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Analisis Sentimen Seputar UU ITE Menggunakan Algoritma Support Vector Machine Yoga Vikriansyah Wijaya; Adhitia Erfina; Cecep Warman
Progresif: Jurnal Ilmiah Komputer Vol 17, No 2: Agustus 2021
Publisher : STMIK Banjarbaru

AbstractSentiment analysis of twitter tweets from the Indonesian people can be used as one of the parameters to be a support for the government in evaluating decision making and policies in the future. This study aims to find out the sentiments of Indonesian people's tweets on Twitter about the Information and Electronic Transaction Law. The data material used in this study uses a query on the Information and Electronic Transaction Law, Hate Speech, Defamation, Online Fraud, and Data Theft. The test is carried out by calculating accuracy, precision, recall and F1-score, using a variety of training data and test data. The highest accuracy results were obtained from the composition of 90% training data and 10% test data with an accuracy value of 84% with an average precision of 84%, recall 65%, f1-score 71% for each sentiment class.Keywords: Sentiment Analysis, Support Vector Machine Algorithm, Community TweetAbstrakAnalisis Sentimen cuitan twitter dari masyarakat Indonesia dapat dijadikan sebagai salah satu parameter untuk menjadi penunjang bagi pemerintah dalam mengevaluasi pengambilan keputusan dan kebijakan di masa yang akan datang. Penelitian ini bertujuan untuk mengetahui sentimen dari cuitan masyarakat Indonesia di twitter seputar Undang-Undang Informasi dan Transaksi Elektronik. Bahan data yang digunakan dalam penelitian ini menggunakan query Undang-Undang Informasi dan Transaksi Elektronik, Ujaran Kebencian, pencemaran nama baik, Penipuan Online, dan Pencurian data. Pengujian dilakukan dengan perhitungan akurasi, precision, recall dan F1-score, dengan menggunakan variatif data latih dan data uji. Hasil akurasi tertinggi didapatkan dari komposisi data latih 90% dan data uji 10% dengan nilai akurasi 84% dengan rata-rata precision 84%, recall 65%, f1-score 71% tiap kelas sentimen.Kata Kunci: Analisis Sentimen, Algoritma Support Vector Machine, Cuitan Masyarakat

Decision Support System For New Employee Recruitment In PT. Prosweal Indomax Using The Simple Additive Weighting Anti Aprianti; Yayatillah Rubiati; Muhamad Renaldi Aripin; Cecep Warman; Dudih Gustian
INTERNATIONAL JOURNAL ENGINEERING AND APPLIED TECHNOLOGY (IJEAT) Vol. 3 No. 1 (2020): International Journal of Engineering and Applied Technology (IJEAT)
Publisher : Nusa Putra University

The eligibility for hiring new employees based on certain criteria that the company expects is something that is very rarely obtained. The acceptance process may result in inaccurate and productive decisions due to inefficient admission processes. There are several criteria in making decisions about the recruitment of new employees at PT. Prosweal Indomax, which is based on latest education, expertise, age and work experience. Purpose made This system is able to help make decisions to determine the optimal recruitment process using the method Simple Additive Weighting (SAW). This method was chosen because this method determines the weight value for each attribute, then is followed by a ranking process that will select the best alternative. The research was conducted by finding the weighted value for each criterion, and then creating a ranking process that would determine which alternative was the best applicant.

Design and Build a Population Administration Data Collection Application System Using the Zachman Framework Anti Aprianti; Yayatillah Rubiati; Muhamad Renaldi Aripin; Cecep Warman
INTERNATIONAL JOURNAL ENGINEERING AND APPLIED TECHNOLOGY (IJEAT) Vol. 4 No. 1 (2021): International Journal of Engineering and Applied Technology (IJEAT)
Publisher : Nusa Putra University

The purpose of this study is to determine how the application of the Zachman Framework in designing a population administration data collection system in Kadaleman village, because the current process is still carried out conventionally so that data storage is irregular. In analyzing the system using the Zachman Framework approach with a Planner, Owner, Designer, and Builder perspective and perspective issues, namely What, How, Where, Who, and When. For system modeling using UML. Data was collected by using observation, interview, literature study, and distributing questionnaires involving 35 respondents. Measurement of data using a Likert scale, with correlation test analysis using Spearman rho. The implementation of a web-based prototype system with the results of the usability value test on the system application shows that the system has met the aspects of learnability, efficiency, memorability, errors, and satisfaction. The results of the correlation analysis of the Zachman Framework model on the system built have a very strong correlation, which implies that the system built through the Zachman Framework approach can be realized and implemented

