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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 492 Documents
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Sistem Monitoring dan Analisis Penggunaan Energi Listrik Rumah Berbasis Internet of Things Menggunakan Prophet Algorithm Vipkas Al Hadid Firdaus; Meyti Eka Apriyani; Nurus Laily Aprilia
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 3 (2023): Maret 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5896

Abstract

Electrical energy is one of the necessities of human life, especially in modern society in urban areas. With a monitoring device for electrical energy consumption using IoT technology, the results of the development show that the monitoring system works well, but the results show that current and voltage measurements are still less accurate. Therefore, in this study, an Electrical Energy Analysis and Monitoring System was developed using the IoT-Based Prophet Algorithm. Data collection was obtained from electrical energy using the PZEM-004T module sensor device used at home and the energy data obtained were stored in a MySQL database. This PZEM data retrieval will appear in real-time on the Monitoring Website. The dataset was processed by implementing the Prophet Algorithm, evaluating the model and visualizing the prediction results on the analysis website. Testing using Mean Absolute Percentage Error (MAPE). For design, this system uses energy data and data retrieval time as parameters in the monitoring system for the use of electrical energy at home. Analysis of data taken from electrical energy monitoring was predicted by the model created by the Prophet Algorithm and tested with MAPE to see how accurate the predicted value is in the Prophet Algorithm model. Predictions in this study get an error value of less than 10%, namely 6.87%, which means it is very accurate in predicting the prophet algorithm at home.
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 : STMIK 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 Sistem untuk Mendeteksi Jarak Aman Kendaraan Bermotor menggunakan Arduino dan Sensor Ultrasonik Yonas Juniantiko Putro; Theophilus Wellem
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 3 (2023): Maret 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5853

Abstract

Traffic accidents can occur due to driver error, natural factors (bad weather, heavy rain), or technical factors (for example, uneven roads or potholes). When driving a vehicle on the highway, a driver must always maintain a distance from the vehicle in front of it to reduce the risk of an accident. Likewise, when parking the vehicle, the driver must be able to maintain a distance from other objects around the vehicle so as not to crash into the object. To assist the driver in obtaining information about the vehicle's distance to the surrounding objects, this research designs and implements a system to detect the safe distance from a vehicle to other vehicles or objects in front of it. The hardware used in this system is an Arduino Uno R3 microcontroller board, an HC-SR04 ultrasonic sensor to measure distance, an LCD to display measurement results, and an LED and an electronic buzzer used as indicator and alarm when the distance is not safe. The test results show that the implemented system can measure the distance from a motorbike to objects in front of it and warns the driver by activating the LED and the buzzer if the distance is ranging 40 cm to 50 cm which indicates that the distance is not safe.
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 : STMIK 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..
Perbandingan Metode K-NN dan SVM Berdasarkan Kinerja Pegawai Sinarring Azi Laga
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 3 (2023): Maret 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5816

Abstract

Limited qualified human resources cause employees not to do the job in accordance with the company's operational standards properly and correctly. At this time PT. XYZ does not have tools to identify employee performance, therefore researchers conduct research to assist PT. XYZ in classifying employee performance. The methods used in this study were K-NN and SVM with a sample of 873 PT. XYZ employee data. Based on the trials conducted, the K-NN method has the highest accuracy rate of 90.13%, 91% precision rate, and 98.95% recall rate. The most optimal number of neighbors (k value) for the K-NN method is 5 with an accuracy rate of 88.35%.
Klasifikasi Tingkat Keberhasilan Produksi Ayam Broiler di Riau Menggunakan Algoritma K-Nearest Neighbor Beni Basuki; Alwis Nazir; Siska Kurnia Gusti; Lestari Handayani; Iwan Iskandar
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 3 (2023): Maret 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5800

Abstract

Livestock is a crucial component of the Indonesian agriculture sector. One of the most widely practiced types of livestock farming is broiler chicken farming. The production of broiler chickens continues to increase due to the increasing consumption of broiler chickens. Presently, companies are facing an urgent requirement to support farmers, regardless of their level of experience, whether they are newly entering the sector or have been established for some time. Core companies encounter challenges in modeling the success rate of broiler chicken farmer production because of the vast quantity of data coming from collaborating farmers, which makes it arduous for the company to establish the success rate of broiler chicken production. Establishing the level of production success is very helpful in selecting the appropriate farmers to be guided, thus enabling accurate decision-making. A classification procedure utilizing data mining and K-Nearest Neighbor (KNN) algorithm is necessary to manage the growing volume of data. The study examined 927 livestock production data from Riau, where the data was divided into two sets, with 80% allocated for training and the remaining 20% for testing purposes. The findings of the confusion matrix analysis showed that the optimal result was achieved at k = 3, with an accuracy rate of 86.49%, precision of 75.00%, and recall of 70.21%.
Klasifikasi Sentimen Tragedi Kanjuruhan Pada Twitter Menggunakan Algoritma Naïve Bayes Iqbal Salim Thalib; Siska Kurnia Gusti; Febi Yanto; Muhammad Affandes
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 3 (2023): Maret 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5852

Abstract

The Kanjuruhan Malang incident occurred on October 1 and resulted in 132 deaths, 96 serious injuries and 484 minor injuries. The cause of the riot occurred due to provocation between Arema Malang supporters and Persebaya Surabaya supporters who mentioned harsh words and other provocative actions that caused anger on both sides. Sentiment analysis of the Kanjuruhan tragedy using the Naive Bayes method was conducted through tweets taken through Twitter to understand the public's perception of the incident. The Naïve Bayes algorithm is performed for the sentiment classification of tweet data which is applied by processing the tweet text and classifying it into positive, negative, and neutral. In this study using data as much as 4843 data and carried out with tweet data that has been crawled resulting in 2,042 data. This research aims to classify sentiment and determine the level of accuracy in the Multinomial Naïve Bayes algorithm in the Kanjuruhan tragedy using a dataset in the form of tweets from twitter social media. The processed tweet data is divided into two types, namely 90% training data and 10% test data.  The results of this classification get a Naïve Bayes accuracy of 75% with a precission of 73%, recall of 75%, and f1-score value of 74%. The results of the tweet data used in this study can be concluded that the Naïve Bayes algorithm has a fairly good accuracy value.
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 : STMIK 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.
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 : STMIK 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. 
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 : STMIK 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.

Page 23 of 50 | Total Record : 492

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