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JURIKOM (Jurnal Riset Komputer)
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma
ISSN : 2407389X     EISSN : 27157393     DOI : http://dx.doi.org/10.30865/jurikom.v7i1.1927
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
JURIKOM (Jurnal Riset Komputer) membahas ilmu dibidang Informatika, Sistem Informasi, Manajemen Informatika, DSS, AI, ES, Jaringan, sebagai wadah dalam menuangkan hasil penelitian baik secara konseptual maupun teknis yang berkaitan dengan Teknologi Informatika dan Komputer. Topik utama yang diterbitkan mencakup: 1. Teknik Informatika 2. Sistem Informasi 3. Sistem Pendukung Keputusan 4. Sistem Pakar 5. Kecerdasan Buatan 6. Manajemen Informasi 7. Data Mining 8. Big Data 9. Jaringan Komputer 10. Dan lain-lain (topik lainnya yang berhubungan dengan Teknologi Informati dan komputer)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 39 Documents
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Search results for , issue "Vol 10, No 1 (2023): Februari 2023" : 39 Documents clear
Xiaomi Smartphone Sentiment Analysis on Twitter Social Media Using IndoBERT Priyan Fadhil Supriyadi; Yuliant Sibaroni
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5540

Abstract

The extraordinary evolution of technology has resulted in smartphones becoming important devices in people's daily lives. As a result, today's smartphones impact many people's lives, with more and more people owning smartphones. One of the most popular smartphone products today is Xiaomi. This popularity cannot be separated from various opinions on Twitter. Twitter is a social media that makes it easy for people to express their opinions regarding Xiaomi products called sentiment. Sentiment analysis is needed to classify various opinions on Twitter into positive, neutral, and negative classes. This study aims to analyze the sentiment of public opinion on Xiaomi smartphone products on Twitter social media. The models used in this study were BERT and IndoBERT because they produced a good performance in previous studies. This study's stages of work consisted of collecting, preprocessing, separating training and test data, building models with BERT and IndoBERT to detect sentiment, and carrying out training and testing stages. Test results using IndoBERT get a very good accuracy value with an accuracy value above 90%. The sentiment classification results for Xiaomi smartphone products show that positive sentiment on batteries has a greater number, with a positive percentage of 78%. In comparison, neutral sentiment is 4%, and negative sentiment is 18%. Furthermore in the camera aspect, positive sentiment has a greater number, with a positive percentage of 68%, while neutral sentiment is 18% and negative sentiment is 14%. Moreover, on the screen, positive sentiment has more numbers, with a positive percentage of 67%, neutral sentiment is 10%, and negative sentiment is 23%. Last, in the ram aspect, positive sentiment has a greater number with a positive percentage of 76%, while neutral sentiment is 17% and negative sentiment is 7%. The highest number of positive sentiments is in the camera aspect, which has 1935 positive sentiments from 2830 data. The sentiment analysis results can be used as an evaluation along with insights for the Xiaomi company so that in the future, the company can maintain and even improve the quality of the aspects that smartphone users like about Xiaomi products, namely cameras.
Penerapan Metode K-Nearest Neighbor pada Sentimen Analisis Pengguna Twitter terhadap KTT G20 di Indonesia Herda Andriana; Shofa Shofia Hilab; Agustia Hananto
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5427

Abstract

Indonesia will host the KTT (Konferensi Tingkat Tinggi) G20 summit on the island of Bali on November 15, 2022. The G20 was formed with one goal in mind: to boost the global economy, which had just entered a period of crisis. However, Indonesia's participation as a full member of the Group of Twenty (G20) has sparked controversy among the country's general populace and the population of Indonesia itself, necessitating a thoughtful analysis of the group's motives. Sentiment analysis was gleaned from tweets on KTT G20 posted on the social media platform Twitter. Data scraping yielded a total of 2,500 tweets for inclusion in the collection. Methods for classifying tweets into positive, neutral, and negative groups are required because of the large amount of data that has already been collected. The purpose of this study was to analyze public opinion on Twitter during the KTT G20. The data was processed using the Orange neural network using a number of tools and the K-Nearest Neighbor method, yielding a total of 1,107 tweets that were successfully added to the original set, with an average recall and precision of 99%. According to the analysis of sentiment, there were 89 negative tweets, 614 neutral tweets, and 404 positive tweets, with the most common emotions being happiness, surprise, and fear
Comparative Analysis of Naive Bayes Model Performance in Hate Speech Detection in Media Social Twitter Muhammad Hadyan Baqi; Yuliant Sibaroni; Sri Suryani Prasetiyowati
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5493

