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Journal of Computer System and Informatics (JoSYC)
Published by Forum Kerjasama Pendidikan TInggi
ISSN : 27147150     EISSN : 27148912     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering
Journal of Computer System and Informatics (JoSYC) covers the whole spectrum of Artificial Inteligent, Computer System, Informatics Technique which includes, but is not limited to: Soft Computing, Distributed Intelligent Systems, Database Management and Information Retrieval, Evolutionary computation and DNA/cellular/molecular computing, Fault detection, Green and Renewable Energy Systems, Human Interface, Human-Computer Interaction, Human Information Processing Hybrid and Distributed Algorithms, High Performance Computing, Information storage, Security, integrity, privacy and trust, Image and Speech Signal Processing, Knowledge Based Systems, Knowledge Networks, Multimedia and Applications, Networked Control Systems, Natural Language Processing Pattern Classification, Speech recognition and synthesis, Robotic Intelligence, Robustness Analysis, Social Intelligence, Ubiquitous, Grid and high performance computing, Virtual Reality in Engineering Applications Web and mobile Intelligence, Big Data
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 443 Documents
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Sistem Pendukung Keputusan Menentukan Lokasi Pembangunan Jaringan Internet Menggunakan Metode Profile Matching Agustina, Safira; Rohayani, Hetty; Marthiawati H, Noneng; Azzamy, Muhammad Nabil
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4889

Abstract

Decision Support Systems are interactive information systems that provide information, modeling, and data manipulation. This system is used to assist decision-making in semi-structured s and unstructured situations, where no one knows for sure how the decision should be made. The problem in this study is, determining the location of Internet network installation by BeeBeeNet, carried out based on the company's decision to determine a new location to build an Internet network in a new area, namely by surveying the area, as well as looking at the population density in an area and the current interest of the community. The purpose of making a Decision Support System for determining the location of building an internet network is to determine the right location. The method used in this research is the Profile Matching method, which is a Decision Support system in the location criteria determined by PT Batanghari Vision so that the difference in competence (GAP) can be known. The results obtained are the results of ranking using the profile matching method, using several candidates to get results in determining the new location for building an internet network, from the calculations carried out the highest value is 2.9555, this value is the highest value of the existing location criteria. the results obtained can be the right result for the company to choose the location of the internet network construction in the new location.
Decision Support System for Selection of Internet Services Providers using the ROC and WASPAS Approach Soares, Teotino Gomes; Sinlae, Alfry Aristo Jansen; Herdiansah, Arief; Arisantoso, Arisantoso
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4892

Abstract

Along with the growth of the internet service provider industry, selecting an Internet Service Provider (ISP) has become an important decision to ensure optimal internet access. However, with so many ISP options available, consumers often face difficulties in choosing the service that best suits their needs. The aim of this research is to produce a decision support system that can help users choose the ISP that best suits their needs and preferences using the ROC (Rank Order Centroid) approach as a weighting technique and the WASPAS (Weighted Aggregated Sum Product Assessment) approach to determine the best alternative. The ROC approach is used to obtain criteria weights based on the ranking order of the importance of the criteria. On the other hand, the WASPAS method is used to determine the best alternative through weighted addition and multiplication, producing a final value that reflects the extent to which each alternative meets the specified criteria. The outcomes of the case study reveal a ranking of alternatives from highest to lowest scores, as follows: First Media (A2) achieving 0.8629, Indihome (A3) at 0.8416, MyRepublic (A5) with 0.7954, Biznet (A1) scoring 0.7844, and Oxygen (A4) at 0.7469. The usability testing yields an average score of 89%, suggesting that the system is apt for utilization, as it aligns with the functionalities users are seeking.
Sistem Pendukung Keputusan Penentuan Lokasi Pabrik Baru Menggunakan Metode ROC dan MAUT Naufal Rifqi, Muhammad; Tamara Aldisa, Rima
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4893

