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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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Klasifikasi Sentimen Masyarakat di Twitter Terhadap Ancaman Resesi Ekonomi 2023 dengan Metode Naïve Bayes Classifier Dea Ropija Sari; Yusra Yusra; Muhammad Fikry; Febi Yanto; Fitri Insani
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.v4i4.6276

Abstract

Economic recession is a condition in which the economic turnover of a country changes to slow or bad that can last for years as a result of the growth of the Gross Domestic Product (GDP) a country decreases over two decades significantly. Early warnings of the emergence of a global recession become a concern for all countries in the world, even global recessions also have a major impact on Indonesia. Such as declining public spending due to decreasing incomes, increasing unemployment, increasing poverty, and many of whom have to accept PHK or salary cuts. Economic strengthening will be important in minimizing these threats, this research needs to be done to see the response of the public to the threat of economic recession. Twitter provides a container to users to comment on the problem of the economy recession 2023 which can be used as sentiment classification information to know positive and negative comments. This research uses the naive bayes classifier algorithm. In this study there are seven main processes, namely data collection, manual labelling, processing, feature weighing (tf-idf), tresholding, naive bayes method classification, testing. From the 1408 comments data on Twitter about the threat of a 2023 economic recession. Based on the results of the classification, using 2 testing models namely data balance and non-balance data obtained the best balance data test results with the highest accuracy result with the process of classification using algortima naïve bayes classifier resulted in accurateness of 78% obtainable by using a comparison of 90% training data and 10% test data.
Sistem Pakar Diagnosa Gizi Buruk Pada Balita Berbasis Mobile Menggunakan Metode Certainty Factor Afdal Muhammad Efendi; Tengku Khairil Ahsyar; M Afdal; Febi Nur Salisah; Syaifullah Syaifullah
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.v4i4.6307

Abstract

Maltnutrition has a significant impact on children’s development and is a common problem in developing countries, including Indonesia. There are many factors that contribute to malnutrition, one of which is the lack of understanding and knowledge among parents regarding childcare and proper nutrition. This has motivated the author to develop an expert system application for diagnosing malnutrition in toddlers, aiming to facilitate the community, especially mothers with toddlers, in early diagnosis of malnutrition symptoms and diseases through mobile devices. This expert system application is built using Java Programming language with the assistance of Android studio as the development tool. The system analysis employed is the Unified Modeling Language (UML) to provide an overview of the application to be created. Testing is conducted using the Black Box method and data validation yields nearly 100% accuracy. The calculation for diagnosing symptoms and diseases utilizes the Certainty Factor methode, which serves as the calculation of value within the expert system application. The testing results based on symptoms and diseases through the applied calculation method achieve a 92% accuracy rate. The development of this application is expected to assist the community, especially mothers with toddlers, in identifying early symptoms and diseases of malnutrition in children, as well as obtaining solutions for the experienced illnesses.
Sistem Pakar Diagnosa Gangguan Stress Pasca Trauma Menggunakan Metode Certainty Factor Marliana Safitri; Fitri Insani; Novi Yanti; Lola Oktavia
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.v4i4.6309

Abstract

Mental health disorder or commonly called Mental Health Disorder is a disturbing psychological behavior and is followed by traumatic events such as shock shell, war fatigue, accidents, victims of sexual violence, and the covid pandemic. Cases of post traumatic stress disorder data from Indonesian Psychiatric Association amounted to 80% of 182 examiners experiencing symptoms of post-traumatic stress due to exposure to covid, 46% experienced severe symptoms, 33% moderate, 2% mild and others did not show symptom. This study aims to diagnose post traumatic stress disorder using the assurance factor method with 35 symptom data and 3 levels of post traumatic stress disorder as a knowledge base. The certainty factor is a circulation management method and a decision-making strategy using the confidence factor in the system. Based on the research results of the expert system for diagnosing post traumatic stress disorder, the test results obtained an accuracy of 80%. The results of the accuracy of this expert system indicate that the expert system can potentially be used to diagnose post traumatic stress disorder.
Klasifikasi Sentimen Masyarakat di Twitter Terhadap Kenaikan Harga BBM dengan Metode Support Vector Machine Siti Nurhaliza; Yusra Yusra; Muhammad Fikry
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.v4i4.6322

