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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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Metode Bidirectional Associative Memory (BAM) Kontinu Pengenalan Pola Karakter Untuk Keamanan Data Andri Yunaldi; Very Karnadi
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 2 (2022): Desember 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5339

Abstract

Artificial Neural Networks have a paradigm for processing information with the concept of working using biology in processing information similar to how the human brain works. Artificial neural networks solve many problems using the concept of uncertainty such as the discussion in the material, namely the introduction of Continuous BAM Character Patterns. The problem in this research is the lack of data security in maintaining information so that a lot of data can be accessed by people who do not have authority. The main objective of this research is to maintain data security using the Continuous BAM concept. The BAM Kontine method is a method that has the ability to have associative memory or content addressable memory that can be called by using the part stored in the memory itself. The Continuous BAM method will change input to output more smoothly with values that lie in the range [0,1]. The activation function used is the sigmoid function. The results obtained from x1 = [7,-11], x2= [7,13] and x3=[-1,9], All Signs can be recognized by the Continuous BAM algorithm. All Sign Patterns have the same target but have different values.
Analisis Sentimen Tindakan Pemerintah Indonesia Dalam Penanganan Covid-19 Menggunakan Metode Support Vector Machine Ariesta Damayanti; Helda Ludya Safitri; Rudy Cahyadi
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 2 (2022): Desember 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5341

Abstract

Corona Virus Disease 2019 (Covid-19) which has hit the world including Indonesia since the beginning of 2020 is an outbreak that has become a serious threat to world health. The Indonesian government is taking various actions to deal with this problem, while the public, with the existence of social media, has provided many responses to these government policies. Twitter is one of the social media that is widely used by the public to convey comments in the form of responses, suggestions, to criticism of the government regarding the handling of Covid-19. The comments that appear should be used by the government as part of the reference in evaluating a policy or action taken in handling Covid-19. So that one way that can be used to deal with this is one of the methods that exist in the domain of text mining, namely sentiment analysis. This research was conducted by analyzing sentiment using the Support Vector Machine (SVM) method with the Kernel Radial Basis Function (RBF). Tweets will be classified into positive, negative and neutral sentiments, so that the percentage of each opinion category can be known. This study uses data of 600 tweets obtained from the results of scraping using a Twitter scraper. The result of this study is that the training accuracy rate is 77% in classifying positive, negative, and neutral sentiments. From the results of the data classification, it was found that most of the tweets consisted of negative sentiments.
Personality Detection of Twitter Social Media Users using the Support Vector Machine Method Salsabila Anza Salasa; Warih Maharani
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 2 (2022): Desember 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5345

Abstract

 Personality is a person's psychological tendency to carry out certain social behaviors, whether in the form of feelings, thoughts, attitudes and will or actions. Big Five is the most popular and widely used personality model, therefore this proposal uses the Big Five Personality model. In this technological era, humans interact using social media. One of the fastest growing social media is Twitter. Twitter is a social media used by all groups. Every human being has a different personality. Personality detectors are needed for employee recruitment to dig up information about the personality of prospective employees. So personality detection or BigFive Personality can be done through tweets that are shared on Twitter. With this, it is necessary to detect personality using the Support Vector Machine (SVM) method. From the results of the study, it was found that the maximum performance parameter combination in detecting personality on Twitter users was a combination of Linear parameters and C = 10 which obtained an accuracy of 0.979. The data used is the result of crawling on the Twitter site with 146 user usernames and 38853 tweets.
Analisis Intensitas Cahaya Lampu Pijar dengan Menerapkan Metode Gray Level Co-occurence Matrik Elvianto Dwi Hartono; Bagus Hardiansyah
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 2 (2022): Desember 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5366

