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The Covid-19 Chatbot Application Using A Natural Language Processing Approach Cahyo Prianto; Nisa Hanum Harani
IJISTECH (International Journal of Information System and Technology) Vol 5, No 2 (2021): August
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v5i2.133

Cases exposed to the Covid-19 virus in Indonesia until June 2021 continue to experience a spike in increases, to handle it, various government policies continue to be rolled out and the public needs to be given correct, precise and fast information so that mutual awareness can be built to suppress cases exposed to COVID-19. With this background, this study aims to design and build a COVID-19 chatbot system based on artificial intelligence based on the Natural Language Processing algorithm. This chatbot is expected to be a place to ask questions about all things related to covid-19 so that it can become a personal assistant with two-way communication that can be accessed quickly for 24 hours. This chatbot system was built using the Python programming language, Node.js server and MariaDB as the database. As a client, this chatbot is integrated with the popular instant messaging application in Indonesia, namely WhatsApp. The data set used to train the chatbot was 369 question data and spread into 46 question tags. Testing the chatbot system using blackbox testing, and to test the expected output, the chatbot was tested using 350 testing data and the accuracy rate of the chatbot in answering reached 54%.

Rancang Bangun Aplikasi Penentuan Kelayakan Pemberian Pinjaman Kepada Pensiun Menggunakan Metode Weighted Product Kezia Tirza Naramessakh; Cahyo Prianto
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 3, No 4 (2019): Oktober 2019
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v3i4.1289

In supporting the operation of PT. Pos Indonesia, one of which is offering retirees such as pension savings and pension credit. Credit is the provision of money or bills based on an agreement or loan agreement between a company and another party. In providing pension credit, it must be accurate and accurate for retirees who are entitled or eligible to get a loan. So the author makes an application to determine the feasibility of lending in order to make it easier to determine which retirees are eligible to be given a credit loan. This study uses the Weighted Product (WP) method, which is one method of decision making. By using the Weighted Product method can help in making lending decisions by doing a ranking process that will determine the best alternative from retirees. The author uses five criteria, namely, the amount of salary, loan amount, age, credit period, and what credit. The application of determining the feasibility of lending to pensions is based on a website using the code igniter framework. For designing or modeling this application uses UML (Unified Modeling Language). This research resulted in an application that helped in determining a proper pension to be given a pension loan using Weighted Product.

Analisis Sentimen Terhadap Kandidat Presiden Republik Indonesia Pada Pemilu 2019 di Media Sosial Twitter Cahyo Prianto; Nisa Hanum Harani; Indra Firmansyah
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 3, No 4 (2019): Oktober 2019
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v3i4.1549

The development of technology today has been growing rapidly and has an impact on the behavior patterns of people who feel it. The Ministry of Communication and Information (KOMINFO) released a data that of 265 million people of Indonesia, there are around 54% have used internet technology or about 143 million people. In one survey IDN Research Institute said that there are three Social Media that are widely used in Indonesia, namely Facebook, Instagram and Twitter. This study focuses on extracting data in the form of text produced from social media twitter that responds to the account of the RI presidential candidates in the 2019 elections. Sentiment analysis is obtained through tweet classification using sentiment analysis tools such as NRC Lexicon and Bing Lexicon so that information is obtained in the form of positive polarity and negative polarity from community tweets towards the Presidential candidates in the 2019 elections. Using March data before the 2019 election, for candidate 01 Joko Widodo, the NRC Lexicon analysis gave a value of 249 and bing lexicon of 267 with an average value of 0.11, while for candidate 02 Prabowo Subianto the NRC Lexicon analysis gave a value of 195 and bing lexicon of 204 with an average value of 0.085. Using april data after the 2019 election. Candidate 01 Joko Widodo still received a lot of responses from netizens but the sentiment value shifted more negatively compared to candidate 02 Prabowo Subianto. For candidate 01 Joko Widodo the NRC Lexicon analysis gave a value of 17 and bing lexicon of -273 with an average value of -0,246, while for candidate 02 Prabowo Subianto the NRC Lexicon analysis gave a value of 238 and bing lexicon of -73 with an average value of -0.02430939.

