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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 42 Documents
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Search results for , issue "Vol 6 No 1 (2024): November 2024" : 42 Documents clear
Sistem Pemantauan Kesehatan Kambing Berdasarkan Suhu Tubuh dan Detak Jantung Berbasis Internet of Thing Sibarani, Yohana Oktavia; Rismawan, Tedy; Nirmala, Irma
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.5012

Abstract

The goat is one of the species of livestock that has many benefits. Goat benefits become one of the important aspects of keeping goats healthy. Predicting diseases in goats is quite difficult because of the lack of medical experts in the field to check the health condition of the goats. The research has created a system that can determine the health of goats based on their body temperature and heart rate. The system is designed to make it easier for farmers to monitor their health and perform early treatment when there are sick goats. The data used in this study is the normal body temperature and heart rate of the goat. This research uses NodeMCU as a microcontroller and also a sensor. The sensors used are the DS18B20 temperature sensor and the MAX30102 heart rate sensor. The system can measure both temperature and heart rate at the same time. The test results showed an average data error on the measurement of the DS18B20 sensor and the MAX30102 sensor. In these measurements, the average error of the sensor was 0.77% and the sensor MAX30102 was 2.23%. The data from the monitoring of this sensor will be displayed on the website so that it can be viewed by the user.
Sistem Telemetri Cerdas untuk Monitoring Kadar Particulate Matter (PM10) Menggunakan Algoritma Rule-Based Berbasis Internet of Things Salwa, Septi Lailatis; Shodiq, Muhammad; Handoyo, Eko
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.5739

Abstract

Air pollution by Particulate Matter (PM10) is a pressing environmental issue that has a major impact on public health. PM10 consists of particles with a diameter of less than 10 μm that can penetrate the respiratory tract, causing respiratory infections, heart disease, and lung disorders. The main sources of this pollution include vehicle emissions, industrial activities, and organic matter burning, which often exceed the thresholds set by health agencies. This research aims to develop an Internet of Things (IoT)-based intelligent telemetry system to monitor PM10 levels in real-time using Rule-Based Algorithm sensors. This algorithm adopts artificial intelligence techniques to solve problems based on a set of predefined rules. This process involves several important stages of knowledge base collection. IoT as a low-cost device enables extensive data collection, interaction with other IoT devices, and utilization of cloud-based services and storage. This system is expected to provide accurate and fast information about the level of air pollution and increase public awareness about the importance of maintaining air quality. The results showed that the measured PM10 level in Table 1 reached 2.48 µg/m³, while in Table 2 it was 1.768 µg/m³. Both values are categorized as dangerous. The system is also equipped with a notification feature via Telegram, allowing users to monitor air quality conditions effectively. This research contributes to the development of air quality monitoring technology and environmental health.
Perbandingan Algoritma K-Means dan K-Medoids untuk Clustering Pada Transaksi Penjualan Minimarket Alganiu, Ajeng Shalwa; Juwita, Ayu Ratna; Rahmat, Rahmat; Faisal, Sutan
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.5873

Abstract

When shopping, buyers often have difficulty finding daily necessities. One of the causes of this is because the product arrangement process in minimarkets is still carried out randomly and does not match consumer shopping patterns. On the contrary, buyers usually want to buy products through daily necessities packages, but these packages are usually not yet available in minimarkets. Identifying relationship patterns in minimarket transaction data can help overcome product arrangement and product packaging problems. By using the clustering method, objects are grouped into groups that have many similarities with each other. This method allows the grouping process to be carried out. Some of the methods in clustering include the K-Means and K-medoids methods. The purpose of this study is to group the data on goods in the minimarket which can be a guide for more neatly arranged product planning. Data grouping is divided into 3 categories namely slow, medium and fast. The results obtained show that the two algorithms produce different Davies-Bouldin Index values, with the K Medoids algorithm obtaining a lower value of 0.50387 while K-Means obtains a value of 0.50391 where the K-Medoids clustering results have better quality compared to K-Means. With the results of the grouping of these goods data, minimarkets can balance the stock of goods to prevent excess or shortage of inventory of these goods.
Sistem Rekomendasi Pemberian Kredit : Solusi SMART (Simple Multiple Attribute Rating Technique) untuk Perusahaan Multifinance Arief, Rachman; Marbun, Paniel Yose Yanwin
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.5907

