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All Journal JUSIFO : Jurnal Sistem Informasi Sistemasi: Jurnal Sistem Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal CoreIT JURNAL MEDIA INFORMATIKA BUDIDARMA INOVTEK Polbeng - Seri Informatika Jurnal Sistem Cerdas Building of Informatics, Technology and Science Malcom: Indonesian Journal of Machine Learning and Computer Science The Indonesian Journal of Computer Science
Rice Novita
Universitas Islam Negeri Sultan Syarif Kasim Ria
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Literature Review: Trend Penerapan MCDM Metode ELECTRE, EDAS dan ARAS Sasmita, Indah; Novita, Rice; Rozanda, Nesdi Evrilayan; Hamzah, Muhammad Luthfi
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 7, No 1 (2021): Juni 2021
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (933.781 KB) | DOI: 10.24014/coreit.v7i1.13088

Abstract

Pengambilan keputusan multi-criteria (MCDM) menunjukkan tujuan dari pengambilan keputusan beberapa kriteria yang saling bertentangan. Pengambilan keputusan multi-criteria merupakan salah satu topik permasalahan yang paling banyak ditangangani oleh para peneliti. Sekitar 120 makalah tentang pengambilan keputusan multi-criteria pada metode ELECTRE, EDAS dan ARAS telah diidentifikasi melalui serangkaian pertanyaan dan diklasifikasikan berdasarkan tahun 2015 sampai 2020. Penggunaan metode MCDM (ELECTRE, EDAS ARAS) terkait dengan bidang tertentu (Ilmu Keputusan, Ilmu Komputer, Seni dan humaniora) diidentifikasi. Hasilnya menunjukkan bahwa metode ELECTRE, EDAS dan ARAS disukai untuk permasalahan ilmu keputusan dengan persentase 20%, 35%, 24% dan energi dengan persentase 12%, 14%, 16%. Selanjutnya, hasil penelitian menunjukkan bahwa metode ELECTRE merupakan metode yang paling trend dikalangan peneliti dengan mengacu pada banyaknya kasus penerapan multi-criteria.
Sentiment Analysis Classification Of Political Parties On Twitter Using Gated Recurrent Unit Algorithm And Natural Language Processing Andriawan, Ahmad Rizky; Mustakim, Mustakim; Novita, Rice
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 7 No. 2 (2024): Vol. 7 No. 2 (2024): Issues January 2024
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v7i2.10709

Abstract

General elections cannot be separated from the issue of political parties. The issue can be in the form of surveys to sentiment. The results of the current survey need to be done in-depth validation related to the truth. Sentiment analysis aims to validate the truth of the survey institution. There are 5 political parties used as datasets in this study, namely Partai Demokrasi Indonesia Perjuangan Party (PDIP), Gerakan Indonesia Raya Party (Gerindra), Golongan Karya Party (Golkar), Partai Kebangkitan Bangsa Party (PKB), and Nasional Demokrat Party (Nasdem). The Gated Recurrent Unit (GRU) algorithm is implemented in this research as an experiment in data calculation. Based on the results of the GRU algorithm calculation in calculating sentiment on political parties, it produces the highest data at 56.50% accuracy, 72.76% precision, and 100% recall
Sales Management System with Rapid Application Development and PIECES Approach Al-Qadr, Nola Ardelia; Novita, Rice; Ahsyar, Tengku Khairil; Zarnelly, Zarnelly
JUSIFO : Jurnal Sistem Informasi Vol 10 No 1 (2024): JUSIFO (Jurnal Sistem Informasi) | June 2024
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v10i1.22222

Abstract

In this era of rapid technological advancement, the computer business plays a crucial role in providing goods and services to meet societal needs. However, Andalas Computer, despite offering a diverse range of products and services, faces challenges in its sales process and stock management, which still rely on manual methods. This study aims to develop a sales management system to facilitate the company's sales. By employing the Rapid Application Development (RAD) method, system requirements analysis can be addressed with feedback from users. This research utilizes PIECES analysis to identify opportunities from various aspects. The study results in a sales management system tailored to user needs. System testing was conducted using blackbox testing, followed by user acceptance testing to gauge user reception of the system. The results of the testing showed a positive acceptance rate of 90%.
Sentiment Analysis ChatGPT Using the Multinominal Naïve Bayes Classifier (NBC) Algorithm Sri Rahayu, Dwi; Novita, Rice; Khairil Ahsyar, Tengku; Zarnelly
Jurnal Sistem Cerdas Vol. 7 No. 1 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i1.388

