapat 1013 komentar positif dan negatif para pengguna twitter dengan kata kunci “vaksin” yang didapatkan untuk diproses terkait tanggapan masyarakat atas pelaksanaan vaksinasi masal yang dilaksanakan di Indonesia. Dengan menggunakan Algoritma Support Vector Machine (SVM) dan Naïve Bayes berbasis SMOTE dilakukan perbandingan pengujian atas komentar positif dan negatif tersebut. Dari proses pengujian tersebut didapatkan hasil akurasi dari algoritma SVM menggunakan teknik SMOTE didapatkan nilai akurasi =70.51% dan nilai AUC =0.827, sedangkan proses pengujian menggunakan algoritma Naïve Bayes dengan teknik SMOTE didapatkan nilai akurasi = 64.36% dan nilai AUC = 0.423. dari proses diatas, penggunaan Support Vector Machine berbasis teknik SMOTE memiliki akurasi yang lebih tinggi sehingga dapat digunakan untuk memberikan solusi terhadap analisis sentimen vaksinasi Covid-19.
Co-Authors Antonius Yadi Kuntoro Hermanto Hermanto Hermanto Hermanto Hermanto Hermanto Hermanto Wahono Lasman Effendi Ridatu Ocanitra RIZA FAHLAPI Taufik Asra Wahono, Hermanto