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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Faktor Exacta JISICOM (Journal of Information System, Infomatics and Computing) Journal of Information System, Applied, Management, Accounting and Research INTI Nusa Mandiri
Elly Indrayuni
Universitas Bina Sarana Informatika
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Sistem Informasi Pendaftaran Vaksinasi Covid-19 Acmad Nurhadi; Elly Indrayuni
Journal of Information System, Informatics and Computing Vol 5 No 2 (2021): JISICOM: December 2021
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisicom.v5i2.491

Abstract

Saat ini bangsa indonesia sedang dilanda pandemi virus COVID-19. Penyebarannya sangat cepat hingga berdampak pada semua aspek kehidupan. Atas dasar hal tersebut maka pemerintah mengadakan vaksinasi dalam rangka menanggulangi pandemi COVID-19. Tujuan Utama dari Vaksinasi adalah untuk mengurangi transmisi /penularan covid-19 , menurunkan angka kesakitan dan kematian akibat covid-19. Pelaksanaan vaksinasi dilakukan melalui jalur pendaftaran yang telah dibuat oleh pemerintah, namun dibeberapa daerah di indonesia masih melakukan pendaftaran secara manual yaitu para calon vaksinasi secara langsung datang ke lokasi untuk melakukan pendaftaran . Cara tersebut dianggap kurang efektif dimasa pandemi saat ini. Oleh karena itu penulis membangun sebuah sistem informasi pendaftaran vaksinasi berbasis web dalam memudahkan para calon vaksinasi dalam melakukan pendaftaran dan membantu petugas pendaftaran dalam menginput data pasien. Selain itu tampilan yang terdapat pada sistem ini sangat sederhana dan mudah di gunakan. Pada pembuatan sistem ini menggunakan metode waterfall sehingga sistem ini layak diterapkan dalam proses pendaftaran vaksinasi.
PERANCANGAN APLIKASI SISTEM INFORMASI PELAYANAN PEMBUATAN AKTA KELAHIRAN KELURAHAN JEMBATAN LIMA JAKARTA BARAT Acmad Nurhadi; Elly Indrayuni
Journal of Information System, Applied, Management, Accounting and Research Vol 4 No 4 (2020): JISAMAR: Volume 4, Nomor 4, November 2020
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

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Abstract

Manusia akan mengalami suatu peristiwa penting di dalam kehidupan. Salah satu dari peristiwa penting adalah kelahiran. Akta kelahiran merupakan bukti catatan bukti otentik yang melekat pada diri seseorang yang diatur dalam UU No. 23 Tahun 2006 tentang adminitrasi kependudukan serta merupakan bukti yang sah mengenai status anak yang dikeluarkan oleh Catatan Sipil. Mengingat begitu pentingnya peristiwa kelahiran, maka demi terciptanya keadaan masyarakat yang tertib dan teratur serta demi terjaminnya kepastian hukum, maka diperlukan suatu instansi pelaksana adminitrasi untuk mencatatnya. Kelurahan Jembatan Lima adalah instansi pelaksana adminatrasi yang bertugas untuk mendaftar, mencatat, membukukan, serta mengarsipkan Akta Kelahiran bagi peristiwa kelahiran seseorang. Masalah yang mucul pada layanan pembuatan akta kelahiran yaitu pelayanan yang diberikan kepada pemohon dinilai kurang maksimal serta ketidakteraturan pengolahan data dari layanan pembuatan akta kelahiran, sehingga petugas sulit dalam mengontrol dan mengetahui informasi status perkembangan dari setiap layanan pembuatan akta kelahiran. Maka, diperlukan perancangan sistem pembuatan akta kelahiran agar terkomputerisasi. Hasil dari penelitian ini adalah sebuah rancangan Sistem Pelayanan Pembuatan Akta Kelahiran pada Kelurahan Jembatan Lima Jakarta Barat. Metode yang diambil penulis yaitu dengan melaksanakan observasi, wawancara, analisis dan perancangan sistem informasi dilanjutkan dengan pembuatan sistem informasi. Manfaat dari penelitian ini adalah untuk memberikan solusi meningkatkan kinerja petugas dalam layanan pembuatan akta kelahiran, serta mampu mempermudah pelayanan kepada masyarakat Kelurahan Jembatan Lima
Implementasi Algoritma Naive Bayes, Support Vector Machine, dan K-Nearest Neighbors untuk Analisa Sentimen Aplikasi Halodoc Elly Indrayuni; Acmad Nurhadi; Dinar Ajeng Kristiyanti
Faktor Exacta Vol 14, No 2 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i2.9697

