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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 121 Documents
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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.
Rancang Bangun SPK Kualitas Air Sungai Metode Fuzzy Tsukamoto (Studi Kasus: 4 Kecamatan Karawang) Putra, Fery Anuar Ramadhan; Hendriadi, Ade Andri; Ridwan, Taufik
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.v4i2.3358

Abstract

Research reveals that the Citarum River in West Java is seriously polluted, causing problems for humans and the environment, so monitoring of river water quality is necessary. This research proposes the development of a web-based Decision Support System with the SDLC Waterfall model, using the Fuzzy Tsukamoto method based on seven water parameters (EC, TDS, Salinity, pH, ORP, SG, Temperature) to simplify the determination of water quality. This research uses the Research and Development methodology with the SDLC Waterfall model software development approach, which includes the stages of analyzing data requirements and fuzzy logic systems, designing system architecture and features, implementing technology and process results, functional manual testing and model accuracy, and maintenance in the form of documentation and storage. The results showed that the developed DSS can classify water quality based on 7 parameters with a value scale divided into five categories, namely Good, Poor, Medium, Good, and Very Good. Testing was conducted using data from 8 river points in 4 sub-districts in Karawang Regency on April 1, 2024. The accuracy of the DSS model reached 80%. The development of this DSS is expected to provide an initial overview of water quality in a location without requiring an in-depth technical understanding of the water parameters used. This data can also be an initial indicator to determine whether further action or further investigation is required. Suggestions for future research are to integrate the system with IoT to improve its performance and benefits.
Penerapan Algoritma C4.5 Untuk Menentukan Kepuasan Pengguna Aplikasi E-Open Study Kasus : Kelurahan Jati Makmur Komalasari, Yuli; Puspitasari, Nabila Rahmah; Chalimatusadiah, Chalimatusadiah
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.v4i2.3387

Abstract

To support government administration activities in an area, an application called E-Open is needed. The application was created to support the Bekasi City government, one of which is Jatimakmur Village, which is a government agency in Pondok Gede, Bekasi City. The application has been implemented, evaluation of the administration process using the E Open application is required. The aim is to measure, help, test and analyze the level of satisfaction using the E-Open application for Jatimakmur Village residents. Using data mining in data processing to determine the accuracy of each process, accurately converting information so that information is quickly understood and includes collecting, using historical data, patterns or relationships in large data sets. The research population was taken as 150 samples. Quantitative research using the C4.5 algorithm method is used, because it can make predictions by providing an ideal level of accuracy for prediction. Test this research with RapidMiner version 10.1. The results of the processing have a significant effect in determining the classification of the level of citizen satisfaction with the E-Open application. With an accuracy level of 91.33%, while in manual calculations the accuracy was 90.67% for the Satisfied percentage, or also known as the Very Good category. Keywords : Accuracy, C4.5 Algorithm,E-Open
Aplikasi Pencatatan Kalori Harian Berbasis Android Dengan Arsitektur MVVM Ulhaq, Alfi Zia; Adilukito, Abilawa Zulfiqar; Neru, Sultan Muhamad Pascal Gadja; Agisfio, Muhammad Daffa
Computer Science (CO-SCIENCE) Vol. 5 No. 1 (2025): Januari 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i1.3443

Abstract

An imbalance between calorie intake and expenditure is considered the main cause of obesity or being overweight. So by controlling your calorie intake in a balanced manner and according to your needs you will be able to prevent obesity. The daily calorie recording application can help someone record, control and obtain information on their calorie intake. This article discusses the development of this type of application, named Nutrizen, using the waterfall method, during the development process by the CH2-PS076 team in the Bangkit 2023 batch 2 program. The application created is an Android-based application created with the Kotlin programming language and MVVM architecture as a design pattern that is easy to learn and makes the code easy to understand and manage. Testing this application uses the usability test method with the System Usability Scale tool to determine the level of user acceptance of usability. The results obtained include a marginal level of usability acceptance, so improvements are needed so that this application can be more accepted and relied on by the wider community.
Penerapan Model Design Thinking Pada Perancangan Aplikasi Informasi Desa Wisata Kabupaten Bantul Hidayat, Wahyutama Fitri; Malau, Yesni; Purnama, Rachmat Adi; Setiadi, Ahmad
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.v4i2.3459

Abstract

Tourism actors in the current technological era have implemented information systems. With the rapid growth of tourist villages in Bantul Regency, there is a need for promotion and digital information delivery media. However, in developing digital media it is also necessary to pay attention to aspects of the users who are the target market. The design of the application called sidewi mobile (mobile tourist village information system) is based on user experience and needs, using the Design Thinking methodology which has five stages as follows: Empathize, Define, Ideate, Prototype, and Test. The design of the Sidewi mobile application was created using FIGMA software. This research has direct benefits, namely that it can be used as a benchmark for design needs before the development process. The results of the design are then tested using the usability testing method. Using a user friendly design approach and conducting testing using usability testing with the results of five users being able to complete the testing proves that when it was created using user experience there were no significant difficulties when used and it covered all needs.
Klasifikasi Perilaku Pemain Game Online Menggunakan Naïve Bayes Berbasis Particle Swarm Optimization Heristian, Sujiliani; Anwar, Rian Septian; Kautsar, Hanggoro Aji Al; Sujiliani, Sujiliani Heristian; A
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.v4i2.4433

Abstract

Much research has been conducted to understand player behavior as a result of the rapid growth of online gaming. In this research, we use the Naive Bayes method optimized using Particle Swarm Optimization (PSO) to analyze the behavior classification of online game players. The classification accuracy value of the baseline method is 75.09% and the Area Under the Curve (AUC) value is 0.798. We use PSO-based optimization on Naïve Bayes to improve model performance. The results showed that the combination of Naïve Bayes and PSO increased classification accuracy to 95.28% with an AUC value of 0.990. This is a major advance that shows that combining the PSO algorithm with Naive Bayes can enable better classification of online game player behavior. These findings will make a significant contribution to the process of making plans that can improve the gaming experience.

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