Analisis Sentimen UU Omnibus Law pada Twitter Menggunakan Metode Support Vector Machine Syafrial Fachri Pane; Alfadian Owen; Cahyo Prianto
InComTech : Jurnal Telekomunikasi dan Komputer Vol 11, No 2 (2021)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v11i2.10874

Pada media sosial Twitter semua orang bebas memberikan opini ataupun memberikan tweet yang bermanfaat bagi pengguna media sosial tersebut. Namun dalam memberikan opini masyarakat harus bisa membedakan opini yang positif, negatif, ataupun netral. Permasalahan yang ada adalah belum adanya pemberian sentimen otomatis dalam tema tertentu. Maka dari itu dibuatlah sistem untuk memberikan sentimen secara otomatis agar masyarakat tahu opini yang positif, negatif, dan netral. Dalam analisis sentimen ini dilakukan dengan memanfaatkan machine learning salah satu metodenya adalah Support Vector Machine yang merupakan metode pengklasifikasian supervised learning yang dapat membedakan opini positif, negatif, dan netral dalam penelitian ini, menggunakan Bahasa pemrograman Python, dan menggunakan data yang berasal dari Twitter sebanyak 150. Data tersebut diambil pada tanggal 3 November 2020 sampai 9 November 2020 setelah Omnibus Law disahkan. Penerapan metode Support Vector Machine memiliki tiga tahap yaitu mengambil data opini masyarakat Indonesia tentang UU Omnibus Law dengan melakukan Scraping, lalu dilanjutkan ke tahap Text Preprocessing, dan Feature Extraction. Menghasilkan akurasi sebesar 83% dengan menggunakan teknik K-Fold Cross-Validation sehingga hasil yang didapatkan cukup akurat.

Sentiment Analysis of Covid-19 As A Social Media Pandemic Cahyo Prianto; Nisa Hanum Harani
IJISTECH (International Journal of Information System and Technology) Vol 4, No 1 (2020): November
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

A large amount of information about Covid-19 that spreads quickly can lead to a perception of opinion and sentiment for those who read it. This research studies how text networking is formed, sentiment analysis and topics modelling that is widely discussed related to the Covid-19 theme. The text networking analysis was carried out on data taken from 4 different times, namely on 26 March, 29 March, 28 June and 23 July 2020 giving the result that the largest edge, nodes and modularity were in the conversation data on July 23, 2020. Sentiment analysis shows how the public responds to the Covid-19 pandemic. Sentiment analysis from tweet data in March 2020 showed 51% as positive sentiment and 49% as negative sentiment, with an accuracy rate of 0.7586, specificity 0.6667, prevalence 0.5862. Then tweet data in June 2020 showed 59% as negative sentiment and 41% as positive sentiment, with an accuracy rate of 0.6486, specificity 0.6111, prevalence 0.5135. Analysis of topic modelling has succeeded in collecting words related to certain topics, such as the data on March 26, 2020, representing talks related to the topic of "doing activities from home", "health", and "government policy". The data on March 29, 2020, represent talks related to the topic of "activities from home", "expression of feelings", "new habits". The data on June 28, 2020, represent talks related to the topic of "health protocol", "social assistance", "health". And on July 23, 2020 data represents talks related to the topic of "data security", "fine policy", and "policy".

The Covid-19 Chatbot Application Using A Natural Language Processing Approach Cahyo Prianto; Nisa Hanum Harani
IJISTECH (International Journal of Information System and Technology) Vol 5, No 2 (2021): August
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Cases exposed to the Covid-19 virus in Indonesia until June 2021 continue to experience a spike in increases, to handle it, various government policies continue to be rolled out and the public needs to be given correct, precise and fast information so that mutual awareness can be built to suppress cases exposed to COVID-19. With this background, this study aims to design and build a COVID-19 chatbot system based on artificial intelligence based on the Natural Language Processing algorithm. This chatbot is expected to be a place to ask questions about all things related to covid-19 so that it can become a personal assistant with two-way communication that can be accessed quickly for 24 hours. This chatbot system was built using the Python programming language, Node.js server and MariaDB as the database. As a client, this chatbot is integrated with the popular instant messaging application in Indonesia, namely WhatsApp. The data set used to train the chatbot was 369 question data and spread into 46 question tags. Testing the chatbot system using blackbox testing, and to test the expected output, the chatbot was tested using 350 testing data and the accuracy rate of the chatbot in answering reached 54%.

Implementation of K-Means Methods In Clustering Students Ability Levels in English Language Cahyo Prianto; Rd Nuraini; Andi Tenri Wali
The IJICS (International Journal of Informatics and Computer Science) Vol 3, No 2 (2019): September 2019
Publisher : STMIK Budi Darma

Nowadays, English extremely needs to be controlled, especially students, in communicating and reading also understanding literature written in English. In achieving mastery of English, the students, in this case, the students who are not majoring in English are given a common base subject of English. In Politeknik Indonesia, especially majoring in a Bachelor's Degree in Informatics Engineering, teaching English is using the direct method, to find out the results of teaching English within three semesters. Therefore, by doing this research for classifying the level of ability of students into three categories, they are Beginner, intermediate and advanced. The objective of the grouping is to determine how many students who have the capability level is low, medium and high so that the faculty can determine the average level of students' proficiency and the lecturers can intervene to conduct teaching in developing the students' knowledge of English. The classification used the K-Means clustering algorithm, which is one algorithm that classifies the same data on specific groups and different data in the other group. The results of this study by applying the k-means clustering method is the researchers can classify the students based on students' ability levels either they are beginner, intermediate or advanced.