Abstract

Providing credit is one of the services provided by the WOM Finance company for financing motorbike units. When applying for credit, selection must be carried out to select good prospective customers so that there is no risk of congestion in the payment process later. In the selection process, the company takes up to three days to approve credit funds. The selection process carried out by the company currently still uses manual methods with Ms.Excel to compare data where the data will be analyzed and considered by the marketing team manually. There are several stages when applying for credit, customers who apply for credit funds will not be immediately accepted and disbursed, because each prospective customer must meet several criteria so that their application for credit funds can be approved. In this case, the author concludes that he will create a decision support system using the website-based Simple Multiple Attribute Rating Technique (SMART) method as a solution to speed up the process of providing credit funds, as well as making it easier for marketing parties in the selection process. This multi-attribute technique is used to support decision makers in choosing between several alternatives that have been ranked. Of the 5 alternative data used in the calculation, rank 1 with alternative code A3 produces a final score of 30.67 and is worthy of being recommended for credit. Based on tests carried out 20 times by comparing the results of the sales marketing survey recommendations with the results of the system recommendations, the results were obtained 20 times in accordance with an accuracy level of 100%. So it can be concluded that this system can run well in carrying out the process of ranking or selecting prospective motorbike cash credit customers.
Integrasi IoT pada Lahan Tanaman Wakaf Sebagai Media Monitoring dan Alerting pada Tumbuh Kembang Bibit Pohon Mahoni Hernawan, Septian Rico; Novianto, Irwan; Rina, Fadmi
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.5972

Abstract

Environmental damage in Indonesia is concerning, with the massive loss of green spaces in recent years. Consequently, there has been a drastic decline in air quality, contributing to the rise of diseases such as stroke, heart conditions, lung diseases, and even birth defects. The implementation of environment-based waqf activities, with waqf items in the form of trees, offers a practical solution that is accessible to the public. However, this effort has not yet been widely adopted or well-facilitated. The growth of tree seedlings is influenced by several factors, such as air temperature, soil pH, humidity, and carbon particles. A method for monitoring and alerting users is needed to ensure optimal plant growth. An IoT system is integrated for the plants in the waqf areas. Sensor data will be displayed on a screen, with an alerting function that sends alarms through a speaker. Mahogany seedlings were selected for testing due to their rapid growth. Integrating IoT devices for monitoring and alerting effectively increased the height of mahogany seedlings. Based on a two-month test on 10 seedlings across two different areas, a difference of 2.4 cm per two months, or 14.4 cm per year, was observed between IoT-integrated and non-IoT areas.
Klastering Kecepatan Internet Operator Telkomsel Berdasarkan Sebaran Site BTS (Base Transceiver Station) Menggunakan Metode DBSCAN Alifudin, Arif; Pratama, Irfan
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.5984

Abstract

The development of cellular telecommunications technology has now reached a more advanced stage with the presence of 4G LTE technology. Compared to previous technologies such as 3G and 2G, this technology offers data transfer capabilities with much better access speeds. This makes 4G LTE the backbone for modern communication services that support users' needs for fast and stable internet access. However, even though 4G LTE technology has been widely implemented, there are still challenges that need to be overcome to ensure network quality remains optimal. The quality of the internet network in the Special Region of Yogyakarta (DIY) has experienced significant improvements in recent years, but obstacles such as limited infrastructure are still felt, especially in rural and outermost areas. This research aims to analyze and group areas based on network quality. Therefore, data mining analysis of existing data is needed using the DBSCAN algorithm so that clusters will be formed which are divided according to network quality. After carrying out analysis using the epsilon value = 0.5 and the minpts value = 5, the clusters formed were 5 clusters with a silhouette value of 0.216471397367446, which indicates that the quality of the clustering is relatively low, which is possibly caused by less than optimal distribution of data or parameters. Nevertheless, the clustering results obtained still provide useful insights for analyzing site distribution and network performance.
Decision Support System for Platform Selection in E-Commerce Using the OWH-TOPSIS Method Wang, Junhai; Isnain, Auliya Rahman; Suryono, Ryan Randy; Rahmanto, Yuri; Mesran, Mesran; Setiawansyah, Setiawansyah
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.5990

Abstract

Platforms in e-commerce are digital systems that allow online transactions to buy and sell products or services. E-commerce platforms also provide benefits for business actors because they are able to reach a wider market without geographical restrictions, while offering efficiency in business operations. The main problem in choosing a platform for e-commerce is often related to the sheer number of options available and the variety of criteria that must be considered. Criteria such as fees, platform popularity, transaction security, ease of use, features provided, as well as customer service support are important factors in determining the most suitable platform. The implementation of a decision support system to help select the optimal e-commerce platform by applying the OWH-TOPSIS method shows that this system can provide accurate and effective recommendations, so that it can be used as a reference for users in determining the e-commerce platform that suits their needs. The decision support system using the OWH-TOPSIS method provides an efficient and objective solution in the selection of e-commerce platforms. The results of the ranking of the best e-commerce platforms show that Platform D occupies the top position with the highest score value, which is 0.882. In second place is Platform E which obtained a score of 0.8599, followed by Platform A with a score of 0.8341.
Analisis Algoritma JST untuk Prediksi Perkembangan PDRB Menurut Lapangan Usaha Atas Dasar Harga Berlaku Robiansyah, Wendi; Okprana, Harly
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.5994

