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Jurnal Teknik Informatika C.I.T. Medicom
Published by Institute of Computer Science
ISSN : 23378646     EISSN : 2721561X     DOI : -
Core Subject : Science,
Computer Science & IT
The Jurnal Teknik Informatika C.I.T a scientific journal of Decision support sistem , expert system and artificial inteligens which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 99 Documents
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Application of data mining on inventory grouping using clustering method Suci Ramadani; Syukri Hidayat; Ramahdanty Ramahdanty
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 5 (2023): November : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol15.2023.608.pp228-239

Abstract

Data mining in the business field is considered important, because the inventory system of goods in a store and what types of goods are the top priorities that must be stocked to anticipate the vacancy of goods, so that the store owner can find out the most sold items and the lack of stock items. The existence of daily sales transaction activities at Sahabat Komputer stores will produce a pile of data that is getting bigger and bigger, so that it can cause new problems. If this is allowed, the transaction data will become a pile of data that is detrimental because it requires an increasingly large storage media or database. One way to overcome this is to keep the availability of various types of continuous goods in the warehouse. To find out what items are purchased by consumers, the technique of analyzing the inventory of goods in the warehouse is carried out. Application of Clustering, helps in grouping data of the same characteristics into the same region. And from the whole it can be concluded that in cluster 1 the stock is available on average 1-100 pcs, the number of sales is 1-100 pcs and the sales volume per month is 1-100 units. In cluster 2 there is an average available stock of 101-200 pcs, 101-200 pcs sales quantity of 101-200 units, and monthly sales volume of 101-200 pcs. And in cluster 3 there is an average available stock of 301-400 pcs, the number sold is 401-500 pcs, and the monthly sales volume reaches 301-400 pcs.
Privacy-Preserving machine learning in edge computing environments Deni Kurniawan; Dedi Triyanto; Mochamad Wahyudi; Lise Pujiastuti
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 3 (2023): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol15.2023.621.pp118-125

Abstract

Edge computing has transformed data processing by moving computation closer to the source, enabling real-time analysis and decision-making. Edge devices are decentralized, which creates privacy and confidentiality concerns, especially when applying machine learning algorithms to sensitive data. Privacy-preserving machine learning methods for edge computing are examined in this research. Federated learning, homomorphic encryption, differential privacy, and secure aggregation are examined as data protection methods for network edge machine learning. A thorough study of these methods shows the challenges of balancing privacy, computational economy, and model correctness. Federated learning has promise for collaborative model training without raw data sharing, but communication overhead and convergence speed remain. A fictional healthcare use case shows how federated learning may be used to train collaborative models across many edge devices while protecting patient data. The case study stresses the necessity for sophisticated optimizations to overcome edge device limits and regulatory compliance. Federated learning algorithms, privacy-preserving procedures, and ethics must be improved, according to the research. Future directions include improving heterogeneous edge algorithms, addressing data ownership and consent ethics, and increasing model decision-making openness. This paper presents essential insights on privacy-preserving machine learning in edge computing and advocates for robust techniques for different edge environments. The paper emphasizes the importance of technological advances, ethical frameworks, and regulatory compliance for secure and privacy-aware machine learning in decentralized edge computing
Explainable artificial intelligence (XAI) for trustworthy decision-making Deni Kurniawan; Dedi Triyanto; Mochamad Wahyudi; Lise Pujiastuti
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 5 (2023): November : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol15.2023.622.pp240-246

Abstract

This research delves into the optimization of loan approval decisions by integrating the Trustworthy Decision Making (TDM) framework into a mathematical model. The study aims to strike a balance between maximizing loan approvals and ensuring fairness, transparency, and accountability in AI-driven decision-making processes. Leveraging principles of transparency, fairness, and accountability, the mathematical model seeks to optimize loan approvals while adhering to ethical considerations. The formulation emphasizes the importance of interpretable models to maintain transparency in decision explanations, ensuring alignment with trustworthy AI practices. Implementation results demonstrate the efficacy of the model in achieving a balanced approval rate across demographic groups while providing transparent explanations for decisions. This study highlights the significance of ethical considerations and mathematical formulations in fostering responsible AI implementations. However, continual refinement and adaptation of such models remain essential to align with evolving ethical standards and societal expectations. Overall, this research contributes to the discourse on responsible AI by showcasing a methodological approach that integrates ethical principles and mathematical formulations to promote fairness, transparency, and accountability in AI-driven decision-making.
Quantum computing in cryptography: Exploring vulnerabilities and countermeasures Deni Kurniawan; Dedi Triyanto; Mochamad Wahyudi; Lise Pujiastuti
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 4 (2023): September : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol15.2023.625.pp206-213

