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CSRID
Published by Universitas Potensi Utama
ISSN : 20851367     EISSN : 2460870X     DOI : https://doi.org/10.22303/csrid
Core Subject : Science,
Computer Science & IT
CSRID (Computer Science Research and Its Development Journal) is a scientific journal published by LPPM Universitas Potensi Utama in collaboration with professional computer science associations, Indonesian Computer Electronics and Instrumentation Support Society (IndoCEISS) and CORIS (Cooperation Research Inter University).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 45 Documents
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Deteksi Spam Bot Pada Komentar Youtube: Tinjauan Literatur Sistematis Syafrial Pane; Dzul Jalali Wal Ikram
Computer Science Research and Its Development Journal Vol. 15 No. 2: June 2023
Publisher : LPPM Universitas Potensi Utama

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Abstract

YouTube is a very popular social media platform and is used by millions of people around the world. However, the presence of spam in comments can disrupt the user experience and affect the overall quality of the platform. Therefore, in this article, we conducted a Systematic Literature Review (SLR) to evaluate methods for detecting spam in comments on YouTube. In this SLR, we search for related research published between 2018 and 2023 in trusted databases such as Science Direct, IEEE Xplore, and Springer using Publish or Perish software. After making the selection, 17 of the 80 selected articles met our research criteria. The SLR results show that the Email dataset is the most widely used in spam detection research, and the most frequently used approach is supervised learning. In addition, most of the research focuses more on selecting features to improve accuracy in spam detection. The findings from this SLR can provide important insights for researchers who wish to conduct further research on spam detection on comments on YouTube.
Malware Detection pada Static Analysis Windows Portable Executable (PE) Menggunakan Support Vector Machine dan Decision Tree Mohammad Mirza Qusyairi; Rio Guntur Utomo; Rahmat Yasirandi
Computer Science Research and Its Development Journal Vol. 15 No. 2: June 2023
Publisher : LPPM Universitas Potensi Utama

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Abstract

Malware has become a major issue for computer system security today. Due to its ability to spread rapidly and negatively impact system performance, malware detection becomes crucial. One of the methods for malware detection is performing classification using Machine Learning, which learns the variable values of an application without executing it. In this study, the author evaluates the method of malware detection in the static analysis of Windows Portable Executable (PE) using Support Vector Machine (SVM) and Decision Tree. The author uses a dataset of PE files related to malware and safe applications from malware Using SVM and Decision Tree algorithms to classify the PE files as malware or not, determining the best machine learning algorithm for malware detection in PE files.
Klasifikasi Penyakit Tanaman Bawang Merah Menggunakan Convolutional Neural Network dan K-Nearest Neighbor Fifi Febrianti Usman; Purnawansyah; Herdianti Darwis; Erick Irawadi Alwi
Computer Science Research and Its Development Journal Vol. 15 No. 3 (2023): October 2023
Publisher : LPPM Universitas Potensi Utama

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Abstract

The potential for yield loss due to shallot plant disease is the main trigger that can reduce agricultural productivity. Pest and disease attacks can be minimized and overcome quickly if farmers are able to classify the types of diseases that attack plants based on the characteristics and symptoms that appear. This study aims to classify shallot plant diseases, namely purple spotting and moles with a total of 320 datasets using Hue Saturation Value color feature extraction using the K-Nearest Neighbor (Euclidean Distance) and Convolutional Neural Network methods. Based on the results of the study, the accuracy, f1-score was 94% and precision, recal was 97%, 91% in purple spot disease while in moler disease it was 94% in accuracy, precision, recall, and f1-score in HSV and KNN classifications. Classification using HSV and CNN yielded high scores in accuracy, precision, recall, and f1-score with a value of 100% in both purple spot and moler shallot leaf diseases. Classification using deep learning CNN obtains very good accuracy, precision, recall and f1-score, namely 100%. With this description, the classification of shallot plant diseases using HSV and CNN, and CNN deep learning are stated to be able to classify shallot plant diseases, namely purple spotting and moles effectively and accurately.
Klasifikasi Citra Digital Daun Herbal Menggunakan Support Vector Machine dan Convolutional Neural Network dengan Fitur Fourier Descriptor Aulia Rezky Rahmadani Darmawati; Purnawansyah; Herdianti Darwis; Lutfi Budi Ilmawan
Computer Science Research and Its Development Journal Vol. 16 No. 1 (2024): February 2024
Publisher : LPPM Universitas Potensi Utama

