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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 22 Documents
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Search results for , issue "Vol 5 No 2 (2024): February 2024" : 22 Documents clear
Penerapan Metode EDAS dan ROC Dalam Rekomendasi Objek Wisata Pantai Terbaik Anggi Farika Sari; Septia Nike bela Sapira; Elsa Adhista Aulia Dewi; Agusta Praba Ristadi Pinem
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.v5i2.4765

Abstract

Indonesia's natural diversity, cultural heritage and history have given rise to attractive tourism destinations, including Central Java Province. Central Java offers a diversity of beaches, attracting millions of tourists every year. However, many tourists have difficulty choosing a beach tourist destination that suits their wishes. Decision Support Systems (DSS) are a solution with various methods, one of which is the Evaluation Method based on Distance from Average Solution (EDAS) which is currently used together with Rank Order Centroid (ROC) weighting. This method has been proven to be effective in complex decision situations. In this context, using 13 Alternative Beaches was taken by taking into account the highest number of visitors in each Regency/City in Central Java Province and applying criteria such as cleanliness, facilities, popularity, rating and ticket prices used for evaluation. This research aims to examine the relationship and integration of the ROC and EDAS weighting methods in recommendations for the best beach tourist attractions in Central Java. It is hoped that this study will be able to provide a better interpretation and obtain a ranking ranking of alternative beach tourist attractions in Central Java, thereby contributing to tourists making more accurate decisions according to the criteria in the context of selecting beach tourist locations. The results of the research show that Jatimalang Beach is the best alternative with the highest score of 1,000. Thus, Jatimalang Beach is declared as the best choice for the best tourist attraction.
Sistem Pendukung Keputusan Dalam Penilaian Kinerja Karyawan Rumah Sakit Menggunakan Metode Multi Factor Evaluation Process Anwarsyah Anwarsyah; Gandung Triyono
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.v5i2.4778

Abstract

Employees are a resource that is a supporting factor for a company or organization. Having employees who meet qualification standards can develop the company and increase company productivity. Employee performance assessments are carried out looking at the company's success in organizing employees and determining the level of employee loyalty and professional performance towards the company. Petukangan Hospital has a large number of employees ranging from health workers to management employees and others. Currently there is no system used to evaluate employee performance, so the assessment process takes a long time and is not timely and there is an element of subjectivity in the assessment. Therefore, we need a system that can help assess employee performance with the aim of the research as an alternative in systematically and objectively assessing employee performance according to the weights and criteria obtained by each employee. In this research, the method used is the Multi Factor Evaluation Process of a Decision Support System, where this method carries out an assessment by calculating weights and criteria. The aim of this research is to provide the best solution and tools for Petukangan Hospital in assessing employee performance, and this research is expected to have benefits that can become effective and efficient problem solving. This research has results obtained from testing in the form of a ranking system where employees with the highest total evaluation score is the employee with the best performance score. The calculation results show that the employee with the best performance and rank 1 is Employee 26 with a value of 0.8375.
Friends Recommendation on Social Networks using the Bayesian Personalized Ranking-Matrix Factorization Muhammad Haidir Ali; Z. K. A. Baizal
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.v5i2.4804

Abstract

In the digital landscape of social networking, the challenge of improving friend recommendation systems is pivotal for enhancing user interaction and fostering social connections. Addressing this challenge, the current study innovates by fusing Bayesian Personalized Ranking (BPR) with Matrix Factorization (MF), culminating in a novel BPR-MF model designed for the intricacies of social network relationships. The study harnesses a rich dataset from LastFM, comprising 27,806 interactions among 7,624 users, to analyze mutual follower patterns and augment the precision of friend recommendations. Through rigorous preprocessing and systematic evaluation of the BPR-MF model against different numbers of latent factors, the research uncovers that a configuration of 20 latent factors is most effective, achieving an RMSE of 0.156 and an AUC ROC of 0.800. This discovery addresses the critical problem of balancing computational complexity with prediction accuracy in recommendation models. It also demonstrates the necessity for a nuanced, data-driven approach to generate relevant social connections. The research sets a new direction for future studies aiming to capitalize on user interaction data to offer precise friend suggestions, all while upholding user privacy and avoiding reliance on personal data.
Implementasi Metode Dempster-Shafer Untuk Deteksi Kesehatan Mental Pada Mahasiswa Berbasis Web Alif Jalaluddin; Endah Ratna Arumi; Dimas Sasongko; Sambodo Sriadi Pinilih; Uky Yudatama; Muhammad Resa Arif Yudianto
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.v5i2.4830