Abstract

This research delves into the critical analysis of vulnerabilities arising from the advent of quantum computing in traditional cryptographic systems. Employing a newly developed mathematical formulation model, the study meticulously evaluates the susceptibility of classical encryption methods, exemplified by XYZ Bank's RSA and ECC algorithms, to quantum algorithms such as Shor's and Grover's. The assessment reveals pronounced vulnerabilities, particularly highlighting the high susceptibility of RSA encryption to quantum attacks, emphasizing the urgent need to fortify existing cryptographic systems. The research rigorously evaluates potential countermeasures, with Post-Quantum Cryptography (PQC) emerging as a promising solution, showcasing superior effectiveness in mitigating vulnerabilities posed by quantum algorithms. The strategic imperative for organizations to transition towards PQC or other post-quantum cryptographic standards is evident, signaling a paradigm shift towards resilient encryption methods resilient to the disruptive capabilities of quantum computing. The research underscores the significance of collaboration among industry stakeholders, continuous research endeavors, and proactive measures in adopting quantum-resistant cryptographic standards to fortify data security strategies against potential quantum threats in an ever-evolving technological landscape.
Advancing fake news detection: a comparative study of RNN, LSTM, and Bidirectional LSTM Architectures Gregorius Airlangga
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.696.pp13-23

Abstract

In the era of information overload, the exponential growth of digital content has coincided with the proliferation of 'fake news,' posing a critical challenge to online information credibility. This study addresses the pressing need for robust fake news detection systems by conducting a comparative analysis of three neural network architectures: Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM). Our primary objective is to assess their effectiveness in identifying fake news in a binary classification setting. To achieve this goal, we employed advanced neural network models and a dataset of news titles. Our applied research method included data preprocessing and the utilization of RNN, LSTM, and BiLSTM models, each tailored to handle sequential data and capture temporal dependencies. we rigorously assessed the performance of RNN, LSTM, and BiLSTM models using a range of metrics, including accuracy, precision, recall, and F1-score. To achieve a comprehensive evaluation, we divided our dataset into training and testing subsets. Specifically, we allocated 67% of the data for training purposes and the remaining 33% for testing. Our research findings reveal that all three models consistently achieved high accuracy levels, approximately 91%, with slight variations in precision and recall. Notably, the LSTM model exhibited a marginal improvement in recall, which is crucial when the consequences of missing deceptive content outweigh false alarms. Conversely, the RNN model demonstrated slightly better precision, making it suitable for applications where minimizing false positives is paramount. Surprisingly, the BiLSTM model did not significantly outperform the unidirectional models, suggesting that, for our dataset, processing information bidirectionally may not be essential. In conclusion, our study contributes valuable insights to the field of fake news detection. It underscores the significance of model selection based on specific task requirements and dataset characteristics.
Improving efficiency and effectiveness of budget, labor, and inventory allocation decision making through decision support system Agung Wahyudi; Bayu Setyawan; Iman Sapuguh; Nur Ahlina; Adinda Sandra Rosalinda
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 6 (2024): January : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol15.2024.712.pp270-276

Abstract

This research aims to improve the efficiency and effectiveness of budget, labor, and inventory allocation decision making at PT Telkom through the application of the TOPSIS method. Using a decision matrix that includes five alternatives and three criteria, this analysis ranks each alternative based on proximity to the positive ideal solution and distance to the negative ideal solution. The results show that Alternative D is the best choice, signifying superiority in the combination of measured values. These recommendations provide strategic guidance for PT Telkom in optimizing resource management, but keep in mind that the results are relative and need periodic evaluation to maintain relevance in the context of dynamic changes. This research contributes to the decision-making and resource management literature by applying systematic methods to complex business situations.
Application of ELECTRE method in business strategic planning: analysis of development, diversification, and market expansion alternatives Bayu Setyawan; Iman Sapuguh; Nur Ahlina; Agung Wahyudi; Adinda Sandra Rosalinda
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 6 (2024): January : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol15.2024.713.pp277-283