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Abstract

Leaves are one component of plants that contain natural properties and are useful for maintaining human health. However, several types of leaves have the same characteristics and characteristics that make it difficult to distinguish. This study aims to classify types of herbal leaves using the SVM method with four kernels (Linear, RBF, Polynomial, Sigmoid) and CNN with Fourier descriptor (FD) feature extraction. The processed dataset is katuk leaf images, and Moringa leaf images of 480 images which are divided into 80% training data and 20% testing data using two scenarios, namely dark and light. From the testing process, it was found that FD + CNN in the light and dark scenarios obtained an accuracy value of 98%. Thus, the FD + SVM algorithm with Linear, RBF, polynomial kernels can be recommended in classifying herbal leaf images to have the best accuracy value of 100%.
Pengiriman Data Realtime PLC ke ERP Odoo dengan Dilengkapi Analisa OEE Fiqrotin Nur Asita; Muhammad Khoirul Hasin; Zindhu Maulana Ahmad Putra; Ryan Yudha Adhitya; Imam Sutrisno
Computer Science Research and Its Development Journal Vol. 15 No. 3 (2023): October 2023
Publisher : LPPM Universitas Potensi Utama

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Abstract

With the integration of IT (Information Technology) and OT (Operational Technology), the data generated by the OT system can be easily processed and analyzed using the IT system, thereby synergistically increasing the efficiency and productivity of the company. In addition, to maximize machine performance, the Overall Equipment Effectiveness (OEE) method can be applied to monitor production results. Therefore, the authors designed an integration system based on a software connector to connect the Programmable Logic Controller (PLC) with Enterprise Resources Planning (ERP) Odoo. This system facilitates three communication protocols, namely Modbus TCP/IP, Ethernet/IP, and Profinet. As proof of the success of the connector software, a bottle-sorting plant controlled by PLC was made. The data from the plant is acquired by the connector software and sent in real-time to ERP Odoo. In ERP Odoo, an analysis is carried out through manufacturing modules that are custom-made by applying the OEE method as a step in monitoring the production process. Through research conducted, this integration system has been successfully designed and implemented in a bottle-sorting plant. The results of a comparison between manual OEE calculations and OEE calculations in ERP Odoo found an error of 0.161%. Thus, the IT and OT integration system created has been successful and has made the production process more efficient and has provided significant benefits to the company
Identifikasi Wanda Janaka berbasis Deep Learning dengan Metode Convolutional Neural Network Benedictus Herry Suharto; Mawar Hardiyanti
Computer Science Research and Its Development Journal Vol. 15 No. 3 (2023): October 2023
Publisher : LPPM Universitas Potensi Utama

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Abstract

UNESCO named Wayang Kulit a "Masterpiece of Oral Intangible Heritage of Humanity" for Indonesian traditional arts. Wayang Kulit's characters show a symbol of personality, identity, image, appearance, and quality of the characters. In certain situations in the Wayang Kulit performance scene, this character has a different appearance image called Wanda. Wanda Wayang Kulit is less well-known among the younger generation. Therefore, technological creativity is needed so the younger generation can distinguish Wanda Wayang Kulit as part of the Indonesian nation's artistic and cultural literacy. One of the potential technologies that can be used is a smartphone application based on Deep Learning CNN to detect and recognize Wanda Wayang Kulit. In contrast to previous studies that have successfully used Deep Learning CNN to recognize Wayang Kulit characters, this research aims to create a CNN Deep Learning model to classify Wanda Wayang Kulit Janaka. The model uses five Wanda Wayang Kulit Janaka images from an Android smartphone camera. The results achieved from this study are the CNN deep learning model, which can classify the image of Wanda Wayang Kulit Janaka using an Android smartphone camera with an accuracy of 84%.
Rancangan Sistem Dalam Pengelolaan Data Penduduk Pada Kantor Desa Boloak Menggunakan Algoritma Sequential Searching Shofia Qitala; Saruni Dwiasnati
Computer Science Research and Its Development Journal Vol. 15 No. 3 (2023): October 2023
Publisher : LPPM Universitas Potensi Utama

