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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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Analisis Kombinasi Algoritma K-Means Clustering dan TOPSIS Untuk Menentukan Pendekatan Strategi Marketing Berdasarkan Background Target Audiens Ngaeni, Nurus Sarifatul; Kusrini, Kusrini; Kusnawi, Kusnawi
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.v5i2.4948

Abstract

The promotion is an annual agenda for STIMIK Tunas Bangsa Banjarnegara. The aim of this promotional activity is to attract more new students every year. On the other hand, campus promotion encounters obstacles in mapping applicant data from previous years so that considerations for new promotion policies are based on data from the school of origin of alumni or students. By using the K-Means Clustering algorithm, applicant data can be grouped according to the background represented through the school origin attribute. , parents' occupation and place of origin. Then the data is processed using DSS with the TOPSIS method to obtain priority references for marketing types for each cluster. The results of calculating the silhouette coefficient value for the five clusters obtained a score of 0.426. Meanwhile, in the ranking process using the TOPSIS method, the first rank was found in cluster 0 with a score of 0.994110. Further stages use the Decision Tree method to obtain output in the form of recommendations for promotion types for each cluster. For example, cluster 0 is recommended to use promotion types with codes P1, P2, P3, P8 and P9.
Penerapan Sistem Pendukung Keputusan Pada Pengambilan Keputusan Penilaian Kinerja Karyawan dengan Menerapkan Metode MOOSRA Utomo, Dito Putro
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.v5i2.4954

Abstract

The quality of the company will increase as employee performance increases. There are many ways that companies or organizations can improve employee performance. One way is to provide awards or rewards for employees who excel and for employees who are not able to provide their best performance, they will be given as a consequence. Rewards will be given by assessing employee performance. So far, Kofipindo has only relied on leaders' decisions to assess employee performance. This makes employees less motivated to show their best performance. Decision support systems are alternative solutions or alternative means and steps to resolve problems so that problems can be resolved effectively and efficiently. The Moosra method is used to calculate the weight of the criteria and determine the level of rejection as the main criterion. Where the decision support system is used as a model or reference in the decision-making process, especially in the employee performance assessment process. The Moosra method examines the suitability of separating a set of alternative value data with certain criteria described in the decision matrix. The aim of the research is to assist in the employee performance assessment process, making it easier for companies to provide rewards for the performance results of selected employees. The results obtained in the research process were Alternative (A10) with the name Roland Zebua as the employee with the best performance assessment.
Penerapan Metode Support Vector Machine Dalam Memprediksi Prediksi Cuaca Rifqi, Muhammad Naufal; Aldisa, Rima Tamara
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.v5i2.4961

Abstract

Weather is a natural that has a big impact on humans. Information about weather predictions is really needed by humans, especially in the city of Medan. This information is very useful for knowing weather events around us. Data mining is a process of collecting important information from large data, so that it can help make decisions and make accurate predictions. So researchers are interested in conducting research in predicting the weather in the city of Medan using the Support Vector Machine (SVM) method as a solution for predicting the weather in the city of Medan. The application of data mining using the SVM method helps produce precise accuracy for weather based on predetermined criteria. This method is suitable for weather predictions because it is able to provide clear and accurate assessments with weather predictions of 54.55%.
Penerapan Data Mining Dengan Menggunakan Algoritma Clustering K-Means Untuk Pembagian Jurusan Pada Sekolah Menengah Atas Setiawan, Ikbal Danu; Triayudi, Agung
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.v5i2.4970

Abstract

Senior High School is the last level that must be taken before continuing education at a higher level such as a Diploma or Bachelor's degree. Where in general high schools have class majors for students who will move up to class XI from class Improving the quality of education carried out in the class majoring process means that students will be more focused in accordance with the field of interest of the major that the student/I should take. The process that occurs in determining majors is only based on the wishes expressed by the students without taking into account the academic grades of the subjects that the students have passed or completed in class X. This problem is not a small problem that should be ignored, it This is an important problem that must be resolved immediately because if the problem is not resolved immediately it will have lasting impacts later. The process of determining the division of majors for students can be seen based on the patterns or values of previous students. Data mining is a process used to complete processing of large data. The data that is processed is a collection of data that becomes Big Data from past data that is stored in a storage container and can then be reused by processing it. Clustering is an appropriate way to solve problems. Where in clustering grouping is carried out based on the distance to each data object. The K-Means algorithm is part of Clustering Data Mining, where this algorithm can be used to carry out new groupings based on how clusters are formed. From the results obtained, there are 2 (two) new formation clusters. In cluster 1 there are 9 (nine) students and in cluster 2 there are 6 (six) students.
Penerapan Algoritma Clustering K-Means Data Mining dalam Pengelompokan Mahasiswa Penerima Beasiswa Setiawan, Ikbal Danu; Triayudi, Agung
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.v5i2.4971