Abstract

Twitter is a popular social media in Indonesia, and for some people, it is a place to find and disseminate information. Hate speech is aggressive behavior against individuals or groups such on race, gender, religion, nationality, ethnicity, sexual orientation, gender identity, or disability. In this study, hate speech is modeled using Naive Bayesian models, which consist of Multinomial, Bernoulli, and Gaussian Naïve Bayes Models. These methods were chosen because Naïve Bayes is a simple method but has good performance in the case of sentiment analysis. This research aims to get the method with the highest accuracy value in analyzing hate speech. Thus, the Naïve Bayes model can provide the best solution for hate speech problems. The process carried out in this study is to process all data which obtained from Twitter social media and then classify it using the Multinomial Naïve Bayes, Gaussian Naïve Bayes, and Bernoulli Naive Bayes models based on the classification of HS and non-HS sentiment categories.  In this study, to get the best accuracy, two different scenarios were used. The result of the analysis of the accuracy is 82.13% of the Multinomial Naïve Bayes model which is the best accuracy rate value compared with other models.
Rancang Bangun Sistem Informasi Pengadaan Barang Menggunakan Teknologi Cloud Computing Deni Hardiansyah; Ade Priyatna
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5580

Abstract

Cloud computing or what is commonly called cloud computing is being widely discussed in this digital era, the ease of procuring servers is very helpful in the system design process within an organization or company in implementing it, PT Arcelon uses this technology in building a procurement information system to speed up the development stage, procurement of goods and management of goods in and out of goods is currently still in a manual process so a goods procurement information system is needed in the form of a web that can be accessed from the internet network to be able to process and make reporting in every transaction in and out of goods, PT Arcelon has a problem in build a system into a server, by buying a very expensive server, long installation and procurement time, and maintenance that is done often causes a problem, such as an electric short circuit when carrying out maintenance, hindering ongoing business processes in carrying out system deployments that are being carried out because it requires a Cloud Computing technology to bridge this where the system development model that will be used is the waterfall SDLC model (waterfall) by utilizing cloud technology which can guarantee very high server availability. high and can serve many requests from many users under certain conditions. The results of the study show that the system built makes it easier for companies to carry out transactions both procurement, checking, and entering and leaving goods.
Audit Sistem Informasi Aplikasi Absensi Pada Inl International Technology Menggunakan Framework Cobit 5 Muchlis Imam Santoso; Eva Zuraidah
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5402

Abstract

An information system audit is carried out to ensure that the system procedures in the institution function properly, using the Cobit 5 framework in auditing the attendance system at INL International Tehnology Pte. Ltd., because there is no standard operating attendance system, there are still deficiencies where in the application to use it in an orderly and correct manner, if we are late for absence there is no warning that we are late for absence from work. The results obtained are the sequence of domains with the highest value, namely the EDM01 domain maturity level value of 3.64 Fully Achieved achievement value, gets the Established Process level in IT capability, and a gap value of 0.64 from the target level 3. MEA01 maturity level value of 3.18 Fully Achieved achievement value, gets the Established Process level in IT capability, and the value gap of 0.18 from the target level 3. BAI08 maturity level value of 2.93 Fully Achieved achievement value, got the Managed Process level in IT capabilities, and a gap value of 0.07 from the target level 3. DSS02 maturity level value of 1.59 Fully achievement value Achieved, got the Performed Process level in IT capabilities, and the gap value is -1.41 from the target level 3. and APO11 mend get a maturity level value of 0.95, the Fully Achieved achievement score, get the Incomplete Process level in IT capabilities, and a gap value of -2.05 from target levels 3 and 2 and the highest score domain order questionnaire, namely domain EDM01, maturity level value 3.64, gap value 0, 64. MEA01 maturity level value is 3.18, gap value is 0.18. BAI08 has a maturity level of 2.93 with a gap value of 0.07. DSS02 maturity level value is 1.59, gap value is -1.41. and APO11 maturity level value of 0.95 gap value of -2.05
Perancangan Aplikasi Pembelajaran Qur’an Edu Berbasis Android Chairul Rizal; Supiyandi Supiyandi; Barany Fachri
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5621

Abstract

At present, mobile devices are developing so rapidly that they have various functions, as well as for multitasking and also have a system such as a computer, so they are also commonly referred to as Smart Devices. Smartphones circulating in the market generally the most in demand are android devices, from android devices are widely used for utilization, the use of smartphone functions, among others, is used for several purposes, so that its development is increasingly widespread, one of which is learning media. The method applied in the learning media is the Asy Syafi'i method where the method was developed at Ma'had Imam Asy Syafi'I and uses the waterfall method in research because this method is very easy to apply in several research cases. The results obtained in this study were to create an Android-based Al-Qur'an learning application in which there was learning about the introduction of hijaiyyah, tajwid, tahsin and murottal Qur'an letters
Comparison of Word2Vec with GloVe in Multi-Aspect Sentiment Analysis Classification of Nvidia RTX Products with Naïve Bayes Classifier Wira Abner Sigalingging; Sri Suryani Prasetyowati; Yuliant Sibaroni
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5528

