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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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Prioritas Penanganan Anemia pada Ibu Hamil Menggunakan Metode TOPSIS Naufal Rifqi; Agus Iskandar
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 1 (2023): September 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i1.6820

Abstract

During pregnancy, women experience anemia which can negatively impact maternal health and fetal development. The government has taken various measures to address anemia in pregnant women, but the reduction in anemia rates has not been significant. Therefore, the treatment needs to be focused on individuals with high risk to be more effective. Decision Support System (DSS) is a tool used in complex decision-making processes and one of the methods is TOPSIS. TOPSIS is used to set priorities by comparing each alternative against predetermined positive and negative ideal solutions. In this study, there are 10 alternatives and 5 criteria. Based on the results of calculations with the TOPSIS method, Alternative 3 (A3) with a preference value of 0.246561061 is designated as a pregnant woman who must be prioritized in handling anemia.
Penentuan Mahasiswa Berprestasi dengan Menerapkan Metode Multi Attribute Utility Theory (MAUT) Wulan Kartika Murti; Agung Triayudi; Mesran Mesran
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 1 (2023): September 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i1.6823

Abstract

Being an outstanding student in higher education is certainly a positive and proud thing. Where national education aims to develop the potential of students, in order to become educated, creative students and become democratic and responsible citizens. In determining outstanding students, there are several criteria that must be met by each student as a condition for determining outstanding students. The problem that occurs is that sometimes there are obstacles when assessing the criteria set for each prospective participant. To help the evaluation team in determining outstanding students, a decision support system is needed, sometimes experiencing obstacles when assessing the criteria set for each candidate. In the assessment carried out directly there are prospective candidates who do not meet the criteria standards but excel in other criteria. The Multi Attribute Utility Theory (MAUT) method is a quantitative comparison method used to convert several interests into numerical values on a scale of 0-1 with 0 representing the worst value and 1 the best value. The result of this research is a student determination decision that has the highest score value, namely Netralman (A1) with a utility value of 0.462.
Pemilihan Auditor Internal dalam Mengimplementasikan Pendekatan Metode Multi Attribute Utility Theory (MAUT) dan Menerapkan Pembobotan Rank Order Centroid (ROC) Mohammad Aldinugroho Abdullah; Rima Tamara Aldisa
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 1 (2023): September 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i1.6795

Abstract

The Internal Auditor of a company is a professional responsible for evaluating, overseeing, and providing independent assessments of the effectiveness of internal controls, accounting systems, and business processes of a company. The primary task of the internal auditor is to assist the company's management in ensuring that its operations adhere to applicable standards, policies, and regulations. The presence of an internal auditor helps the company maintain integrity, accountability, and compliance with relevant standards and regulations. They provide an independent view of the company's performance and aid in enhancing operational efficiency and effectiveness. In the complex business world, selecting a candidate for the role of Internal Auditor poses a challenge. Diverse selection criteria, such as auditing skills, industry knowledge, and integrity, often prove difficult to objectively assess. Decision-making based solely on experience can lead to inconsistent outcomes. The importance of accuracy and objectivity in selecting an Internal Auditor demands a scientific approach. The Multi-Attribute Utility Theory (MAUT) analysis method is employed to address the complexity of criteria. Meanwhile, the Rank Order Centroid (ROC) method is used to assign weights to each criterion. By combining MAUT and ROC in a support system, companies have a more structured and measurable way to select potential Internal Auditors. This approach is expected to help overcome issues in candidate selection that often do not align with the company's needs, and to provide more accurate and objective decisions. The ultimate result obtained by applying the MAUT method is a value of 0.794, which is the highest ranking among the 7 selected alternatives. The highest ranking result associated the seventh alternative, named Poppy Rosana.
Sistem Pendukung Keputusan Perbandingan Metode MOORA Dengan MOOSRA Dalam Pemilihan Hair Stylish Mohammad Aldinugroho Abdullah; Rima Tamara Aldisa
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 1 (2023): September 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i1.6824