Abstract

This paper proposes primary dataset with 13 images thermal capture, 8 high frequency and 4 low frequency. We utilize thermal images fluorescent lamp and using image processing with extraction feature GLCM method. Furthermore, Contrast, Correlation. Energy, Homogeinity dan sudut 0°, 45°, 90°, 135°, these feature texture using for calculated validation compare with both exsperiment qualitative results in Table1 and Table2. Therefore, exsperiment with fluorescent lamp Figure 2 quantitative results significant in Table1. Quantitative results with fluorescent lamp in Table2 extraction feature GLCM method with angle 0°, 45°, 90°, 135° and in Table1 quantitaive result with low frequency 50 Hz with T (oC) 50 is significantly robust. Comparable quantitative results in Table2 with low frequency 50 Hz from extraction feature mean value angle 0°, 45°, 90°, 135° Contrast (0.0363), Correlation (0.9959), Energy (0.1353), and Homogeneity (0.9832).
Implementasi Natural Language Processing (NLP) dan Algoritma Cosine Similarity dalam Penilaian Ujian Esai Otomatis Daniel Oktodeli Sihombing
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 2 (2022): Desember 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5374

Abstract

Evaluation of learning is an activity that is routinely carried out in the lecture process. The essay exam is a test in the form of questions that aim so that the answers given are in the form of descriptions based on student’s understanding in accordance with what they know. The results of the various answers are a separate consideration in correcting whether the answer is in accordance with the answer key or not. This resulted in each question on the essay exam having its own weight which would later be added up cumulatively to get a total score. This study implements Natural Language Processing (NLP) and Cosine Similarity algorithms to automatically assess essay exams. Document Similarity is one of the tasks in Natural Language Processing (NLP) to check the degree of document similarity. The algorithm used to check the level of similarity is Cosine Similarity which uses two vectors to measure the degree of similarity of documents with the results ranging from 0 to 1. Processing student answer data for three essay questions gets the expected results. The results of the Cosine Similarity calculation in question no 1 show that M3 students have answers with a similarity level of 90.58%. Whereas for question no 2 M1 students had answers with a similarity level of 87.71% and finally for question no 3 M1 students had answers with a similarity level of 76.70%.
Analisis Sentimen Terhadap Program Kampus Merdeka Menggunakan Naive Bayes Dan Support Vector Machine Irma Putri Rahayu; Ahmad Fauzi; Jamaludin Indra
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 2 (2022): Desember 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5381

Abstract

In order to prepare students to face the rapid development of technology, changes in work life and skills, students must be better prepared to face the progress of the times. Universities must be able to carry out innovative learning processes so that students achieve optimal learning outcomes which include aspects of knowledge, skills and attitudes. So the MBKM program was launched to answer these demands. However, MBKM has pros and cons in its implementation, so it is necessary to analyze and evaluate policies to improve performance through feedback from the public by conducting sentiment analysis of MBKM policies on twitter users from 2019 to 2022 with the hashtag #kampusmerdeka. This study used the Naïve Bayes and SVM algorithms to determine accuracy based on sentiment classification. The data used 1118 data with positive sentiment 618 data and negative sentiment 500 data. This study resulted in an accuracy of 86%, precision of 87% and recall of 80% with testing data using the Naïve Bayes algorithm. Then using the linear kernel SVM algorithm with the same testing data resulted in accuracy of 93%, precision of 100% and recall of 84%. Therefore, it is important to conduct studies to improve the MBKM program so that its implementation is clearly in accordance with existing procedures.
Komparasi Algoritma Naïve Bayes Dan Support Vector Machine (SVM) Pada Analisis Sentimen Spotify Ayu Sri Rahayu; Ahmad Fauzi; Rahmat Rahmat
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 2 (2022): Desember 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5398

Abstract

The Spotify app is a subject of interest to social networking communities with significant disagreements or sentiments. Sentiment Analysis is a solution to automatically categorize opinions or ratings into negative or positive opinions. The techniques used in this research are Support Vector Machines (SVM) and Naïve Baye. The advantages of Naïve Bayes are simple, fast and high accuracy. SVM, on the other hand, can identify different hyperplanes that maximize the margin between two different classes. The classification results of this study have two category labels, namely negative and positive. The resulting accuracy value indicates the best test model for sentiment classification cases. Accuracy is measured by the confusion matrix and the results show that the accuracy value of the SVM algorithm is 84% while the accuracy value of the Naïve Bayes algorithm is higher than SVM which is 86.4%.
Estimasi Data Insight Social Media Ads Menggunakan Neural Network, Linear Regression dan Deep Learning Zurni Laila; Nuri Cahyono
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.v4i3.5451

