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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Faktor Exacta JEEE- Journal of Electrical And Electronics Engineering
Didi Rosiyadi
LIPI
Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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MiTE: Program Penyunting Topologi Jaringan untuk Pembelajaran SDN Muhammad Fajar Sidiq; Akbari Basuki; Didi Rosiyadi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 5 (2020): Oktober 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (689.029 KB) | DOI: 10.29207/resti.v4i5.2473

Abstract

Software-defined networking (SDN) is a network programmability concept that separates the control plane from the data plane by proposing a centralized control plane called a controller. Thus, network administrators are able to program the entire networks and their components via the controller. However, learning SDN is challenging due to its complex network setup and the different types of SDN networks such as OpenFlow, and P4. To ease the learning curve, the use of network emulation and a graphical-based network editor is necessary. This paper discusses the implementation of such an application, called MiTE. It satisfies both requirements: a visual network editor enforced with a configuration generator for emulation purpose. We evaluate the program by implementing IP routing cases for both, OpenFlow-based and P4-based networks. The result shows that both cases can be created easily by using a mouse command. The program has more interactive user interface while the created topologies are more informative. Compared to similar applications, our proposed application has better support for a wider range of SDN networks (Openflow and P4), fine-grain network configuration, and a more informative user interface.
Klasifikasi Komentar Instagram untuk Identifikasi Keluhan Pelanggan Jasa Pengiriman Barang dengan Metode SVM dan Naïve Bayes Berbasis Teknik Smote nanang ruhyana; didi rosiyadi
Faktor Exacta Vol 12, No 4 (2019)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v12i4.4981

Abstract

Customer satisfaction is one of the things expected by a company when the product produced has been marketed, both in the form of goods and services. How to complain through customer service is very diverse, lately not only by telephone, customers submit their suggestions or complaints. Customers can submit their suggestions or complaints via e-mail or e-mail or forums in the virtual world that are made by product-producing companies to accommodate a variety of complaints, suggestions, and direct criticism from consumers, especially social media, who are free to express their opinions on shipping services. they use. Instagram is a social media that is more inclined to images and on the other hand has text captions and comments, from the above problems trying to make a research for customer complaints of users of goods delivery services on an Instagram account shipping service company. From the background of the problem, the researchers tried to solve the problem for text mining classifiers by using the Support Vector Machine (SVM) and Naïve Bayes methods and using the SMOTE technique with the usual processes for text mining so that they could produce 69.68% accuracy for Support Vector Machine (SVM) and Naïve Bayes with an accuracy of 88.54%, using the Instagram comment text dataset of 776 records that have been done with preprocessing text.
Perbandingan Kinerja Algoritma K-Nearest Neighbor, Naïve Bayes Classifier dan Support Vector Machine dalam Klasifikasi Tingkah Laku Bully pada Aplikasi Whatsapp Irwansyah Saputra; Didi Rosiyadi
Faktor Exacta Vol 12, No 2 (2019)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v12i2.4181

Abstract

WhatsApp is the most popular messaging application in Indonesia. This causes the emergence of cyberbullying behavior by its users. This study aims to classify WhatsApp chat to two classes, namely bully and not bully. The classification algorithms used are k-NN, NBC and SVM. The results show that the SVM algorithm is better at solving this case with an accuracy of 81.58%.
Analisis Sentimen Media Sosial Opini Ujian Nasional Berbasis Komputer menggunakan Metoda Naive Bayes Fajar Priyono; Surti Kanti; Iqbal Dzulfiqar I; Imam Amirulloh; Endang Sri P; Alvi Alvi; Didi Rosiyadi
Journal of Electrical And Electronics Engineering Vol 1, No 2 (2016): JOURNAL OF ELECTRICAL AND ELECTRONICS ENGINEERING
Publisher : President University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (519.105 KB) | DOI: 10.33021/jeee.v1i2.189

Abstract

Ujian Nasional merupakan proses tolak ukur kemampuan hasil belajar siswa dan siswi selama proses belajar di sekolah, seiring perkembangan zaman, terdapat perubahan pada proses ujian nasional, yaitu sistem pengerjaan ujian nasional secara terkomputerisasi atau dapat di sebut dengan Computer Based Test (CBT). Dengan adanya Ujian Nasioal Berbasis komputer tentu menjadi bahasan-bahasan baru bagi masyarakat, baik bahasan pro dan kontra sehingga banyak masyarakat yang melontarkan opini-opininya melalui media sosial. Penelitian ini telah membahas mengenai analisa sentimen opini ujian nasional berbasis komputer. Sample yang di ambil sebanyak 181 kalimat sentimen yang di olah menggunakan algoritma Naive Bayes dengan mengelompokkan data menjadi tiga kelas Sentimen Positif, Netral, Negatif. Hasil pengolahan data menunjukan kelas sentimen netral memiliki nilai tertinggi sebesar 79% dan nilai terendah di peroleh kelas negatif dengan nilai 0.09%. Sedangkan tingkat akurasi ketiga kelas sentimen mencapai 100%.Keywords— Naive Bayes, Opini, Sentimen, Twitter The National exam is a benchmark of students’ learning capability result during learning process in schools. As the era develops, national exam is changing in its process. Nowadays, the test uses Computer Based Test (CBT) system. The existence of this system leads to new topics for society. Consequently, pro and contra opinions are thrown by them through social media. Therefore, this research discuses about analyses of sentiment opinions on CBT national exam. Naive Bayes’ Algorithm is used for processing 181 data samples in form of sentiment sentences. The data samples are grouped into three classess; Positive, Neutral, and Negative. The results of data processing have shown that Neutral sentiment gains the highest percentage 79% while the Negative sentiment is the lowest with value 0.09% Overall, accuracy degree of the three sentiment classes reach 100%.Keywords— Naive Bayes, Opini, Sentimen, Twitter
Co-Authors Akbari Basuki Alvi Alvi Endang Sri P Fajar Priyono Imam Amirulloh Iqbal Dzulfiqar I Irwansyah Saputra Muhammad Fajar Sidiq nanang ruhyana Surti Kanti