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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 457 Documents
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Clustering Data Persediaan Barang Menggunakan Metode Elbow dan DBSCAN Berliana, Trisia Intan; Budianita, Elvia; Nazir, Alwis; 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.v5i2.7089

Abstract

In the world of business and inventory management, efficient inventory management is very important. If a company does not have inventory, it is impossible to fulfill consumer desires. Managing inventory requires careful inventory management and good data analysis. Challenges in inventory involve unpredictable fluctuations in demand, making it difficult to determine optimal inventory levels. Product diversification with various characteristics is also an obstacle, hindering grouping and formulating inventory management strategies. The lack of clear product segmentation adds to the inhibiting factor, making it difficult to identify groups of similar goods. Inefficient stockpiling can be detrimental to the business as a whole, so implementing clustering is necessary to optimize inventory strategies based on product characteristics. By analyzing product groups, companies can develop more efficient and effective inventory management strategies. This research uses a clustering method using the elbow method and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). The elbow method is used to determine the most optimal EPS and Minpts values. The aim of this research is to group goods inventory data using the attributes Initial quantity (initial stock), quantity sold (stock sold), and quantity available (available product stock). So that grouped data can make it easier for companies to optimize the inventory of the most sold goods. and fans. Based on the elbow and DBSCAN test results, 144 clusters and 0 noise data were obtained, with cluster 2 being the product with the largest number of sales and inventory. The DBSCAN method which was tested without using elbows obtained cluster 3 results and 959 noise data.
Klasifikasi Sentimen Komentar Youtube Tentang Pembatalan Indonesia Sebagai Tuan Rumah Piala Dunia U-20 Menggunakan Algoritma Naïve Bayes Classifer Hasibuan, Ilham Habibi; Budianita, Elvia; Agustian, Surya; Pizaini, Pizaini
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.v5i2.7096

Abstract

Text mining is a method used to perform tasks such as document classification, clustering, information extraction, sentiment analysis, and information retrieval. The Federation Internationale Football Association (FIFA), the international football governing body, has designated Indonesia as the host country for the U-20 World Cup starting in 2019. Indonesia is expected to be the choice venue for the U-20 World Cup in 2021. However, due to the Covid outbreak -19, the World Cup was rescheduled and is now scheduled to take place in 2023. Indonesia officially relinquished its position as host on March 31 2023. One of the reasons is the many factions that oppose the presence of the Israeli national team in Indonesia. As a result, various public reactions responded to Indonesia's decision to cancel holding the U-20 World Cup, especially on the Narasi tv YouTube channel video entitled "The U-20 World Cup Failed to Be Held in Indonesia, Let's Look at it from Two Perspectives | Discussion". Since the video was uploaded until August 16 2023, the total comments generated were 4,629 comments. This research uses a Naïve Bayes classifier approach. Naïve Bayes Classifier (NBC) is a direct probabilistic classifier that exploits Bayes' Theorem under strong independence conditions. The tests carried out show that the model performance when using stopword removal and stemming techniques is superior in classifying classes in the dataset. The F1-Score is 59.70% and the Accuracy value is 63.43%. Furthermore, after identifying the most efficient model for applying naïve Bayes classification, evaluation was carried out on validation data resulting in an F1-Score of 58.72% and an accuracy rate of 61.65%. Classification analysis shows that Indonesian people have a negative view or are disappointed with the cancellation
Analisis Sentimen Tentang Penggunaan Galon Bebas BPA di Indonesia Menggunakan Algoritma Support Vector Machine Tri Atmojo, Muhammad Iqbal; Sinduningrum, Estu
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.v5i2.7101

Abstract

This research employs the Support Vector Machine algorithm to classify sentiment in comments on the X, Youtube, and Tiktok platforms regarding the use of BPA-free water gallons in Indonesia. From a total of 1200 data points, post-labeling, 772 data points were obtained, with 552 classified as positive and 220 as negative. The experimental results reveal that SVM achieves an accuracy of 96.15%, while Naïve Bayes achieves an accuracy of 84.55%. These findings indicate that SVM is effective in classifying sentiment with a high accuracy rate, providing valuable insights for manufacturers, government entities, and consumers regarding the use of BPA in water gallons in Indonesia. This study contributes to a better understanding of the role of social media in shaping public opinion and policies related to environmental and health issues.
Sistem Pendukung Keputusan Penerimaan Dosen Tetap Menggunakan Metode MOORA dan MOSRA Mesran, Mesran; Aldisa, Rima Tamara; Rangkuti, Wanda Tofani Devi; Sari, Cindy Nanda
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.v5i2.7140

