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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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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.
Analisis Sentimen Terhadap Review Google Maps Jogja City Mall Menggunakan Algoritma Support Vector Machine Aryanto, Putri Marceliana; Mardhiyyah, Rodhiyah
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.v6i1.6045

Abstract

Sentiment analysis works with calculating the total of reviews based on given labels. Reviews that had specific labels would be classified with other reviews that had same labels. This study used Support Vector Machine (SVM) method for analysing of reviews of Google Maps users who had visited Jogja City Mall in Sleman, Special Region of Yogyakarta. In previous studies, SVM was proved several times superior in accuracy score and the accuracy of sentiment classification. SVM model for sentiment analysis in this study was successfully created with accuracy score was 84% and 85% in K-Folds testing. With the total of testing data was 20% out of 1694 data, the total amount of positive label was so much more than the total amount of negatif label. This means the rate of 4.6 stars for Jogja City Mall is relevant with the reviews that were given. The analysis of imbalance review data and the correlation between the stars rating and sentiment that were extracted may contribute to a deeper understanding of consumer behavior in the mall, providing practical implications for mall management in improving customer experience.
Sistem Pendukung Keputusan TOPSIS untuk Memilih Kamera Mirrorless bagi Fotografer Jalanan Putri, Dhea Desliana; Waluyo, Anita Fira
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.v6i1.6056

Abstract

Choosing the right mirrorless camera is a challenge for street photographers, given the many options available in the market. This research aims to develop a decision support system based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to assist photographers in selecting a camera that suits their needs. The main problem identified is the lack of clear criteria in camera selection, which can lead to non-optimal decisions. The TOPSIS method is used to evaluate various camera alternatives based on criteria such as image quality, weight, price, and other features. The results show that the system can provide better and more efficient recommendations, with an accuracy rate of up to 85 percent in predicting the most suitable choice for street photographers. From the application of the TOPSIS method, the Nikon Z9 camera received the highest preference value of 0.85768125, making it the best alternative in this study. Thus, the system not only helps photographers in decision-making, but also enhances their photography experience. This research makes a significant contribution in the field of information technology and photography, hoping to guide photographers in choosing the right equipment.
Utilizing Knowledge Discovery in Databases (KDD) for Hotel Guest Feedback Analysis 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.v6i1.6094

Abstract

This research explores the application of Knowledge Discovery in Databases (KDD) to analyze hotel guest feedback and improve service quality at Bintang Flores Hotel in Labuan Bajo. Utilizing KDD methodologies, the study processed 589 guest reviews to identify key factors influencing customer satisfaction, including cleanliness (1.00), location (0.82), and staff service (0.71). The analysis also highlighted issues such as limited breakfast variety (0.59) and inconsistent Wi-Fi connectivity (0.41) as recurring concerns, especially for long-term guests and business travelers. The data revealed that guests staying in the Deluxe Double or Twin Room frequently rated their experience as "Excellent" or "Very Good," with couples and families expressing high satisfaction levels. In contrast, suite categories received fewer and more varied ratings, signaling areas for targeted improvement. Through KDD, the study effectively combined structured numerical ratings and unstructured written feedback to pinpoint areas needing operational enhancement. Addressing challenges related to service consistency during peak periods, infrastructure maintenance, and food variety is essential for boosting guest satisfaction. The findings support implementing targeted strategies to ensure that Bintang Flores Hotel maintains a competitive edge and meets evolving customer expectations in the hospitality market.
Implementasi Pengenalan Ekspresi Wajah dengan Menggunakan Metode Convolutional Neural Network dan OpenCV Berbasis Webcam Melatisudra, Raifvaldhy Jounias Luppus; Utomo, Suharjanto; Sutjiningtyas, Sri; Hernawati, Hernawati
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.v6i1.6114

Abstract

Facial expressions play an important role as a non-verbal language rich in emotional information, especially in psychological contexts. The main challenge in this research is to understand and analyse complex facial expressions, which are often difficult for psychologists to interpret. The implementation of webcam-based facial expression recognition leverages the computer's ability to visually recognise human emotions, supported by artificial intelligence and machine learning. Convolutional Neural Network (CNN) and OpenCV methods are used to detect and classify facial expressions directly. The CNN model is trained using a dataset with six expression classes (happy, sad, angry, surprised, neutral, afraid), with four convolution layers for multi-class classification. The implementation of facial expression recognition is successful, the system captures facial images from a webcam, detects faces in the frame, and classifies facial expressions directly on the screen window. The performance of training data against the trained model measured using Classification Accuracy shows an accuracy of 72.34% in training accuracy and 60.54% in validation accuracy. While the performance of the facial expression recognition system calculated using Confusion Matrix resulted in an accuracy of 70.55%. The calculation results show that the model is at the Fair Classification parameter level or able to classify facial expressions in humans with a fairly good level of accuracy, this research has great potential for application development in the field of psychology. However, further optimisation is needed by involving experts to ensure its effectiveness.

Page 40 of 45 | Total Record : 443

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