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All Journal JSM (Jurnal SIFO Mikroskil) Jurnal Informatika dan Teknik Elektro Terapan E-Dimas: Jurnal Pengabdian kepada Masyarakat Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Yayasan Cita Cendikiawan Al Khwarizmi Journal of Social Responsibility Projects by Higher Education Forum
Sinaga, Frans Mikael
Univeritas Mikroskil, Medan
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Journal : Indonesian Journal of Artificial Intelligence and Data Mining

 1 
Classification of Big Data Stunting Using Support Vector Regression Method at Stella Maris Medan Maternity Hospital Chen, Kelvin; Adriansyah, R. A. Fattah; Juliandy, Carles; Sinaga, Frans Mikael; Liko, Frederick; Angkasa, Aswin
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.31112

Abstract

This study aims to classify big data related to stunting using the Support Vector Regression (SVR) method at Stella Maris Maternity Hospital, Medan. Stunting, a condition of impaired growth in children due to chronic malnutrition and repeated infections, affects physical and cognitive development. With increasing health data, big data processing methods are essential for accurate information. SVR was chosen for handling high-dimensional and non-linear data, providing precise results. The study uses medical information, nutritional history, and socio-economic factors collected from hospital patients. The research process includes data collection, pre-processing to address missing values and outliers, normalization, and SVR application. Final results use SVR with Voting Classifier combining Support Vector Classifier (SVC), Random Forest (RF), and Gradient Boosting (GB), achieving an accuracy of 91.67%. This approach effectively identifies main stunting factors, aiding clinical decision-making and intervention programs. The study showcases big data and machine learning's potential in healthcare, serving as a model for improving health services and monitoring children's health conditions.
Forecasting Climate Change Patterns to Improving Rice Harvest Using SVR for Achieving Green Economy Juliandy, Carles; Kelvin, Kelvin; Halim, Apriyanto; Pipin, Sio Jurnalis; Sinaga, Frans Mikael; Lestari, Wulan Sri
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.32393

Abstract

The consistently declining rice harvest will cause several economic and environmental problems. The unstable and unpredictable climate change was believed as the main problem of the declining rice harvest. We proposed a method for forecasting climate change to help the farmer in their rice cultivation. We used Support Vector Regression (SVR) to improve algorithm steps such as normalizing the data and applying an Adaptive Linear Combiner (ALC) to optimize the dataset before we processed it with the algorithm. Our model gets 95% accuracy as measured with the confusion matrix. We believe our model will help the farmers in their rice cultivation with good climate forecasting. A further benefit of this research we belief that with the well-forecasted climate, the usage of pesticides will decrease and will help the vision of the Indonesian government with a green economy
Exploring New Frontiers: XCEEMDAN, Bidirectional LSTM, Attention Mechanism, and Spline in Stock Price Forecasting Kelvin, Kelvin; Sinaga, Frans Mikael; Winardi, Sunaryo; Susmanto, Susmanto
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.29649

Abstract

The Attention Mechanism is acknowledged as a machine learning method proficient in managing relationships within sequential data, surpassing traditional models in this regard. However, the unique characteristics of stock data, including substantial volatility, multidimensionality, and non-linear patterns, present challenges in attaining accurate forecasts of stock prices. This research aims to tackle these hurdles by enhancing a prior model through the incorporation of an Attention Mechanism, resulting in an enhanced model. The forecasted data are standardized and prepared for analysis before undergoing signal decomposition into high and low-frequency components. Subsequently, the Attention Mechanism processes the high-frequency signals. Evaluation entails comparing the performance of the proposed model with that of the previous model using identical parameters. The findings indicate that the proposed model achieves a reduced RMSE value of 0.5708777053 compared to the previous model's average RMSE value of 0.5823726212, indicating enhanced accuracy in stock price prediction. This approach is anticipated to make a substantial contribution to the advancement of more dependable and effective stock price prediction models, addressing the limitations of prior methodologies
Enhancing Image Quality for the Detection of Underwater Debris with Adaptive Fuzzy Filter Halim, Apriyanto; Ulina, Mustika; Tanti, Tanti; Sinaga, Frans Mikael
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.33010

Abstract

The image quality improvement process plays a very important role. This is because the process can increase the clarity and accuracy of image detection. One type of image detection that exists is the detection of garbage found under the sea. One of the image quality improvement processes is related to noise removal. Noise is a sudden increase in pixel intensity in an image. This can cause various problems that occur such as in medical photos, satellites, and photography. One method used to remove noise from images is using Adaptive Fuzzy Filter (AFF). This method is carried out by first finding the average value of the mean fuzzy set and the gray level fuzzy. After that, the value comparison process is carried out. From the results of the research conducted on 689 images from the dataset obtained, there is a decrease in the amount of noise of around 96,23% of the total noise obtained previously. This can certainly provide good results in terms of changes in noise that have been made.
Random Forest Optimization Using Recursive Feature Elimination for Stunting Classification Marpaung, Sophya Hadini; Sinaga, Frans Mikael; Rambe, Khairul Hawani; Simamora, Fandi Presly; Kelvin, Kelvin
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.35295

