cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Naety
Contact Email
jurnalmedicom@iocscience.org
Phone
+6281381251442
Journal Mail Official
jurnalmedicom@iocscience.org
Editorial Address
Perumahan Romeby Lestari Blok C, No C14 Deliserdang, Sumatera Utara, Indonesia
Location
Unknown,
Unknown
INDONESIA
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 5 Documents
 1 
Search results for , issue "Vol 16 No 5 (2024): November : Intelligent Decision Support System (IDSS)" : 5 Documents clear
Customer segmentation analysis using DBSCAN method in marketing research of retail company Saragih, Hondor; Manurung, Jonson
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 5 (2024): 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.Vol16.2024.906.pp321-328

Abstract

Customer segmentation is an important aspect of an effective marketing strategy, yet many traditional methods are unable to capture the complexity of diverse customer behaviors. This research aims to apply the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method for customer segmentation in retail companies, focusing on identifying patterns of purchasing behavior and product preferences. Data was collected through a questionnaire distributed to 500 respondents, then analyzed using the DBSCAN method. The results showed that DBSCAN successfully identified several customer segments with unique characteristics, and provided an average Silhouette Score of 0.67 and Davies-Bouldin Index of 0.45, indicating good cluster quality. The findings imply that a density-based approach can improve a company's understanding of customer dynamics, and enable the development of more targeted and effective marketing strategies. This research makes an important contribution to the marketing literature, while opening up opportunities for further exploration of the use of machine learning methods in customer segmentation.
Evaluation of ARIMA model performance in projecting future sales: case study on electronic products Saputra, Bagus Hendra
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 5 (2024): 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.Vol16.2024.993.pp329-337

