cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Jordy Lasmana Putra
Contact Email
jordy.jlp@nusamandiri.ac.id
Phone
+6221-231170
Journal Mail Official
jurnal.coscience@bsi.ac.id
Editorial Address
Jl. Kramat Raya No.98, RT.2/RW.9, Kwitang, Kec. Senen, Kota Jakarta Pusat, Daerah Khusus Ibukota Jakarta 10450 (Gedung Rektorat Universitas Bina Sarana Informatika)
Location
Kota adm. jakarta barat,
Dki jakarta
INDONESIA
Computer Science (CO-SCIENCE)
Published by Universitas Bina Sarana Informatika
ISSN : -     EISSN : 27749711     DOI : https://doi.org/10.31294/coscience
Core Subject : Science,
Computer Science & IT
Computer Science (CO-SCIENCE) pertama kali publikasi tahun 2021 dengan nomor ISSN (Elektonik): 2774-9711 yang diterbitkan oleh Lembaga Ilmu Pengetahuan Indonesia (LIPI). Computer Science (CO-SCIENCE) adalah jurnal yang diterbitkan oleh Program Studi Ilmu Komputer Universitas Bina Sarana Informatika. Computer Science (CO-SCIENCE) terbit 2 kali setahun (Januari dan Juli) dalam bentuk elektronik. Redaksi menerima naskah berupa artikel ilmiah dan penelitian pada bidang: Networking, Aplication Mobile, Software Engineering, Web Programming, Mobile Computing, Cloud Computing, Data Mining, dan Aplikasi Sains.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 131 Documents
 5   6   7   8   9 
Analisis Sentimen Pemanfaatan Artificial Intelligence di Dunia Pendidikan Menggunakan SVM Berbasis Particle Swarm Optimization Saepudin, Atang; Aryanti, Riska; Fitriani, Eka; Royadi, Royadi; Ardiansyah, Dian
Computer Science (CO-SCIENCE) Vol. 4 No. 1 (2024): Januari 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i1.2921

Abstract

The utilization of Artificial Intelligence (AI) in the field of education in Indonesia has witnessed significant developments in recent years. The advancements in AI technology have opened up new opportunities to enhance the quality of education, and address various challenges faced by the Indonesian education system. This has naturally sparked diverse opinions and comments from the public, particularly on the social media platform X/Twitter. This research focuses on sentiment analysis of reviews expressed on the X/Twitter social media platform. The primary goal of this study is to develop an effective sentiment analysis method by leveraging the Support Vector Machine (SVM) algorithm optimized with Particle Swarm Optimization (PSO) for feature selection. In this research, user reviews from X/Twitter were collected and analyzed to identify positive or negative sentiments within the context of each comment. The SVM algorithm was used to classify sentiments based on similarity to comments with known sentiments. Feature Selection PSO was employed to optimize the parameters within SVM to enhance sentiment analysis accuracy. The results of sentiment analysis on comments or tweets on the X/Twitter social media platform using both SVM and PSO-based SVM algorithms indicated that the PSO-based SVM algorithm achieved a higher accuracy. The SVM algorithm with feature selection PSO produced accuracy 89.50%, precision 86.98%, recall 93.00%, and AUC 0.964. Meanwhile, the SVM algorithm had accuracy 87.50%, precision 85.46%, recall 90.50%, and AUC 0.956. This demonstrates that the use of feature selection PSO in the SVM algorithm is capable of improving the accuracy of the results.
Analisis Sentimen Ulasan Aplikasi Identitas Kependudukan Digital Pada Play Store Menggunakan Metode Naïve Bayes Komarudin, Ahmad; Hilda, Atiqah Meutia
Computer Science (CO-SCIENCE) Vol. 4 No. 1 (2024): Januari 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i1.2955