Abstract

Chatbots have become one of the popular solutions for improving customer service. One well-known chatbot is ChatGPT, a language model developed by OpenAI. As time goes by and more and more people use ChatGPT, sentiment analysis is needed about users' opinions about the ChatGPT service. Therefore, it is necessary to carry out sentiment analysis of the ChatGPT service on Twitter to find out how users respond to this chatbot service. In this research, the results showed positive sentiment of 57%, negative sentiment of 29% and neutral sentiment of 14%. Topics for each sentiment were also obtained and sentiment prediction results from 40% of the test data with results of 96% positive, 3.5% negative and 0.5% neutral with a test accuracy of 63%.
Classification of Beef and Pork with Deep Learning Approach Akhiril Anwar Harahap; Novita, Rice; Ahsyar, Tengku Khairil; Zarnelly, Zarnelly
Jurnal Sistem Cerdas Vol. 7 No. 1 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i1.393

Abstract

Beef is one of the most consumed meats in Indonesia. However, the high price of beef has led to rogue traders mixing pork with beef. This condition occurs due to the lack of public knowledge about the difference between the two meats. To maintain food safety in Indonesia and especially in Riau province, the Livestock Service Office of Riau province conducts market surveys. There are several methods that are usually used to check the content of beef or pork, including Rapid Test Kit and Elisa. Both methods are time consuming and costly. One other solution that can be used is the artificial intelligence method, namely deep learning. In this research, a classification approach using deep learning is used to distinguish between beef and pork in the form of a web application. This research compares Convolutional Neural Network algorithm with Inception-V3 and Inception-Resnet-V2 architecture with hyperparameter optimization. From several experiments that have been carried out, the best model is the Inception-Resnet-V2 architecture with an experimental scenario using a learning rate of 0.001, and an optimizer Adam with an accuracy of 96.50%, Precision 96.48%, Recall 96.55% and F1-Score 96.50%. By using this model, web-based applications can be developed using the flask framework well and can perform classification accurately.
Comparison of Service and Ease of e-Commerce User Applications Using BERT Yuda, Afi Ghufran; Novita, Rice; Mustakim; Afdal, M.
Jurnal Sistem Cerdas Vol. 7 No. 2 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i2.403

Abstract

The development of e-commerce has transformed shopping patterns by harnessing the internet, enabling consumers to shop online. In Indonesia, e-commerce has experienced rapid growth, with numerous options such as Tokopedia, Shopee, and Lazada, leading to intense competition. Sentiment analysis using machine learning techniques has become crucial for understanding consumer views on these e-commerce services. This study analyzes user comments on Tokopedia, Shopee, and Lazada e-commerce platforms from Instagram social media, totaling 3900 data points, using the Bidirectional Encoder Representations from Transformers (BERT) model with 5 epochs and a batch size of 32. Sentiment analysis utilizes 3 types of labels: positive, neutral, and negative. The final results of the study include the performance analysis of the BERT model, as well as comparisons for each predefined category, namely Promotions & Offers, and Services. The final results of the model indicate good performance, with accuracy rates of 95%, 97%, and 99%, respectively.
Sentiment Analysis on the Impact of Artificial Intelligence (AI) Development to Determine Technology Needs Abror, Naufal; Novita, Rice; Mustakim; Afdal, M.Afdal
Jurnal Sistem Cerdas Vol. 7 No. 2 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i2.404

Abstract

Artificial Intelligence (AI) has become a hot topic in recent years in Indonesia. To determine the influence of AI developments in determining technology needs, a sentiment analysis needs to be carried out. Sentiment analysis is a process used to help identify the contents of a dataset in the form of opinions or views (sentiments) in text form regarding an issue or event that is positive, negative or neutral. The algorithm applied in this research is the Multinominal Naive Bayes Classifier method. The Multinominal Naive Bayes Classifier method was chosen because it has quite high processing speed and accuracy when used on large, varied and large amounts of data. In this research, the sentiment results were "Negative" for the topic of data security and privacy with a testing accuracy of 75%, "Positive" for Economic Topics with a testing accuracy of 50%, "Negative" for Industrial Topics with a testing accuracy of 58%, "Positive" for Field Topics jobs with a testing accuracy of 75%, “Negative” Transportation Topics with a testing accuracy of 50%, and “Negative” for Education Topics with a testing accuracy of 67%.
Klasifikasi Sentimen Masyarakat Terhadap Pemberlakuan Pembatasan Kegiatan Masyarakat Menggunakan Text Mining Pada Twitter Mustofa, Asdar; Novita, Rice
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (549.373 KB) | DOI: 10.47065/bits.v4i1.1628

