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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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Pemanfaatan Algoritma Floyd Warshall dalam Menentukan Jalur Terpendek Bencana Banjir Erwadi, Yetman; Handayani, Sri; Saputera, Surya Ade; Fernandez, Sandhy
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5483

Abstract

Bengkulu Province experienced floods and landslides that caused loss of life and property. These events mainly occur in the Central Bengkulu region and Bengkulu City, caused by damage to the Bengkulu River Watershed (DAS). During the rainy season, the river overflows. In this case the community is also confused when evacuating related to flood disasters, the community is confused in determining which route is the closest for the evacuation route because at the location of the flood disaster there are many routes that can be passed. From the problem the purpose of this research is to create an application that can determine which path is the closest in order to evacuate quickly. From the results of the analysis of this application, it is clear that the implementation of the Floyd-Warshall algorithm in this web application has a very important purpose, namely to assist the community in finding the shortest path to the evacuation point during a flood. The Floyd-Warshall algorithm is known to be effective in finding shortest paths in weighted graphs, as implemented in the context of this application to calculate the shortest distance between every pair of vertices or points within a defined area. The Floyd-Warshall algorithm can only compute shortest paths within areas that are well-defined in terms of vertices and edges, for the test results of the distance obtained in test 1 as far as 0.76 KM, in test 2 as far as 0.40 KM and in test 3 as far as 0.41 KM.
Perbandingan Support Vector Machine dan Naïve Bayes Terkait Kepuasan Pengguna Bus Listrik Kota Medan Mesanda, Zery; Ikhsan, Muhammad
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5556

Abstract

The city government has introduced an initiative to use electric buses as a cleaner and more sustainable alternative. The success of a public transportation system is not only determined by the availability of fleet and infrastructure, but also by the level of user satisfaction. User satisfaction is an important indicator that reflects the extent to which the service meets users' expectations and needs. Therefore, an in-depth understanding of the factors that influence user satisfaction, as well as the ability to predict and manage them, is key in improving the quality of public transportation services, including usage. In an effort to understand and improve trolleybus user satisfaction in Medan City, an adequate predictive analysis approach is required. By using methods such as Support Vector Machine (SVM) and Naïve Bayes, we can develop predictive models that can identify patterns and trends in user data, thus enabling relevant parties to take appropriate actions to improve services. In this context, the comparison between SVM and Naïve Bayes methods will provide valuable insights into the effectiveness of each method in predicting the satisfaction of electric bus users in Medan City. Based on the comparison results, the Naive Bayes algorithm shows slightly better performance compared to the Support Vector Machine in this sentiment analysis. The accuracy value generated by applying the Naive Bayes method is 58%, while applying the Support Vector Machine method is 57%. Nonetheless, both algorithms provide valuable insights into the sentiment of Medan people towards Electric Buses.
Prediksi Bahan Baku Kerupuk Rambak UMKM Tiga Berlian dengan Single Moving Average Sebastian, Devara Avila; Yudatama, Uky; Primadewi, Ardhin
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5604

Abstract

The "Tiga Berlian" rambak cracker MSME is an MSME in the snack food sector located in Banyuwangi Bandongan Village which produces crackers made from cowhide. Rambak crackers are a superior product with total demand per month reaching 150 kg. Tiga Berlian UMKM has difficulty in predicting and calculating raw material supplies for the coming period so excess raw material stock often occurs, such as cowhide or buffalo as the main raw material that is wasted due to uncertain demand. Single Moving Average (SMA) is used as a prediction method for raw materials in Tiga Berlian. Calculations with several periods (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, and 11) will be tested for accuracy using MAD, MSE and MAPE. The 11 predictions made with Tiga Berlian raw material data for 12 months, the 11th-period calculation resulted in a MAD value of 2.56198, a MAPE value of 2.561983, and an MSE value of 72.20135 as the smallest and most relevant accuracy value.
Implementasi Metode Analytic Hierarchy Process (AHP) dalam Sistem Rekomendasi Mitigasi Bencana Alam Nugraha, Nur Budi; Santosa, Yaqutina Marjani; Puspaningrum, Alifia; Saiful, Ibnu
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5626

