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Jurnal Sistem Komputer dan Informatika (JSON)
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma
ISSN : -     EISSN : 2685998X     DOI : https://dx.doi.org/10.30865/json.v1i3.2092
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
The Jurnal Sistem Komputer dan Informatika (JSON) is a journal to managed of STMIK Budi Darma, for aims to serve as a medium of information and exchange of scientific articles between practitioners and observers of science in computer. Focus and Scope Jurnal Sistem Komputer dan Informatika (JSON) journal: Embedded System Microcontroller Artificial Neural Networks Decision Support System Computer System Informatics Computer Science Artificial Intelligence Expert System Information System, Management Informatics Data Mining Cryptography Model and Simulation Computer Network Computation Image Processing etc (related to informatics and computer science)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 755 Documents
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Implementasi Klasifikasi Data Mining Untuk Penentuan Kelayakan Pemberian Kredit dengan Menggunakan Algoritma Naïve Bayes Agung Triayudi; Sumiati Sumiati
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 1 (2022): September 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4653

Abstract

Credit today is very widely used in the transaction process. At first, lending was only done by banks, but with the development of time and also the increasing needs and purchases from the public, lending is not only done by banks. The granting of credit for financing goods by the company to the buyer is not done haphazardly, but must go through several selection processes. The process of granting credit must be carried out through detailed and strict stages. This causes the process to be lengthy and also lengthens the work of the selection team. Data mining is a data processing technique that is useful for obtaining important patterns from data sets. The Naïve Bayes algorithm is part of the data mining classification process. The process of the Naïve Bayes algorithm is based on the concept of the Bayes theorem. The result of the research is that the new alternative data is ACCEPTABLE for credit applications, it can be seen that the probability value of ACCEPTED is greater than the probability value of REJECTED, which is 0.011108
Analisis Sentimen Produk Kecantikan Jenis Serum Menggunakan Algoritma Naïve Bayes Classifier Muhammad Hamka; Naila Alfatari; Dhani Ratna Sari
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 1 (2022): September 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4740

Abstract

The increased consumption of beauty products as a lifestyle has increased public opinion on the beauty products used. Generally, reviews are given through posts on social media. This study discusses the classification of sentiment analysis on the use of serum beauty products on Twitter using the Naïve Bayes Multinomial algorithm. Sentiment analysis of serum beauty products is carried out to provide information and preferences to the public regarding the quality of a product. The results of the information and preferences become a reference for consideration in choosing the appropriate serum beauty product. The data used in this study were 27,587 tweets using three keywords, namely "serum," "face serum", and "beauty serum". Tweet data is divided into training data and test data with the number of training data as much as 22,070 tweets and test data as much as 5,518 tweets. The data is categorized using the lexicon senticnet 7 dictionary based on polarity values. The results of the analysis of positive sentiment are 35%, negative sentiment is 63.8%, and neutral sentiment is 1.2%. The classification results using Naïve Bayes Multinomial obtain the highest accuracy value of 80%. The Confusion Matrix results get the highest precision value of 88%, the highest recall of 81%, and the highest f1-Score of 86%.
Sistem Pendukung Keputusan Untuk Pemilihan Auditor dengan Menggunakan Metode MOOSRA Ahlan Ismono
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 1 (2022): September 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4743

Abstract

Auditors have an important role in the company. This is because the results of the auditor's work are related to the continuity of the company's organization. The role of the auditor is so important for the company that the auditor should have extensive knowledge and good analytical skills. in the selection process does not have a special reference that causes the selected auditor is not in accordance with what is expected by the company. Therefore, it is necessary that the process for selecting auditors has been computerized with a computer. Decision Support System is a system that is already connected to a computer. Decision Support System is part of an information system on a computer that is used to process data. The Multi-objective Optimization method on the basis of Simple Ration Analysis (MOOSRA) is a method that can be used to solve problems on semi-structured and multidisciplinary attributes. The results showed that Alternative 1 (A1) was chosen as the auditor with the highest score of 5.60
Implementasi Algoritma K-Nearest Neighbors Pada Penentuan Jurusan Siswa M. Daffa Alkhussayid; Ferdiansyah Ferdiansyah
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 1 (2022): September 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4759

