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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 757 Documents
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Implementasi Metode K-Means Untuk Memprediksi Status Kredit Macet Muthia Nur Rizky Fitriani; Bayu Priyatna; Baenil Huda; April Lia Hananto; Tukino Tukino
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 3 (2023): Maret 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5953

Abstract

A credit card is one of the legal payment media owned by a bank in making a payment transaction within the agreed timeframe. In particular, credit services are provided by institutions or bodies that have the authority to distribute funds in the form of financial assistance to individuals and groups. However, in practice there are bound to be obstacles, especially during payback periods that often occur, such as when a customer wants to submit a Repeat Order or apply for funds again. Obstacles that are usually encountered in the process of granting credit are substandard credit and bad credit payments. Before PT Esta Dana Ventura wants to decide to approve applications for re-granting credit cards from prospective repeat order customers, a classification of assessment criteria is needed to determine the feasibility of granting credit to prospective repeat order customers. This study made the decision to use Data mining clustering classification with Rapidminer tools as a tool to obtain accurate results by processing data using the K-Means clustering method to help PT. Esta Dana Ventura in analyzing potential non-performing loans. By comparing survey data for Repeat Order candidates with previous credit granting data and classifying them in the form of bad or non-bad credit classifications.From the results of research using the k-means method it can produce grouping data into 3 criteria, namely (C0) 69 data with current customers, (C1) 3 data with very current customers, and (C2) 52 data with Bad customers..
Sistem Pendukung Keputusan Pemilihan Aplikasi Chat Terbaik Dalam Mendukung Pembelajaran Daring di Masa Pandemi Covid Menggunakan Metode Multi Attribute Utility Theory Mesran Mesran; Amanudin Harahap; Fifto Nugroho
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 3 (2023): Maret 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5991

Abstract

The Chating application is a virtual communication service either through messages, voice calls, or video calls that can assist in conducting online lectures during a pandemic. There are many types of applications that can be used to communicate during online lectures. Such as the Telegram application, Whatsapp application, Discord, KaKaoTalk, Line or WeChat and other social media applications that can be used to communicate personally or in groups. However, these applications have their own advantages and disadvantages that can affect the comfort and optimization of learning. Therefore, to determine the best Chating application, each alternative must have its own criteria as a requirement for the optimization of the application. As in this study, the authors added some data as criteria, namely Group Support, Member Capacity, Security, Account Privacy and Services provided by the application. Which, each criterion has a predetermined weight and is carried out with a settlement process using the Multi Attribute Utility Theory (MAUT) method for the ranking process. From calculations using the Multi Attribute Utility Theory (MAUT) method, it will produce sequential alternatives from the highest to the lowest. The alternative value which is Qi or the highest ranking will be used as an alternative recommendation for chat applications that will be used as a tool to support online learning during the Covid pandemic.
Analisa Tingkat Kepuasan Masyarakat Terhadap Kualitas Pelayanan CCTV Lalu Lintas Menggunakan Metode Naïve Bayes Pandu Dharma Putra; Jemakmun Jemakmun
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 3 (2023): Maret 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.6002

Abstract

This study aims to determine the level of public satisfaction with the quality of traffic CCTV services at the Palembang City Police Station based on the results of a questionnaire containing Tangibles, Reliability, Responsiveness, Assurance, Emphaty. This research is a quantitative research. The subject of this study was the community of Palembang city which amounted to 300 people Data collection uses questionnaire questionnaires, while data analysis is processed and calculated in mining applications, namely rapidminers using the Naïve Bayes method. The results showed that out of 300 respondents, 73 were very satisfied, 139 satisfied, 34 neutral, 29 dissatisfied, and 25 very dissatisfied. While the most respondents based on gender attributes are men with a total of 166, age attributes are 20-30 with a total of 86, education attributes are high school with a total of 150, job attributes are private employees with a total of 140, vehicle attributes are motorcycles with a total of 150. And from the category is Satisfied with the number of 139.
Perbandingan Metode Perhitungan Jarak pada Nilai Centroid dan Pengelompokan Data Menggunakan K-Means Clustering Budi Hartono; Sri Eniyati; Kristophorus Hadiono
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 3 (2023): Maret 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.6021