Abstract

This research applies the ELECTRE Method in the analysis of PT Djarum's business strategic planning, focusing on business development, product diversification, and market expansion. Using three main criteria with assigned weights, this study evaluated five strategic alternatives. The results show that New Business Development (PBB) dominates the ranking with the highest Net Flow, signaling high conformity with the criteria and weights. Product Diversification (DP) follows as a viable alternative, while Global Market Expansion (EPG), Strategic Partnership (KS), and Regional Market Expansion (EPR) are ranked according to their respective suitability levels. These conclusions provide strong strategic guidance for PT Djarum in making informed business decisions and support the long-term growth and sustainability of the company amidst the changing dynamics of the competitive cigarette industry.
Development of fuzzy logic based student performance prediction system Iman Sapuguh; Nur Ahlina; Agung Wahyudi; Bayu Setyawan; Adinda Sandra Rosalinda
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 6 (2024): January : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol15.2024.714.pp284-290

Abstract

Improving students' academic performance is a key goal in the context of higher education. However, the process of identifying students who require additional support is often complicated and complex. Traditional approaches in analyzing student performance data tend to be limited in handling data uncertainty and complexity. Therefore, the development of fuzzy logic-based decision-making systems is becoming increasingly important. This research aims to develop a fuzzy logic-based decision-making system to predict student performance accurately and efficiently. This approach utilizes fuzzy logic concepts to handle uncertainty and complexity in data, and allows the integration of various input factors, such as exam results, class participation, and other variables, in the decision-making process. The research methods include collecting historical student performance data, modeling fuzzy variables for inputs and outputs, developing fuzzy inference rules, and implementing and testing the system using split test data. Numerical example results show that the system is able to provide predictions of student performance by considering relevant input variables. In addition, the system also offers the potential to improve the efficiency of educational interventions by identifying at-risk students faster and more precisely. As such, the development of this fuzzy logic-based decision-making system is expected to make a significant contribution to efforts to improve the quality and equity of higher education by ensuring that every student gets the support they need to reach their full academic potential.
A mathematical model for predicting the spread and detection of rumors in online communities Nur Ahlina; Agung Wahyudi; Bayu Setyawan; Iman Sapuguh; Adinda Sandra Rosalinda
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 6 (2024): January : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol15.2024.715.pp291-296

Abstract

This study investigates the development of a mathematical model to predict the spread and suppression of rumors in online communities. Through mathematical formulation and numerical simulation, the dynamics of rumor spreading are explored by considering factors such as transmission and suppression rates, as well as strategic interventions such as early detection and information intervention. The results show that the model can provide valuable insights into rumor spreading behavior and the effectiveness of control strategies. The findings can support efforts to reduce the negative impact of rumor spreading in online environments and promote healthier and safer online communities.
Sentiment, toxicity, and social network analysis of virtual reality product content reviews Yerik Afrianto Singgalen
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.716.pp24-34

Abstract

Virtual Reality (VR) technology has garnered significant attention in recent years due to its potential to revolutionize various industries. This study aims to investigate consumer sentiments toward VR products, mainly focusing on Meta Quest 3 in the context of the AI era. The background section outlines the rising popularity of VR products and their impact on consumer behavior, emphasizing the need for a comprehensive understanding of consumer sentiments to inform marketing strategies effectively. Methodologically, the study adopts the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework to guide the analytical approach, which includes sentiment classification, toxicity scoring, and social network analysis (SNA). A dataset comprising 2,115 consumer interactions and evaluations was utilized, with 1,302 interactions for the ALINE tech video and 813 interactions for The Tech Chap video, to derive insights into sentiment patterns and interaction dynamics. The findings reveal a positive reception towards VR products, with Meta Quest 3 particularly well-received. The sentiment classification algorithm achieved an accuracy of 77.92% without SMOTE and 85.66% with SMOTE, demonstrating competency in sentiment prediction. The precision, recall, and f-measure for SVM without SMOTE were 85.78%, 99.83%, and 92.27%, respectively, while with SMOTE, they were 100%, 55.82%, and 71.50%, respectively. Toxicity scoring yielded an average toxicity score of 0.05. Social network analysis (SNA) identified a network diameter of 6, modularity of 0.6072, and a density of 0.002815, highlighting the intricate dynamics of consumer interaction within the VR domain.

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