Abstract

SMA Negeri 8 Palembang has two majors, namely science and social studies. Determination of majors is done when in class X to determine the majors of the student. This research was conducted because the teacher had difficulty in determining the majors at SMA Negeri 8 Palembang. In the research case, the researcher uses the classification with the k-Nearest Neighbors algorithm, and the Euclidean distance measurement method to predict the students in determining the majors that will be taken by students. The source of the data for this research is the report card scores for the X grade students of SMA Negeri 8 Palembang, and the data for this research are 335 data on the grade X students of SMA Negeri 8 Palembang. The data collection was taken from the grade X class report cards, namely mathematics, physics, biology, English, Indonesian, history, geography, economics, and psychological test scores. In determining the majors, students in science and social studies get the average score of all subjects and the psychological test scores produced by these students to enter the science department with an average score of 80, math score 78, physics 78, biology 78, and psychological test 80. If students get an average score below 80, it will be predicted to enter social studies, to enter the social studies department with a minimum score of 70 geography subjects, 70 economics, 70 history, and 70 psychological test scores. The results obtained in this study used the K method. -Nearest Neighbors based on training data obtained from 335 student data, 101 classified social studies class according to predictions, 10 data predicted social studies, but data declared natural science, 2 science prediction data and, 222 data according to natural science predictions, and the accuracy got 96% and the results of observations using the Website using K-NN show the same data results obtained through an accuracy of 96%.
Deteksi Dini Anak Disleksia dengan metode Support Vector Machine Ardhian Ekawijana; Akhmad Bakhrun; Zulkifli Arsyad
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 1 (2022): September 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4776

Abstract

Dyslexia is a brain disorder caused by genetics. People with dyslexia can live a normal life and even have certain advantages if they get the correct education. People with dyslexia often get the predicate stupid because teachers do not know the case of their students. Early detection of dyslexic children can be done with a series of tests so that the system can conclude that the data is dyslexic or not. Support Vector Machine is a data classification method to share dyslexia test results or not. This system is trained with test results data that are already available using the SVM method. This study uses gamification data to detect dyslexic children or not. SVM proves a good level of accuracy in predictions up to 94%.
Implementasi dan Analisis Profil Sistem Pada Virtualisasi Paloalto Firewall Berdasarkan Metrik Sumber Daya Komputasi Ni Made Meliana Listyawati; Adityas Widjajarto; M Teguh Kurniawan
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 1 (2022): September 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4780

Abstract

On the security aspect, it is necessary to know how effectively a firewall can protect network devices from DDoS attacks. The characteristics of a firewall have different functions in protecting the system from various external attacks that can attack and retrieve data. In this research, the implementation of Paloalto firewall virtualization aims to obtain the system profile function on the firewall based on the use of computing resources. Profiling of the firewall system of this experiment based on the consumption of computing resources in load testing. This experiment used a DDoS SYN flood attack on Kali Linux as an attacker and a virtualization Paloalto firewall that protects a web server on Ubuntu Server as an attack target. This research distinguished based on two test scenarios, namely based on testing the service HTTP allow and service HTTP block with Paloalto memory specifications at RAM 5.5 GB and RAM 8 GB specifications. Measurements were made based on computing resources on CPU, memory, and a session focused on before, during, and after DDoS SYN flood attacks. The pattern of usage of computing resources tends to be linear when a DDoS SYN flood attack occurs. The experimental results obtained on the highest use of computing resources during the attack were CPU usage with an average percentage of 95.8% and the second increase was in memory usage with an average percentage of 44%, and the session usage was 138682. For further research, it can use variations of DDoS attacks to get a wider profile.
Implementasi Algoritma Naïve Bayes Classifier (NBC) untuk Klasifikasi Penyakit Ginjal Kronik Qurotul A'yuniyah; Ena Tasia; Nanda Nazira; Pangeran Fadillah Pratama; Muhammad Ridho Anugrah; Jeni Adhiva; Mustakim Mustakim
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 1 (2022): September 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4781

Abstract

Degenerative disease is a non-communicable disease that arises from an unhealthy lifestyle, so that it can reduce the physical and mental quality of the sufferer. Chronic Kidney Disease (CDK) is a degenerative disease that is included in the world's top 10 causes of death according to the World Health Organization (WHO). This study used CDK data with attributes of age, blood pressure, weight, albumin levels, sugar levels, red blood cells, pus cells, pus cell clots, bacteria, blood sugar levels, blood urea levels, creatinine serum, sodium, magnesium, hemoglobin, the volume occupied by red blood, indications of hypertension, indications of diabetes mellitus, indications of coronary heart disease, appetite, indications of swelling in the calves or feet, and indications of anemia. Therefore, the classification of kidney disease data is carried out with the implementation of the superior Naïve Bayes Classifier (NBC) algorithm and produces a high level of accuracy. The classification results using the RapidMiner tools carried out by the application of the NBC algorithm, the accuracy value is 96.43%, the average recall is 93.18%, the average precision is 93.02%, and the AUC is 93.2%. so it can be concluded that the performance of NBC in classifying chronic kidney disease data is excellent.
Analisis Sentimen Customer Feedback Tokopedia Menggunakan Algoritma Naïve Bayes Aldian Umbu Tamu Ama; Deva Nita Mulya; Yashinta Putri D Astuti; Ignatius Bias Galih Prasadhya
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 1 (2022): September 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4783