Abstract

The sales performance of electronic products is significantly affected by a variety of internal and external factors, necessitating precise forecasting models to aid strategic decision-making. This research investigates the effectiveness of ARIMA models in predicting future sales, focusing on a case study involving electronic products. The study utilizes monthly sales data obtained from company records and industry databases. The methodology includes assessing data stationarity through the Augmented Dickey-Fuller (ADF) test, applying differencing when required, and determining ARIMA parameters using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) analyses. The findings reveal that ARIMA models effectively capture seasonal variations and trend patterns. Their performance is assessed using metrics such as Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). This study highlights the need to incorporate external factors into prediction models to enhance accuracy and recommends exploring alternative approaches that can better adapt to dynamic market conditions.
A Systematic Literature Review on the Theoretical Foundations of Machine Learning in Intelligent Computing Systems Payton, Henry Quinn; Shiloh, Thomas
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 5 (2024): November : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study presents a comprehensive theoretical review of the foundations that underpin modern intelligent computing systems, integrating perspectives from statistical learning theory, computational learning theory, optimization theory, information theory, probabilistic modeling, neural computation, and cognitive as well as bio-inspired approaches. Using a systematic review methodology supported by structured search strings and rigorous data extraction, the study identifies core theoretical constructs including VC dimension, PAC learning, sample complexity, entropy, mutual information, Bayesian inference, convergence principles, and universal approximation that collectively shape the development, capabilities, and limitations of intelligent systems. The analysis reveals how these theories complement one another in addressing challenges related to generalization, learnability, optimization efficiency, uncertainty modeling, and biological plausibility. The findings highlight that existing theoretical frameworks provide strong foundations but remain limited in explaining the behavior of high-dimensional, non-convex, and black-box models common in deep learning. The review contributes an integrated conceptual map that clarifies how different theories support robust system design and identifies gaps that future research must address, including scalability of theoretical guarantees, unified frameworks for hybrid systems, and deeper mathematical understanding of modern neural architectures. Overall, the study offers a coherent synthesis that strengthens theoretical grounding and guides future advancements in the construction of reliable and intelligent computing systems.
A Mapping Study on Dynamic Latent State Models in Behavioral and Psysiological Prediction Research Edward, Alexander Grant
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 5 (2024): November : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Dynamic Latent State Models (DLSMs) have become increasingly central to behavioral and physiological prediction due to their ability to represent hidden psychological states and temporal dynamics that static machine-learning models cannot capture. This research conducts a systematic mapping study to analyze the evolution, methodological trends, application domains, and dataset usage of DLSMs published over the last decade. Using a structured search strategy across major scientific databases, studies were screened following PRISMA guidelines, and relevant information was extracted to construct a comprehensive taxonomy of model types, signal modalities, and prediction tasks. The results reveal a significant rise in the adoption of DLSMs, particularly after 2018, driven by advances in deep generative models such as deep Kalman filters and variational state-space models. EEG, HRV, and EDA emerge as the most dominant physiological signals, while stress, emotion, and fatigue prediction constitute the primary application areas. Benchmark datasets including DEAP, WESAD, and DREAMER are frequently used but remain limited in ecological diversity, indicating a continuing need for more realistic, multimodal datasets. Comparison with earlier research shows a shift from interpretable probabilistic models toward more expressive but less transparent deep latent models. This study contributes a consolidated overview of theoretical foundations, research patterns, and methodological gaps in the field. The findings highlight key challenges related to interpretability, dataset diversity, and evaluation consistency, while identifying opportunities for hybrid modeling approaches and more comprehensive data resources. Overall, this mapping study provides a structured foundation to guide future work in advancing dynamic latent-state modeling for behavioral and physiological prediction.
Explainable AI for Public Sector Decision Making: A Systematic Literature Review Karl, Roland Vincent
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 5 (2024): November : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The growing adoption of Artificial Intelligence (AI) in government has intensified the need for transparent, accountable, and trustworthy decision-making systems. This study conducts a systematic literature review to examine how Explainable AI (XAI) is applied within the public sector, identify the dominant techniques used, and analyze their benefits and challenges. Using PRISMA guidelines, studies were collected from major academic databases including Scopus, Web of Science, IEEE Xplore, SpringerLink, ACM Digital Library, and Google Scholar. The findings reveal that XAI development in government contexts has grown significantly over the past decade, with SHAP, LIME, decision trees, counterfactual explanations, and rule-based models emerging as the most frequently used methods. These techniques support public-sector decision making by enhancing transparency, strengthening accountability, reducing bias, improving auditability, and fostering public trust. However, persistent challenges remain, including technical complexity, trade-offs between accuracy and interpretability, limited AI literacy among officials, lack of standard frameworks, and legal or ethical risks. The review highlights the need for more domain-specific XAI guidelines, user-centered explanation tools, and integrated evaluation frameworks. This research contributes a comprehensive synthesis of current XAI applications in government and outlines a future research agenda to support the development of responsible, explainable, and ethically aligned AI for public administration.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol 17 No 5 (2025): November : Intelligent Decision Support System (IDSS) Vol 17 No 4 (2025): Intelligent Decision Support System (IDSS) Vol 17 No 3 (2025): July: Intelligent Decision Support System (IDSS) Vol 17 No 2 (2025): May: Intelligent Decision Support System (IDSS) Vol 17 No 1 (2025): March: Intelligent Decision Support System (IDSS) Vol 16 No 6 (2025): January : Intelligent Decision Support System (IDSS) Vol 16 No 5 (2024): November : Intelligent Decision Support System (IDSS) Vol 16 No 4 (2024): September: Intelligent Decision Support System (IDSS) Vol 16 No 3 (2024): July: Intelligent Decision Support System (IDSS) Vol 16 No 2 (2024): May: Intelligent Decision Support System (IDSS) Vol 16 No 1 (2024): March: Intelligent Decision Support System (IDSS) Vol 15 No 6 (2024): January : Intelligent Decision Support System (IDSS) Vol 15 No 5 (2023): November : Intelligent Decision Support System (IDSS) Vol 15 No 4 (2023): September : Intelligent Decision Support System (IDSS) Vol 15 No 3 (2023): July: Intelligent Decision Support System (IDSS) Vol 15 No 2 (2023): May: Intelligent Decision Support System (IDSS) Vol 15 No 1 (2023): March: Intelligent Decision Support System (IDSS) Vol 14 No 2 (2022): September: Intelligent Decision Support System (IDSS) Vol 14 No 1 (2022): March: Intelligent Decision Support System (IDSS) Vol 13 No 2 (2021): September: Intelligent Decision Support System (IDSS) Vol 13 No 1 (2021): March: Intelligent Decision Support System (IDSS) Vol 12 No 2 (2020): September: Intelligent Decision Support System (IDSS) Vol 12 No 1 (2020): March: Intelligent Decision Support System (IDSS) Vol 11 No 2 (2019): Informatik Vol 11 No 1 (2019): Informatika Vol 10 No 2 (2018): Informatika More Issue