Abstract

Scholarships are a program intended to help students with economic problems. For universities, especially private universities, scholarships are an attraction or a campus promotional event to attract prospective students to register at the campus. The scholarships provided by the campus are independent scholarships which are based on funding from the university's foundation. This is very important to pay attention to, where apart from the achievements of prospective students, they must also consider their readiness or ability to participate in the learning process that takes place at the university. Therefore, paying attention to the grades obtained from prospective students is very important to pay attention to. Another problem is that the quota given by the foundation for scholarships is also limited, which is not covered by all prospective students who register or submit scholarship applications. In terms of determining or awarding scholarships, there is not yet a reference standard that is used for determination in the decision-making process, so scholarship awards are often misdirected. Mistakes in awarding scholarships are of course very detrimental to the campus. Therefore, this problem should require special attention and treatment. This problem can be easily resolved by finding a pattern of rules for accepting scholarships. Data mining is a process method that is widely used today, this is because data mining is very helpful in the decision making process. The process carried out by data mining is divided into several techniques such as Clustering. Clustering is a way to group new data. The K-Means algorithm carries out a solution process based on grouping, therefore the K-Means algorithm is classified as a clustering part of data mining. The aim of the research to be carried out is to assist in the process of grouping prospective students who will be prioritized in receiving scholarships. Based on the results of this research, it can later help to find students who are truly worthy of receiving the scholarship. The results obtained from the research are that there are 2 (clusters) obtained from the K-Means algorithm process. Where in cluster 1 there are 10 grouping data and in cluster 2 there are 5 grouping data.
Penerapan Model Waterfall dalam Pengembangan Perangkat Lunak Pemantauan Tanaman Anggur Berbasis Mobile Menggunakan IoT Kasliono, Kasliono; Ruslianto, Ikhwan; Erniajan, Yunita
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5099

Abstract

Unfavorable tropical climatic conditions as well as the status of grapes as not a national priority commodity, have led to high production costs and low productivity in grape cultivation in the pontianak area. So this research has The purpose to conceptualize and develop an IoT-based Android application that allows observation of grape plants in the Greenhouse. Thus, it is hoped that this application can provide solutions for farmers in monitoring plant conditions in real-time, increasing productivity, and improving the quality of grape crops in the area. In previous research, The app is technically less user-friendly, not suitable for the average users, especially farmers. Key components include an Android app, a data processing system, and sensors measuring values like air temperature, humidity, and soil moisture.. The data processing system receives data from sensors and sends it to the Android app via the internet network.. The Android app allows users to view Greenhouse environmental statistics. The research was carried out in stages, beginning with hardware and software ideation and ending with real-world testing of the application. According to the research, the system's implementation is functional, nodes can send data and be displayed on mobile applications, and tests were conducted using the black box testing method, which yielded a "successful" statement on eight tests performed on the Android mobile application.
Rekomendasi Pemberian Kredit Pemilikan Rumah Menggunakan Kombinasi Metode VIKOR dan Pembobotan Entropy Irmansyah Lubis, Ahmadi; Siregar, Rosma
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5118