Abstract

This study aims to compare the effectiveness of the MOORA (Multi-Objective Optimization on the basis of Ratio Analysis) and MOOSRA (Multi-objective Optimization on the Basis of Simple Ratio Analysis) methods in the context of selecting stylish hair at the barbershop. In the growing hair care industry, the selection of stylish hair does not only affect the appearance of the customer but also plays an important role in the image and success of the barbershop itself. Therefore, it is important for barbershop owners to choose the right stylish hair. The MOORA method is known for its ability to solve multi-objective decision-making problems by utilizing ratio analysis. Meanwhile, MOOSRA is another method that focuses on optimization by considering relative preferences. In the context of selecting stylish hair, both can be useful tools in guiding barbershop owners to choose stylish hair according to customer needs and preferences. This research involves collecting data regarding customer preferences and hair stylish characteristics from various barbershops. This data was then analyzed using the MOORA and MOOSRA methods to choose the most suitable hair style for each scenario. The results of the analysis will be compared to assess the relative performance of the two methods in this context. It is hoped that the results of this research will provide valuable insights for barbershop owners and the hair care industry in general. By understanding the advantages and limitations of each method, barbershop owners will be able to make more informed decisions in selecting stylish hair. In addition, this research can also contribute to the development of a methodology in more complex multi-objective decision-making, by providing concrete examples in practical applications. The final results of the calculations of the two methods are proven to produce the same highest ranking result, which is obtained by alternative 1 on behalf of Poppy Sukma.
Perbandingan Metode ARAS dan MOORA dalam Seleksi Penerimaan Pegawai Baru Non ASN Susanto, Susanto; Ningrum, Setya; Cahyono, Yudi
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 3 (2024): Maret 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i3.7449

Abstract

The acceptance of new non-ASN candidates is a major problem in recruiting new employees, this happens because of the large number of new prospective employees who register and the number of vacancies that are inadequate, therefore research is carried out that aims to create a decision support system that can speed up the process of recruiting candidates. new employee. The methods used in this research are ARAS (Additive Ratio Assessment) and MOORA (Multi-Objective Optimization on the basis of Ratio Analysis) methods. The selection of this method is able to filter out the most alternatives based on weighting and this method is able to choose goals based on different criteria, namely benefits and costs. The results obtained from the ARAS method are the calculation rankings from the comparison of alternative utility functions with the optimal utility function values, while the results obtained from the MOORA method are the calculation rankings of the maximum and minimum values. From this value, an alternative that meets the criteria through calculations using the ARAS and MOORA methods, the employee alternative chosen is Yuniar Pilakso Angkasa with a final result of 0.935 for the ARAS method and 0.163 for the MOORA method. The results of the Spearman Rank coefficient test obtained a value of 0.9357% for the ARAS method and 0.7428% for the MOORA method, meaning that the two methods have a strong correlation level and can be used in recruiting new non ASN employees.
Klasifikasi Sentimen Terhadap Topik Pindah Ibu Kota Negara Pada Twitter Menggunakan Metode Naïve Bayes Classifier Dermawan, Jozu; Yusra, Yusra; Fikry, Muhammad; Agustian, Surya; Oktavia, Lola
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 3 (2024): Maret 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i3.7475

Abstract

Towards the middle of 2019, President Joko Widodo announced plans to relocate Indonesia's capital city. This caused pros and cons in the community, which were widely observed in various social media. To quickly measure the level of public sentiment towards the policy of moving the National Capital City (IKN), whose construction is already underway, a classification system that has good performance is needed. This research proposes a classification of public sentiment on the topic using the Naïve Bayes Classifier method. The data used in this study amounted to 4000 tweets that have been classified into two classes, namely 2000 positive class data and 2000 negative class data. The purpose of this research is how to apply the Naïve Bayes Classifier method in classifying sentiment on the topic of moving the nation's capital and determine the accuracy level of the method. The application of the Naïve Bayes classification method using TF-IDF features to classify 10% of the data as testing data resulted in an accuracy of 77.00%, for a precision value of 77.06%, recall 77.08% and f1-score of 77.00%. Based on the results achieved, the Naïve Bayes Classifier method is good at text classification tasks, with a fairly good accuracy rate.
Implementation of Global Vectors for Word Representation (GloVe) Model and Social Network Analysis through Wonderland Indonesia Content Reviews Singgalen, Yerik Afrianto
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 3 (2024): Maret 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i3.7569