Abstract

This study will observe the process of grouping data or forming clusters using K-Means clusters with three methods of measuring distances, namely Euclidean distance, Manhattan distance, and Minkowski distance. Observations are more focused on changing the centroid value and the results of grouping data, as well as the number of iterations required. Experimental data amounted to 20, 30, 40, and 50 pieces of data which were grouped into 2 groups. This research also summarizes the application of K-Means clusters which have been widely used in various fields, including Health, Education, and Disaster. The results of grouping data with the three distance measurement methods are not too much different, namely the highest difference is 2 members of the data on 50 test data. The most iterations on 40 test data use the Euclidean distance, namely 7 iterations, and the least iteration on 20 test data uses Minkowski distance i.e. 3 iterations. On the 50 test data it takes 4 iterations. The amount of test data is not directly proportional to the number of iterations needed to reach the cluster in a stable state.
Implementasi Teorema Bayes Pada Sistem Informasi Posyandu Dalam Mendeteksi Stunting Pada Balita Dedi Gunawan; Verania Nur Andika
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 4 (2023): Juni 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6146

Abstract

Abstract–The management of posyandu data in Majegan Village is still carried out manually. This causes health monitoring to be not optimal, especially in detecting stunting in toddlers. One of the efforts to overcome this problem is to provide a posyandu information system that can make it easier for posyandu officers to record and analyze fetal growth until toddlerhood and can detect symptoms of stunting. Through the implementation of Bayes' theorem in a web-based application, stunting symptoms can be observed earlier. Bayes' theorem calculates the values of the symptoms experienced by toddlers so as to obtain the results of probability numbers that can be used to predict stunting in toddlers. System design uses the waterfall method which goes through the stages of SDLC (System Development Life Cycle). After the system was developed, to test the quality of the application and the accuracy of naïve bayes in predicting stunting, two types of testing were carried out, namely black box testing and system usability testing (SUS). The black box test results show that the application functionality runs well with an error percentage of 0%, while the SUS test results show that the application has a usability level at Level B which means the application can be used and help users. Meanwhile, the results of the prediction of naïve bayes produced the model with the highest prediction of 60%.
Implementasi Algoritma Knuth Morris Pratt Dalam Pencocokan String Pada Kamus Indonesia–Korea Rakhmat Kurniawan R; Aidil Halim Lubis; Siti Ayu Hadisa
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 1 (2023): September 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i1.6149

Abstract

Currently, South Korean culture is very popular with many Indonesians, and the rapid development of Korean culture in Indonesia is currently very widespread and very popular. Many Indonesians even learn Korean to keep up with current trends, but due to the different structure of the language, learning Korean becomes more difficult for most people. The dictionary is an effective guide for translating foreign languages/terms. Conceptually, dictionaries are arranged alphabetically, along with explanations of definitions, uses or translations. This is also required for Indonesian to Hangul Korean translation. Many Indonesian-Korean dictionaries are currently published in printed form, but it is still difficult to use because users have to look up the meanings manually. We need practical and effective new media such as smartphone media. There are many algorithmic methods that can be used to create dictionary applications, one of which is using the Knuth Morris Pratt (KMP) algorithm. With this algorithm, every text to be translated is checked for word search and then a match is found with the appropriate word from the desired word. In this study, the final results of the study found differences in the use of the word hangul in formal and informal forms. In this study, the authors tested the application of the algorithm on an Android-based Indonesian-Korean dictionary application.
Analisis Sentimen Tanggapan Masyarakat Terhadap Calon Presiden 2024 Ridwan Kamil Menggunakan Metode Naive Bayes Classifier Neni Sari Putri Juana; Elin Haerani; Fadhilah Syafria; Elvia Budianita
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 4 (2023): Juni 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6168

Abstract

Reaction to public facts about the election of the presidential candidate Ridwan Kamil, which will later be obtained, the data is taken from Twitter based on these problems, it is necessary to do sentiment analysis research. Based on the results of this study, the classification process for the Naïve Bayes Classifier has 3 scenarios for dividing training data and test data, namely with 90%:10% training data, the test data produces an accuary value of 85.43%, a recall value of 100.00%, and a precision of 85.33%. For training data 80%: 20% of the test data produces an accuracy value of 86.38%, a recall of 100.00% and a precision value of 86.38% and for data on the distribution of training data 70%: 30% of the test data produces an accuary value of 84.29 %, 100.00% recall and 84.29% precision. From the tweet data that has been used, there are 1262 positive comments and 242 negative comments. These results prove that the Naïve Bayes classifier is very good for conducting sentiment analysis on Twitter comments about the 2024 presidential candidate Ridwan Kamil. The naïve Bayes classifier process gets the highest accuracy value of 86.38% by dividing the training data 80%:20% test data.
Sistem Pendukung Keputusan Menentukan Siswa Berprestasi dengan Metode SAW (Simple Addtive Weighting) Isnia Anjar Setyani; Yoannes Romando Sipayung
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 4 (2023): Juni 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6179

