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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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Implementasi Support Vector Machine untuk Kendali Lampu Ruangan Adam, Chairul; Hidayati, Rahmi; Suhardi, Suhardi
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 3 (2024): Maret 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i3.6964

Abstract

The use of lights in daily activities is a necessity for everyone. Lights are useful as lighting when the room has minimal light, so lights are needed in the room in the house. If there are many rooms in a house, there are also many lights used. Often users forget to turn on and off the lights in each room if done manually. An automatic control is needed to make it easier for users to turn on and off lights according to user habits. Support Vector Machine is used in this in this study to classify the condition of lights based on user habits when turning on and off the lights. User habit data used for 15 days with a total of 620 data. Testing was carried out for 3 days with a total of 72 data. The results of system testing obtained the accuracy value of kitchen lights by 87.50%, accuracy of main room lights by 95.83%, accuracy of second room lights by 91.67%, accuracy of living room lights by 95.83%, accuracy of terrace lights by 95.83%, and accuracy of toilet lights by 94.44%.
Sistem Deteksi Dini dan Pemadaman Kebakaran Otomatis di Rumah Berbasis IoT Menggunakan NodeMCU ESP32 Saputra, Dimas Bagus; Hidayati, Rahmi; Suhardi, Suhardi
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 3 (2024): Maret 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i3.6974

Abstract

Fire is a serious disaster that causes significant losses in terms of both material assets and loss of life, especially in densely populated residential areas. Fires that occur in one house can easily spread to other houses. To prevent this issue, an early fire detection and automatic fire suppression system has been developed for homes based on the Internet of Things (IoT) using NodeMCU ESP32. This system has the ability to detect early signs of fire, gas, and temperature conditions, and takes automatic action when potential fire hazards are detected. Additionally, the system sends real-time push notifications to users' Android applications to provide warning messages. Test results indicate that the fire sensor can detect flames up to a distance of 130 cm with an average notification delay of approximately 2,37 seconds. The MQ-2 gas sensor can detect butane gas up to a distance of 170 cm with an average notification delay of about 2,84 seconds, while the DHT22 temperature sensor has an average accuracy of approximately 98,52% with a notification delay of about 2,03 seconds.
Sistem Pendukung Keputusan Penentuan Jasa Ekspedisi Terbaik Menerapkan Metode ARAS Saragih, Nova; Triayudi, Agung; Mesran, Mesran
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.6976

Abstract

The increase in users of goods delivery services throughout Indonesia has resulted in more and more new companies in this field. Consumers also believe in using their services in sending their goods delivered using goods delivery services. The large number of companies that run the goods delivery service business, makes users confused in shipping goods to consumers. There are factors that influence the freight forwarding company's services to be known by the public and the level of promotion from each expeditionary party so that people are confident in using the freight forwarding services. In determining the best expedition services in the city of Medan there are several criteria, namely service, experience, responsiveness, cost and age. Based on these problems, a decision support system is needed as a problem solving technique and is assisted by a method that can produce an accurate final value. The method is the Additive Ratio Assessment (ARAS) method in which the method is very helpful in producing the best weight and preference values from alternative data and criteria so that the final result is to determine the best expedition services in the city of Medan in Alternative A14 with a value of 1.0000, namely expedition services TNT.
Sistem Pendukung Keputusan Pemilihan Wi-Fi Extender dengan Pendekatan Complex Proportional Assessment dan Rank Reciprocal Nugroho, Nurhasan; Fatmayati, Fryda; Alexander, Allan Desi; Tonggiroh, Mursalim
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.6984

Abstract

A Wi-Fi Extender is a device needed to expand the range and improve the quality of the Wi-Fi signal. To determine the choice, decision makers must know one by one the specifications of the existing products. This results in making decisions difficult and requiring a long time. So the aim of this research is to develop a decision support system for choosing a Wi-Fi Extender using the Rank Reciprocal and COPRAS (Complex Proportional Assessment) weighting approach to make it easier to make decisions in a relatively short time. The Rank Reciprocal approach is used to rank or weight the criteria given by decision makers. Meanwhile, the COPRAS approach is used to obtain the best alternative which is evaluated by calculating the effectiveness index directly proportional to the criteria considered to provide benefits and costs. Based on the case study that was carried out, the highest utility result was obtained, namely the Mercusys MW300RE (A4) which obtained a score of 100. The output produced by the decision support system in the case study that was carried out obtained the same score as manual calculations. Apart from that, the usability testing results obtained an average value of 88.75%. This shows that the system is declared suitable for use because it is in accordance with its function and use.
Clustering Pecandu Narkoba Menggunakan Algoritma K-Means Clustering Amal, Ikhlasul; Putri, Raissa Amanda
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7009

