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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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Sistem Monitoring Polusi Udara Mengggunakan Sensor Nitrogen Carbon Berbasis Internet of Thing Ferry Fachrizal; Julham Julham; Antoni Antoni
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.v4i1.4903

Abstract

Technological developments in the field of Embedded Systems and Internet Of Thing, can be implemented in a system capable of monitoring air pollution, where the system is capable of transmitting data in real time using sensors located at near or far distances. To transmit data from various sensor devices that are located remotely, the use of LoRA technology is a solution for sending data between the client and the gateway, LoRA communication technology can be implemented into an air pollution monitoring system where the sensors are placed far from the gateway and cloud server. The prototype of this air pollution detector uses an Arduino Uno microcontroller and a Nitrogen Carbon sensor that can detect CO, NO2 and NH3 connected to LoRA Shield with the IoT platform as a monitoring and notification system. The measurement results of the three sensors in this study are displayed on the Smart Phone as a notification. From the research, it was found that the content of NH3 was 1.88ppm, CO was 17.76 ppm and NO2 was 0.12ppm. Based on the Air Pollutant Index, the results of the air quality in this area are not good.
Analisis Sentimen Vaksinasi Booster Berdasarkan Twitter Menggunakan Algoritma Naïve Bayes dan K-NN Afid Rozaqi; Agung Triayudi; Rima Tamara Aldisa
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.v4i1.4907

Abstract

Covid-19 or the corona virus has spread throughout the world, one of which is Indonesia. There have been many problems due to this virus for 2 years in Indonesia, and various efforts and policies have been made by the government to control the impact that does not become worse by this corona virus, these efforts are vaccination actions against the Indonesian people, and in early 2022 the government started a new program, namely booster vaccination. Many people are pro and contra to the program on social media Twitter. This study was conducted with the aim of knowing the sentiment of Indonesians towards booster vaccination in Indonesia.The data obtained as many as 2000 tweets obtained from the keyword "booster vaccine" on Twitter. Then the data is divided into training data and test data (training) then made into three different portions, namely 60/40, 70/30, and 80/20. The test results are that the best performance is found in testing a portion of 80% of the training data 20% of the test data using the K-NN algorithm, the test produced the highest value results, namely 78.62% accuracy and AUC 0.845 and categorized as good classification. The results show that the K-NN algorithm model with an 80% portion of training data is the best in the classification of booster vaccination sentiment analysis. The sentiment results in the test data were positive with 303 tweet data and negative sentiment totaled 93 tweet data. The results of more positive sentiments show that booster vaccinations in Indonesia are acceptable and get a lot of support from the Indonesian people on social media Twitter.
Pembangunan Smart Detection Absensi Berbasis Kartu RFID dan ESP 32 Fikri Fajar Asshiddiqi; Agung Triayudi; Rima Tamara Aldisa
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.v4i1.4912

Abstract

In today's era with the rapid development of technology and the development of semiconductor technology, it is possible to create integrated circuits on an increasingly large scale and can integrate many different systems. One of the benefits of current technological developments in the attendance recording tool, whose data is integrated into the learning information system to replace the manual recording model. This tool is designed by integrating the work of a radio frequency identification (RFID) microcontroller into one system. The extracted data as a unique number from the RFID tag is used as student data. After the card is attached to the assessment device, student data will automatically be entered into the attendance database. By making this tool, it can facilitate the work and activities of students and lecturers in conducting lecture activities and also as learning for all in this period of rapid technological development
Penerapan Monitoring Locator Dies pada Perusahaan Stamping Part Otomotif Denny Riandhita Arief Permana; Morgan Suseta; 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.v4i1.4914

