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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

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

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

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

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 Netizen Tweets Using Random Forest, Decision Tree, NaÃ¯ve Bayes, and Ensemble Algorithm Rianto, Yan; Kuntoro, Antonius Yadi
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 1 (2020): Article Research, October 2020
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v5i1.10565

The current Governor of DKI Jakarta, even though he has been elected since 2017 is always interesting to talk about or even comment on. Comments that appear come from the media directly or through social media. Twitter has become one of the social media that is often used as a media to comment on elected governors and can even become a trending topic on Twitter social media. Netizens who comment are also varied, some are always Tweeting criticism, some are commenting Positively, and some are only re-Tweeting. In this research, a prediction of whether active Netizens will tend to always lead to Positive or Negative comments will be carried out in this study. Model algorithms used are Decision Tree, NaÃ¯ve Bayes, Random Forest, and also Ensemble. Twitter data that is processed must go through preprocessing first before proceeding using Rapidminer. In trials using Rapidminer conducted in four trials by dividing into two parts, namely testing data and training data. Comparisons made are 10% testing data: 90% Training data, then 20% testing data: 80% training data, then 30% testing data: 70% training data, and the last is 35% testing data: 65% training data. The average Accuracy for the Decision Tree algorithm is 93.15%, while for the NaÃ¯ve Bayes algorithm the Accuracy is 91.55%, then for the Random Forest algorithm is 93.41, and the last is the Ensemble algorithm with an Accuracy of 93, 42%. here.

Analisis Faktor-Faktor Yang Mempengaruhi Kepuasan Pelanggan Fixpay Menggunakan SEM Dengan PLS Antonius Yadi Kuntoro; Moh. Arie Hasan; Dedi Dwi Saputra; Dwiza Riana
Jurnal Informatika Vol 6, No 1 (2019): April 2019
Publisher : LPPM Universitas Bina Sarana Informatika

Penelitian ini bertujuan untuk mengetahui pengaruh kualitas pelayanan, nilai pelanggan, dan kepercayaan terhadap kepuasan pelanggan pada Fixpay. Fixpay adalah sebuah platform Mobile Payment yang dapat melakukan beragam jenis pembayaran dan pembelian secara online dari smartphone..Penelitian ini menggunakan pendekatan kuantitatif dengan metode asosiatif. Data yang digunakan menggunakan data primer berupa kuesioner yang diperoleh melalui google form. Pengambilan sampel menggunakan teknik non random sampling sehingga diperoleh sampel penelitian sebanyak 100 responden. Hasil penelitian menunjukkan bahwa kualitas pelayanan, nilai pelanggan, dan kepercayaan berpengaruh signifikan terhadap kepuasan pelanggan Fixpay baik secara parsial maupun simultan. Disarankan kepada pihak perusahaan untuk terus meningkatkan kepuasan pelanggan, seperti dengan membuat mudah aplikasi Fixpay untuk dioperasionalisasikan, mudah mengakses aplikasi, dan meningkatkan nilai kegunaan dari aplikasi Fixpay. Pengolahan data dalam penelitian ini menggunakan Structural Equation Modeling (SEM) dengan Partial Least Square (PLS).

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

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

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.

ALGORITMA KLASIFIKASI NAIVE BAYES DAN SUPPORT VECTOR MACHINE DALAM LAYANAN KOMPLAIN MAHASISWA Hermanto Hermanto; Ali Mustopa; Antonius Yadi Kuntoro
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol 5 No 2 (2020): JITK Issue February 2020
Publisher : LPPM Nusa Mandiri

Service in the world of education is an important element for the creation of an academic atmosphere that is conducive to the implementation of a successful teaching and learning process. The process of service to students there is a tendency to be implemented not following the minimum service standards that must be provided to students so that students tend to complain about the services provided. Submission of criticism, complaints, input, or suggestions for dissatisfaction and problems that exist in the university environment is still very limited. Complaints can be constructive if submitted to the right place and party. In this research the data processing of email complaints from students conducted at the academic student body (students.bsi.ac.id). Student complaint data that will be processed is data in the form of * .xls complaint file. Before text data is analyzed using text mining methods, the pre-processing text needs to be done including tokenizing, case folding, stopwords, and stemming. After pre-processing, the classification method is then performed in classifying each complaint category and dividing the status into two parts, namely complaint and not complaint so that the status becomes a normal condition in text mining research. The purpose of this study is to obtain the most accurate algorithm in the classification of student complaints and can find out the results of the classification of the Naïve Bayes algorithm method and Support vector Machine used and compared. In this study, the results of testing by measuring the performance of these two algorithms using Cross-Validation, Confusion Matrix, and ROC Curves. The obtained Support vector Machine algorithm has the highest accuracy value compared to Naïve Bayes. AUC value = 0.922. for the Support vector machine method using the student academic data collection dataset (students.bsi.ac.id) has 84.45%, from the Naïve Bayes algorithm has an accuracy rate of about 69.75% and AUC value = 0.679.