Abstract

Mental health is a person's soul condition to budaptasi in its environment to feel happy or get the comfort of life, so as not to experience mental disorders. Often mental health is ignored by most people because it is different from physical health that can be seen directly with the eyes and can be identified easily. Lack of awareness of mental health in the life of the people of Indonesia and the assumption that a person who goes to psychologists is a person inseasonable, often the individual who actually undergoes mental health problems reluctant to get help from experts or deny that he does not have mental health problems. Limitations of time and costs are also one of the constraints of a student reluctant to get help from experts like psychologists. Therefore, a web-based expert system is built with a dempster-shafer method to use as detection on the student and allows the user to know whether the user has a tendency of the problem on its mental health or not before the official consultation is required from the expert. Testing Accuracy Comparison System between the results of the system and experts by using 100 correspondents from students at Muhammadiyah Magelang University (UNIMMA) 89% know mental health and 65% have experienced mental disorders. The results of the SRQ29 data used and were spread among campus students, this study has used 20 sample data and produces 70% expert suit compliance. From the results of expert suitability obtained from the calculation of the system by selecting symptoms and automatically the system will calculate the accuracy of the existing Belief Valident in every symptom. Then the system will take decisions based on the results of the largest calculation value.
Sistem Pakar Diagnosa Penyakit Angsa Menggunakan Metode Forward Chaining Berbasis Web William Yviis Guko; Ade Eviyanti; Hindarto Hindarto
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.v5i2.4835

Abstract

Geese have many benefits for human needs. Starting from meat, eggs, feathers, fat, and other uniqueness such as guarding other livestock if there are animals or strangers approaching their territory. However, in Indonesia, goose utilization is less desirable and only a few do it. This poses a probӏem for goose breeders due to the absence of knowӏedge about the diseases experienced and the handӏing soӏutions. An expert system is an artificiaӏ inteӏӏigence that supports expert decision-making. The forward chaining method provides conclusions through rules derived from existing facts. This Expert System for Diagnosing Swan Diseases Using the Web-Based Forward Chaining Method is designed to make it easier for farmers or users to consult about the diseases experienced and treatment solutions with 90% diagnostic results. This expert system is made based on a website that can be accessed easily and at any time. Based on black box testing, the results obtained show 100% system functionality, so this expert system application can be used.
Prediksi Harga Kelapa Sawit Menggunakan Metode Extreme Learning Machine Jul Hariansyah; Elvia Budianita; Jasril Jasril; Iis Afrianty
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.v5i2.4858

Abstract

Palm oil is one of the keys to the Indonesian economy and the main commodity for attracting foreign investment. The palm oil and palm kernel industry generates most of the foreign currency from palm oil. The price of palm oil often goes up and down every month resulting in instability in the income received by people who own oil palm plantations. The aim of predicting palm oil prices is to carry out appropriate planning or steps for palm oil business actors. One way to overcome this problem is to make predictions. One method that can make predictions is the Extreme Learning Machine (ELM). ELM is an artificial neural network method used to predict palm oil prices. The ELM method is a feedforward method with a single hidden layer which is better known as a single hidden layer feedforward neural network (SLFNs). In this research, the best implementation was 5 inputs with 20 neurons in the hidden layer with output in the form of palm oil price predictions. Based on the tests carried out, the research produced the smallest error rate of 0.0027111424247658633 using 20 neurons in the hidden layer so that the latest data prediction test results for 5 price rotations in September rotation 1 were 1400.314191, September rotation 2 were 1846.798921, September rotation 3 amounted to 1505.430419, September rotation 4 amounted to 2301.853412, September rotation 5 amounted to 2645.082489 in palm oil price predictions.
Evaluation of Salesperson Performance in the Sales Allowance Decision Support System Using the MARCOS and PIPRECIA Methods Sitna Hajar Hadad; Abhishek R Metha; Setiawansyah Setiawansyah; Heni Sulistiani
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.v5i2.4863

Abstract

Optimal salesperson performance is the main key to a company's success in achieving sales targets and business growth. A reliable salesperson is not only able to sell products or services, but also has the ability to build strong relationships with customers. The purpose of this study is to assess the performance of salesperson in providing sales allowances based on performance results carried out by applying a combination of MARCOS and PIPRECIA methods, so as to produce a recommendation for the final assessment of salesperson performance that will assist the company in providing sales benefits to salespersons. The combination of Pairwise Relative Criteria Importance Assessment (PIPRECIA) and Measurement of Alternatives and Ranking According to Compromise Solution (MARCOS) forms a powerful holistic approach to decision making. PRCIA facilitates the identification and assessment of the relative weights of each decision criterion, providing a solid foundation for assigning value to the relative importance between criteria. The results of the salesperson performance evaluation ranking above show the final results for rank 1 with a value of 4.3446 obtained by Rini, rank 2 with a value of 3.5369 obtained by Murniasih, rank 3 with a value of 3.1807 obtained by Hana Ferbi.
Sistem Pendukung Keputusan Menentukan Lokasi Pembangunan Jaringan Internet Menggunakan Metode Profile Matching Safira Agustina; Hetty Rohayani; Noneng Marthiawati H; Muhammad Nabil Azzamy
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 Teotino Gomes Soares; Alfry Aristo Jansen Sinlae; Arief Herdiansah; 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 Muhammad Naufal Rifqi; Rima Tamara Aldisa
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.

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