Pelatihan Peningkatan Keterampilan Video Editing Di Sman 2 Lembang Bandung Barat Cahyo Prianto; Nisa Hanum Harani; Woro Isti Rahayu
DHARMA RAFLESIA Vol 19, No 1 (2021): JUNI (ACCREDITED SINTA 5)
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/dr.v19i1.15927

Perkembangan teknologi media sosial hadir sebagai sarana untuk berkomunikasi, pola komunikasi jarak jauhpun hadir dengan berbagai format seperti berbasis teks, suara, gambar maupun video. Komunikasi didalam media sosial banyak didominasi dengan pola komunikasi visual, sehingga kemampuan mengedit gambar dan video menjadi sebuah kebutuhan agar tercipta komunikasi yang lebih baik dan menarik. Pada pengabdian kepada masyarakat ini dilaksanakan dalam bentuk pelatihan dengan tema video editing yang bertujuan untuk meningkatan keterampilan komunikasi visual pengolahan video dengan mengambil objek siswa dan guru di SMAN2 Lembang Bandung Barat. Pelatihan dilaksanakan selama satu pekan yang dilakukan secara daring dengan menggunakan dua metode komunikasi yaitu tatap muka secara daring menggunakan zoom dan pendampingan peserta melalui whatsapp grup. Pelatihan mencakup materi seperti 1)Memotong video, 2)Input audio, 3)Transisi efek, 4)Clip grafis, 5)Voice over, 6)Layer croma key, 7)Kemampuan edit video dan gambar, 8)Ekstrak audio dari video, 9)Eksport dan share video, 10)Membuat skenario film pendek, 11)Memproduksi produk video. Dengan menggunakan metode pretest-posttes diperoleh hasil bahwa ada kenaikan nilai yang signifikan antara sebelum dilakukan pelatihan dengan setelah dilakukan pelatihan, sebelum pelatihan tingkat pemahaman peserta untuk seluruh materi sebesar 12% dan setelah pelatihan tingkat pemahaman peserta menjadi sebesar 66.5%, sehingga terdapat kenaikan pemahaman peserta sebesar 53.5%. Kesimpulannya adalah pelatihan yang dilakukan dapat meningkatkan pemahaman yang menunjang keterampilan komunikasi visual pengolahan video. Pelatihan Peningkatan Keterampilan Video Editing Di Sman 2 Lembang Bandung Barat

Sentiment Analysis of Student Emotion During Online Learning Using Recurrent Neural Networks (RNN) Nisa Hanum Harani; Cahyo Prianto
IJISTECH (International Journal of Information System and Technology) Vol 5, No 3 (2021): October
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

There are many limitations in online learning process where communication effect student productivity, such as interpretation in the delivery of information can be different if it is in text form . The unstable internet network in some parts of Indonesia is also an obstacle in the learning process. Emotional factors are very influential on student motivation in learning, in online learning emotions can be read from textual dialogue in providing responses. We propose trainable model capable of identifying the tendency of emotions / responses felt by students. With using natural language processing we can extract information and insights contained in conversations from WhatsApp, then organize them into their respective categories. The selection of the RNN algorithm can increase the accuracy by 75% in analyzing student emotions in online learning.

Development of E-Commerce Information System at Az-Zahra Shop Using Laravel Framework Nawaf Naofal; Muhammad Rifqi Daffa Ulhaq; Cahyo Prianto
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 1 (2022): March
Publisher : Yayasan Literasi Sains Indonesia

Az-zahra Furniture Store is a furniture store which is located at Jalan Raya Pasar Cipunagara, Subang, West Java. In its business process, as an effort to expand the reach of consumers and move towards digitization, a website-based e-commerce system called e-fazastore is designed which is expected to help az-zahra furniture store in running business processes. With this e-commerce system, it will assist in several activities such as selling and managing furniture data, managing customer order data and facilitating transactions between the two parties, namely the seller and their customers, as well as making it easier to find out the available inventory. The system is built using the Laravel framework with an MVC (Model, View, Controller) architectural design system, which is a design method that divides the program structure into three main parts, namely data (Model), system view (View) and how to operate a data flow (Model). controller) in the system

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