Abstract

One of the government's new innovations is the implementation of Digital Population Identity which is to bring transformation in the way people prove their own identity, making it easier, safer, and more efficient in various contexts of everyday life. It is known that on October 20, 2023 the Digital Population Identity application only received 3.3 stars from 32.7 thousand reviews, this shows that the application still has not reached the maximum level of satisfaction in serving the needs of the community. Therefore, sentiment analysis of user reviews is needed to gain deeper insight into how people respond to this application, so this research aims to obtain sentiment data from the Digital Population Identity application on the Play Store in the form of reviews from users and the results of the analysis can be used as evaluation material for application developers. Data collection is done using the scrapping method through Google Colab as many as 1000 reviews whose results will be labeled negative and positive. Then the data will be cleaned and simplified through preprocessing, and will be classified with the Naïve Bayes algorithm with 90% test data and 10% training data for each iteration. The classification results are then calculated performance with confusion matrix and obtained an accuracy value of 82.23%, precission of 76.08%, and recall of 94.02%.
Optimasi Algoritma Naïve Bayes Berbasis Particle Swarm Optimization Untuk Klasifikasi Status Stunting Pahlevi, Omar; Amrin, Amrin; Handrianto, Yopi
Computer Science (CO-SCIENCE) Vol. 4 No. 1 (2024): Januari 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i1.2963

Abstract

Every parent wants their children to grow up healthy. Eating a healthy diet can minimize stunting. Long-term nutritional deficiencies can lead to stunting, a chronic nutritional problem that impairs physical growth and development, including low body weight and height. Preventive action against stunting is a fundamental activity that must be done immediately in the form of counseling and taking further medical action.  In data mining there are several methods for extracting information including classification. There are various methods for extracting information using data mining, such as classification. In this research, researchers will apply Naïve Bayes with Particle Swarm Optimization (PSO) for the classification of stunting status in order to determine whether a child has a case of stunting or not based on gender, age, birth weight, body weight, body length, and breastfeeding. In the final results of the research, it is known that the accuracy of the truth obtained through the performance of the Naïve Bayes algorithm model is 80.69% and a score of 0.801 resulting from Area Under the Curva (AUC). Then based on the calculation results with the Naïve Bayes algorithm model with Particle Swarm Optimization, it can be obtained a truth accuracy rate of 83.06% with an Area Under the Curve (AUC) value of 0.801. Based on the final value obtained, the pattern of applying Particle Swarm Optimization to the Naïve Bayes algorithm can improve the performance of the classification method used in this research activity.
Pengembangan Aplikasi Manajemen Litabmas Pendanaan Mandiri dengan Pendekatan Hierarchical Model-View-Controller (HMVC) Wahyuni, Eka Dyar; Afandi, Mohamad Irwan; Putra, Agung Brastama
Computer Science (CO-SCIENCE) Vol. 4 No. 1 (2024): Januari 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i1.2965

Abstract

In higher education, overseeing the execution of litabmas activities is a challenging and error-prone task, particularly when done manually through the physical submission of documents. Information is not always readily available, archives are frequently lost or even damaged, and tracing Litabmas external responsibility (articles, journals, patents/IPRs, etc.) from earlier periods can be challenging. This article describes how the Waterfall approach was used to construct the Litabmas management application. The Hierarchical Model-View-Controller (HMVC) technique is used to build the code architecture during the coding process. By reducing intermodular dependencies, this method makes it easier for a team to design an application. This application was developed to facilitate the management of the full cycle of Litabmas activity implementation for users, from generating Litabmas master data to initiating the Litabmas period, submitting proposals, conducting evaluations, and reporting Litabmas outputs and outcomes. The application has been tested using the black box method, and as a result, 23 use cases from six levels of users —administrator, head of institutional research, researcher, reviewer, auditor, and general visitor access rights —have been generated. All of these usecase have 100% of the expected output produced by the application.
Metode Vulnerability Assesment Dalam Pengujian Kinerja Sistem Keamanan Website Points of Sales Wahyudin, Wahyudin; Kuswara, Heri; Resti, Resti; Dalis, Sopiyan
Computer Science (CO-SCIENCE) Vol. 4 No. 1 (2024): Januari 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i1.2978