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Abstract

Fast and accurate information will support the smooth operation or management of the village government, in managing community data and information. Currently, various administrations and data management at the Boloak village office are still being carried out manually, and have not been computerized so that the performance in managing population data is still not optimal. For this reason, in utilizing information technology for managing population data and assistance data, an information system design is needed for the scope of the alternating village office, so that it can support smooth operations and improve performance for village government employees. With the aim of this research being to design a population data management system and assistance data by implementing a web-based Sequential Searching Algorithm using php and using the MySql database, this research will be designed using the waterfall method, making it easier for employees to search population data, identify population data as well as in managing social assistance data more efficiently and improving the performance of village officials in managing data.
Retweet Prediction Using ANN Method and Artificial Bee Colony Jondri Jondri; Kamaludin Hanif Farisi; Kemas Muslim Lhaksmana
Computer Science Research and Its Development Journal Vol. 15 No. 2: June 2023
Publisher : LPPM Universitas Potensi Utama

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Abstract

In the ongoing modern era, the rapid dissemination of information takes place, utilizing various channels for data exchange. One such platform is the social media platform Twitter, renowned for its swift and extensive information propagation. A pivotal factor contributing to information distribution on Twitter is the retweet feature, whereby users can redistribute content to their audience. A study has been conducted to forecast this retweet activity by employing the Artificial Neural Network classification method in conjunction with the Artificial Bee Colony optimization approach. This study leverages diverse features, encompassing content-based feature, user-based feature, and time-based feature. The evaluation results from this study reveal that the proposed method achieves an accuracy value of around 83% with the highest accuracy value reaching 84%. These findings indicate that the fusion of the Artificial Neural Network classification method executed with optimization using the Artificial Bee Colony algorithm yields dependable and consistent performance in predicting retweet activities.
Perbandingan Klasifikasi Jenis Sampah Menggunakan Convolutional Neural Network Dengan Arsitektur ResNet18 dan ResNet50 Christin Evasari Nainggolan; Muhammad Nasir; Fatoni; Devi Udariansyah
Computer Science Research and Its Development Journal Vol. 16 No. 1 (2024): February 2024
Publisher : LPPM Universitas Potensi Utama

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Abstract

Complex problems occur in dealing with waste, both in developing and developed countries, such as Indonesia. According to data from the Ministry of Environment and Forestry, in 2022 the total waste pile will reach 34,439,338.12 tons per year. In this research, machine learning will be used by comparing CNN architecture, ResNet18 with ResNet50, for the classification of waste types. This research uses 2527 images of garbage image data consisting of 6 classes, namely cardboard, glass, metal, paper, plastic and trash. Convolutional Neural Network is a component of the Deep Neural Network method that has the ability to identify objects in images with a high level of complexity. From the ResNet18 model test in this study, the accuracy was 98.69% and the test results on ResNet50 resulted in an accuracy of 99.41%. The precision and recall results of both models reflect excellent performance and accuracy of around 99%. So it can be concluded that both CNN models, ResNet18 and ResNet50, have excellent performance in classifying garbage images.
PENERAPAN DATA MINING CLASSIFICATION UNTUK PENENTUAN JENIS BANTUAN SOSIAL MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER Shinta Siti Sundari; Evi Dewi Sri Mulyani; Cepy Rahmat Hidayat; Dede Syahrul Anwar; Teuku Mufizar
Computer Science Research and Its Development Journal Vol. 16 No. 1 (2024): February 2024
Publisher : LPPM Universitas Potensi Utama

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Abstract

The social assistance program is a program held by the government as an effort to overcome poverty. Mekarjaya Village is one of the villages running the program. In carrying out this social assistance process, there are obstacles in terms of collecting data on its citizens because there are often discrepancies in the recipient data collected by the community with the type of assistance. To make it easier to determine the appropriate type of social assistance, an analysis of the data on the recipients of the social assistance is needed. The data analysis method in this research uses Data Mining including Data Selection and Preprocessing, while the classification method uses the Naïve Bayes Classifier. Testing using the Confusion Matrix produces an accuracy of 94.53% with a comparison of training data and testing 80:20. With this model, it is hoped that village officials can determine the type of social assistance that is appropriate for the community.

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