Abstract

PT. Ganding Toolsindo is an automotive stamping parts company that has been established since 1998. This design is illustrated by conducting research at the kruing plant which is placed in the production division to assist the audit process at first and collect customer data, then get a mandate from the managerial to create an information system that is integration and innovation in the area of return and use of dies. Then look for accurate data with various methods so that the implementation of this system goes well, on this occasion the design was made with the concept of a dies locator information system that tracks the existence of dies for production and contains the identity of dies data in the production process. The problem that occurs is that the operator often places the dies not according to the existing shelves and in the end the dies become unorganized and neat when the production process is running. The method of developing this system is to use SDLC (system development life cycle), namely Waterfall. This information system uses mysql and codeigniter, then to run this system using xampp. The whole of this information system is about tracking the dies whether they are in production or on the shelf, if they are being repaired, the status of the dies will change as well as the status of the dies when they are being used in production. In this system feature there are customer data, part data, shelf data, user data and the duration of use of the dies. In the end, this system generates a report to the production supervisor to be seen and in what dies data is being used in production so that there will be no more misplaced shelves.
Analisis Sentimen Komentar Youtube Tentang Relawan Patwal Ambulance Menggunakan Algoritma Naïve Bayes dan Decision Tree Abd Wahid; Galuh Saputri
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.v4i2.4941

Abstract

The presence of the ambulance patrol is sometimes considered disturbing by the community, many people's opinions are still pro and contra about the actions carried out by civilian ambulance patrol volunteers because they are considered illegal and sometimes also arrogant. In this study. the researchers wanted to know the opinions and responses of the public about the actions taken by civilian ambulance patrol volunteers. The method used in this study is to perform sentiment analysis with data mining techniques to find the polarity of sentences in a document using the Naïve Bayes and Decision Tree algorithms. The initial steps in this research were collecting comment data by scraping YouTube comments using API Key Youtube V3, followed by manual data labeling, data cleaning, data preprocessing, and word weighting using TF-IDF. From the overall results of testing with 600 training data, the Naïve Bayes algorithm has a higher accuracy value of 66.72% while the Recall value is 64.98%. Testing with the Decision Tree algorithm in this study has a higher Recall compared to Naïve Bayes. %. From the results of the YouTube comment dataset used in this study, it can be concluded that the Naïve Bayes algorithm has a higher accuracy value than the Decision Tree algorithm, so it can be concluded that Naïve Bayes has the best accuracy in the YouTube comment dataset used in this study.
Komparasi Jarak Euclidean dan Manhattan Pada Algoritma K-Nearest Neighbor Dalam Mendeteksi Penyakit Diabetes Mellitus Agustin Ely Rahayu; Abd. Charis Fauzan; Harliana Harliana
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.v4i2.5046

Abstract

Diabetes Mellitus is a chronic disease. This disease is caused by an increase in blood sugar levels in the body, it can cause diseases such as heart disease, obesity, and eye, kidney, and nerve diseases. Detection of Diabetes Mellitus is usually carried out by laboratory tests, so that patients have to undergo several medical tests to provide input values to a computerized diagnostic system which has proven to be expensive and has long queue times. From these problems, an artificial intelligence system is needed to diagnose this disease more easily and quickly. Therefore, the researcher aims to use an intelligent system to produce the highest accuracy from the results of the classification test using the K-Nearest Neighbor (K-NN) method with Euclidean distance and Manhattan distance. The class classifications used were pregnancy calculations, blood sugar in blood, blood pressure, skin fold thickness, insulin, body weight, diabetes genealogy dysfunction, and age. The research data in the form of datasets amounted to 450 datasets and the data was divided into two to determine the highest accuracy of 80% test data and 20% for training data. The highest accuracy using Euclidean distance is 84% with a value of K=5, and secondly, the Manhattan distance has the highest accuracy of 82% with a value of K=7.
Text Mining dan Klasifikasi Sentimen Berbasis Naïve Bayes Pada Opini Masyarakat terhadap Makanan Tradisional Sunneng Sandino Berutu
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.v4i2.5138