Abstract

The increasing number of gamers has increased the demand for Graphics Processing Unit (GPU) products, one example of which is the Nvidia RTX product. Many users submit their reviews on social media Twitter in the form of tweets. These Tweets can be analyzed to determine the quality of a product. But most of the tweets talking about the product as a whole ignoring the category aspects of the product, making it difficult for both users and companies to pinpoint which aspects need attention. In this research, a multi-aspect based sentiment analysis will be carried out on tweets on Nvidia RTX products based on aspects of the product. The classification method used is Naive Bayes Classifier which will then compare feature extraction using Word2Vec and GloVe. Performance parameters are measured using a confusion matrix to produce values for accuracy, precision, recall, and f1-score. The highest accuracy results obtained were 60.71% on the price aspect, GloVe feature extraction, and classification with Gaussian Naive Bayes.Keywords: naive bayes classifier; Word2Vec; GloVe; confusion matrix; multi-aspect sentiment analysis
Performance of ANN and RNN in Predicting the Classification of Covid-19 Diseases based on Time Series Data Ridho Isral Essa; Sri Suryani Prasetyowati; Yuliant Sibaroni
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5557

Abstract

Indonesia is one of the countries with the highest confirmed cases of COVID-19. The city of Bandung is an area in Indonesia where the number of confirmed cases have continued to increase from 2021 to 2023. Currently there are around 103,574 cases with a total of deaths of around 1485 people. This is bad news for the city of Bandung because of the increasing number of confirmed cases. Various precautions against factors that might affect the rapid spread of COVID-19 in the city of Bandung have been carried out. But the confirmation cases still can't be stopped. Therefore, in this study we made a classification of the spread of COVID-19 in the city of Bandung with 25 features which will later be expanded using feature expansion techniques. This aims to analyze what factors have a major influence on the spread of COVID-19 in the city of Bandung. The method used are ANN and RNN methods. Where in this study the two methods were compared to determine which model had the best performance. Modeling is done by building models 2, 3, 4, and 5 months then the best model accuracy results from the ANN method are 79% and 81% for the RNN method. The author's contribution in this research is to build 2, 3, 4, and 5 month models, compare the performance results of ANN and RNN models, analyze the results of the confusion matrix, and make conclusions about what features are often used in each modeling.
Klasifikasi Kualitas Jagung Terhadap Data Percobaan Penanaman dengan Metode Decision Tree Tomy Nanda Putra; Darmansah Darmansah; M Yoka Fathoni
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5495

Abstract

Corn is one type of plant that is widely cultivated in Indonesia because it has a high enough price, especially the need for this plant. This plant is used as a substitute for carbohydrates and protein after rice, is also used as feed ingredients for livestock. This lack of corn production is caused by several factors such as the age of trees, fertilizers and pests. The impact that occurred was a decrease in corn production at a time when there were many needs of the company and the community would corn. The corn data processed in this study were sourced from the Department of Horticultural Food of Pasaman Regency and the owners of corn crops. Furthermore, the data is processed using the Rapid Miner software. Aimed to find out the prediction of the quality of the corn planting experiment. The method used in solving this problem is C4.5 Algorithm. From the testing of this method, it was found that two predictions in corn cultivation were ‘good and‘ not good. This analysis makes it easier for farmers to plant corn, especially in the selection of seeds to fertilize them so as to produce good fruit for consumption
Pemilahan Sampah Menggunakan Model Klasifikasi Support Vector Machine Gabungan dengan Convolutional Neural Network Miftahuddin Fahmi; Anton Yudhana; Sunardi Sunardi
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5468

Abstract

Waste sorting is a vital process in waste management. The problem with the waste sorting process is that humans feel uncomfortable with the smell of garbage for too long. The problem can be solved by creating a machine learning system to identify the waste type. The purpose of this research is to solve waste management problems using machine learning using the most accurate classification model. The types of wastein this research are limited to only two types: organic and inorganic. Data was collected and revised from the Kaggle dataset. Data were imported into the system using Python. Data was trained and used for classifying the waste based on the image source. Waste images will be determined in their category using the Support Vector Machine model with feature extraction using the Convolution layer. The system successfully performs waste classification using the Support Vector Machine model combined with the Convolutional Neural Network with an accuracy of 96,16% and a loss of 7,25% on the overall category

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