Abstract

With the rise of increasingly sophisticated technology in this time of globalization, it is exceptionally simple for the more extensive local area to manage exchanges (drugs). For this reason, the government is constantly trying to stop the spread of drugs among Indonesian people by using any media, ranging from verbal invitations, banners, posters, videos and photos displayed in schools, government and public places. The maltreatment of opiates and perilous medications (drugs) in Indonesia as of late has turned into a difficult issue and has arrived at a condition of concern with the goal that it has turned into a public issue.In order to make it easier for BNN to conduct monitoring and counseling to areas where there are many drug addicts, it is necessary to cluster data on drug addicts in Medan city. To solve the problem, it can be solved by clustering drug addicts in Medan city using the K-Means Clustering Algorithm. The data used comes from the BNN of North Sumatra Province, the data used is data on drug addicts in Medan City in 2020-2023. The purpose of clustering drug addict data in Medan city is to find out areas that are very high, high, low and very low levels of drug addicts.This study found that there are 2 sub-districts with the highest level of drug addiction, 7 sub-districts with a high level of addiction, 7 sub-districts with a low level of drug addiction, and 5 sub-districts with a lowest level of addiction.
Klasifikasi Tingkat Keberhasilan Produksi Ayam Broiler di Riau Menggunakan Algoritma Naïve Bayes Hamwar, Syahbudin; Nazir, Alwis; Gusti, Siska Kurnia; Iskandar, Iwan; Insani, Fitri
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7038

Abstract

Livestock is becoming one of the important animal protein source providers, along with the fisheries sector, to meet the protein needs of the community at large. One type of livestock business that is popular is the maintenance of broiler chickens because of the potential for meat yield. Today, many breeders run a partnership pattern with large companies where breeders play the role of the main supplier and the company as the core. This step helps maintain the stability of production and income of farmers. The success of farmers in broiler chicken production can be measured by looking at the performance index (IP), if the performance is not good then coaching from the core company is needed. The large amount of data obtained from farmers makes it difficult for core companies to model the success rate of farmer production, this can make it difficult for core companies to choose farmers who need coaching. The application of data mining methods using the Naïve Bayes algorithm classification model has the potential to provide solutions to this problem. The purpose of this study was to predict how much success rate of broiler chicken production in Riau region by utilizing the Naïve Bayes Classifier algorithm. This study utilizes a production data set involving 952 broiler chicken farmers in Riau, with 3 scenarios dividing the data ratio of 90:10, 80:20, and 70:30. The results of the analysis showed that through the evaluation of the confusion matrix, it was best found in a data ratio of 90:10 with accuracy results reaching 89,58%, precision reaching 89,89%, and recall reaching 90,16%.
Kontruksi Sosial Network Analysis Untuk Menilai Kepopuleran Brand Sepatu di Twitter Prayoga, Setyo Hendro; Handoko, Widiyanto Tri
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7046

Abstract

Shoes are one of the most popular products to buy. Therefore, many well-known shoe brands such as Adidas, Converse, New Balance, Nike, Reebok and Skechers are competing to create the latest shoe models. Many shoe brand users share their experiences on the Twitter platform. The aim of this research is to assess the level of popularity of the Adidas, Converse, New Balance, Nike, Reebok, Skechers shoe brands on the Twitter platform. This research will analyze the social network patterns of Twitter users in the period January - March 2023 which discusses the shoe brands Adidas, Converse, New Balance, Nike, Reebok, Skechers and will be analyzed using Social Network Analysis (SNA). SNA is a method that can be used to analyze interactions between individuals that occur on the Twitter platform by looking at network properties. This research will also use the Weighted Aggregated Sum Product Assessment (WASPAS) method which is used to support decision making about which shoe brands are the most popular in the period January – March 2023. The results of this research show that New Balance obtained rank 1 with a value of 0.985172467, Adidas was ranked 2nd with a value of 0.946380309, Nike was ranked 3rd with a value of 0.851347847, Reebok was ranked 4th with a value of 0.83674501, Converse was ranked 5th with a value of 0.831588591, and Skechers was ranked 6th with a value of 0.759538181.
Analisis Perbandingan Algoritma C4.5 dan Modified K-Nearest Neighbor (MKNN) untuk Klasifikasi Jamur Rahmadhani, R.; Nazir, Alwis; Syafria, Fadhilah; Afriyanti, Liza
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7052

