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All Journal InComTech: Jurnal Telekomunikasi dan Komputer JISA (Jurnal Informatika dan Sains) Jurnal Teknologi Informatika dan Komputer Academic Journal of Computer Science Research (AJCSR) Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan SIGMA: Information Technology Journal Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Lentera Pengabdian SAINTEK
Donny Maulana
Universitas Pelita Bangsa
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences

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Journal : SIGMA: Information Technology Journal

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Optimasi Metode Naïve Bayes Particle Swarm Optimization Analisis Sentimen Formula E Jakarta Pada Twitter Donny Maulana; Hasim Budi Jatmiko; Nanang Tedi Kurniadi
Jurnal SIGMA Vol 13 No 1 (2022): Maret 2022
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

The city of Jakarta plans to hold a Formula E racing event to promote electric cars as the vehicle of the future. The Covid-19 pandemic that hit Jakarta forced the plan to be postponed. The postponement caused a polemic in the community on social media due to the condition of Jakarta being hit by Covid-19 but the Jakarta city government still wants to hold Formula E by paying commitment money to the organizers which is not small. This difference of opinion on social media is used as material for sentiment analysis using the Naive Bayes classification method. The Naive Bayes method, which has a weakness in feature selection, is optimized by applying the Particle Swarm Optimization (PSO) feature selection. The results of the application of PSO optimization on the Naive Bayes method show an increase in performance with an accuracy value of 89.16%, precision 91.10%, recall 86.81% and AUC 0.690. Keywords: Naive Bayes, Particle Swarm Optimization, Sentiment Analysis, Jakarta E-Prix.
Analisa Extrasi Informasi Pada Abstraksi Jurnal Skripsi Berbahasa Indonesia Menggunakan Algoritma K Nearest Neighbor Donny Maulana; Asep Saepudin
Jurnal SIGMA Vol 12 No 4 (2021): Desember 2021
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

Information Extraction is the extraction of structured facts and information from the contents of a large collection of texts. The definition of facts here is a variety of entities that are calculated or connected in the form of structured information as input to the database. Processing information extraction data in thesis journal abstracts using the KNN algorithm starts from the data selection stage (attributes used and determination of training data and data testing), the algorithm testing stage (KNN), and the accuracy test stage (using split validation). The classification process in thesis journal abstracts using the KNN algorithm is one way to classify information extraction in thesis journal abstracts. The classification process in the thesis journal abstract using the KNN algorithm is used to avoid information extraction errors in the thesis journal abstract. processing data starting from the data preprocessing stage and text mining calculations consisting of weighting term frequency and weighting concept frequency and Cosine Similarity D7 0.0332, D15 0, D10 0.1296, D14 0.1296 Keywords: Text Mining, Information Extraction, K-NN
Klasifikasi Analisis Sentimen Terhadap Calon Presiden 2019 Pada Media Sosial Twitter Menggunakan Metode Algoritma Naïve Bayes Donny Maulana
Jurnal SIGMA Vol 11 No 3 (2020): September 2020
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

The existence of Twitter has been widely used by various levels of society in recent years. The public's habit of posting tweets to evaluate the presidential candidates is one of the media in representing the public response to the presidential candidates. Therefore in this study an analysis of public sentiments towards the 2019 presidential candidates will be revealed through the Twitter social network. The analysis was carried out using a tweet classification that contained public sentiment towards the 2019 presidential nomination, namely jokowi and prabowo. The classification method used in this study is Naive Bayes Classification (NBC). NBC is used to get the classification of positive and negative responses to the twitter and get a preference value from the community towards the 2019 presidential candidates. The results of the jokowi data percentage test methods are 25%, 50%, 75%, and 100% of the amount of data from the training data yielding an accuracy of 64.67%, 70.57%, 87.56%, 97.50% and for the test results the percentage of Pre -owo data 25%, 50%, 75%, and 100% of the amount of data from the training data resulted in an accuracy of 64.57 %, 81.67%, 64.22%, 62.67%. And for the results of testing the positive response of the people on Twitter with a value of perference value of 53% for Jokowi and 48% for Prabowo. Therefore sentiment classification using the Naive Bayes classification method can be used to measure the public response to the performance of 2019 presidential candidates. Keywords: Twitter, naive bayes, sentiment analysis
Sistem Pendukung Keputusan Pemilihan Jasa Pengiriman Terbaik Menggunakan Metode Maut (Multi-Attribute Utility Theory) Wildan Trio Munawarudin; Donny Maulana; Ucok Darmanto Soer; Rensi Suryanti; Edri Fauzan
Jurnal SIGMA Vol 15 No 2 (2024): September 2024
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v15i2.6037

Abstract

Karena kemajuan teknologi, pelaku e-commerce kini dapat menggunakan media sosial untuk menjual produk mereka. Toko online seperti Shopee, Bukalapak, dan Lazada memberikan bantuan yang lebih besar kepada masyarakat. Layanan pengiriman dibutuhkan jika orang ingin menjual barang mereka di toko online. Penelitian ini bertujuan untuk membangun Sistem Pendukung Keputusan (SPK) yang dapat membantu konsumen dalam proses pengambilan keputusan terkait layanan pengiriman dengan menggunakan metode Multi-Attribute Utility Theory (MAUT). MAUT digunakan untuk menyelesaikan masalah pengambilan keputusan yang melibatkan beberapa kriteria dengan mengumpulkan informasi dan memberikan bobot pada setiap kriteria berdasarkan tingkat kepentingannya. Hasil akhirnya berupa nilai numerik dalam skala 0–1. Dari hasil penelitian, ditemukan bahwa alternatif A1 (JNE) merupakan layanan pengiriman terbaik, diikuti oleh J&T Express, Pos Indonesia, SiCepat, dan AnterAja. Kriteria ketepatan waktu, keamanan pengiriman, biaya pengiriman, layanan pelanggan, cakupan layanan, dan kemudahan penggunaan sangat penting bagi pengguna. Penelitian ini menyimpulkan bahwa SPK yang dibangun mampu memberikan solusi efektif dalam pemilihan layanan pengiriman. Penerapan metode MAUT membantu mengurangi subjektivitas dan memungkinkan penilaian yang lebih objektif serta sistematis, sehingga membantu konsumen membuat keputusan yang lebih informasional dan rasional.
Co-Authors Agung Nugroho Agung Nugroho Agus Suwarno, Agus Ahmad Turmudi Zy Ahmad Turmuzdy ZY Alfatan Dzulatkha2 Andri Firmansyah Asep Saepudin Edri Fauzan Emi Lia Nurjanah Gatot Tri Pranoto Hasim Budi Jatmiko Ikhsanudin ikhsanudin Irfan Afriantoro Ismamudi, Ismamudi Lutfi Adji Ardiansyah Maryatul Kiptiyah Maulana Sofyan Muhtajuddin Danny Nanang Tedi Nanang Tedi Kurniadi Nawangsih, Ismasari Novita Sari Raden Rangga Bramantya Putra Rensi Suryanti Rezayadi Yahya Riady, Sasmitoh Rahmad Rudi Rahmanto2 Shinta Mawarni Slamet Nur Ihsan Suherman Suherman Taufik Hidayat Ucok Darmanto Soer Vivi Mustikaningtyas Wildan Trio Munawarudin Wiyanto - Yoga Religia Yusup Solikhun