Abstract

Home Ownership Credit (KPR) is the process of buying a house by way of credit or installments for a certain period of time and a determined interest rate. Problems that often arise in the process of granting mortgages are when providing an analysis of recommendations for the suitability of a house, such as credit analysis errors which often result in bad credit payments and disrupt the fund circulation system between credit payments and receivables. Billing. In addition, there is no filter in determining the eligibility of providing credit computerized, the system is equipped with a decision support system that can describe the supporting factors that are used as a calculation weight in making decisions on determining home ownership loans for those who deserve to receive them. To analyze the feasibility of providing home ownership loans, a decision support system is needed that can provide a good analysis to determine the creditworthiness of home ownership with the VIKOR method and the Entropy method. The Entropy method is applied to determine the weight of each criterion and the VIKOR method is used to rank alternatives. Based on the results of the study, the VIKOR method and the Entropy weighting method can provide recommendations for providing home ownership loans to eligible customers based on specified criteria. The ranking results were obtained by testing variations in the VIKOR index value with the best alternative result, namely A3 (3rd Alternative). And the Entropy method produces objective and proportional criteria weights and obtains valid criteria weights without having to give criteria weights manually or subjectively, so it is useful for determining the weight of criteria that need not be doubted about their validity.
Decision Support System for Best Honorary Teacher Performance Assessment Using a Combination of LOPCOW and MARCOS Putra, Ade Dwi; Arshad, Muhammad Waqas; Setiawansyah, Setiawansyah; Sintaro, Sanriomi
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5127

Abstract

Teachers are the main pillars in the formation and development of future generations. They not only pass on knowledge, but also play an important role in guiding, inspiring, and shaping the character of students. The main problems in selecting the best honor teachers are limited resources and a less systematic evaluation process, lack of transparency and consistency in the selection process can also lead to dissatisfaction and injustice among honor teachers. Through the combination of LOPCOW and MARCOS, this research succeeded in producing a more accurate and accountable ranking in the selection of the Best Honorary Teacher. The LOPCOW approach provides a deep understanding of percentage changes against set criteria, while MARCOS assists in weighting compromise solutions that can optimally meet those criteria. Thus, the study provides a more holistic and detailed view of various aspects of teacher quality and performance, enabling better decision-making in selecting the Best Honorary Teachers who meet educational needs and advance student learning. The ranking results showed that the assessment results from the LOPCOW and MARCOS methods gave results, namely rank 1st with a final value of 0.3404446 obtained by SF Teachers, 2nd place with a final value of 0.3367384 obtained by LBS Teachers, and 3rd place with a final value of 0.3343083 obtained by ASB Teachers.
Penerapan Chatbot pada Aplikasi Web Tanya Jawab Tentang Fiqih Jual Beli Islam Menggunakan LangChain Nurhapiza, Nurhapiza; Harahap, Nazaruddin Safaat; Fikry, Muhammad; Affandes, Muhammad
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5148

Abstract

Fiqh is the field that studies Islamic rules on how humans behave, both in speech and action. Islamic Fiqh of buying and selling is a branch of fiqh that concentrates on the laws and rules relating to transactions and social interactions that occur in the daily lives of Muslims. There are many sources of learning about the fiqh of buying and selling, including books and the internet. However, manual searches can take a long time and make it difficult for some people to gain in-depth understanding. The application of a chatbot to a question and answer web application can provide a solution to provide easier access. This research aims to provide an effective and efficient solution in understanding fiqh muamalah (Islamic buying and selling). This research develops a question and answer system about the fiqh of Islamic buying and selling to make it easier for users to understand, by utilizing a deep learning approach through technologies such as LangChain, OpenAI, Large Language Model, and ChatGPT 3.5 turbo. Implementation is done through a chatbot web application that provides an initial display and menu, allowing users to ask questions about the fiqh of Islamic buying and selling and see the answers and references. Testing was conducted by students of UIN Sultan Syarif Kasim Riau and an ustaz who has a good understanding of fiqh muamalah using ten questions that were tested through the question and answer web application as a guide. The test results showed an answer evaluation of 88.8% with a very suitable category regarding the correctness of the responses given.
Klasifikasi Penyakit Cacar Monyet Menggunakan Metode Support Vector Machine Anugrah, Wendy; Haerani, Elin; Yusra, Yusra; Oktavia, Lola
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5149

Abstract

Monkey pox is a zoonotic disease caused by the monkey pox virus and this disease is very dangerous. Monkey pox can be detected in advance by using information contained in patient data and applying machine learning techniques. This study aims to classify monkey pox using the Support Vector Machine (SVM) method. This test is carried out using a confusion matrix by comparing the ratio of training data and test data with a ratio of 70:30, 80:20, 90:10 and using the RBF kernel. Based on the test results, the highest ratio results were obtained at 90:10 with the best accuracy value of 65% with SVM parameter testing, namely the value C= 10 and y (gamma)= 1. Based on the results of tests carried out using the Support Vector Machine method, the accuracy values ​​were quite good.

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