Abstract

During the Covid-19 pandemic, many people access information and even consult health problems online with the best doctors via smartphones. The Halodoc application is considered the most popular with 18 million users in 2020. So that many people have reviewed the application on the Google Play Store application provider. It may take a while to read the full review. However, if only a few comments are read, they are biased. For that, a platform is needed which can automatically identify positive or negative opinions. Sentiment analysis is a solution for the technique of classifying texts or sentiments into positive or negative opinion categories. The method used in this research is an experiment using the Naive Bayes algorithm, Support Vector Machine, and K-Nearest Neighbors. Evaluation is carried out using 10 Fold Cross-Validation. The results showed that K-Nearest Neighbors (KNN) had the best and most accurate performance in the sentiment classification because it produced the highest accuracy value of 95.00% and the largest AUC value of 0.985 compared to the Naive Bayes and Support Vector Machine algorithm.
Sentiment Analysis About COVID-19 Booster Vaccine on Twitter Using Deep Learning Elly Indrayuni; Achmad Nurhadi
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2022): Article Research Volume 7 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11485

Abstract

The rapid spread of COVID-19 cases to various countries has made the COVID-19 outbreak a global pandemic by the World Health Organization (WHO). The effect of the designation of COVID-19 as a pandemic has prompted the government to take preventive action against vaccination, as well as the WHO which has asked the public to immediately get a third or booster dose of vaccine. Various responses regarding the COVID-19 booster vaccine continue to emerge on social media such as Twitter. Twitter is often used by its users to express emotions about something either positive or negative. People tend to believe what they find on social networks, which makes them vulnerable to rumors and fake news. Sentiment analysis or opinion mining is one solution to overcome the problem of automatically classifying opinions or reviews into positive or negative opinions. In this study, the Deep Learning algorithm was used to analyze public opinion sentiment regarding the COVID-19 booster vaccine on Twitter. The data collection method used is crawling data using an access token obtained from the Twitter API. Meanwhile, to evaluate the model, the K-fold Cross-Validation method is used. The results of testing the model obtained the highest accuracy value at iterations = 10, which is 82.78% with AUC value = 0.836, precision = 83.33% and recall = 95.89%.
Work schedule system application at PT. Asima Jaya Teknik Bekasi Acmad Nurhadi; Elly Indrayuni
JISICOM (Journal of Information System, Informatics and Computing) Vol 6 No 2 (2022): JISICOM: December 2022
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisicom.v6i2.969

Abstract

With the Covid-19 virus still making us have to innovate in the field of technology to support a job. and to find out the problems that exist in PT. Asima Jaya Teknik, the method used is the waterfall method, where the Waterfall Model is divided into four stages, requirements analysis, design, coding, and testing. PT. Asima Jaya Teknik, in the process of recap work it is still not computerized so that in carrying out the process, errors are still encountered starting from when inputting data to getting work to billing data. The process is still manual using paper, so there is often loss and damage, sometimes between one employee and another having data that is not the same, so they have to work twice to correct data from other employees. The results of this study create an application that is used to recap the work of employees that can help and alleviate and speed up the work process at PT. Asima Jaya Engineering. In addition, it can also save time and energy from the employees of PT. Asima Jaya Engineering.
OPTIMASI NAIVE BAYES BERBASIS PSO UNTUK ANALISA SENTIMEN PERKEMBANGAN ARTIFICIAL INTELLIGENCE DI TWITTER Elly Indrayuni; Acmad Nurhadi
INTI Nusa Mandiri Vol 18 No 1 (2023): INTI Periode Agustus 2023
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v18i1.4282

Abstract

At present the development of Artificial Intelligence technology is progressing rapidly. There are many new artificial intelligence technologies available in various fields. Artificial Intelligence is an artificial intelligence program that can study data, perform processes of thinking and acting like humans. The presence of Artificial Intelligence technology has many positive impacts, especially in increasing work effectiveness and efficiency. However, AI is also a threat to human resources because slowly human work is being replaced by Artificial Intelligence. Various opinions about the development of Artificial Intelligence are widely discussed on social media such as Twitter. Sentiment analysis is a computational study to automatically categorize opinions into positive or negative categories. In this study, the Naive Bayes algorithm was used to analyze sentiment or public opinion regarding the development of Artificial Intelligence for Twitter users. The data collection method used is crawling data on Twitter. The results of the sentiment classification test for the development of Artificial Intelligence using Naive Bayes yield an accuracy value of 86.42%. Meanwhile, the results of the sentiment classification test using Naive Bayes based on Particle Swarm Optimization (PSO) increased with an accuracy value of 87.55%. Based on the results of this study, the use of PSO as an optimization technique for the Naive Bayes algorithm is proven to be the best algorithm model in sentiment analysis for the development of Artificial Intelligence for English text.
Co-Authors Achmad Nurhadi Acmad Nurhadi Dinar Ajeng Kristiyanti