Abstract

Lecturers are the forerunners and places to gain knowledge for the nation's children, good lecturers will produce good students too, and good students will become successors to the progress of the nation to be even better, the large number of lecturers at Budi Darma University results in a density of lecturers, it is important to do acceptance of permanent lecturers to provide rewards to lecturers who have worked diligently and earnestly, each lecturer has their own quality but permanent lecturers are lecturers who have a safer position and are trusted by the campus, the importance of selecting permanent lecturers using a system decision support to prevent fraud in the election process. In this study, the MOORA (Multi-Objective Optimization on the Basis Of Ratio Analysis) and MOOSRA (Multi-objective Optimization on the basis of Simple Ratio Analysis) methods are used to assist the selection process in a logical, systemic manner and can produce a decision value on the ranking value. which are different from each formula or algorithm, but these values are equally real and fair without any cheating. In this study the authors also used the ROC (Rank Order Centroid) value to obtain an effective and correct weighting value to perform calculations on the criteria values that had been set by the campus or college of the Budi Darma Medan University. The results in this study based on the calculation of the MOORA method, the highest result was achieved by A1, which is worth 0.4742 and in the MOOSRA method, the highest alternative result was achieved by A1, which is worth 28.1366.
Penerapan Data Mining Untuk Clustering Kualitas Udara Rifqi, Ahmad; Aldisa, Rima Tamara
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.v5i2.7145

Abstract

Human health at this time is the key to the continuity of life. Human health is very necessary in the process of development of human life. Environmental health is related to the circumstances or conditions that exist in the surrounding area where you live, whether in a small environment or a large environment. Air quality is the condition of the surrounding air. Air quality is very important for human life because air is what helps humans to live by breathing. With the availability of good air quality, it will certainly be an important factor for an area, not only for health but also for other sectors that interact directly in open areas. The important role of air quality for humans means that more attention needs to be paid and special treatment is given to areas exposed to bad air. The above is a very important problem that must be resolved immediately, if the problem is not resolved immediately it will have an impact on health. The process of solving problems requires a way to resolve them. Where the process of measuring air quality can be seen based on certain conditions or criteria that occur in an area. Data mining is a method used to carry out the problem solving process by processing data. In the process carried out in data mining, there are various ways of solving it. One thing that can be used is clustering. In clustering itself there are various kinds of algorithms such as DBSCAN, K-Means and K-Medoids. In this research, the solution process will use the three algorithms K-Means, K-Medoids and DBSCAN. The purpose of using these three algorithms is to compare the results obtained. In the process carried out in completing data mining, clustering techniques are used using 3 (three) algorithms, namely K-Means, K-Medoids and DBSCAN. The results obtained were that the K-Means algorithm had the highest accuracy value obtained at K=4 with a value of 0.843, for the K-Medoids algorithm the highest value was obtained at K=5 with a value of 0.896 and for the DBSCAN algorithm the highest value was obtained at K=2 with a value of 0.885.
Studi Kinerja Jaringan LoRa: Optimalisasi dan Analisis Efisiensi Komunikasi Nirkabel Astutik, Liya Yuni
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.v5i2.7147

Abstract

LoRa is a long-range wireless connectivity technology, considered an innovative solution for implementing the Internet of Things (IoT). This research encompasses a series of performance tests conducted under various conditions, including Line of Sight (open) and Non-Line of Sight (closed), utilizing the ISM 915 MHz frequency. The importance of testing optimization and efficiency analysis of LoRa is a key factor in ensuring the optimal performance and sustainability of this technology. The testing process involves several stages. The first test assesses the communication range of LoRa SX1276 within a building, determining its communication capabilities indoors. The second test focuses on the maximum communication range, identifying the farthest distance at which LoRa SX1276 can maintain a stable and reliable connection. The third test evaluates the performance of data transmission in a closed environment, specifically Non-Line of Sight conditions, to assess how well LoRa SX1276 maintains data transmission performance in situations with obstacles or hindrances. The fourth test assesses the resilience of LoRa SX1276 in long-term operations, evaluating the network's durability and reliability over an extended period. The fifth test conducts a comprehensive analysis of the system's performance, including aspects such as delay, data delivery, and data loss. This research aims to provide a deeper understanding of the capabilities of LoRa SX1276 in various usage scenarios. The results are expected to contribute to the development and implementation of LoRa SX1276 technology in diverse applications requiring wireless connectivity with extensive range and high efficiency.
Deteksi Lesi Pra-Kanker Serviks Pada Citra Kolposkopi Menggunakan Convolutional Neural Network dengan Arsitektur YOLOv7 Nurqolbiah, Fatihani; Nurmaini, Siti; Saputra, Tommy
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.v5i2.7152