Abstract

Integrating the Global Vectors for Word Representation (GloVe) Model with Social Network Analysis presents a promising approach for extracting nuanced semantic relationships from Wonderland Indonesia's content reviews. However, the lack of comprehensive studies exploring the effectiveness of this integration, specifically within the context of Wonderland Indonesia's content reviews, necessitates focused research to uncover its potential impact and applications. This study investigates the reception and impact of the "Wonderland Indonesia" video content by Alffy Rev ft. Novia Bachmid (Chapter 1) within the YouTube community using a comprehensive methodology based on CRoss-Industry Standard Process for Data Mining (CRISP-DM), topic analysis, and Social Network Analysis (SNA). Through topic analysis, the content's main themes and narrative elements were identified, shedding light on its storytelling effectiveness. Furthermore, sentiment analysis using Vader was conducted on 2204 out of 24185 posts, revealing that 1369 (92%) exhibited positive sentiment, 427 (31.19%) had neutral sentiment, and 850 (62.09%) contained negative sentiment. Additionally, sentiment analysis using TextBlob was performed on the same subset of posts, with 1369 (40) posts exhibiting positive sentiment, 599 (43.75%) with neutral sentiment, and 730 (53.32%) expressing negative sentiment. Notably, metrics such as toxicity (highest value: 0.90780) and severe toxicity (highest value: 0.95021) exhibited varying prominence within the analyzed content. These findings enable targeted interventions and content moderation strategies to promote healthier online discourse. The SNA uncovered intricate social dynamics and interaction patterns among viewers, emphasizing the video's ability to foster engagement and community interaction. This study underscores the significance of creative storytelling and community engagement strategies in digital content creation, with implications for audience participation and community development within the digital sphere. Future research could explore the longitudinal effects of such content strategies on audience retention and community engagement.
Pemanfaatan Metode Decision Tree dengan Algoritma C4.5 Untuk Prediksi Potensi Kunjungan Wisatawan Iman, Hadad Karsa Nur; Latifah, Noor; Supriyono, Supriyono; Nugraha, Fajar
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 3 (2024): Maret 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i3.6684

Abstract

This research aims to predict potential tourism visits in Pati Regency, Indonesia, utilizing data mining methods, specifically the Decision Tree with the C4.5 algorithm. The significance of the tourism sector in a region's economy and sustainability, along with the potential of data in formulating more effective and targeted strategies and decisions, motivated this objective. The initial experiment results demonstrated the superior performance of the Decision Tree C4.5 method in predicting potential tourism visits in Pati Regency, with an accuracy of 96.42%, precision of 96.42%, and recall of 96.66%. This performance exceeded the Naive Bayes method, which yielded an accuracy of 82.14%, precision of 84.49%, and recall of 83.07%. The research highlights the potential of data mining methods in the tourism sector, especially for predicting tourism visits. The results are expected to assist stakeholders in formulating more effective strategies and decisions, contributing positively to the wider development of the tourism sector.
Sentiment Classification of Food Influencer Content Reviews using Support Vector Machine Model through CRISP-DM Framework Singgalen, Yerik Afrianto
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 3 (2024): Maret 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i3.7509

Abstract

The research problem revolves around the challenges in effectively marketing culinary tourism aligned with tourist preferences in Indonesia, necessitating a substantial exploration of consumer sentiments related to culinary diversity through the lens of food influencer content. Food influencers are crucial in stimulating tourists' interest in gastronomy through culinary tourism in Indonesia. This research reveals challenges in culinary tourism marketing aligned with tourist preferences, necessitating substantial exploration of consumer sentiments related to culinary diversity through food influencer content. The sentiment classification method employed is the Cross-Industry Standard Process for Data Mining (CRISP-DM) using the Support Vector Machine (SVM) algorithm and the SMOTE operator. The data source is derived from a video with the ID PMhfLy_buV8, containing 114,422 comments. This study collects and processes 30,000 comments, resulting in 9,323 data points. The findings highlight the vital performance metrics of SVM models, both with and without SMOTE, showcasing high accuracy, precision, recall, and F-measure values. Specifically, SVM without SMOTE achieves 95.28% accuracy, while SVM with SMOTE achieves 98.67%. Despite some limitations in discerning positive and negative sentiments, indicated by moderate Area Under the Curve (AUC) values (0.608 to 0.658), the overall efficacy of SVM in sentiment analysis for food influencer content is apparent. Drawing from a dataset of 30,000 comments, these insights contribute to advancing sentiment analysis methodologies and offer practical implications for understanding consumer perceptions and behaviors in digital media and influencer marketing. Additionally, the prominence of frequent words such as "bang" (1322), "nonton" (1064), "makan" (921), "yang" (801), "puasa" (711), "tahun" (484), "ngiler" (448), "lagi" (384), "tanboy" (311), and "enak" (315), as extracted from RapidMiner analysis, underscores the significance of language patterns in the realm of food influencer content.
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 : STMIK 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%.

Page 27 of 50 | Total Record : 492

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