Abstract

Flood disasters are a serious threat faced by various regions in Indonesia, including Indramayu Regency, West Java, as one of the most vulnerable areas. The geographical characteristics of Indramayu, which are dominated by lowlands and surrounded by large rivers such as Cimanuk and Cipunagara, cause this area to experience annual flooding, especially during the rainy season. The impact of this disaster extends from material losses to significant social and economic disruption for local communities. This research aims to develop an effective and targeted flood disaster mitigation recommendation system. Using a quantitative approach, this study focuses on developing a system based on the Analytical Hierarchy Process (AHP) method. The research methodology includes several key stages. First, identify and determine criteria and sub-criteria that are relevant to flood disaster mitigation. Second, weighting criteria using pairwise comparisons. Third, consistency calculations to ensure the validity of the assessment. Finally, the construction of a decision hierarchy that integrates all analysis elements. The results of the AHP analysis are then integrated into a system specifically designed to support decision making in disaster mitigation. The system combines geographic, demographic and historical disaster data to provide comprehensive and contextual recommendations. The system being developed is expected to facilitate several key aspects of disaster management in terms of quick and effective decision making by the authorities, increasing public awareness and knowledge about disaster management and understanding disaster distribution patterns.
Perbandingan Metode Deep Learning dengan Model LSTM dan GRU untuk Prediksi Perubahan Iklim Mustofa, Amin; Setiawan, Hendra
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5671

Abstract

Climate plays a critical role in determining the quality of life in Indonesia, which demands in-depth understanding through the Koppen climate classification and the latest technology. From agriculture to urban infrastructure, public health, to ecosystem sustainability, every aspect of our lives is affected by climate conditions. Deep Learning methods such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) are used to predict time series data because of their adaptive ability in learning complex data patterns. The LSTM and GRU models were tested with 2010-2021 data using batch_size 64, epochs 150, optimizer adam, and showed high accuracy (<10%). LSTM recorded MAPE: Rainfall 5.50%, Humidity 7.60%, Temperature 4.36%, Sunlight 8.29%. GRU recorded MAPE: Rainfall 5.01%, Humidity 6.86%, Temperature 4.35%, Sunlight 8.28%. Predictions for 2028 show that the Special Region of Yogyakarta has a Tropical Monsoon (Am), Tropical Savannah (As) and Tropical Rain Forest (Af) climate. These climate changes have significant impacts: increased rainfall increases the risk of flooding, threatening infrastructure and lives, while the As climate reduces agricultural productivity and increases food insecurity. Changes in rainfall and temperature affect people's health, with high humidity increasing the risk of tropical diseases and high temperatures causing heat stress. Climate change in the Am type increases the risk of floods and landslides, while in the Af type it threatens tropical rainforest ecosystems.
Penerapan Algoritma Support Vector Machine Untuk Mendeteksi Autisme Khoiriah, Miftahul; Kurniawan, Rakhmat
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5692

Abstract

Autism is a type of developmental disorder that can cause a neurological condition to disrupt brain function and impact a person's growth process, communication skills and social interaction abilities. In general, autism spectrum disorders can be detected in babies as early as 6 months. Things that interfere with a child's development occur because the structure of brain function is disturbed. This widespread disability is described as a spectrum disorder due to the considerable variation in how an individual manifests symptoms and their severity. By carrying out this detection, it can make it easier for parents to know whether their child has autism or not so they know what action to take. This research was conducted using a quantitative research methodology, where the research approach focuses on collecting and analyzing data that can be measured in numerical form using statistical techniques to obtain numbers and generalize. This approach involves the relationship between phenomena and cause and effect using a larger sample. After the previous stages are completed, then continue testing the prediction results using testing and accuracy data to obtain classification results. From the classification results above, the resulting classification value reaches 100% using test data and using accuracy values. Support Vector Machine (SVM) algorithm ) with a linear kernel has been applied to a dataset of autism in children. This model succeeded in separating classes well, showing that SVM is an effective algorithm for this classification problem.
Prediksi Penjualan Produk Pepsodent Unilever dengan Algoritma K-Nearest Neighbor Maulida, Dzikra; Nasution, Yusuf Ramadhan
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5718

Abstract

In the era of globalisation and increasingly fierce market competition, companies are striving to improve their operational efficiency and marketing strategies to maintain market share and increase revenue. PT Unilever Tbk, as one of the multinational companies that operates various types of consumer products, including dental care products such as Pepsodent, requires reliable sales prediction to maximise its product performance in the market. The main objectives of this research are to apply the K-Nearest Neighbor method to Unilever pepsodent products in a prediction model that can preprocess pepsodent product data for the last 1 year using Rapid Miner and to measure the accuracy of Pepsodent product sales predictions. The data used is the number of stocks, types of pepsodent, sales, seasonal factors. From the results of analysis and evaluation, it can be concluded that the prediction accuracy in the K-NN algorithm is able to provide fairly accurate sales predictions for Pepsodent Whitening products with a value of 161, 186, 165 equally 114. Pepsodent Economy with a value of 982 predictions 1021, a value of 638 and 774 predictions are both 927. Pepsodent Herbal with a value of 173 predicted 193 and a value of 129 and 118 predicted values are both 207. Accurate sales predictions are helpful in production planning and marketing strategies, which in turn can improve operational efficiency and customer satisfaction. The K-NN algorithm proved to be effective in this case, although proper selection of the K parameter is essential to obtain the best results.
Sentiment Analysis Study Tour Bus Ban on Twitter Using Support Vector Machine Method Purba, Ony Hizri Kaifa; Zufria, Ilka
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5726