Abstract

Factories are places where goods and services are mass produced. Factories collect and integrate various resources, such as labor, capital, and machines. Factories have an important role in a country's economy, because they produce goods and services that society needs. Apart from that, factories can also create jobs and improve community welfare. However, factories can also have a negative impact on companies and the environment. Companies that produce goods with certain raw materials must consider the availability of raw materials at the factory location. If raw materials are not available at that location, the company must bring them in from other locations, which can increase production costs. Apart from that, companies must also consider the availability of adequate infrastructure, such as roads, electricity, air and telecommunications. If infrastructure is inadequate, companies have to invest to build it, which can also increase production costs. In this study, the ROC method and the MAUT method were used, the ROC method was used to determine the level of importance of the criteria used in selecting the factory location. The MAUT method is used to rank factory locations. The five criteria used in selecting factory locations are: availability of raw materials, availability of infrastructure, labor costs, transportation costs, and environmental impact. A total of 10 factory locations were selected based on predetermined criteria. The final results show that the best factory location is location E with a final score of 0.682, followed by location H and location F with a final score of 0.619 and 0.453 respectively.
Sentiment Classification of Over-Tourism Issues in Responsible Tourism Content using Naïve Bayes Classifier Afrianto Singgalen, Yerik
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4904

Abstract

The research problem addressed in this study is the analysis of public sentiment regarding over-tourism issues. Utilizing the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology and the Naive Bayes Classifier (NBC) algorithm, the study navigates through stages of business understanding, data processing, modeling, evaluation, and deployment. The central focus lies in understanding and classifying public sentiments surrounding the challenges associated with over-tourism. The findings reveal that the NBC algorithm, particularly when augmented with Synthetic Minority Over-sampling Technique (SMOTE), demonstrates superior performance metrics, showcasing an accuracy of 84.82%, precision of 91.69%, recall of 76.75%, f-measure of 83.47%, and AUC of 0.838. The comparison with NBC without SMOTE, which registers an accuracy of 78.16%, precision of 87.61%, recall of 74.56%, f-measure of 80.51%, and AUC of 0.745, underscores the significance of addressing class imbalance for improved predictive performance. Integrating CRISP-DM with the NBC algorithm and SMOTE proves instrumental in advancing sentiment analysis methodologies, providing nuanced insights into public perceptions and attitudes concerning the critical issue of over-tourism.
Sentiment Classification of Climate Change and Tourism Content Using Support Vector Machine Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4908

Abstract

This research aims to classify public sentiment regarding the issue of climate change and tourism. The research problem addressed in this study pertains to the classification of public sentiment concerning climate change within the tourism sector. Specifically, the study aims to explore and classify the public's sentiments regarding the impact of climate change on tourism activities.The methodology employed is CRISP-DM, which encompasses stages of business understanding, data understanding, modeling, evaluation, and deployment. Specifically, the SVM and SMOTE algorithms are utilized in the modeling stage to achieve optimal results. By leveraging this systematic approach and advanced algorithms, the study seeks to comprehensively analyze public sentiment towards climate change within the context of tourism, thus contributing valuable insights to academia and industry practitioners. Applying CRISP-DM methodology coupled with SVM and SMOTE algorithms enhances the rigor and effectiveness of sentiment analysis in addressing the complexities of climate change discourse in the tourism sector. The findings of this research demonstrate that the SVM and SMOTE algorithms yield promising results in sentiment classification, with an accuracy of 86.15% +/- 1.68% (micro average: 86.15%), precision of 85.17% +/- 2.16% (micro average: 85.11%) (positive class: Positive), recall of 87.64% +/- 3.39% (micro average: 87.64%) (positive class: Positive), f_measure of 86.34% +/- 1.79% (micro average: 86.35%) (positive class: Positive), and AUC of 0.923 +/- 0.012 (micro average: 0.923) (positive class: Positive). These metrics indicate the effectiveness and reliability of the SVM and SMOTE algorithms in accurately classifying sentiment toward climate change in the context of tourism. The high accuracy, precision, recall, f_measure, and AUC scores suggest that the models produced by these algorithms are robust and capable of capturing nuanced sentiment patterns, thereby contributing to the advancement of sentiment analysis techniques in climate change research within the tourism domain.
Penerapan Algoritma Artificial Neural Network dan Economic Order Quantity dalam Memprediksi Persediaan Pengendalian BBM Ula, Walid Alma; Afdal, M; Zarnelly, Zarnelly; Permana, Inggih
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4916

