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Zahratul Fitri, Zahratul
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Journal : JURIKOM (Jurnal Riset Komputer)

 1 
Klasterisasi Data Stunting Pada Balita Di Puskesmas Xyz Dengan Menggunakan Metode Mixture Modelling Delianda, Anggun; Asrianda, Asrianda; Fitri, Zahratul
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8580

Abstract

This research is motivated by the high prevalence of stunting in Indonesia, reflecting nutritional imbalances in early childhood. To address this issue, an information technology approach is employed to identify at-risk infant groups. The analyzed data consists of anthropometric information, including height, weight, and age of infants, collected from the Peusangan Health Center. The applied method is the Gaussian Mixture Model (GMM) with the Expectation-Maximization algorithm to cluster the data into two groups: "Potential Stunting" and "Not Stunting." The research results indicate that several Posyandu and villages have notably high potential stunting rates, such as Posyandu Bungong Seulanga (141 infants) and Pante Gajah village (116 infants), with a higher prevalence among male infants (34.67%) and those aged 52–60 months (24.18%). Model evaluation using a confusion matrix on 1,465 data points showed a True Positive of 958 (65.36%), False Negative of 4 (0.27%), False Positive of 503 (34.33%), and True Negative of 0 (0%), with an accuracy of 65.36% and an error rate of 34.64%. However, a previous accuracy test on 1,665 data points only achieved 34.55%, indicating unsatisfactory individual prediction performance. In conclusion, Mixture Modelling is effective for clustering and identifying at-risk groups but lacks accuracy in individual predictions, with a bias toward the "Potential Stunting" class that requires improvement in future research.
Penerapan Algoritma K-Means Clustering untuk Segmentasi Kepadatan Penduduk Berbasis GIS Putri, Rizki Amelia; Safwandi, Safwandi; Fitri, Zahratul
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8668

Abstract

This study aims to develop a clustering system using the K-means algorithm to analyze demographic data of sub-districts from 2020 to 2023. The system is designed to cluster sub-districts based on variables such as population size, population percentage, population density, and gender ratio. The clustering results reveal different grouping patterns each year, reflecting the dynamics of demographic data over time. Evaluation using the Davies-Bouldin Index (DBI) indicates that the clustering results are of reasonably good quality, with DBI values of 1.1492 in 2020, 0.6859 in 2021, 1.2470 in 2022, and 0.6805 in 2023. The best DBI value was recorded in 2023 at 0.6805, demonstrating that the clustering results in that year were the most optimal compared to other years. The system also facilitates Users with interactive map visualizations, supporting better data analysis and decision-making processes. This research is expected to contribute to the management of demographic data and support more accurate data-driven policy-making.
Implementasi Algoritma XGBoost dengan Walk Forward Validation untuk Prediksi Harga Emas Antam Hisyam, Mochammad; Fitri, Zahratul; Aidilof, Hafizh Al Kautsar
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8693

Abstract

Accurate gold price prediction is crucial in supporting financial and investment decision-making. This study aims to develop and optimize a daily gold price prediction model using the Extreme Gradient Boosting (XGBoost) algorithm based on historical price data and technical indicators. The model was constructed to predict two types of prices, namely "Close" and "Buyback" prices in IDR/gram. Optimization was carried out using Bayesian Optimization to obtain the best hyperparameter combinations. The model was evaluated using a Walk Forward Validation (WFV) approach with a 14-day sliding window and two main evaluation metrics: Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results show that the model provides excellent predictive performance, with an average RMSE of 15,431.92 and MAPE of 1.03% for Close price, and RMSE of 15,382.64 and MAPE of 1.15% for Buyback price. The prediction visualizations indicate that the model consistently follows the actual price trend. Feature importance analysis reveals that technical indicators such as RSI, EMA, and MACD significantly contribute to the model. The success of this study demonstrates that an optimized XGBoost model can serve as a reliable approach for gold price forecasting and opens opportunities for developing more advanced predictive models in future research.
Klasterisasi Data Stunting Pada Balita Di Puskesmas Xyz Dengan Menggunakan Metode Mixture Modelling Delianda, Anggun; Asrianda, Asrianda; Fitri, Zahratul
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8580