Abstract

Corona Virus Disease 2019 (Covid-19) is currently a pandemic in the world, including in Indonesia. Various policies have been carried out to break the chain of the spread of Covid-19, one of which is the government's policy of implementing Community Activity Restrictions (PPKM). PPKM is one of the most discussed topics on social media, including Twitter. Tweets on Twitter given by the public to the PPKM policy that was held to evaluate the implementation of PPKM, it is necessary to classify public sentiment using text mining, in this study using the K-Nearest Neighbor (KNN) and Naïve Bayes Classifier (NBC) algorithms with data from tweets. Twitter during the PPKM last year with 3,516 data. Where the results are that the NBC algorithm is better than the KNN algorithm with an accuracy of 79.67% compared to 78.86%, the polarity of public sentiment towards PPKM is also obtained with positive sentiment of 36.83% with a total of 1,295, neutral sentiment of tweets 54.15% with the number of 1,902 tweets, and 9.02% negative sentiment with a total of 317 tweets
Prediksi Kebutuhan Energi Listrik Menggunakan Metode Jaringan Syaraf Tiruan Pulungan, Joliando; Novita, Rice
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (453.197 KB) | DOI: 10.47065/bits.v4i1.1649

Abstract

Government owned company of electricity play an important role in the distribution of electrical energy services. The Bagan Batu Auxiliary Service Unit (ULP) is one of the ULPs that plays an important role in the distribution of electrical energy in the Bagan Batu area. Along with the increase in the number of customers every year, the problem of demand for electrical energy changes and increases every year. To predict short-term electrical energy needs, this study uses the Backpropagation Artificial Neural Network method with the help of the MATLAB R2015B tool. The research data for training and network testing uses the history of energy sold (kWh) for the last 10 years with other variables consisting of household customers, business, social, industrial, population growth, Gross Regional Domestic Product (GRDP). The results of the research produce predictions of electrical energy for the next 3 years from 2022 to 2024. This research produces the best architectural model 6-6-1 with the smallest MSE error of 0.003312731 and produces Mean Absolute Percentage Error (MAPE) value of 6%. The research implies benefits for stakeholders to take action on the provision of electrical energy
Analisis Sentimen E-Wallet Menggunakan Support Vector Machine Berbasis Particle Swarm Optimization Vamilina, Vina; Novita, Rice
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3526

Abstract

E-Wallet applications in Indonesia have started to be in demand since the Covid-19 pandemic. The object being analyzed is an e-wallet application that is widely used in Indonesia and can be downloaded on the Google Playstore. The applications analyzed are Dana, Ovo, PayPal Link Aja and Doku. The advantages of these five applications are that Dana is user friendly or easy to use, while using Ovo is superior in terms of benefits, and Doku is superior in terms of security, Link Aja tends to be perceived by consumers in a neutral condition between security and user convenience because it is an e-wallet. It is still considered new in Indonesia, and PayPal has become a successful online payment system in C2C field. The focus of this research is to compare the comments of the users of the five applications. The method used in this study is the Support Vector Machine (SVM) algorithm. To produce high accuracy it is optimized using the Particle Swarm Optimization (PSO) algorithm. This was taken based on previous studies which stated that SVM-PSO has the highest percentage of accuracy compared to other algorithms. The data used is a thousand (1000) per application. So, the total amount of data is five thousand (5000) data. The results of the research show that the Ovo e-wallet is superior because it has the most positive comments, namely 579 and 421 negative comments, while the lowest position is occupied by Link Aja which only has 579 positive comments and 421 negatuve comments. In the process of sentiment analysis, the accuracy percentage of the SVM-PSO algorithm was also obtained, which was 91.10% in the Link Aja application. This means that SVM-PSO is very suitable to be combined to get the highest accuracy
Co-Authors Abror, Naufal Afandi, Rival Afdal, M.Afdal Afifah, Tiara Afrah Ahsyar, Tengku Khairil Aini, Delvi Nur Akhiril Anwar Harahap Al-Qadr, Nola Ardelia Andriawan, Ahmad Rizky Anisa Putri Auliani, Sephia Nazwa Azzahra, Aura Dwinnie, Zairy Cindy F. Safiesza, Qhairani Frilla Hamzah, Muhammad Luthfi Khairil Ahsyar, Tengku Kurniawan, Viki M Afdal M. Afdal Muhammad Iqbal Munzir, Medyantiwi Rahmawati Munzir, Medyantiwi Rahmawita Mustakim Mustakim Mustakim Mustofa, Asdar Novansyah, Rizky nursalisah, febi Pangestu, Prayugo Pertiwi, Tata Ayunita Pulungan, Joliando Putri, Suci Maharani Rahayu, Dwi Sri Ramadani, Faradila Rozanda, Nesdi Evrilayan Sasmita, Indah Vamilina, Vina Wibisono, Yudistira Arya Yuda, Afi Ghufran Zarnelly Zarnelly Zarnelly