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INDONESIA
Faktor Exacta
Published by Universitas Indraprasta PGRI
ISSN : 1979276X     EISSN : 2502339X     DOI : -
Core Subject : Science, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering
Faktor Exacta is a peer review journal in the field of informatics. This journal was published in March (March, June, September, December) by Institute for Research and Community Service, University of Indraprasta PGRI, Indonesia. All newspapers will be read blind. Accepted papers will be available online (free access) and print version.
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "Vol 17, No 4 (2024)" : 5 Documents clear
SISTEM PENDUKUNG KEPUTUSAN PENENTUAN BONUS KARYAWAN PADA PT GAHAKA KARYA PRIMA MENGGUNAKAN METODE SAW Subekti, Aditya Rindang; Yulianingsih, Yulianingsih; Prasetya, Rudi
Faktor Exacta Vol 17, No 4 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i4.25117

Abstract

Analisis Model Matematika dan Simulasi Penyebaran dan Penanganan Penyalahgunaan Narkoba di Indonesia Ristiawan, Rifki; Endaryono, Endaryono; Mahyudi, Mahyudi
Faktor Exacta Vol 17, No 4 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i4.25201

Abstract

he increasing abuse of narcotics, psychotropic substances and addictive substances is a big problem in Indonesia today. The spread of drugs is increasing rapidly to the point that it can be considered a disaster. This research uses literature study methods and data analysis to determine assumptions and distribution models, then analyzes the models and carries out numerical simulations. A mathematical model was created to see the pattern of the spread of drug abuse and analyzed analytically to determine the existence of an equilibrium point and the type of stability, as well as to obtain the basic reproduction number . Numerical simulations were carried out to see distribution patterns in the next few years. From the results of the numerical simulations, information was obtained that to suppress the spread of drug abuse, efforts that can be made are to reduce the rate of recruitment of vulnerable classes by dealers and the rate of change from users to dealers.
Learning object reusability evaluation on the free national e-learning system Risaf, Karin A.; Khalida, Rakhmi; Supono, Riza Adrianti
Faktor Exacta Vol 17, No 4 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i4.26562

Abstract

Optimalisasi Model Klasifikasi Naive Bayes dan Support Vector Machine Dengan Fast Text dan Chi Square Pada Analisis Sentimen Penyelenggaraan Pembelajaran Pemrograman di Fasilkom Universitas Mercu Buana Fajriah, Riri; Kurniawan, Denni
Faktor Exacta Vol 17, No 4 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i4.24751

Abstract

The implementation of effective programming learning at the Faculty of Computer Science, Universitas Mercu Buana is one of important strategy. This expectation is constrained because the results of the evaluation of the competency achievements of many graduates have not mastered programming skills well. Therefore, the research conducted is related to analyzing the sentiments of all stakeholders who have been involved with the implementation of programming learning. The data source based on the results of an online questionnaire. The sentiment data analysis process uses the Cross Industry Standard Process for Data Mining method with the Naive Bayes and Support Vector Machine classification models. The result of the research is an increase in the accuracy of sentiment analysis data processing which previously only used the Naive Bayes Algorithm only achieving an accuracy of 65.56% and by optimizing with Feature Extraction Fast Text, the accuracy achievement increased to 90.49%. While optimizing the algorithm using Feature Selection Chi Square can make the Support Vector Machine classification model optimized to achieve an accuracy value of 99.58% from the previous accuracy achievement was 90.72%. This research can prove that optimizing the application classification model algorithms can use using Fast Text and Chi Square techniques.
Sistem Informasi Manajemen Pemesanan Produk RIZAL Wedding Organizer Berbasis Web Mayanti, Rina; Pratiwi, Sekar Ageng; Hidayat, M Irfan; Zana, Sanusi Ibnu
Faktor Exacta Vol 17, No 4 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i4.27733

Abstract

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