Design and Build a Child's Learning Room Application, in Increasing Learning Motivation Based on Android Antonius Yadi Kuntoro; Hermanto Hermanto
Jurnal Mantik Vol. 4 No. 3 (2020): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.Vol4.2020.1011.pp1788-1796

Millennials are often associated with the term “kids zaman now” or "children of today." If we associate today's children with previous habits, then we will find different things there. It affects their motivation to study at school. While teaching in schools still uses a conventional model, better known as Teacher-Centered Learning (TCL), the model is boring for today's students. As a result, many students today find it challenging to understand the lessons learned at school, and arriving at their homes is again preoccupied with their gadgets and forgetting the subject matter learned in school. Children's Learning Room application ranging from early childhood to elementary and junior high school students can provide a pleasant atmosphere in the child's learning process; besides, this App can be played without any difficulty by the child in its use. At the app creation stage, there are three stages of testing: functional testing, device testing, and user interest testing. From due diligence using questionnaires from 30 respondents from three professions, namely students, teachers, and parents of students with 12 questions asked on the questionnaire, most respondents agreed, or positive responses got 95.4% of the responses strongly agreed. It can be said that this Children's Learning Room Application is suitable for early childhood to elementary school children computer technology.

KLASIFIKASI KELUHAN PENGGUNA KAI ACCESS UNTUK PEMESANAN TIKET DENGAN ALGORITMA SVM DAN NAÏVE BAYES Hermanto -; Antonius Yadi Kuntoro; Taufik Asra
Jurnal Informatika Vol 6, No 2 (2022): JIKA (Jurnal Informatika)
Publisher : University of Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/jika.v6i2.6187

Perkembangan dan kemajuan Teknologi Informasi dan Komunikasi (TIK) sangat diperlukan guna untuk memudahkan dan menyelesaikan berbagai masalah yang dihadapi oleh manusia dengan cepat dan singkat. Disamping itu, masyarakat zaman sekarang ingin semuanya serba praktis dan tidak menyita banyak waktu. Salah satu contoh permasalahan sehari-hari yang menjadi perhatian masyarakat sekarang adalah transportasi. Kereta api nampaknya menjadi salah satu alat transportasi favorit orang Indonesia terbukti dengan meningkatnya layanan khusus Kereta Api diberbagai perangkat Android, IOS, dan Windows Phone. Penelitian ini fokus terhadap menganalisa kepuasan pengguna aplikasi KAI Access terhadap pemesanan tiket, Penelitian ini bertujuan untuk menganalisis keluhan pengguna aplikasi KAI Access dalam pemesanan tiket kereta api secara online. Terdapat 1321 komentar positif dan negatif pada pengguna aplikasi kai access untuk keluhan pemesanan tiket. Dengan menggunakan Algoritma SVM dan Naïve Bayes dilakukan perbandingan pengujian atas komentar positif dan negatif tersebut. Dari proses pengujian tersebut didapatkan hasil akurasi dari algoritma SVM nilai akurasi = 73.36% dan nilai AUC = 0.794. sedangkan untuk algoritma Naïve Bayes nilai akurasi dan nilai AUC dari algoritma yaitu untuk SVM nilai akurasi = 67.10% dan nilai AUC = 0.573. Dapat disimpulkan bahwa algoritma yang lebih unggul adalah memiliki nilai akurasi tertinggi adalah Algoritma SVM dibanding dengan algoritma Naïve Bayes.

Prediction of PrivyID Application Comments Use as an Electronic Document (e-doc) using the Ensemble Vote method Riza Fahlapi; Hermanto Hermanto; Taufik Asra; Antonius Yadi Kuntoro; Ridatu Oca Nitra; Lasman Effendi
Jurnal Teknik Komputer AMIK BSI Vol 9, No 1 (2023): JTK Periode Januari 2023
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jtk.v9i1.14245

Indonesia is developing one of the more efficient and effective Financial Technology (Fintek) support services innovations by using electronic documents. The Electronic Document provider business that is used as a reference and utilized by fintech companies is PrivyID. In this study, how is the commentary aspect of using the PrivyID application for digital signature services to become a legal electronic document. Web-based application platforms and mobile applications in the community are indispensable for the use of Electronic Documents developed by PrivyID as a service provider in business and personal transactions that are needed by the community. More in-depth research regarding the Prediction of PrivyID Application Comments in Its Use as an Electronic Document (e-doc) taken from 818 data of PrivyID application users. The research was conducted by combining 3 (three) algorithms (k-Nearest Neighbor, Na¨ıve Bayes, and C4.5) in the Ensembles Vote method which resulted in the best Prediction Comment value with an accuracy of 86.80.

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