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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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Compares the effectiveness of the bagging method in classifying spices using the histogram of oriented gradient feature extraction technique Muhathir Muhathir
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 1 (2023): 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.Vol15.2023.386.pp48-57

Abstract

Spice classification is a crucial task in the food industry to ensure food safety and quality. This study focuses on the classification of spices using the Histogram of Oriented Gradient (HoG) feature extraction method and bagging method. The objective of this research is to compare the performance of three different models of bagging method, including Bootstrap Aggregating (Bagging), Random Forests, and Extra Tree Classifier, in classifying spices. The evaluation metrics used in this research are Precision, Recall, F1-Score, F2-Score, Jaccard Score, and Accuracy. The results show that the Random Forest model achieved the best performance, with precision, recall, F1-score, F2-Score, Jaccard, and Accuracy values of 0.861, 0.8633, 0.8587, 0.8607, 0.7694, and 0.8733 respectively. On the other hand, the Extra Tree Classifier had the lowest performance with precision, recall, F1-score, F2-Score, Jaccard, and Accuracy values of 0.7034, 0.7958, 0.7037, 0.7047, 0.5635, and 0.72 respectively. Overall, the results indicate a fairly good success rate in classifying spices using the HoG feature extraction method and bagging method. However, further evaluation is needed to improve the accuracy of the classification results, such as increasing the number of training data or considering the use of other feature extraction methods. The findings of this research may have significant implications for the food industry in ensuring the quality and safety of food products.
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.
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.
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.
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.
Hungarian maximization model approach for optimizing human resource assignment in multi-site projects Riandari, Fristi; Dalimunthe, Yulia Agustina; Ginting, Ramadhanu; Afifa, Rizky Maulidya; Afrisawati, Afrisawati
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 2 (2025): May: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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

Abstract

Digital transformation in project management demands the implementation of computational models that are able to handle the complexity of human resource (HR) allocation efficiently and objectively. This study examines the application of the Hungarian algorithm in the form of maximization as a computer science-based optimization solution to the HR assignment problem in multi-location projects. By constructing a benefit matrix calculated from weighted attributes such as technical expertise, experience, and location preference, this study implements linear transformations and matrix processing procedures using a numerical approach in Python. This digitalization process allows the system to perform assignment evaluation and allocation automatically and with high precision. Simulation results on a case study of five workers and five project locations show that the model produces optimal assignments with a total benefit score of 420. This model proves its effectiveness in solving polynomial assignment problems, while expanding the use of the Hungarian algorithm in the domain of applied computer science to support data-driven decision making. This study emphasizes the role of classical algorithms in supporting scalable and replicable digital solutions for modern HR management systems.
Smart City Weather and Disaster Monitoring Architecture: LoRaWAN Integration with COBIT 2019 Governance Yulistiawan, Bambang Saras; A, Galih Prakoso Rizky; Widyastuti, Rifka; Mulianingtyas, RR Octanty
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 2 (2025): May: 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.Vol17.2025.1267.pp59-74

Abstract

Climate change, urbanization, and the increasing frequency of natural disasters such as floods and forest fires demand that Indonesian cities adopt real-time, integrated, and reliable environmental monitoring systems. Within the context of smart cities, LoRaWAN technology offers wide coverage, low power consumption, and cost-efficient operations, making it highly relevant for city-scale multi-sensor monitoring systems. This study proposes the design of a LoRaWAN-based weather and disaster monitoring system architecture integrated into the smart city framework, while simultaneously adopting the IT governance principles of COBIT 2019. The methodology includes a literature review and the mapping of five COBIT domains (EDM03, APO03, BAI03, DSS02, MEA01) to LoRaWAN’s technical components, ranging from sensors, gateways, and network servers to application servers, dashboards, and public notification modules. The analysis demonstrates that the proposed design enhances data standardization, end-to-end security, monitoring, scalability, and device governance. The integration of COBIT 2019 further enables the optimization of risk management, monitoring effectiveness, incident response, and regulatory compliance. In conclusion, the proposed architecture provides a comprehensive framework to support resilient, adaptive, and sustainable smart cities. However, this architecture has not yet been implemented in practice, thus necessitating further implementation and evaluation to ensure the system’s effectiveness and sustainability in operational environment.

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