Abstract

MI Kalirejo is an Islamic Madrasah located in Kalirejo Village, East Ungaran District, Semarang Regency. Based on the number of students who have achievement in academic and non-academic fields, a process is needed to determine student achievement. Currently MI Kalirejo still uses the manual method in the assessment process and only through the results of report cards in determining outstanding students. The author is interested in building a website-based decision support system in determining outstanding students based on predetermined criteria, so that it can help make it easier for school institutions to process and determine appropriate, fast and accurate assessments there are several criteria such as the average value of report cards, attitude scores. This system designs and creates and implements a website-based decision support system using the Simple Additive Weighting method. In this study, researchers collected data throught observation and interviews. Then the researchers used the waterfall method in system development. In testing the system using a black box to obtain accuracy results. The conclusions obtained in making a decision support system in determining outstanding students using simple additive weighting based on a website are obtained alternative values from the result of calculations that have been carried out. The author shows accuracy of 100% using the sample and population of Sugiono’s theory. From the result of calculations using the SAW method, it shows that alternative A2 is obtained by a student named Faeza with a value of 1 in the first rank. Thus alternative A2 students on behalf of Faeza were chosen as the best alternative to achieve achievements as outstanding students at MI Kalirejo. Based on the trials and the resulting values have the same result so that the purpose of making this website has been achieved
Penerapan Metode Weighted Product Berbasis Visualisasi Graph Database dalam Merekomendasikan Parfum Isi Ulang Defy Lukbatul Qolbiah; Abd. Charis Fauzan; Tito Prabowo
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 4 (2023): Juni 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6181

Abstract

Perfume is useful for increasing self-confidence, creating satisfaction, eliminating bad odors, and making self-assessment more attractive. Refill perfumes are made from certain perfume seeds dissolved in a suitable solvent. Perfume has many types and strengths of aroma, but there are obstacles when people want to choose the desired perfume scent. This problem becomes research material because it is expected that this problem can be solved. To determine perfume recommendations, it is calculated using the Weighted Product method and visualized using a graph database. In the Neo4j Graph Database visualization, the perfume category and perfume name are used as nodes and the ranking results are used as edges. From the ranking results using the Weighted Product method, 21 perfumes for each category are entered into the Graph Database visualization and a total of 63 perfumes will appear in the perfume recommendation system.Refill perfume is a perfume made from certain perfume seeds dissolved in the appropriate solvent.
Klasifikasi Citra Daging Sapi dan Daging Babi Menggunakan CNN Arsitektur EfficientNet-B6 dan Augmentasi Data M. Fadil Martias; Jasril Jasril; Suwanto Sanjaya; Lestari Handayani; Febi Yanto
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 4 (2023): Juni 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6195

Abstract

In daily life, beef often serves as a staple food for humans. However, the high and expensive price of beef has prompted traders to adulterate it with pork for the sake of profit. Such adulteration has serious implications in the Islamic religion, where not all types of meat are considered halal (permissible for consumption), such as pork. As a result, consumers often remain unaware that the beef they purchase has been adulterated with pork. At a glance, both types of meat exhibit similar appearance and texture, making them difficult to differentiate. This research aims to classify beef and pork using a deep learning model with the Convolutional Neural Network (CNN) method, combined with data augmentation. The model used is EfficientNet-B6 with variations in the testing scenario. The variations include the ratio of training and testing data, learning rates, and optimizer for EfficientNet-B6. Data augmentation is performed using techniques such as random rotation, shifting, image scaling, vertical and horizontal flipping, and nearest pixel filling. Evaluation results using the confusion matrix show that the model with data augmentation achieves the highest accuracy for the classes of beef, pork, and adulterated samples at 92.00%, while the model without augmentation achieves an accuracy of 91.67%. However, from this experiment, the best scenario to avoid misclassifying pork and adulterated samples as beef can be obtained. This scenario involves a model with data augmentation, a 90:10 data split, SGD optimizer, and a learning rate of 0.01, which achieves the highest precision for the beef class at 96.05%. The research findings demonstrate that the use of data augmentation on images can improve the model's performance, and the model with data augmentation, a 90:10 data split, SGD optimizer, and a learning rate of 0.01 exhibits the best performance in classifying beef images.

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