Abstract

The increase in the price of fuel oil (BBM) in Indonesia has always been a controversy which can be seen from online media such as Twitter which has an effect on the Indonesian economy, with this problem it has a change in the impact of cost instability due to an increase in fuel prices which will also affect the rate of increase in transportation costs and the rate of inflation. The effect of these changes leads to many different public opinions so as to produce pros and cons of these changes, with the existence of the problems above, the classification process is needed. This study uses 3000 tweet data obtained from the crawling process. This study obtains an accuracy of 85% at a ratio of 90:10, for a precision value of 85%, 99% recall and 91% f1-score for negative sentiment, while 83% precision value, 19% recall, 30% f1-score for positive sentiment. Then in the 80:20 comparison experiment, an accuracy of 83% was obtained, for a precision value of 83%, a recall of 99% and an f1-score of 91% for negative sentiment, while a precision value of 82%, a recall of 16%, an f1-score of 26% for positive sentiment.
Analisis dan Implementasi Market Basket Analysis (MBA) Menggunakan Algoritma Apriori dengan Dukungan Visualisasi Data Septembri Rio Bagaskara; Dwi Hosanna Bangkalang
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.v4i4.6351

Abstract

Culture Coffee MSME is one of the MSMEs engaged in the culinary field and is experiencing business competition. A marketing strategy is needed with the right decision-making process so that the business can survive and excel. UMKM Culture Coffee uses a point of sales application to accommodate the transaction process and record transactions. Historical customer data can be processed into a basis for decision making for marketing strategies that effectively increase sales. However, the transaction data has not been used optimally. There is a need to analyze historical customer data that can generate information to form marketing strategies. Market Basket Analysis (MBA) is one of the methods in data mining used in knowing products that tend to be purchased together by customers known as Association Rule.  Association rules produce products in the form of packages or bundling which are used as marketing strategies. The marketing strategy obtained is supported by data visualization which contains information from the data. Apriori algorithm is used to generate association rules. The result of this research is an association rule on the historical data of MSME Culture Coffee customer purchases. Based on these rules, recommendations for selling menu packages to customers can be given. The purpose of this research is to find customer purchasing patterns which are used as the basis for decision making in determining menu sales. The results showed 2 product packages, namely, nuggets and french fries with sausages and french fries with a support and confidence value of 12.5% and 37.6% with 10.8% and 29% respectively. The results of this study can be used as a basis for the sales and marketing strategy of Culture Coffee MSMEs to increase business revenue.
Analisa Gambar X-Ray Mammography dengan Convolution Neural Network pada Deep Learning dengan Arsitektur Resnet Nur Islamiati Sanusi; Siti Ramadhani; Muhammad Irsyad
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.v4i4.6365

Abstract

Cancer is a disease that occurs when cells in the body undergo changes and grow uncontrollably. Breast cancer is one of the common types of cancer that affects women worldwide. Early detection of breast cancer is crucial to improve the survival rate. Mammography is a medical imaging method used for the early detection of breast cancer. In this context, deep learning technology and computerized classifiers, such as Convolutional Neural Network (CNN) with the Resnet model, have been used for the analysis and prediction of mammography images with promising results. Previous studies have shown high accuracy in classifying breast masses as benign or malignant using CNN and Resnet. Furthermore, CNN has also been employed for the classification of malignant and benign breast cancer, prediction of breast cancer risk, as well as detection and classification of breast masses with satisfactory accuracy rates. The use of deep learning in medical image analysis, including mammograms and X-ray images, has proven to be an effective tool in improving cancer diagnosis and treatment. The dataset used consisted of 322 images divided into 7 classes. After testing, an accuracy of 72% was achieved with a 90:10 ratio of test data to training data, along with the corresponding confusion matrix values. Therefore, it can be concluded that the Resnet method is capable of identifying breast cancer.
Penerapan Metode Simple Multi Attribute Rating Technique (SMART) Untuk Seleksi Penerimaan Bantuan Usaha Produktif Raihan Mahdy; Fitra Kurnia; Iwan Iskandar; Eka Pandu Cynthia
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.v4i4.6370

Abstract

Productive business assistance is assistance provided to improve business capabilities, depending on the type of business being running. The goal is to develop work productivity and also increase income. As for the distribution of productive business assistance at BAZNAS Pekanbaru City, it still uses an old system and is not yet effective, so the process takes quite a long process. In order for the selection process to be effective, a decision system was created for the alternative of recipients of productive business assistance. The method in this research using the simple multi attribute rating technique (SMART) method. This research uses 6 criteria and 22 sub-criteria. The application is build with using PHP and MySQL programming languages. The results of the application of the SMART method which has been tested on 10 sample recipients obtained the order of the highest value to the smallest. With the highest value is 0.75. This system has been tested using the Blackbox testing method and the user acceptance test (UAT) with an assessment final value is 94.4%.
Analisis Sentimen Ulasan Pelanggan Online Ubi Madu Cilembu Abah Nana Menggunakan Algoritma Naïve Bayes Muhammad Rafly Al Fattah Zain; Mia Kamayani
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.v5i1.6646