Abstract

Gross Regional Domestic Product (GRDP) data plays a vital role as a reference in regional development planning. However, the main challenge faced is the inaccuracy of GRDP growth predictions due to complex and fluctuating economic dynamics, especially in areas such as Simalungun Regency. Therefore, this study aims to analyze the development of Gross Regional Domestic Product (GRDP) by business field based on current prices in Simalungun Regency using three Artificial Neural Network (ANN) algorithms, namely Backpropagation, Bayesian Regulation, and Levenberg-Marquardt. The research data is GRDP times-series data for 2015-2023 obtained from the Central Statistics Agency of Simalungun Regency. The analysis used five models of the same architecture, namely 7-5-1, 7-10-1, and 7-15-1, with a target error of 0.01 and a maximum epoch of 1000 iterations. The results of the study indicate that the Levenberg-Marquardt algorithm with the 7-10-1 architecture model provides the best performance with an accuracy rate of 100% and the smallest Mean Squared Error (MSE) value of 0.0000214320 compared to other algorithms and architecture models. This finding indicates that the Levenberg-Marquardt algorithm is superior in predicting the development of GRDP in Simalungun Regency. The implementation of the results of this study is expected to help local governments and related agencies provide information on the development of GRDP in Simalungun Regency so that they can design more accurate and effective economic policies. In addition, this study also contributes to the development of artificial intelligence-based economic prediction methods, especially in the application of JST for the analysis of complex and dynamic regional economic data.
Algoritma Bayesian Regulation untuk Prediksi Kemiskinan Sebagai Evaluasi Awal Mendukung Kebijakan Ekonomi Hijau Firzada, Fahmi; Darma, Surya
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6011

Abstract

This study aims to utilize the Bayesian Regulation algorithm to predict poverty in Simalungun, Pematangsiantar, Asahan, Batu Bara, and Tebing Tinggi, as an initial step to evaluate the Green Economy policy. Poverty remains a serious issue, particularly in Pematangsiantar and Simalungun, where social inequality and limited access to basic services are prevalent. High poverty rates and limited resources present significant challenges to improving community welfare. The Green Economy policy could be a potential solution to reduce the negative environmental impact of development and enhance community well-being. This research uses secondary time-series poverty data from 2012 to 2023, obtained from the Central Bureau of Statistics of North Sumatra, based on the basic needs approach. The applied Machine Learning algorithm is Bayesian Regulation, used to predict poverty levels in these areas based on five architectural models (10-5-1, 10-10-1, 10-15-1, 10-20-1, and 10-25-1). The 10-25-1 model was selected as the best model due to its smallest MSE (error), 0.00218055780, compared to the other four models. This study aims to provide insights into the development of poverty in these regions and offer an initial evaluation of the effectiveness of the Green Economy policy. It is also expected to propose more effective policy recommendations for reducing poverty and supporting environmental sustainability, particularly in Pematangsiantar and Simalungun.
Understanding Hotel Customer Experience through User-Generated Reviews using Knowledge Discovery in Databases (KDD) Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6014

Abstract

This research explores the analysis of 388 hotel customer reviews to understand guest experiences, employing advanced analytical methodologies to uncover valuable insights for service quality enhancement. Utilizing the Knowledge Discovery in Databases (KDD) framework, the study applies Latent Dirichlet Allocation (LDA) for topic clustering and k-nearest Neighbors (k-NN), enhanced by the Synthetic Minority Over-sampling Technique (SMOTE) for sentiment classification. The integration of these techniques allows for the extraction of coherent thematic patterns and the accurate differentiation of sentiment categories within the reviews. The findings reveal that LDA, evaluated through metrics such as log-likelihood (-54,886.092) and coherence scores (-14.949), effectively captures the underlying themes discussed by guests, providing a clear representation of customer priorities and concerns. Additionally, applying SMOTE significantly improves the k-NN model's performance, achieving an accuracy of 91.43% and a precision of 97.26% by balancing class distributions and enhancing classification accuracy. This approach demonstrates the potential of combining topic modeling and sentiment analysis to derive actionable insights, which can be strategically utilized to optimize service delivery and elevate the overall customer experience in the hospitality industry. The study concludes that leveraging such data-driven methodologies facilitates a deeper understanding of customer feedback, ultimately supporting informed decision-making and continuous service improvement.

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