Abstract

Mushrooms are organisms that consist of several cells, contain spores, are eukaryotic (have a cell nucleus membrane), and do not have chlorophyll, so fungi depend on other organisms to get food. Mushrooms have very identical shapes, starting with size, shape, smell, and color. So it is difficult for ordinary people to differentiate between poisonous mushrooms and non-poisonous mushrooms. Mistakes in identifying mushrooms can have fatal consequences because they can cause poisoning when consuming mushrooms. Therefore, there is a need for education in classifying poisonous and non-poisonous mushrooms. By applying various classification algorithms, it can be determined which algorithm performs better. In previous research conducted by several researchers on classifying mushrooms, there were differences in the accuracy results for each algorithm. Therefore, this research will raise the question of how to measure or comparion algorithm performance in classification using the C4.5 algorithm and the Modified K-Nearest Neighbor (MKNN) algorithm. The results obtained by comparion the performance of the C4.5 algorithm and the Modified K-Nearest Neighbor (MKNN) algorithm in this research show that the C4.5 algorithm managed to obtain an accuracy level of 98.52%, precision of 98.55%, recall of 98.52%, and f1-score of 98.51%. In contrast, the Modified K-Nearest Neighbor (MKNN) algorithm using the value K=10 achieved an accuracy level of 96.62%, precision of 96.69%, recall of 96.62%, and f1-score value of 96.57%.
Klasifikasi Sentimen Masyarakat Terhadap Prabowo Subianto Bakal Calon Presiden 2024 di Twitter Menggunakan Naïve Bayes Classifier Dwitama, Raja Zaidaan Putera; Yusra, Yusra; Fikry, Muhammad; Yanto, Febi; Budianita, Elvia
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7071

Abstract

The Indonesian President who has served for 2 consecutive terms cannot nominate again to become President. The public's attitude towards the three presidential candidates, Prabowo Subianto, Anies Baswedan, and Ganjar Pranowo, who are predicted to run for the 2024 presidential election, is also a matter for netizens' opinions from which conclusions can be drawn. Testing will be carried out in this research using information collected from tweets posted by Twitter users. Naïve Bayes Classifier is a technique that will be applied for sentiment assessment. In the upcoming presidential election, this research will be a source when determining the presidential choice. 2100 tweets with the search keywords "Presidential Candidate" and "Prabowo Subianto" are data collected by dividing 1050 positive data and 1050 negative data. Then implementation was carried out using Google Colab starting from data processing (cleaning, case folding, tokenizing, normalization, negation handling, stopword removal, stemming) followed by classification using the Naïve Bayes Classifier. According to test findings using the Confusion Matrix with three experimental test data 90:10, 80:20 and 70:30. Obtained the highest accuracy results of 89%, with a precision value of 89.7%, 88.6% recall and 88.9% f1-score in the 90:10 trial test.
Penerapan CRISP-DM dalam Klasifikasi Sentimen dan Analisis Perilaku Pembelian Layanan Akomodasi Hotel Berbasis Algoritma Decision Tree (DT) Singgalen, Yerik Afrianto
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7081

Abstract

The Cross-Industry Standard Process for Data Mining (CRISP-DM) approach is very relevant in identifying business challenges and producing recommendations in the form of appropriate models to face various business challenges. Sentiment classification is needed to identify and analyze consumer trends and preferences in order to plan risk mitigation strategies related to business sustainability. This study adopts the CRISP-DM method in classifying hotel guest sentiment through review data on the Agoda platform and analyzing sentiment data based on the purchase behavior of related products and services. Meanwhile, the stages in the CRISP-DM method are as follows: the stage of understanding the business context (business understanding), the stage of understanding data characteristics (data understanding), the modeling stage (modeling), the evaluation stage, and the implementation stage (deployment). The results of this study show that ten words are the attention of hotel guests and are dominated by positive sentiment, namely shopping, great, stay staff, clean, location, room, good, mall, and hotel. The classification results using the DT algorithm showed good performance with an accuracy value of 93.91%, a precision value of 90.98%, and a recall value of 97.77%.  In addition, the AUC value is 0.943 or 94.3%, and the f-measure value is 94.18%. Furthermore, sentiment analysis data can be developed into a Customer Relationship Management (CRM)  supporting application to analyze guest purchase history data related to sentiment, country of origin, guest type, room type, and length of stay by day, month, and year. Thus, the marketing strategy of hotel accommodation services can be optimized for personalization and increase interest and intention of returning stays.

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