Abstract

The development of electronic commerce through point of sales based websites is closely related to the growth rate of the internet, because electronic commerce runs through networks and Internet connections. However, the more point of sale based websites that are built, the greater the possibility of cyber attacks that could harm the website. Therefore, website security is very important to pay attention to. One method that can be used to maintain website security is to carry out a Vulnerability Assessment. Vulnerability Assessment is a process of searching for security gaps in an information system or computer network with the aim of identifying potential security vulnerabilities and taking preventative steps before an attack occurs. The vulnerability assessment technique used is using a weakness scanner application to identify security gaps in systems and applications such as Nikto, Nmap, Zenmap and Owasp ZAP. Based on testing with the Owasp ZAP tool, the results of scanning carried out on the sakupos.com website, which is a points of sales based website, show that there is a vulnerability on the website. The test results show the Level of Vulnerability (Risk Assessment) as well as recommended solutions that can be used to prevent it. There were 10 vulnerabilities detected, 7 vulnerabilities were found with a Medium risk level, 2 vulnerabilities with a Low risk level, and 1 other vulnerabilities at the Informational risk level.
Rekomendasi Pemilihan Jenis Tanaman Menggunakan Algoritma Random Forest dan XGBoost Regressor Rahman, Abdul; Udjulawa, Daniel; Mulyati, Mulyati
Computer Science (CO-SCIENCE) Vol. 4 No. 2 (2024): Juli 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i2.2987

Abstract

Recommendations for plants that suit a particular planting location's environmental conditions and soil nutrients can lead to optimal harvest outcomes. Machine learning applications in agriculture have been widely explored, particularly in enhancing crop yields. In this study, two machine learning algorithms, Random Forest and XGBoost Regressor, were implemented to recommend plants based on environmental conditions and soil nutrient levels. The implementation of both algorithms was compared in terms of accuracy using three accuracy metrics: Mean Absolute Error (MAE), Mean Square Error (MSE), and R2. The results indicated that both algorithms exhibited comparable accuracy levels. The Random Forest algorithm demonstrated superior accuracy in terms of MAE and MSE, with values of 36.73681574 and 1.848396760, respectively. Meanwhile, the XGBoost Regressor algorithm displayed good accuracy, mainly when measured using the R2 accuracy metric, achieving a high accuracy level of 0.98542963509705.. Keywords : Crop Recommendation, Machine Learning, Random Forest, XGBoost
Penerapan: Penerapan Metode SMOTE Untuk Mengatasi Imbalanced Data Pada Klasifikasi Ujaran Kebencian Ridwan, Ridwan; Heni Hermaliani, Eni; Ernawati, Muji
Computer Science (CO-SCIENCE) Vol. 4 No. 1 (2024): Januari 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i1.2990

Abstract

Hate speech is the spread of hatred towards individuals or groups on the basis of ethnicity, religion, race, and other characteristics that can lead to discrimination, violence, and social conflict. Unbalanced data can cause negative results in classification results. The Synthetic Minority Oversampling Technique (SMOTE) method is used to deal with unbalanced data. Feature extraction uses Bag of Words and TD-IDF, then the training data are oversampled using the SMOTE, SVM-SMOTE, Kmeans-SMOTE, and Borderline-SMOTE methods. This classification uses the Random Forest, Support Vector Machine, Logistic Regression, and Naive Bayes algorithms using Twitter data. The research results show that the application of the Borderline-SMOTE method to handle imbalanced data produces better performance than other SMOTE methods based on accuracy, recall,precision and F1-Score values with respective values of 84.09%, 85.25%, 84,55% and 81.16%. The Random Forest algorithm produces higher performance values than other algorithms.
Pengembangan Sistem Self Ordering Mimi Cakes and Cookies Berbasis Web Dengan Metode Rapid Application Development (RAD) Herdiansah, Arief; Ramadhani, Ryan Zulham; Mahpud, Mahpud; Frithadila, Najmah
Computer Science (CO-SCIENCE) Vol. 4 No. 2 (2024): Juli 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i2.2996