Abstract

Indonesia has several famous traditional foods and is available in some cities. In addition, several international foods also are interesting to Indonesian. This article analyzes the netizen sentiment for these food categories where the data source is Twitter. The foods are rendang, sate, gudeg, pizza, hamburger, and spaghetti. The text mining approach is adopted to process data. The research steps are data crawling, cleaning, filtering, translating, and splitting. Furthermore, the classifier model based on the Naïve Bayes algorithm is developed. The analysis result shows that the gudeg food reaches a high percentage of positive sentiment with 57,9.  Then, the high rate of negative sentiment is achieved by the rendang food with 21,9 %. Moreover, hamburger food obtains a high percentage of neutral sentiment. Meanwhile, the evaluation of classifier model performance shows that the model with the hamburger dataset achieves a high score for accuracy, precision, and recall parameters with 0.72, 0.72, and 0.68 sequentially. 
Penerapan Metode Buffer Stock dalam Prediksi Ketercukupan Bahan Baku Elsa Violina Damayanti; Muhammad Arifin; Syafiul Muzid; Yudie Irawan
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.v4i3.5140

Abstract

UD. Elvajaya has sales and purchases of stock of bag raw materials, but until now UD. Elvajaya experienced difficulties in terms of predicting or estimating the calculation of raw materials according to incoming orders. Raw materials often experience advantages and disadvantages in the ongoing process of making bags, causing service to customers to experience delays in the process of making and shipping goods to be delayed. The absence of proper calculations when there is an incoming order results in an unstable stock of raw materials with the number of incoming orders. In making raw material reports, books are still used as storage so that raw materials are not controlled. This study proposes the application of the buffer stock method to overcome existing problems. With the existence of a prediction system for the adequacy of raw materials using the buffer stock method, it can assist in the process of calculating raw materials to be purchased according to incoming orders so as to reduce the risk of stock shortages in the ongoing manufacturing process. The buffer stock method has several advantages, namely, minimizing risks in production regarding insufficient raw materials, being able to handle ordering requests with quite a large number. With this system, it is hoped that the problems faced by UD owners can be resolved. This research produces a prediction of how many raw materials will be issued for the following month by looking at the bag orders in the previous month. UD.Elvajaya produces 1440 cm of Buffer Stock for fabric raw materials, 1440 cm of zippers, 2160 cm of hose, 2160 cm of bisban, 15 threads, and 44 zipper heads. 
Optimasi Akurasi Klasifikasi Pada Prediksi Smokte Detection dengan Menggunakan Algoritma Adaboost Amin Nur Rais; Warjiyono Warjiyono
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.v4i2.5154

Abstract

The problem of fire is a threat to nature and the environment. To deal with fire incidents, a smoke detector was created and developed in combination with an IoT device so that incident data can be recorded properly where the recorded data will be used as a reference for increasing the accuracy of early detection. Increasing the accuracy of smoke detectors so that they can be combined with artificial intelligence technology. This research proposes prediction optimization using the adaboost algorithm combined with the naïve Bayes classification algorithm with a measurement matrix based on accuracy, recall, and precision. The results showed that using the adaboost algorithm could increase the resulting accuracy value with a value of 0.987. If you look at the evaluation from the precision side, it also shows that the use of the adaboost algorithm can increase the precision value with a value of 0.971. But the recall evaluation showed that without boost it got a better score with a value of 0.995
Sistem Pendukung Keputusan Rekomendasi Dalam Pemilihan Pemeliharaan Ikan Air Tawar Ekonomis Menerapkan Metode Additive Ratio Assesment (ARAS) Agus Iskandar
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.v4i2.5176

Abstract

Developing a business in fresh water is a promising business, because food ingredients such as fresh water fish are always needed by every community. Freshwater fish are fish that live in fresh water, such as living in lakes, rivers and so on. if someone wants to build a freshwater business, then the type of freshwater fish that is suitable and the easiest to maintain must be chosen. The many types of freshwater fish make prospective business owners confused in choosing which type of water fish is suitable for business. In helping the owner solve these problems, a decision support system is needed. A decision support system is a system developed on a computer, where the development uses computer-based steps or methods. SPK requires a method in its application. The method used in this study is the ARAS method. The ARAS method is an acronym for the Additive Ratio Assessment Method (ARAS). The ARAS method is the method used in ranking each alternative by using the reference, namely the criteria as the calculation material. By using the SPK using the ARAS method, the result is that by using the ARAS method, the selection of freshwater fish that is highly recommended to be kept is Alternative A1 with the type of fish, namely catfish as the best alternative.

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