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All Journal Faktor Exacta Cyberspace: Jurnal Pendidikan Teknologi Informasi Telematika MKOM
Denni Kurniawan
Universitas Budi Luhur
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Other

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SISTEM PENDUKUNG KEPUTUSAN DALAM PENILAIAN PRESTASI KERJA MENGGUNAKAN FUZZY-AHP DAN SAW Denni Kurniawan; Catur Nugroho
CYBERSPACE: Jurnal Pendidikan Teknologi Informasi Vol 3, No 2 (2019)
Publisher : UIN Ar-Raniry

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (879.469 KB) | DOI: 10.22373/cj.v3i2.5359

Abstract

Employee appraisal is one of the company's efforts to evaluate employee performance and productivity. As the result, the company can also give awards to employees who are considered gives high contribution to company.  However, it is not easy to measure employee performance, because most them only based on the leaders valuation which is subjective and do not based on standards. The objective of this study is to develop a system to assess employee performance by using a combination of Fuzzy Logic, Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods. The AHP is a method of weighting in based on multi-criteria decisions. This method uses a pairwise comparison matrix to  calculate the weight value. The Fuzzy logic is used to overcome the problem, where the AHP method is indicated still have subjectivity in criteria evaluation. After calculation based on combination of Fuzzy-AHP methods, the final result of employee performance will determined by using SAW method. The employee with the highest weight value will considered as the most productive employee and also gives the best performance in the company.
Optimization Sentimen Analysis using CRISP-DM and Naive Bayes Methods Implemented on Social Media Denni Kurniawan; Muhammad Yasir
CYBERSPACE: Jurnal Pendidikan Teknologi Informasi Vol 6, No 2 (2022)
Publisher : UIN Ar-Raniry

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/cj.v6i2.12793

Abstract

Freedom of expression on social media Twitter not always give positive value, because sometimes can contains negative things such as fake news, spreads hate speech, and racism, where these kinds of tweet can be categorized as an act of Cyberbullying. Where this cyberbullying tends to increase every time. The aim of this study is to use the Naïve Bayes method in classifying types of sentiment on Twitter. The keyword used is Saipul Jamil, and the tweet was taken in September 2021. A total of 18,067 tweets were collected and then they will be labelled with a positive or negative value. This study also uses the CRIPS-DM method which is consist of Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment stages. The results of this study obtained the value of Accuracy (85.6%), Negative Recall (82.1%), Positive Recall (90.23%), and Negative Precision (91.76%) Positive Precision (79.18%).
PENGAMANAN DATA BERBASIS MOBILE ANDROID DENGAN PENGGABUNGAN LINEAR FEEDBACK SHIFT REGISTER (LFSR) DAN MODIFIKASI MATRIKS KUNCI ALGORITMA KRIPTOGRAFI PLAYFAIR CIPHER Denni Kurniawan; Bayu Priyatna
Telematika MKOM Vol 10, No 1 (2018): Jurnal Telematika MKOM Vol. 10 No. 1 Maret 2018
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (554.688 KB)

Abstract

Playfair cipher merupakan metode enkripsi klasik yang sulit untuk dikriptanalisis secara manual namun selain dari kelebihan yang terdapat pada plyafair cipher terdapat juga banyak kekurangan diantaranya, dapat dipecahkan dengan menggunakan informasi frekuensi kemunculan bigram, tidak dapat memasukkan huruf kecil, angka dan karakter khusus pada saat melakukan enkripsi.. Penelitian ini melakukan modifikasi pada matriks kunci algoritma kriptografi playfair dan menggabungkan dengan algoritma Linear Feedback Shift Register (LFSR), dengan merubah ukuran matriks kunci 13x13 maka playfair cipher mampu menyisipkan karakter sebanyak 196 karakter terdiri dari huruf kapital, huruf kecil. Hasil perhitungan dengan metode avalanche effect didapatkan nilai rata-rata 43,59% pada playfair cipher yang dilakukan modifikasi kunci matriks 13x13 dan digabung dengan generator LFSR, 2,15% pada playfair cipher kunci matriks 10x10 tanpa digabung dengan LFSR dan 34,41% pada playfair klasik 5x5. Bahwa playfair cipher yang telah dimodifikasi dan digabung dengan generator LFSR ini lebih kuat dari playfair cipher sebelumnya. Hasil pengujian kompleksitas waktu memiliki enkripsi dan dekripsi yang cepat.
Komparasi Pengaruh Model Klasifikasi Naive Bayes dan Support Vector Machine Pada Analisis Data Sentimen Di Bidang Pendidikan Fajriah, Riri; Kurniawan, Denni
Faktor Exacta Vol 17, No 2 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i2.22342

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.
Co-Authors Bayu Priyatna Catur Nugroho Fajriah, Riri Muhammad Yasir