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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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Penerapan Algoritma Gronsfeld Dengan Pembangkit Kunci Quicksort Untuk Mengamankan Pesan Rahasia Ria Ramadhani Syahputri; Mesran Mesran; Murdani Murdani
Journal of Computer System and Informatics (JoSYC) Vol 4 No 4 (2023): August 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i4.4090

Abstract

The numerous issues often encountered in securing confidential messages revolve around message leaks. Message leaks occur through various means, such as eavesdropping. Eavesdropping happens when unauthorized parties manage to access and obtain information from ongoing communications. One way to maintain the confidentiality of secret messages is through encryption and decryption, known as cryptography, achieved by transforming the original message (plaintext) into a secret message (ciphertext). In this study, the results of the encrypted character ciphertext are v, v, w, ^, ™, ʽ, ¦, ¥, -, ¼, Ý, ó, ⁒, x, [], ", [], #, S, a, I, p, p. These findings can serve as an alternative solution to safeguard message confidentiality, ensuring only the message owner and those with knowledge of the key can access it. The Gronsfeld Cipher cryptographic method can also enhance the security of the Quicksort algorithm, a cryptographic algorithm, by generating a more randomized text message that does not reveal patterns connecting it to the original text message.
Analisis Dalam Pendukung Keputusan Seleksi Content Creator Mahasiswa Terbaik Menerapkan Metode EDAS dan ROC Mesran Mesran; Dwina Pri Indini
Journal of Computer System and Informatics (JoSYC) Vol 4 No 4 (2023): August 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i4.4093

Abstract

One of the uses of technology is social media, which is an application that can convey information to many people. Around 49 percent of Indonesia's population are active users of social media because the use of social media as a means of promotion is considered easier and more efficient to get a wider target audience. In creating content on social media, a quality content creator is needed. The selection of student content creators at Budi Darma University in Medan is important to be able to get students who are creative and have the potential to become social media content creators. In selecting content creators, students at Budi Darma University have several predetermined criteria, namely the number of social media, the number of tiktok followers, the number of Instagram followers, content production per day, content creativity and content design updates. In the process of selecting student content creators, a system is needed that can speed up and simplify the process of selecting student content creators, namely the SPK (Decision Support System). In this study the EDAS (Evaluation Based on Distance from Average Solution) method and the Rank Oder Centroid (ROC) method used are expected to help the selection process for accepting student content creators. So the best alternative to become a student content creator lies in alternative A8 on behalf of Arsyillah with a score of 182001
Implementasi Metode Additive Ratio Assesment (ARAS) Dalam Pemberian Promo Tiket Umroh Pada Member Zaza Mutiara Arini; Mesran Mesran; Melda Panjaitan
Journal of Computer System and Informatics (JoSYC) Vol 4 No 4 (2023): August 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i4.4142

Abstract

PT Anugerah Quba Mandiri (AQM) Tour & Travel every year has the charm to be able to attract the attention of customers or customers on the promo route. the selection of Umrah ticket promos held by the travel party is not given to all pilgrims who register for Umrah. The promo will be given only to pilgrims who get the general qualifications set by the company. Members who have been officially registered can have the opportunity to get a promo to be given to the selected congregation, but ticket promos can only be given to one congregation, therefore it must be based on the appropriate requirements and qualifications. Members who have registered will be recorded using a computer system to record the number of members who are entitled to the ticket promo. With the large number of registered members, the company really needs a decision system that can help in making decisions on ticket promotions. A Decision Support System (SPK) that can help companies find out which members are eligible for ticket promotions using the Additive Ratio Assessment (ARAS) method. The ARAS method is a method or method of decision making that uses the concept of utility degree ranking by comparing the overall index value of each alternative to. In its implementation, the decision support system in providing promos to PT AQM tour & Travel members uses the ARAS method, obtaining effective and objective ranking results. From the results of research conducted at PT AQM Tour & Travel it can be concluded that Alternative A13 on behalf of Nurlela Br Sitepu is stated as a member who gets an Umrah ticket promo with a preference value of 0.2963.
Klasifikasi Buah Pinang Berdasarkan Data Sensor Menggunakan Metode K-Nearest Neighbor Berbasis Web Dea Rizki Febrinamas; Rahmi Hidayati; Irma Nirmala
Journal of Computer System and Informatics (JoSYC) Vol 4 No 4 (2023): August 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i4.3805