Abstract

PT IlmuKomputerCom Braindevs is a professional training company that sells products in the form of training services (courses). In service companies such as PT IlmuKomputerCom Braindevs Sistema, holding courses with high demand is the key to increasing company profits. The marketing division is a very important division in the context of holding a course/training. To manage the target participants needed in organizing training. In addition, PT IlmuKomputerCom Braindevs also needs to estimate advertising costs and ad duration in the training promotions that will be held. To analyze the marketing division using data mining techniques and the Cross-industry standard process for data mining (CRISP-DM) method to obtain the desired estimate. So to get the final result of the participant's estimated value, ad duration and ad cost, an algorithm that has the most accurate accuracy is needed according to the reference from the results of the comparison of algorithms by looking at the value of RMSE (Root Mean Square Error). The closer the resulting value is to 0, the better the estimated accuracy of the RMSE (Root Mean Square Error) estimate will be.
Kombinasi Algoritma Kriptografi Vigenere Cipher dan SHA256 untuk Keamanan Basis Data Rian Oktafiani; Erik Iman Heri Ujianto; Rianto Rianto
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.v4i3.5583

Abstract

An organization must consider and manage the security of data storage in databases or databases, and special procedures are needed to protect data from various security risks. The problem in this study is that the population data contained in the Girisuko village administrative service information system has not been encrypted or secured. This can pose a risk that the data stored in the database can be intercepted and misused. In this study, the cryptographic technique used was a combination of the Vigenere Cipher and SHA 256 algorithms to secure or encrypt databases, especially population data in the Girisuko village administrative service information system. The text in the database is encrypted using the Vigenere Cipher, and SHA-256 is used to generate a hash value or a random value that is different from the text in the database. Messages will be encrypted using the Vigenere Cipher and then hashed with SHA-256 simultaneously. As a result, it will be difficult for an attacker to decrypt the text stored in the database because they have to break the Vigenere Cipher encryption, and also have to solve the hash value generated using SHA-256. This combination aims to increase security and maintain the confidentiality of messages from attackers. The application of the Vigenere Cipher and SHA to the village administration service information system application with a real-time database works well, as evidenced by the fast running-time of 0.39 seconds the data encryption process uses the Vigenere Cipher with 894,968 keys/second and an analyzed key length of 7 characters then text on population database successfully secured. By conducting this research, it is hoped that it can contribute to improving database system security.
Implementasi Data Mining Memprediksi Penjualan Crude Palm Oil Berdasarkan Kapasitas Tangki Menggunakan Multiple Linear Regression Ana Komaria Baskara; Alwis Nazir; Muhammad Irsyad; Yusra Yusra; Fitri Insani
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.v4i3.5665

Abstract

Data mining is a process of discovering information from data that can be used to improve business, product development, and other decision-making processes. One application of data mining is in PT. Kerry Sawit Indonesia, which is an agribusiness company in the Wilmar Group that deals with processing crude palm oil (CPO). Sales of CPO are crucial for palm oil plantation companies. To increase efficiency and profitability, palm oil plantation companies can predict CPO sales to optimize sales and CPO inventory. One method that can be used to predict CPO sales is through data mining techniques. In this study, the data mining technique used is multiple linear regression. Multiple linear regression is used to determine the relationship between the tank capacity variable and CPO sales. The data used in this study are CPO production data, CPO sales data, and tank capacity data obtained from palm oil plantation companies over the last five years. The results of the Multiple Linear Regression calculation in this case study show that the coefficient of determination (R-squared) value is 0.9546, indicating that 95.46% of the CPO delivery variability can be explained by the independent variables. Additionally, the MAPE and RMSE tests show that the regression model obtained has good accuracy in predicting CPO deliveries. Therefore, this regression model can be used to predict CPO deliveries in the future, considering the predetermined independent variable values.

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