Abstract

Pre-cancerous cervical lesions detection is crucial in the diagnosis and analysis of medical images. Because visual observations are weak, computer-based detection is needed. This research proposes a pre-cancerous cervical lesion detection model using a Convolutional Neural Network with the YOLOv7 architecture, capable of accurately detecting these lesions. The data used was 913 colposcopy image data from 200 cases. The dataset is divided into training and testing data, resulting in a detection model for pre-cancerous cervical lesions. The model achieves an mAP of 91.9%, precision of 87.7%, recall of 96%, and an F1-score of 93%. The study demonstrates that the performance of YOLOv7 indicates the model's ability to accurately detect pre-cancerous lesions in the cervix.
Implementasi Metode CRISP-DM dalam Analisis Model Pendukung Keputusan Simple Additive Weighting dan Pengembangan Basis Data Riwayat Pembelian Layanan Akomodasi Hotel 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.v5i2.7153

Abstract

The development of studies on implementing Simple Additive Weighing (SAW) decision support models in purchasing hotel accommodation services or making stay decisions is limited to hotel recommendations calculated from consumer assessments of criteria with predetermined weights. However, it is necessary to develop a database with interactive visualization of hotel accommodation services and make it easier for consumers to compare and provide ratings. Considering this, this study uses the CRISP-DM method to develop a database based on the purchase history of hotel accommodation services in a business operational area, then uses SAW as a decision support model in the calculation process to produce the best hotel recommendations based on purchase data. The CRISP-DM method consists of business understanding, data understanding, modeling, evaluation, and deployment stages. At the business understanding stage,  the customer's purchase history data is collected in the Agoda website review column. At the data understanding stage, the data collection process is carried out based on the supporting data of the criteria used. At the modeling stage, the SAW algorithm is used in the calculation process to get the best recommendations. In the Evaluation phase, hotels with the best recommendations are analyzed based on the guest's country of origin, guest sentiment, type of guest staying, room type used, length of stay, and month and year. In the Deployment stage, the database is developed using Oracle Apex and visualized interactively so that system users can understand consumer trends and behavior, especially in making overnight decisions based on purchase history data. Based on data obtained from the Agoda platform, it can be seen that A2 ranks first with a value weight of 0.983, then A3 ranks second with a value weight of 0.982, and A1 ranks third with a value weight of 0.946. Meanwhile, based on data obtained from the Booking.com platform, it can be seen that A3 ranks first with a value weight of 0.983, then A2 ranks second with a value weight of 0.974, and A1 ranks third with a value weight of 0.951. Thus, the SAW decision support model implementation output is not limited to the results of calculations and recommendation tables but includes a database with interactive visuals.
Klasifikasi Sinyal EEG Untuk Mengenali Jenis Emosi Menggunakan Recurrent Neural Network Utari, Aspirani; Rini, Dian Palupi; Sari, Winda Kurnia; Saputra, Tommy
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.v5i2.7162

Abstract

This research focuses on in-depth exploration and analysis of the application of two types of Recurrent Neural Network (RNN), namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The two models are drilled with the same parameters, consist of three layer, use the relu activation function, and apply 1 dropout level. In order to compare the performance of the two, experiments were carried out using five groups of datasets for training and performance evaluation purposes. The evaluation includes metrics such as accuracy, recall, F1-score, and area under the curve (AUC). The dataset used is Eeg Emotion which contains 2458 unique variables. In terms of performance, LSTM succeeded in outperforming GRU in the task of classifying emotional data based on EEG signals. On the other hand, GRU shows advantages in accelerating the training process compared to LSTM. Although the accuracy of both methods is almost similar in all data divisions, in the evaluation of the ROC curve, the LSTM model demonstrates superiority with a more optimal curve compared to GRU.
Analisis Sentimen Perbedaan Pendapat Netizen Indonesia Terhadap Penutupan Tiktok Shop Menggunakan Algoritma Naïve Bayes Kurnianto, Eko; Febriawan, Dimas
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.v5i2.7170

Abstract

This research uses the Naïve Bayes algorithm to analyze the sentiments of Indonesian netizens regarding the closure of the TikTok Shop. This research focuses on analyzing differences of opinion spread on social media platforms. Data obtained from social media such as Youtube, Tiktok, and Threads. There is data that will later be used in this research with a total of 1366 data. Then, there were 987 positive data and 379 negative data. After conducting research, results will be obtained with an accuracy of 86.97% in the first experiment which does not use the Split Data operator, and an accuracy of 89.23% in the second experiment which uses the Split Data operator. Then the results of this analysis reveal significant variations in sentiment among Indonesian netizens regarding the closure of the TikTok Shop. Some groups of netizens may express disappointment or disapproval while others may show support for the decision. The analysis also identified key factors influencing dissent, such as user experience, expectations of the platform and economic impact. Due to this, this research contributes to the field of sentiment analysis and natural language processing which applies splitting procedures so that netizen comment data on the platform can be classified.

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