Abstract

This research is motivated by the high prevalence of stunting in Indonesia, reflecting nutritional imbalances in early childhood. To address this issue, an information technology approach is employed to identify at-risk infant groups. The analyzed data consists of anthropometric information, including height, weight, and age of infants, collected from the Peusangan Health Center. The applied method is the Gaussian Mixture Model (GMM) with the Expectation-Maximization algorithm to cluster the data into two groups: "Potential Stunting" and "Not Stunting." The research results indicate that several Posyandu and villages have notably high potential stunting rates, such as Posyandu Bungong Seulanga (141 infants) and Pante Gajah village (116 infants), with a higher prevalence among male infants (34.67%) and those aged 52–60 months (24.18%). Model evaluation using a confusion matrix on 1,465 data points showed a True Positive of 958 (65.36%), False Negative of 4 (0.27%), False Positive of 503 (34.33%), and True Negative of 0 (0%), with an accuracy of 65.36% and an error rate of 34.64%. However, a previous accuracy test on 1,665 data points only achieved 34.55%, indicating unsatisfactory individual prediction performance. In conclusion, Mixture Modelling is effective for clustering and identifying at-risk groups but lacks accuracy in individual predictions, with a bias toward the "Potential Stunting" class that requires improvement in future research.
Penerapan Algoritma K-Means Clustering untuk Segmentasi Kepadatan Penduduk Berbasis GIS Putri, Rizki Amelia; Safwandi, Safwandi; Fitri, Zahratul
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8668

Abstract

This study aims to develop a clustering system using the K-means algorithm to analyze demographic data of sub-districts from 2020 to 2023. The system is designed to cluster sub-districts based on variables such as population size, population percentage, population density, and gender ratio. The clustering results reveal different grouping patterns each year, reflecting the dynamics of demographic data over time. Evaluation using the Davies-Bouldin Index (DBI) indicates that the clustering results are of reasonably good quality, with DBI values of 1.1492 in 2020, 0.6859 in 2021, 1.2470 in 2022, and 0.6805 in 2023. The best DBI value was recorded in 2023 at 0.6805, demonstrating that the clustering results in that year were the most optimal compared to other years. The system also facilitates Users with interactive map visualizations, supporting better data analysis and decision-making processes. This research is expected to contribute to the management of demographic data and support more accurate data-driven policy-making.
Implementasi Algoritma XGBoost dengan Walk Forward Validation untuk Prediksi Harga Emas Antam Hisyam, Mochammad; Fitri, Zahratul; Aidilof, Hafizh Al Kautsar
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8693

Abstract

Accurate gold price prediction is crucial in supporting financial and investment decision-making. This study aims to develop and optimize a daily gold price prediction model using the Extreme Gradient Boosting (XGBoost) algorithm based on historical price data and technical indicators. The model was constructed to predict two types of prices, namely "Close" and "Buyback" prices in IDR/gram. Optimization was carried out using Bayesian Optimization to obtain the best hyperparameter combinations. The model was evaluated using a Walk Forward Validation (WFV) approach with a 14-day sliding window and two main evaluation metrics: Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results show that the model provides excellent predictive performance, with an average RMSE of 15,431.92 and MAPE of 1.03% for Close price, and RMSE of 15,382.64 and MAPE of 1.15% for Buyback price. The prediction visualizations indicate that the model consistently follows the actual price trend. Feature importance analysis reveals that technical indicators such as RSI, EMA, and MACD significantly contribute to the model. The success of this study demonstrates that an optimized XGBoost model can serve as a reliable approach for gold price forecasting and opens opportunities for developing more advanced predictive models in future research.
Co-Authors - Fakhrurrazi Ade Irfan Aidilof, Hafizh Al Kautsar Akbar, Muhammad Zulfat Ananda Faridhatul Ulva Andik Bintoro Angga Pratama ANNISA KARIMA Asrianda Asrianda Bakhtiar Bakhtiar Dahlan Abdullah Delianda, Anggun Dewi Anggraini Dita Amelia, Dita Emi Maulani Eva Darnila Fadliani, Fadliani Fuadi, Wahyu Hisyam, Mochammad Irwansyah, Defi Kausar, M Reza Khairul Amna Leni Maulida Liza Afra Merdu, Bunga Mawarni MUHAMMAD TEGUH, MUHAMMAD Muhibuddin Muhibuddin, Muhibuddin Mutammimul Ula Nisa, Miftahul Nisfiani, Nisfiani Nunsina, Nunsina Nurdin Nurdin Nurdin Nurdin Nuriana, Nuriana Putri, Rizki Amelia Rahayu, Cut Fitriani Rahma, Putri Razi, Ar Rita, Fatia Rizal S.Si., M.IT, Rizal Safriana Safriana Safwandi Safwandi, Safwandi Sahira, Dianda Samsuar, Samsuar Saptari, Mochamad Ari Shaleha, Cut Dini Siregar, Siti Chairani Sri Wahyuni Syathira, Najwa Ummi Kalsum Winandar, Aris Zainal Abidin Zul Akli