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All Journal IJCIT (Indonesian Journal on Computer and Information Technology) Indonesian Journal on Software Engineering (IJSE) Paradigma EVOLUSI : Jurnal Sains dan Manajemen IMTechno: Journal of Industrial Management and Technology
Retno Sari
STMIK Nusa Mandiri
Computer Science & IT Engineering Industrial & Manufacturing Engineering

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Penerapan Algoritma Naive Bayes Untuk Analisis Sentimen Pada Wisata TMII Berbasis Website Retno Sari; Ratih Yulia Hayuningtyas
Indonesian Journal on Software Engineering (IJSE) Vol 5, No 2 (2019): IJSE 2019
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijse.v5i2.6957

Abstract

Abstrak: Para wisatawan sebaiknya terlebih dahulu mengetahui kondisi dari objek wisata yang ingin dikunjungi dengan melihat opini wisatawan sebelumnya. Data dalam penelitian ini didapat dari Tripadvisor, yang kemudian diklasifikasikan ke dalam class negatif dan class positif. Dari data tersebut akan dibuatkan aplikasi dengan Naive Bayes. Algoritma Naive Bayes dipilih karena memberikan hasil yang tepat dalam pengklasifikasian opini dalam bentuk paragraf. Pengolahan awal dalam penelitian ini meliputi tokenization, stopword removal, dan stemming kemudian data akan diuji dengan algoritma Naive Bayes. Untuk memudahkan wisatawan mengetahui klasifikasi opini publik maka pada penelitian ini dibuat aplikasi analisis sentimen menggunakan pemrograman PHP dan HTML untuk memudahkan wisatawan mencari kesimpulan dari sebuah opini.  Kata kunci: Analisa Sentimen, Naive Bayes
Perancangan Validasi Permohonan Narasumber Pada Sistem Informasi Permohonan Narasumber Menggunakan Finite State Automata Fadillah Said; Dwi Andriyanto; Retno Sari; Windu Gata
Paradigma Vol 22, No 2 (2020): Periode September 2020
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (626.026 KB) | DOI: 10.31294/p.v22i2.8157

Abstract

The Law and Human Rights Research and Development Agency in carrying out its duties and functions provides services in the form of Providing Resource persons. Managing mail manually requires a long time and allows for document loss. This study aims to reduce the use of time, help minimize the loss of letters, and reduce errors in recording letters by storing them in a database in accordance with the specified format. In the design of the system will check the applicant's input and validate before the data is stored in the database using Finite State Automata (FSA) so that the data stored in accordance with the format specified. The implementation of this research is expected to be able to help the Law and Human Rights Research and Development Agency to reduce the use of time, help minimize the loss of letters, and reduce errors in recording letters in accordance with the specified format.
ANALISIS SENTIMEN PADA REVIEW OBJEK WISATA DUNIA FANTASI MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR (K-NN) Retno Sari
Evolusi : Jurnal Sains dan Manajemen Vol 8, No 1 (2020): Jurnal Evolusi 2020
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/evolusi.v8i1.7371

Abstract

Abstract-Reviews of tourist attractions make it easier for other visitors to know the state of the place and become input for other visitors. Sentiment analysis is needed to make the best decision. This study aims to determine the accuracy of sentiment analysis using the K-Nearest Neighbor algorithm, which reviews using Indonesian language texts. The advantages of k-NN have a simple principle, working based on the shortest distance from the test sample and training sample. In this research, preprocessing used is tokenize, stopword filter and bi-gram. The data used consisted of 50 positive reviews and 50 negative reviews. Experiments have been carried out by changing the value of k and obtained the greatest accuracy with a value of k = 7 with an accuracy of 77.01, precission 92.38, recall 61.56 and AUC value of 0.894.Keywords: sentiment analysis;Review of attractions; k-NNAbstrak-Review mengenai tempat wisata memudahkan pengunjung lain untuk mengetahui keadaan tempat tersebut dan menjadi masukan untuk pengunjung lain. Analisis sentimen diperlukan untuk mengambil keputusan yang terbaik. Penelitian ini bertujuan untuk mengetahui akurasi analisis sentimen dengan menggunakan algoritma K-Nearest Neighbor yang mana review menggunakan teks berbahasa Indonesia. Kelebihan k-NN memiliki prinsip sederhana, bekerja berdasarkan jarak terpendek dari sampel uji dan sampel latih. Pada penelitian ini preprocessing yang digunakan yaitu tokenize, filter stopword dan bi-gram. Data yang digunakan terdiri dari 50 review positif dan 50 review negative. Eksperimen telah dilakukan dengan mengubah nilai k dan didapat akurasi yang terbesar dengan nilai k=7 dengan akurasi 77.01, precission 92.38, recall 61.56 dan nilai AUC 0.894.Kata kunci : analisis sentiment; review objek wisata; k-NN
Rancang Bangun Sistem Informasi Penggajian Karyawan PT. Sinar Mentari Era Jaya Cicilia Regita Valent Cicilia Regita Valent; Adjat Sudradjat; Retno Sari; Miwan Kurniawan Hidayat; Ishak Komarudin
IMTechno: Journal of Industrial Management and Technology Vol. 2 No. 2 (2021): Juli 2021
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1004.25 KB) | DOI: 10.31294/imtechno.v2i2.226