Perancangan Arsitektur Sistem Pemesanan Tiket Wisata Online Menggunakan Framework Zachman Sudin Saepudin; Egit Pudarwati; Cecep Warman; Sihabudin Sihabudin; Giri Giri
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 11, No 2 (2022): JULI
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v11i2.1415

Penerapan sistem teknologi saat ini semakin berkembang dan mulai merambah ke berbagai sektor. Semua aktifitas yang dilakukan oleh sebuah bidang usaha semakin tidak terlepas dari pengaruh teknologi. Salah satu sektor yang berkembang dalam perkembangan teknologi adalah bidang pariwisata. Salah satunya yaitu wisata situ sukarame yang dalam menjalankan kegiatannya masih manual, seperti dalam pemesanan tiket masuk. Cara ini kurang efektif, karena mengakibatkan antrian yang sangat panjang dan membuat petugas loket kesulitan dalam menghitung jumlah wisata yang masuk per rombongan. Oleh karena itu, perancangan suatu sistem informasi penunjang kerja pada suatu objek wisata dirasa sangat dibutuhkan untuk kemajuan ke depannya. Dengan adanya perancangan enterprise architecture tiket wisata ini diharapkan bisa membantu dalam permasalahan yang ada khususnya dalam melakukan proses pemesanan tiket wisata yang lebih cepat, efektif, dan efisien. penelitian ini menggunakan metode pengumpulan data dengan melakukan observasi, wawancara dan studi pustaka. kemudian penelitian ini menggunakan framework zachman untuk melakukan klasifikasi pengorganisasian artifak enterprise. Zachman Framework terdiri dari 6 kolom dan 6 baris. Tiap kolom merepresentasikan fokus, abstraksi, atau topik arsitektur enterprise, yaitu: data, fungsi, jaringan, manusia, waktu, dan motivasi. Berdasarkan hasil penelitian yang telah dilakukan maka dapat diambil kesimpulan bahwa Penelitian ini menghasilkan analisis dan perancangan tiket wisata online yang dapat mengolah data pengunjung dan mengetahui jumlah pengunjung yang datang setiap hari nya dengan menggunakan framework zachman. Analisis dan perancangan ini dapat digunakan sebagai landasan pengembangan sistem informasi tiket wisata agar pengembangan sesuai dengan kebutuhan serta mempermudah wisatwan untuk membeli tiket dengan aplikasi ini

Sentimen Analisis Kegiatan Trading Pada Ap-likasi Twitter dengan Algoritma SVM, KNN Dan Random Forrest Neng Resti Wardani; Sudin Saepudin; Cecep Warman
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.497

This study aims to find out how people comment on trading activities that are currently busy. As we know that lately there have been cases of trading involving affiliates, many people feel that they have been deceived by these activities. From this case, we conducted research using data collection methods regarding trading, which were taken from the Twitter social media platform using the Orange application. The data obtained through the scraping process will then be filtered to separate positive and negative sentiments, so that the data ready for sentiment analysis is 1,400 tweets. Data were analyzed using three methods, namely Random Forest, KNN, and SVM (Support Vector Machines). The results obtained from the research conducted which has 3 variables, namely positive sentiment has a value of 29%, negative is 10%, and neutral has a value of 62%. To analyze sentiment data from Twitter the author uses 3 classification methods and produces an accuracy value of KnN of 0.999, Random forest 0.994 and Naïve SVM 0.992. Based on the results of the analysis that has been carried out regarding trading activities, people think that not all trading is illegal and fraudulent because many sites are still legal

Sentimen Analisis Kegiatan Trading Pada Ap-likasi Twitter dengan Algoritma SVM, KNN Dan Random Forrest Neng Resti Wardani; Sudin Saepudin; Cecep Warman
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.497

This study aims to find out how people comment on trading activities that are currently busy. As we know that lately there have been cases of trading involving affiliates, many people feel that they have been deceived by these activities. From this case, we conducted research using data collection methods regarding trading, which were taken from the Twitter social media platform using the Orange application. The data obtained through the scraping process will then be filtered to separate positive and negative sentiments, so that the data ready for sentiment analysis is 1,400 tweets. Data were analyzed using three methods, namely Random Forest, KNN, and SVM (Support Vector Machines). The results obtained from the research conducted which has 3 variables, namely positive sentiment has a value of 29%, negative is 10%, and neutral has a value of 62%. To analyze sentiment data from Twitter the author uses 3 classification methods and produces an accuracy value of KnN of 0.999, Random forest 0.994 and Naïve SVM 0.992. Based on the results of the analysis that has been carried out regarding trading activities, people think that not all trading is illegal and fraudulent because many sites are still legal

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