Abstract

Stunting is still a major health problem in Indonesia, with a prevalence of 27% in toddlers in 2023, far from the WHO target of below 20%. RSU Mitra Medika Tanjung Mulia in Medan serves patients with various socio-economic backgrounds, which affects the quality of services, including stunting detection. Conventional methods are prone to bias and error. This study used the Random Forest algorithm and the Recursive Feature Elimination (RFE) feature selection method to improve the accuracy of stunting classification. After data preprocessing and feature selection, two main variables were identified, namely age and height. The initial Random Forest model achieved an accuracy of 94.38%, which increased to 94.42% after hyperparameter tuning. The results showed that this approach produced an accurate, efficient model that can be integrated into clinical systems, helping medical personnel identify children at risk of stunting quickly and accurately, increasing the effectiveness of interventions, and supporting government efforts to reduce the prevalence of stunting
Big Data Analytics for Predicting Depression Risk in Generation Z: Integrating Self-Organizing Maps and Long Short-Term Memory Sinaga, Joy Nasten; Nuraina, Nuraina; Sinaga, Frans Mikael; Kelvin, Kelvin; Nurhayati, Nurhayati
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.38011

Abstract

Mental health issues among Generation Z are rising, with depression being one of the most significant challenges. Leveraging the capabilities of big data analytics and artificial intelligence, this study proposes a hybrid method combining Self-Organizing Maps (SOM) and Long Short-Term Memory (LSTM) networks to predict depression risk based on behavioral data. The SOM algorithm is utilized for clustering high-dimensional input data to uncover hidden patterns, while the LSTM network is employed to capture sequential dependencies over time. Data were collected from various digital platforms, processed, and analyzed to train and validate the proposed model. Results show that the SOM-LSTM framework significantly improves the accuracy and reliability of early depression risk detection compared to conventional models. This study contributes a scalable and adaptable model for mental health prediction that can assist in timely interventions for Generation Z
A Smart Architecture for Stunting Prediction: Implementing the SOM–Voting Classifier on Healthcare Big Data Kelvin, Kelvin; Winardi, Sunaryo; Sinaga, Frans Mikael; Hardy, Hardy; Panjaitan, Erwin Setiawan; Wong, Ng Poi; Ferawaty, Ferawaty; Lim, Justine; Wijaya, Grace Putri
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.38000

Abstract

Childhood stunting is a persistent public health challenge in Indonesia. This study developed a predictive classification model using healthcare data from hospitals in Medan to enable early identification of at-risk children. A novel framework was proposed that integrated an unsupervised Self-Organizing Map (SOM) for feature engineering with a supervised Voting Classifier ensemble, which combined a Support Vector Classifier (SVC), Random Forest (RF), and Gradient Boosting (GB). The proposed framework achieved an accuracy of 100% on the test set, a substantial improvement over the 91.67% accuracy of the baseline Voting Classifier without SOM. While this result highlighted the model's high predictive potential, it must be interpreted cautiously, acknowledging the need for validation on more diverse datasets to ensure generalizability. The findings demonstrated that this hybrid machine learning approach can serve as a powerful decision-support tool, enabling proactive clinical interventions and aiding public health officials in strategically allocating nutritional resources to support Indonesia's national stunting reduction goals.
Co-Authors Adriansyah, R. A. Fattah Ananda Putra Brahmana Angkasa, Aswin Apriyanto Halim Arwin Halim Bate'e, David Belferik, Ronald Br Ginting, Geby Laurent Br. Sitorus, Rotua Eka Wati Carles Juliandy Chan, Jacky Chen, Kelvin Edmund, Edmund Fa, Farrell Rio Fandi Presly Simamora Felix Felix Ferawaty, Ferawaty Filbert Filbert, Filbert Gunawan Gunawan Gunawan, Erin Hardy Hardy Heru Kurniawan Heru Kurniawan Hui, David Christian Hutabarat, Yonatan Hutagaol, Grace Helena Irpan Pardosi Jerry Johnson Joy Nasten Sinaga Karina Mannita Tarigan Kelvin Kelvin Kelvin Liko, Frederick Lim, Justine Limbong, Mila Rachmafika Lubis, Muhammad Wahyu Pratama Manullang, Mangasa A.S. Marpaung, Dhea Romantika Maynius, Veilind Michael Michael Muhammad Noor Hakim Neveline, Neveline Ng Poi Wong Nuraina, Nuraina Nurhayati Nurhayati Panjaitan, Erwin Setiawan Pipin, Sio Jurnalis Purba, Michael Emmanuel Purba, Rizki Azzura Saifani Rambe, Khairul Hawani Revelin, Dylan Ronsen Purba Saragih, Yuni Marlina Satria, Krismulia Wignya Sherina Sherina, Sherina Siahaan, Alfa Ulina Stefani Sinaga, Tetti Sintiya, Cindy Sio Jurnalis Pipin Siregar, Roy Antoni Sitompul, Indra Maruba Situmorang, Angga Zefanya Sophya Hadini Marpaung Suhindra, Wahyu Susmanto, Susmanto Syahrul R, Syahrul Syanti Irviantina Tanidi, Davin Tanti Tanti Ulina, Mustika Vanessa Leonora Wijaya, Grace Putri Winardi, Sunaryo Wulan Sri Lestari