Abstract

Motor vehicle production in Indonesia increases every year along with increasing demand for fuel as a raw material. Generally, gas stations carry out the process of ordering fuel from Dempo on an irregular basis, the frequency of orders does not have a certain time, orders depend on sales transactions and the amount of fuel inventory available depends on the fuel in storage. Regarding prediction and control of fuel supplies, the risk at gas stations is that the volume of fuel received is different from that ordered. It is suspected that tank trucks carrying fuel during delivery from the depot to gas stations tend to experience evaporation in the tank (loses), so that the fuel quantity decreases. Requests for fuel filling are only based on monitoring without any special calculations resulting in stock being maintained and not covering consumer demand. This research is to analyze the Artificial Neural Network algorithm in predicting fuel, and determine inventory control using Economic Order Quantity. The research was conducted using data from November 2020 - October 2023. The data was processed using the ANN algorithm using Google Colab, and continued with EOQ using Microsoft Excel. The ANN parameters are 1 hidden layer with 100 units, Adam optimizer, learning rate 0.001, batch size 8 and epoch 200. Pertalite ANN test results are MSE 248852593.81 and MAE 12749.45, while Pertamax Turbo MSE 803842.94 and MAE 672, 74 provides predictions for November and December of 11,1436.82 L and 11,1960.83 L and Pertamax Turbo of 3,782.46 L and 3,660.70 L. Furthermore, in 2023 the fuel EOQ of Pertalite and Pertamax Turbo will be 8,445 L and 5,261 L, Safety Stock 3,516 L and 1,064 L, Maximum Inventory 6,042 L and 5,153 L, Re order point 2,403 L and 108 L, Order frequency 149 times and 6 times with Total Inventory Cost Rp. 178,830,302 and Rp. 7,700,459.
Implementasi Data Mining dengan K-Means Clustering untuk Memprediksi Pengadaan Obat Pane, Putri Pratiwi; Ramadhan Nasution, Yusuf; Furqan, Mhd.
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4920

Abstract

Community Health Center is one of the institutions that provides healthcare services. To ensure the provision of quality healthcare services, the Community Health Center management must be able to effectively manage medicine inventory to avoid the risks of shortages or excess stock. Therefore, the purpose of this research is to observe and perform clustering of medicine demands at Puskesmas Mandala using the K-Means Clustering technique. The data used includes medicine demand data from January to December 2023 at the health center. In its implementation, the RapidMiner application or software is utilized to perform clustering using the K-Means Clustering algorithm. The available medicine data will be grouped into 3 clusters: cluster 0 for high medicine demands, cluster 1 for moderate medicine demands, and cluster 2 for low medicine demands. Out of the 28 test data used, the results show the first cluster consisting of 24 items, the second cluster consisting of 3 items, and the third cluster consisting of 1 item with a Davies Bouldin Index value of 0.276. From this research, the Puskesmas can continue to procure medicine for the types classified under high-demand clusters to ensure that the medicine needs are consistently met.
IOT Rancang Bangun Alat Pengusir Hama Burung pada Padi Sawah Petani Berbasis Internet of Things (IoT) Sufaat, Imam; Juliandri, Juliandri
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4921