Abstract

Study tour is an activity outside the classroom that has the purpose of learning about the process of something directly. This activity is usually carried out by the school once a year. This activity is not only a learning tool for students, but also a recreational activity.In this activity, there are many things that need to be prepared, such as transportation, lodging, meals, and so on. This is sometimes troublesome, because not all tourists or business people have the time and willingness to prepare it. Therefore, they need services during their trip. Especially now that it is even semester, where every school usually holds a study tour, as well as a final class farewell. As a response to concerns, some parents may choose to find alternative activities that are considered safer for their children, such as joining activities in the city or at school. Based on this need, it makes opportunities for business people engaged in the tour agency industry. SVM (Support Vector Machine) is a machine learning method that works on the principle of Structural Risk Minimization (SRM) with the aim of finding the best hyperlane separating two classes in the input space. Simply put, SVM (Support Vector Machine) has the concept of finding the best hyperlane, which serves as the boundary of two classes The results of sentiment classification on Study Tour Buses using the Support Vector Machine algorithm that matches the actual data amount to 176 data out of a total of 240 test data. It is known that of the 1200 data obtained regarding sentiment towards there are 519 reviews that are positive and 681 reviews that are negative.The accuracy value of the Study Tour Bus sentiment classification using the Support Vector Machine (SVM) algorithm obtained is 73%.
Pengenalan Pola untuk Identifikasi Jenis Kain Tenun Sibolga Menggunakan Metode Principal Component Analysis dan K-Nearest Neighbours Piliang, Dinara Sarvina; Sriani, Sriani
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5727

Abstract

Sibolga woven fabric is one of Indonesia's traditional fabrics that has high artistic and cultural value. Sibolga woven fabric motifs are usually inspired by nature, such as flora, fauna, and local culture. Sibolga woven fabric and is famous for its unique and diverse motifs. Sibolga woven fabric motifs are usually inspired by nature, such as flora, fauna, and local culture. Manually classifying the types of Sibolga woven fabrics is a time-consuming process and requires special expertise. This causes the complexity of motifs and color variations found in Sibolga woven fabrics. Therefore, a system is needed that can classify the types of Sibolga woven fabrics automatically and accurately. The method used in this study is the feature extraction method, which is to extract new features from the initial data set. One of the feature extraction techniques that can be used is Principal Component Analysis (PCA). The use of PCA can be used to reduce the lower dimensions of data with very little risk of information loss. The study also uses KNN because this algorithm is used effectively to classify fabrics based on these key features, thereby reducing computational complexity and improving accuracy. The results of the classification of sibolga woven fabrics using the K-NN algorithm by utilizing the feature extraction process using PCA obtained an accuracy of 72%. It can be concluded that the classification of sibolga woven fabrics using an algorithm using the K-Nearest Neighbours (K-NN) algorithm can be done by extracting features using the PCA method (Pricipal Component Analysis).
Clustering Analysis of Bus Fares Trans Metro Deli Medan Using Mean Shif Clustering Method Rambe, Rinanda Putri; Zufria, Ilka
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5728

Abstract

Medan City is the 3rd most populous city in Indonesia, according to data from the Central Statistics Agency, Medan has a population of 2.49 million in 2022, an increase from the previous 2.46 million in 2021. The increasing number of population inhabiting the city of Medan means that the need for transportation for the people of Medan is also increasing. Trans Metro Deli bus data can be grouped effectively using the mean shift algorithm based on several attributes, namely passenger category, payment method and fare. Each passenger group has different needs and ability to pay, which makes setting fair and efficient fares a challenge. Inappropriate pricing can lead to passenger dissatisfaction, reduce the number of public transportation users, and affect bus operators' revenue. Cluster technique is a well-known clustering technique, which aims to group data into clusters so that each cluster contains data that is as similar as possible. Mean shift belongs to the category of clustering algorithms with unsupervised learning that assigns data points to clusters iteratively by shifting the points towards the mode (mode is the highest density of data points in the region in the context of mean shift). Mean shift does not require determining the number of clusters in advance The attributes used in the clustering process, namely passenger category, payment method and fare can properly create a hyperplane between clusters, thus creating significant differences from each cluster, as evidenced by the silhouette score obtained by 0.64. By conducting this analysis, it is expected to find a more efficient and fair fare clustering pattern, and provide practical recommendations for management in setting fares that are more in line with passenger needs. In addition, this research also aims to evaluate the effectiveness of mean shift clustering in the context of transportation fare analysis.

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