Abstract

Buah Pinang merupakan salah satu jenis buah yang banyak diekspor dan memiliki manfaat dalam bidang kecantikan, pewarna makanan serta sebagai bahan baku untuk industri tekstil. Proses klasifikasi buah pinang secara manual membutuhkan waktu dan tenaga yang banyak. Sehingga untuk membantu mempercepat dan mempermudah proses klasifikasi buah pinang dibutuhkan sistem klasifikasi yang dapat mengenali buah pinang berdasarkan warna dalam berbagai tingkat kematangan buah yaitu mentah, matang dan tua. Penelitian ini menggunakan metode K-Nearest Neighbor (K-NN) untuk proses klasifikasi buah pinang. Data yang digunakan sebanyak 600 data yang diperoleh dari sensor, terdiri dari 200 buah pinang mentah, 200 buah pinang matang, dan 200 buah pinang tua. Parameter yang digunakan yaitu mentah, matang dan tua dengan rentang nilai Red, Green, Blue (RGB) yang berbeda setiap kondisinya. Pengujian menggunakan nilai ketetanggaan (K) yaitu 5, 7, 9 dan 11 dan diperoleh nilai ketetanggaan (K) terbaik adalah K = 7. Hasil pengujian dilakukan menggunakan confusion matrix didapatkan nilai accuracy sebesar 98,33%, recall sebesar 97,24%, dan precision sebesar 100%.
Integrasi Mesin Absensi dan Pusher Notification pada Sistem Informasi Akademik Sekolah Untuk Monitoring Absensi Real-Time Monsya Juansen; Septian Simatupang
Journal of Computer System and Informatics (JoSYC) Vol 4 No 4 (2023): August 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i4.3840

Abstract

Integrasi mesin absensi dan pusher notification dalam sistem informasi akademik sekolah untuk pemantauan kehadiran secara real-time merupakan aspek penting dari manajemen sekolah yang efisien. Penelitian ini bertujuan untuk mengintegrasikan mesin absensi dan pusher notifikasi ke dalam sistem informasi akademik sekolah untuk pemantauan kehadiran secara realtime. Sistem ini memberi sekolah kemampuan untuk memantau kehadiran siswa dengan cepat dan efisien serta menyampaikan pemberitahuan real-time kepada pemangku kepentingan terkait. Kajian diawali dengan analisis menyeluruh terhadap persyaratan dan tantangan yang dihadapi sekolah dalam memantau kehadiran secara real-time. Tinjauan komprehensif literatur yang ada dilakukan untuk memahami konsep mesin absensi, notifikasi pusher, dan integrasi sistem. Berdasarkan temuan, desain sistem diusulkan, dengan mempertimbangkan arsitektur, antarmuka pengguna, dan alur kerja sistem terintegrasi. Mesin absensi terhubung ke sistem informasi akademik melalui protokol komunikasi yang sesuai, memastikan transfer dan sinkronisasi data tanpa hambatan. Fitur pusher notification diintegrasikan ke dalam sistem untuk mengirimkan notifikasi kehadiran secara real-time kepada pemangku kepentingan terkait, termasuk administrator, guru, dan orang tua. Ini memfasilitasi tindakan cepat dan pengambilan keputusan berdasarkan informasi kehadiran. Untuk menilai keefektifan solusi terintegrasi, serangkaian pengujian dan evaluasi dilakukan. Ini termasuk skenario seperti check-in siswa, check-out, dan pembaruan catatan kehadiran. Kinerja, keandalan, dan kepuasan pengguna dari sistem terintegrasi dievaluasi. Hasil penelitian menunjukkan manfaat pengintegrasian mesin absensi dan notifikasi pusher ke dalam sistem informasi akademik. Pemantauan kehadiran secara real-time meningkatkan efisiensi administrasi, meningkatkan akuntabilitas siswa, dan mendorong komunikasi yang efektif antara sekolah dan pemangku kepentingan. Penelitian ini berkontribusi pada pengetahuan tentang integrasi sistem kehadiran dan pemberitahuan pusher di lingkungan sekolah. Temuan ini dapat memandu lembaga pendidikan dalam mengadopsi solusi serupa untuk meningkatkan manajemen kehadiran dan operasional sekolah secara keseluruhan.
Penerapan Metode CART Dalam Klasifikasi Jurusan Siswa Baru Destia Arini Hairunnisa; Cucu Suhery; Rahmi Hidayati
Journal of Computer System and Informatics (JoSYC) Vol 4 No 4 (2023): August 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i4.3860