Abstract

Products and customers have a close relationship, therefore UMKM need to build good relationships with customers. The most common way that companies or UMKM do is to look at the reviews given, this is called customer feedback. The results of customer feedback to companies or UMKM can improve service and product quality. The problem that arises is how to process the many reviews given, especially reviews from marketplaces like Tokopedia. Therefore, a method is needed to see user reviews of the products being sold, whether positive or negative. The method that will be used is sentiment analysis. Sentiment analysis is the process of understanding and extracting and automatically processing text data and can produce sentiments that are displayed in a sentence. The steps taken were taking House of Smith customer review data at Tokopedia, manual labeling to get positive and negative data reviews, data preprocessing, TF-IDF weighting and classification using the Naïve Bayes algorithm. The results of sentiment testing using the Naïve Bayes algorithm with TF-IDF weighting quality accuracy of 83% with visualization of the distribution of words that appear the most are the words 'good', 'comfortable' and 'use' for positive reviews. The most frequent negative reviews were 'material' and 'thin' which indicated that some buyers felt that the product had a thin material.
Analisis Vulnerability Management Pada Container Docker Menggunakan Opensource Scanner Berdasarkan Standar Cyber Resilience Review (CRR) Milenia Oktaviana; Adityas Widjajarto; Ahmad Almaarif
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 1 (2022): September 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4787

Abstract

One of the most widely used container technologies to provide IT services is Docker. The vulnerability in container technology, namely Docker, requires special management. Management of this vulnerability can be done technically with a software vulnerability scanner and standard Cyber Resilience Review (CRR) guidelines. Experiments were carried out with Aquasec and Anchore scanners that performed vulnerability scanning on two Docker Images systems. The two vulnerable systems have different versions, namely version – 1 and version – 2. The software elements in version – 2 have a higher versioning level than version – 1. Experimental data in the form of vulnerability reports are analyzed based on Cyber Resilience Review (CRR) which focuses on four stages namely Define a Strategy, Develop a Plan, Implement the Capability, Assess and Improve the Capability. So that the results of Category Vulnerability are obtained, namely 30 Closed Vulnerability, 10 Open Vulnerability, and 13 Newly Vulnerability. Continuation of this research can use aspects of Patch Management with more varied software tools.
Vulnerability Management Pada Vulnerable Docker Menggunakan Clair Scanner Dan Joomscan Berdasarkan Standar GSA CIO-IT Security-17-80 Ryan Supriadi Ramadhan; Adityas Widjajarto; Ahmad Almaarif
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 1 (2022): September 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4789

Abstract

Vulnerabilities in Docker need to be managed considering that this vulnerability is one of the potentials for exploitation, this can happen because Docker is a container related to application and system security. This study analyzes the vulnerability management process in Docker Images and Docker Images Applications using the GSA CIO-IT Security-17-80 standard. This vulnerability search uses two scanning tools, namely Clair Scanner and JoomScan. Vulnerabilities in Docker Images and Docker Images application version - 1, were overcome by creating a new system, namely version - 2 which upgrades the Docker Images software and Docker Images application. The test scenario is run by scanning for vulnerabilities in two versions of the trial system, in the form of a vulnerability report. The data was analyzed using the GSA CIO-IT Security Standard-17-80 which was limited to the stages of Scanning Capabilities, Vulnerability Scanning Process, Vulnerability Scan Reports, Remediation Verification, and Re-Classification of Known Vulnerabilities. The result is the fastest scanning time is in version - 2, the results of the comparison of vulnerabilities obtained are 44.45% on Docker Images and 77.78% on Joomla. So that the contribution that can be given is to provide an overview of the use of the GSA CIO-IT Security-17-80 standard as a guide for managing the security of an IT asset based on the stages carried out. Continuation of research can be in the form of using the 6 stages of GSA with the support of adequate vulnerability data from the right scanner software.

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