Abstract

So far, the payroll system of PT. Sinar Mentari Era Jaya runs manually, so that even miscalculations cannot be avoided. This results in inconvenience for employees at work, which in turn can reduce the work productivity of the employees themselves. Given these problems, it is necessary to build a computerized payroll system using a programming application that can simplify the work of the HRD department, making it more effective and efficient. PT's payroll information system. Sinar Mentari Era Jaya is made with MySQL DBMS and the PHP programming language is used to manage employee data, work attendance, holidays, as well as to perform salary calculations based on the data that has been inputted, and can be used to print reports. Finally, with this payroll information system, the salary calculation process can be done automatically without the need to calculate manually and the salary data obtained is appropriate, provided that all data entered is valid.
Komparasi Algoritma Support Vector Machine, Naïve Bayes Dan C4.5 Untuk Klasifikasi SMS Retno Sari
IJCIT (Indonesian Journal on Computer and Information Technology) Vol 2, No 2 (2017): IJCIT - November 2017
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (473.741 KB) | DOI: 10.31294/ijcit.v2i2.2773

Abstract

AbstrakLayanan pesan singkat atau yang dikenal sebagai SMS, merupakan salah satu cara untuk berkomunikasi oleh para pengguna telepon genggam. SMS terdapat dua macam yaitu sms spam dan sms ham. SMS yang masuk kedalam kotak pesan banyak mengandung SMS yang merupakan spam. Komparasi algoritma Support Vector Machine, Naïves Bayes dan C4.5 untuk klasifikasi sms ini, untuk mengetahui algoritma mana yang memiliki tingkat akurasi yang tinggi. Dapat dilihat dari hasil pengklasifikasian dengan menggunakan 3 metode aplikasi data mining untuk sms berbahasa Indonesia dengan jumlah data sms 200. Akurasi yang didapat dengan menggunakan Naïves Bayes yaitu sebesar 95.00%, sedangkan yang menggunakan Support Vector Machine sebesar 76.00% dan dengan C4.5 akurasi didapat sebesar 95.50%.Katakunci: Klasifikasi SMS, Support vector Machine, Naïves Bayes, C4.5 AbstractShort message service or known as a text message , is one way to communicate by users mobile phone. SMS there are two kinds of the sms junk and sms human rights. The sms in to the inbox message contain many the sms is junk. Comparation algorithm Support Vector Machine, Naïves Bayes and C4.5 for the classification of sms, to know algorithm which is highly accurate. Can be seen from classification with use 3 method data mining aplication for sms Indonesian with the amount of data sms 200. Accuracy obtained by using Naïves Bayes of 98.00%, with use Support Vector Machine of 76.00% and with c4.5 accuracy obtained pf 95.50%Keywords: SMS Classification, Support Vector Machine, Naïves Bayes, C4.5
Co-Authors Adjat Sudradjat Cicilia Regita Valent Cicilia Regita Valent Dwi Andriyanto Fadillah Said Ishak Komarudin Miwan Kurniawan Hidayat Ratih Yulia Hayuningtyas Windu Gata