Abstract

SMK Negeri 3 Pontianak is one of the vocational education schools in Pontianak City. Every new academic year SMK Negeri 3 Pontianak accepts around 320 new students. The large number of prospective new students makes the majoring process carried out by the school become less effective and takes a long time to determine majors for new students. With a system that can classify new student majors, it helps in the process of determining student majors. This study uses the Classification and Regression Trees (CART) algorithm for the classification process in determining majors for new students. The assessment indicators used for classification consist of interest, MTK (US) school exam scores, school exam IPA scores, school exam Indonesian language scores, math report cards, science report cards, social science report cards, Indonesian language report cards, and English report cards. Classification of majors at SMK 3 Pontianak consists of accounting, office, marketing, and hospitality majors. The amount of data used is 320 data which is divided into 224 training data and 96 test data. The CART algorithm generates decision trees, rules, and new student majors that have been classified. Based on the test results using the confusion matrix, the system accuracy results are 84.38%.
Analisis Rantai Pasok Toko Ban dengan Penerapan SCOR dan AHP An-Nisa Firardiansyah Prayitno; Azhar Eka Putra Lasena; Sandhy Fernandez
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.3851

Abstract

The research carried out aims to describe the supply chain of XYZ Tire Shop using the SCOR (Supply Chain Operations Reference) and AHP (Analytical Hierarchy Process) methods. The SCOR method is used to understand and identify important elements in the supply chain, including suppliers, production, distribution and customers. Furthermore, the AHP method is used to evaluate and prioritize relevant criteria in decision making, such as supplier selection, risk management, and distribution improvement. By integrating SCOR and AHP, XYZ Tire Shop can improve their operational efficiency and supply chain performance. This analysis provides valuable insight into optimizing coordination with suppliers, inventory management, and investments in distribution improvements. The results of this research provide practical guidance for XYZ Tire Shop in improving the best decision making in managing their supply chain. And it can be concluded that the best tire recommendation for procurement of goods is Bristone 205/65 R16 Ecopia.
Sistem Pendukung Keputusan Kelulusan Peserta Pelatihan Menggunakan Metode Naïve Bayes Jajang Nurjaman; Andrianingsih Andrianingsih
Journal of Computer System and Informatics (JoSYC) Vol 4 No 4 (2023): August 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i4.4074

Abstract

A decision support system (DSS) is a system that helps decision making in a particular process or situation. In the context of trainee permits, SPK can be used to help predict graduation or not based on several relevant factors, the main objective of this research is to change the system calculation method from manual to automatic. The Center for Tourism and Creative Economy Human Resource Development (PPSDM Parekraf) uses this system to help automate graduation calculations from manual to automatic by inputting several values ​​(Pre & Post Test, Behavior, Assignments and Quizzes, Reports and Comprehensive Test) with all the value provisions reached the test threshold (70). The Naive Bayes method is one of the general classification methods used by SPK and is based on the Bayes theorem with the assumption that each feature or factor used in classification is independent of one another. This system is designed to facilitate an effective and efficient decision-making process in transmitting training participants whether they can continue to the next level of training. This research was carried out in the period from March to June 2023 at PPSDM Parekraf. The data studied uses and analyzes by taking samples of ongoing training data. Hopefully, this SPK will help with more accurate and efficient decisions in determining the graduation of Basic Tourism training participants, the current conditions regarding value processing are still carried out manually. This system is recommended to be used as a medium or tool to support the results of participants' agreements which initially used manual calculations to become automatic. To test the data, it is done by collecting the data and values ​​of the training participants, then preprocessing the data using the Naïve Bayes method into a decision support system.
Analisis Pemberian Bantuan UMKM Menggunakan Algoritma K-NN dan C4.5 Akrim Teguh Suseno; M Al Amin; Fajar Mahardika
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4312