Abstract

The food order recording system can cause errors in recording orders, lose data and require a lot of time in the process of recording and reporting orders. Product marketing carried out using social media requires sellers to upload product photos one by one so that buyers can see them. This is more troublesome if the ordering process is carried out conventionally. This research is applied research on the development of a food ordering information system with a self-ordering concept which can be an alternative solution to replace conventional food ordering systems. This research was conducted at Mimi Cakes & Cookies MSME, which currently still uses conventional methods in the process of recording pre-orders for the food it sells. This research uses the Rapid Application Development (RAD) development method. The system was developed using the PHP Framework Codelgniter programming language with the VS Code text editor. The system development results were tested using the black box testing method. The results of this research produce a pre-order system with a Web-based self-ordering concept that can provide a solution to the problem of recording and reporting orders for Mimi Cakes & Cookies MSMEs. The system developed has also caused the number of orders for Mimi Cakes and Cookies to increase by 20%
Klasifikasi Kualitas Buah Apel Dengan Algoritma K-Nearest Neighbor (K-NN) Menggunakan Bahasa Pemrograman Python Astuti, Puji
Computer Science (CO-SCIENCE) Vol. 4 No. 2 (2024): Juli 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i2.3328

Abstract

Fruit is an important intake for the human body, apples are included in the fruit favored by the people of Indonesia. For this reason, it is necessary to provide apples of good quality, so that they can benefit the body. By using the k-NN method that is considered able to train data quickly and effectively for training data and testing data in large quantities. This study began from the collection of datasets obtained from https://www.kaggle.com/, then perform a preprocessing process followed by separating the training data and testing data with a composition of 25% testing data and 75% training data. Then the k-NN method is applied to this study to be classified based on several existing criteria, so as to obtain the results of performance evaluation K-NN with the value of accuracy that has been calculated with python programming. In implementing datamining using Python programming language by utilizing the library that has been provided as a process to facilitate the implementation of machine learning. From The Matrix confution test, there are 441 data predicted with true data, and 440 data predicted incorrectly. As for the 54 and 65 data predicted to be less precise than 1000 testing data. So that the accuracy value obtained by the k-NN method is equal to 0.88 or 88%. It is seen that the k-NN method can work well, quickly and efficiently in training large amounts of data.
Metode Rapid Application Development Dalam Pengembangan Sistem Informasi Perpustakaan Berbasis Web Hidayatulloh, Syarif; Patyani, Enda
Computer Science (CO-SCIENCE) Vol. 4 No. 2 (2024): Juli 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i2.3332

Abstract

The information system is crucial for business processes in all fields, including education. A library at SMK Negeri 1 Jawai, Sambas Regency, currently manages its data, borrowing, and book return processes manually, using physical records and writing tools. With such management, the library faces various challenges. Common issues include students having difficulty tracking borrowed books, problems with lost returns, and discrepancies in the number of loans and returns. Therefore, this research aims to improve business processes at SMK Negeri 1 Jawai's library by developing a web-based information system tailored to its needs. Rapid Application Development was chosen as the development method for this library information system due to its ability to quickly produce a high-quality system. The method involves stages such as requirement planning, system design, and implementation. RAD is capable of creating a website that provides objective information. The resulting Information System from this research was tested using Blackbox Testing, which confirmed that all features and processing flows functioned correctly.

Page 7 of 14 | Total Record : 131

 5   6   7   8   9 

Filter by Year

2021 2026


Reset
Filter By Issues
All Issue Vol. 6 No. 1 (2026): January 2026 Vol. 5 No. 2 (2025): Juli 2025 Vol. 5 No. 1 (2025): Januari 2025 Vol. 4 No. 2 (2024): Juli 2024 Vol. 4 No. 1 (2024): Januari 2024 Vol. 3 No. 2 (2023): Juli 2023 Vol. 3 No. 1 (2023): Januari 2023 Vol. 2 No. 2 (2022): Juli 2022 Vol. 2 No. 1 (2022): Januari 2022 Vol. 1 No. 2 (2021): Juli 2021 Vol. 1 No. 1 (2021): Januari 2021 More Issue