Abstract

This research aims to analyze the sentiment of online customer reviews for Ubi Madu Cilembu Abah Nana using the Naïve Bayes algorithm. The study has two main objectives: to classify the sentiment analysis of reviews into positive and negative categories regarding the service and products of Ubi Madu Cilembu Abah Nana, as well as to evaluate the accuracy level of the final classification results. The data was collected from online food delivery applications such as Gofood, Grabfood, and Shopeefood. The data used in this study amounts to 259 entries, with 310 positive and 49 negative data points. After conducting experiments, an accuracy result of 86.29% was obtained in Experiment 1 using the Split Data operator, and an accuracy of 86.12% was achieved in Experiment 2 utilizing Cross Validation with the assistance of language experts. The findings of this research indicate that the Naïve Bayes algorithm can be employed to classify customer sentiment towards the service and products of Ubi Madu Cilembu Abah Nana with a significantly high accuracy rate. These results can be valuable for Ubi Madu Cilembu Abah Nana in enhancing their service and product quality based on customer feedback. Additionally, this study also contributes to the field of sentiment analysis and natural language processing by applying classification algorithms to customer review data.
Penerapan Algoritma C4.5 Mengklarifikasi Penerimaan Bantuan Sosial Menggunakan Feature Selection M Wandi Dwi Wirawan; Siska Kurnia Gusti; Jasril Jasril; Pizaini Pizaini
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.v5i1.6653

Abstract

The Indonesian government's efforts to overcome poverty in Indonesia are through the Smart Indonesia Card (KIP) program which is carried out by the government in the form of providing assistance to underprivileged families. The main aim of distributing KIP assistance is to help send underprivileged children to continue their education, the difficulties found in receiving KIP are due to the large number of residents registering, as well as the data having several conditions, the limited time available in providing KIP by sub-district parties, the completion base is relatively low, therefore the provision of assistance must be right on target. Therefore, the aim of this research is to look for the most influential attributes in receiving KIP assistance in order to improve the results of the data verification process. After carrying out Feature Selection using Information Gain, the most influential attributes can be obtained. The influences are Number of Art, Number of Rooms, Cooking Room, Refrigerator, Motorbike. Therefore, we need to know some of the attributes that most influence the selection of KIP assistance so that we can get accuracy values from decision tree modeling using the C4.5 algorithm or decision tree. Test This experiment can produce a decision tree in which the Number of Art attribute is the most influential attribute with the success rate of KIP acceptance. This evaluation uses a confusion matrix to obtain an accuracy value of 98.21%, precision of 98.21%, recall of 99.48%.
Penerapan Seleksi Fitur Untuk Klasifikasi Penerima Bantuan Sosial Pangkalan Sesai Menggunakan Metode K-Nearest Neighbor Muhammad Fauzan; Siska Kurnia Gusti; Jasril Jasril; Pizaini Pizaini
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.v5i1.6654

Abstract

The inability to fulfill basic human needs is how poverty is defined. To address this issue, the indonesian goverment implements various social assistance programs, one of which is Kartu Indonesia Pintar (KIP), aimed at providing free education to children aged 7-18 who are economically disadvantaged. However, in the distribution of aid in the Pangkalan sesai sub-district, distributing officers often face challenges due to the high number of eligible recipients applying, complex data requierements, and limited time for the officers. Distributing this social assistance accurately is crusial. Therefore, this research aims to determine the accuracy value for the data of potential recipients of the Kartu Indonesia Pintar (KIP to enhance the data verification process’s outcomes. To tackle this issue, the research employs the K-Nearest Neighbor (K-NN) algoritm and also employs feature selection using Information Gain to reduce less influential attributes. The data used consists of 1998 records of KIP beneficiaries from the 2023 in excel format, with 33 attributes. After performing data cleaning an Information Gain-based feature selection, the dataset is reduced to 1675 records, with 5 selected attributes. The best classification result in this study is achieved with ratios of 7:3 and 8:2, and a value of k = 5, yielding the highest accuracy of 98,21%. The lowest accuracy is obtained using a ratio of 9:1 with the same k value when not using Information Gain, resulting in an accuracy of 89,82%.

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