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 6 (2025): January : Intelligent Decision Support System (IDSS)" : 5 Documents clear
Comparing optimization hyperparameter long short term memory for rainfall prediction model Nur Hermawan, Ilham; Martanto, Martanto; Dikananda, Arif Rinaldi; Mulyawan, Mulyawan
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 6 (2025): 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.Vol16.2025.942.pp405-414

Abstract

Improving the accuracy of weather prediction, especially rainfall, is very important in various sectors such as agriculture, water resource management, and disaster mitigation. This research aims to optimize the Long Short-Term Memory (LSTM) model in rainfall prediction through the application of hyperparameter optimization using two main techniques: Grid Search and Bayesian Optimization (Optuna). This hyperparameter optimization includes finding the best configuration of important parameters, such as the number of LSTM units, batch size, learning rate, and number of epochs. A historical rainfall dataset from BMKG is used, which is then divided into training and test data to build and test the prediction model. Grid Search performs a thorough exploration of all possible parameter combinations, while Optuna uses a probabilistic Bayesian approach to speed up the optimization process. The results show that hyperparameter optimization significantly improves the performance of LSTM models. The model optimized with Optuna produces a Mean Squared Error (MSE) value of 0.179578 with an execution time of 105.26 seconds, while Grid Search has an MSE of 0.286778 with an execution time of 457.69 seconds. The lower MSE value indicates that the Optuna model has a smaller prediction error, making it more accurate in predicting rainfall. The faster execution time of Optuna also confirms its efficiency in finding the optimal hyperparameter configuration compared to Grid Search. The conclusion of this study confirms that hyperparameter optimization plays an important role in improving the prediction accuracy of LSTM for rainfall. The developed method is expected to be the basis for the development of other weather prediction models as well as support decision-making in various sectors that rely on weather prediction. In addition, this research opens up opportunities for further studies in the optimization of deep learning models in handling complex climate data.
Optimization of academic performance prediction using linear regression with selectk-best Saelan, M. Rangga Ramadhan
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 6 (2025): 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.Vol16.2025.994.pp386-393

Abstract

This study discusses the prediction of student performance by considering factors that can influence academic performance. In this research, the SelectK-Best feature selection technique and linear regression were used to enhance the accuracy of the prediction. The selection of this topic is based on the importance of understanding the factors that influence student performance and how feature selection can help build more efficient models. The methods applied in this study include data exploration through EDA, the use of SelectK-Best to select the most significant features, and linear regression to build the prediction model. The evaluation metrics show that the model with feature selection achieved MAE of 0.6293, MSE of 0.5945, RMSE of 0.7711, and R² Score of 0.9144, demonstrating the model's excellent performance. In contrast, the model without feature selection did not produce better results than the model with feature selection. This emphasizes the importance of applying feature selection techniques in building more accurate prediction models. This study contributes to predicting student performance through the use of systematic and effective methods, while also opening opportunities for further research in the context of education and more diverse data.
Construction of micro scale coral propagation media controller system with Arduino Nano and Flutter SDK Utomo, Aulia Desy Nur; Abimanyu, Abimanyu; Prihantoro, Cahyo
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 6 (2025): 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.Vol16.2025.997.pp373-385

Abstract

Rising sea temperatures due to global warming and human activities in Indonesia threaten coral reef sustainability, leading to bleaching and mass mortality. In 2016, 50% of coral colonies in Gili Matra experienced bleaching, 11% were pale, and 1% faced mortality. To mitigate damage, controlled coral cultivation in isolated media offers an alternative to open-ocean methods, allowing precise water quality management. Coral transplantation, involving fragmentation and placement in controlled environments, enhances rehabilitation efforts. An IoT-based controller enables real-time monitoring and automation of life-support systems, including supplementation pumps, photosynthetic lamps, top-up pumps, cooling fans, and current pumps. System performance shows consistent lamp scheduling, supplementation dosage with a deviation of ±1-2%, precise top-up activation, current pump scheduling with a 1s deviation, and optimal water parameters (alkalinity 8.3 dKH, calcium 420 ppm, magnesium 1050 ppm, salinity 1.025).
Outlier detection in the clustired data Bu'ulolo, Efori; Syahputra, Rian; Simorangkir, Elsya Sabrina Asmita
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 6 (2025): 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.Vol16.2025.1005.pp394-404

Abstract

The purpose of this study is to detect outliers in data clusters. Outliers in data cluster datasets often occur in the data clustering process, especially in the K-Means algorithm. Outliers in cluster data are members/cluster items that are far from the centroid value and are not found in the dominant cluster. Outliers in cluster data are caused by various factors such as inaccurate K values, inaccurate centroid point values, poor data quality and others. To detect outliers in cluster data using the blox plot method, Z-Score and relative size factor (RSF). The input value is the sum of squared error (SSE), calculated by summing the squares of the distance of each data point from the cluster centroid. The dataset used consists of 3 (three) variances, namely high data variance, medium data variance and low data variance. The method used for outlier detection in this study can detect outliers in all data variances used, only not all outlier detection methods are optimal for all data variances. The plox plot method is optimal for high data variance and medium data variance, the RSF method is optimal for medium data variance and the Z-Score method is not optimal for high data variance.
A Comprehensive Review of Machine Learning Paradigms for Large-Scale Smart System Liam, Morgan Jaden
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 6 (2025): January : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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

Abstract

Large-scale smart systems such as smart cities, smart grids, smart healthcare, and IoT-based infrastructures generate massive volumes of complex, heterogeneous data that require intelligent analysis and real-time decision-making. Machine learning (ML) plays a central role in enabling these capabilities, yet the diversity of ML paradigms and the fragmented nature of existing studies make it difficult to determine which approaches are most effective for large-scale environments. This comprehensive review synthesizes and compares major ML paradigms, including supervised learning, unsupervised learning, reinforcement learning, deep learning, hybrid models, federated learning, and graph-based neural networks, across a wide range of smart system applications. The findings reveal that deep learning excels in processing high-dimensional and unstructured data, reinforcement learning performs best in autonomous and real-time control tasks, federated learning supports privacy-preserving analytics in distributed IoT ecosystems, and graph-based models offer superior performance in systems with interconnected network structures. The review also identifies key technological challenges such as data heterogeneity, computational complexity, communication bottlenecks, and privacy concerns that affect the scalability and deployment of ML in smart environments. By providing a unified comparison of ML paradigms and highlighting emerging trends, performance characteristics, and implementation challenges, this study offers valuable insights for researchers, system designers, engineers, and policymakers. The review further outlines future research directions aimed at enhancing scalability, robustness, interpretability, and real-time capability in next-generation smart systems.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2025 2025


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