Abstract

The Covid-19 pandemic has had an impact on the economies of countries in the world, including Indonesia. The low trade balance, public consumption and the implementation of large-scale social restrictions (PSBB) have caused business actors to suffer losses and even go out of business, especially Micro, Small and Medium Enterprises (MSMEs). Assistance to MSMEs has been provided by the government to maintain the functioning of the economy in the micro environment. However, the provision of this assistance needs to be carried out further analysis because there are reports that the assistance is not on target, causing the budget spent to be ineffective. In this research, an analysis will be carried out on the provision of assistance from the government to MSMEs, especially in Pekalongan district, using data mining techniques, especially classification. The algorithms used are K-Nearest Neighbor (K-NN) and C.45 which are then compared to determine the highest level of effectiveness in recommendations for providing MSME assistance in the District. Pekalongan. The data used was 312 MSMEs and after going through the data preprocessing process we got accurate data, namely 279. The data was divided into 2, namely training data of 200 data and testing data of 79 data. The results of this research, the K-NN algorithm obtained an accuracy level of 94.94%, precision 94.73% and recall 94.73%, while the C.45 algorithm obtained an accuracy level of 86.08%, precision 87.21% and recall 88.3%. Based on the results of this research, it can be concluded that the use of data mining techniques with the K-NN and C4.5 algorithms has a high level of accuracy for recommendations for assistance to MSMEs, however for the best results you can use the K-NN algorithm which has an accuracy level of 94.94%.
Penerapan Fitur Seleksi dan Particle Swarm Optimization pada Algoritma Support Vector Machine untuk Analisis Credit Scoring Naufal, Abdul Razak; Suseno, Akrim Teguh
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4409

Abstract

After the Covid-19 pandemic, the banking sector faced significant challenges in contributing to achieving national goals in terms of increasing living standards and equalizing the regional economy. Hundreds of millions of low-income people have no credit or bank accounts because they do not have sufficient credit history to warrant the credit scores assigned to them. An estimated 1.7 billion people (31% of the adult population) do not have an account with a financial institution. People today are usually concentrated in developing countries, especially in China 204 million, India 357 million and Indonesia 102 million people. Because it is very difficult to make accurate predictions in determining credit worthiness for low-income people. Cooperatives are financial institutions that have a crucial role in channeling financing to members and the community to develop their businesses. An inappropriate credit distribution process can have a negative effect on KSP, resulting in frequent losses. This risk is known as problem loans, the cause is the KSP's failure to analyze the credit of prospective debtors. Therefore, calculations are needed to detect opportunities for credit risk default by prospective debtors objectively and precisely so that loan problems do not occur. Credit scoring is a method used to evaluate credit risk in terms of loan applications from consumers [4]. In this research we will provide a solution using classification techniques with feature selection methods in the Particle Swarm Optimization (PSO) Algorithm and Support Vector Machine (SVM) to predict the credit risk of prospective debtors failing to make loan payments. The application of the SVM algorithm in credit scoring research is because SVM is good at data classification. However, the standard SVM model still does not produce optimal results due to the difficulty of determining the best parameters, therefore researchers will optimize it with the Feature Selection and PSO algorithms to determine the best parameters. The results from the combination of several parameters using PSO-SVM obtained an accuracy of 87.23%, therefore the application of this method was proven to improve the performance of the SVM algorithm to increase its accuracy results in predicting the feasibility of granting credit.

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