Abstract

This research aims to develop an effective and efficient bird pest repellent tool in farmers' rice fields by utilizing Internet of Things (IoT) technology. Bird pests often pose a serious threat to agricultural products, especially in rice fields. Through the application of IoT, this tool can monitor real-time bird activity around agricultural areas and take appropriate preventive measures. This tool is designed to utilize motion sensors or pir sensors, surveillance cameras, and a sound or visual signal delivery system to chase away birds. approaching agricultural areas. PIR sensors are widely used in a variety of applications, including home security systems, motion sensor lighting, and building automation. The main advantages of PIR sensors are their low power consumption and ability to provide fast response to movement, making them an efficient and effective choice for many detection applications. motion, data collected from sensors and cameras will be sent to the IoT platform with the Telegram application and which is connected via an internet connection, allowing farmers to monitor and control tools remotely via smart devices such as smartphones or computers. The main advantage of this tool is its ability to provide a quick response to the presence of bird pests, reduce agricultural yield losses and increase crop productivity. Additionally, integration with IoT provides high flexibility and connectivity, enabling continuous system optimization. designing this tool involves the hardware and software design stages. The hardware consists of a pir sensor, an esp 32 cam microcontroller, and a speaker and servo. While the software consists of Arduino Ide, Blynk and Telegram. After design, the tool is field tested for performance. Test results show that this tool is able to detect the presence of birds with good accuracy, provides a fast response in chasing them away, Telegram is able to communicate by giving commands and receiving images, Blynk can provide information on the condition of the tool when it is online and give commands to activate the servo, sound or both of them. With a significant removal success rate, this tool is successful in reducing losses caused by bird strikes in agriculture. Additionally, it has low power consumption and can be integrated with existing IoT platforms for remote monitoring and data analysis. It is hoped that this research can make a positive contribution to efforts to develop sustainable agricultural technology and improve farmers' welfare by protecting agricultural products from bird pest attacks. Thus, it is hoped that the implementation of this tool in the field can provide an innovative and efficient solution in controlling bird pests in farmers' rice fields.
Sentiment Classification of Robot Hotel Content using NBC and SVM Algorithm Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4924

Abstract

Sentiment analysis plays a pivotal role in comprehending public sentiment, notably within digital communication, where copious amounts of textual data are generated daily. This study delves into the efficacy of sentiment classification models, namely the Naive Bayes Classifier (NBC) and Support Vector Machine (SVM), within the imbalanced datasets commonly encountered in sentiment analysis tasks. Employing a comparative analysis methodology, a dataset comprising robot hotel reviews from online platforms is the basis for evaluation. Both NBC and SVM models undergo training and assessment, with and without the Synthetic Minority Over-sampling Technique (SMOTE), to rectify the class imbalance. Performance evaluation relies on critical metrics, including accuracy, recall, precision, f-measure, and Area Under Curve (AUC) to gauge model effectiveness. Findings demonstrate SVM's superiority over NBC in terms of accuracy (SVM: 76.88%, NBC: 67.43%), precision (SVM: 92.03%, NBC: 86.87%), recall (SVM: 58.88%, NBC: 41.00%), f-measure (SVM: 71.78%, NBC: 55.63%), and AUC (SVM: 0.907, NBC: 0.961). Incorporating SMOTE significantly enhances both models' performance, particularly in addressing class imbalance concerns. Although NBC exhibits a more balanced performance across precision and recall metrics, SVM demonstrates heightened accuracy and predictive capability in sentiment classification tasks. These findings underscore the pivotal role of algorithm selection and preprocessing techniques in optimizing sentiment analysis performance, thereby providing invaluable insights for practitioners and researchers alike.
Sistem Pakar Pemilihan Bibit Padi Unggul dengan Metode Forward Chaining Efan, Efan; Sasmita, Sasmita; Suhada, Desmi Aulia
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4926

Abstract

Expert systems are technologies that are widely used in the agricultural sector, one of which is for selecting seeds. Rice is one of the most important food crops in life, rice is the main source of carbohydrates for the majority of the world's population. One of the important factors influencing the growth of rice production is better and better quality seed so that it can increase the productivity of rice plants, using better seed can increase yields up to three times a year. This research aims to develop an efficient system for selecting superior payi seeds using the Forward Chaining method, in order to assist the mappers in selecting payi seeds that are in accordance with their criteria. This research was motivated by the process of selecting rice seeds by farmers who still had little knowledge about rice and was carried out by asking people who only understood rice. The system development method uses the Waiter Faill method, which Taihaipain uses, including analysis, design, coding and testing, uses the system using Axure applications, StarUML for development, Visual Studio Code applications for coding, PHP, HTML for programming skills, and Database XAMPP and MySQL. The testing method uses the Blackbox Testing method, namely to determine whether the functionality, input and output of the software meets the required specifications. The results of the research show that all application features function well after being tested using the black box testing method. This expert system also helps the community in choosing rice seeds, as shown through interviews that 85% of respondents need this system.

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