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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Riset Akuntansi Kontemporer Jurnal Statistika dan Matematika (Statmat) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika
Hendrawati, Triyani
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Transportation Other

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PEMETAAN AREA DI PROVINSI JAWA BARAT INDONESIA BERDASARKAN FAKTOR-FAKTOR YANG BERKONTRIBUSI PADA KEJADIAN DEMAM BERDARAH DENGUE Hendrawati, Triyani; Putri Samsi, Haana Lahanda; Munawwaroh, Ihksa
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 3 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i3.641

Abstract

Dengue haemorrhagic fever (DHF) is endemic in major cities in Indonesia. West Java Province is one of the provinces in Indonesia with a high number of DHF cases every year. To minimise the spread and increase of DHF cases can be done by controlling the factors that influence it. This study aims to categorise cities/districts in West Java based on factors that influence DHF cases. The data used are population density (Soul/ ), proper sanitation (%), healthy and clean living behaviour (%), access to proper water sources (%), and health index (%) in 27 cities/districts in West Java in 2021. In this study, the method used is the Hierarchical clustering method; namely the single linkage method, the complete linkage method, the average linkage method, and the ward method. The clustering methods are then compared based on the value of their standard deviation. The analysis results show that the best method used is the complete linkage method. The results of clustering areas in West Java based on factors affecting dengue cases obtained three clusters. Cluster 1 is the cluster with the highest level of dengue cases compared to other clusters. Cluster 1 consists of Depok city, Bogor city, Cirebon city, Bandung city, and Cimahi city. The characteristic of cluster 1 is that it has the highest average population density compared to other clusters. Cluster 2 consists of Cirebon, Bekasi, Banjar City, Bogor, Bandung, Karawang, West Bandung, Purwakarta, Kuningan, Majalengka, Pangandaran, Indramayu, Garut, Ciamis, Subang, Sukabumi, Cianjur, Tasikmalaya, and Sumedang. Cluster 2 has the characteristics of having the highest average percentage of households with access to proper sanitation, percentage of households with clean and healthy behaviour, and health index. Cluster 3 is Sukabumi City and Tasikmalaya City. Cluster 3 has characteristics with the lowest average in the percentage of households that have access to proper sanitation and the percentage of households with clean and healthy behaviour.
Real-time Emotion Recognition Using the MobileNetV2 Architecture Hendrawati, Triyani; Apriliyanti Pravitasari, Anindya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6158

Abstract

Facial recognition technology is now advancing quickly and is being used extensively in a number of industries, including banking, business, security systems, and human-computer interface. However, existing facial recognition models face significant challenges in real-time emotion classification, particularly in terms of computational efficiency and adaptability to varying environmental conditions such as lighting and occlusion. Addressing these challenges, this research proposes a lightweight, yet effective deep learning model based on MobileNetV2 to predict human facial emotions using a camera in real time. The model is trained on the FER-2013 dataset, which consists of seven emotion classes: anger, disgust, fear, joy, sadness, surprise, and neutral. The methodology includes deep learning-based feature extraction, convolutional neural networks (CNN), and optimization techniques to enhance real-time performance on resource-constrained devices. Experimental results demonstrate that the proposed model achieves a high accuracy of 94.23%, ensuring robust real-time emotion classification with a significantly reduced computational cost. Additionally, the model is validated using real-world camera data, confirming its effectiveness beyond static datasets and its applicability in practical real-time scenarios. The findings of this study contribute to advancing efficient emotion recognition systems, enabling their deployment in interactive AI applications, mental health monitoring, and smart environments. Real-world camera data is also used to evaluate the model, demonstrating its usefulness in real-time applications and its efficacy beyond static datasets. The results of this work advance effective emotion identification systems, making it possible to use them in smart settings, interactive AI applications, and mental health monitoring.
GROUPING REGENCIES/CITIES IN WEST JAVA PROVINCE BASED ON PEOPLE’S WELFARE INDICATORS USING BIPLOT AND CLUSTERING Puspitasari, Priscilla Ardine; Faidah, Defi Yusti; Hendrawati, Triyani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1839-1852

Abstract

The level of people's welfare in West Java Province still requires improvement in each indicator. People's welfare indicators include poverty, employment, education, housing, consumption patterns, health, and population. The level of people's welfare can be known by reviewing all dimensions based on linear relationships between regencies/cities to produce information on indicators that still need improvement. These efforts can assist the West Java Provincial Government determine regional policies and programs for equitable distribution and improve people's welfare in all regencies/cities. The data used in this study are secondary data derived from the Website of the BPS of West Java Province 2023, West Java Open Data Province 2023, and Diskominfo Statistics Division (Jabar Digital Service). The grouping of regencies/cities was done using Principal Component Analysis based on Singular Value Decomposition biplot analysis, and it continued with Ward's Method Clustering based on Euclidean distance calculation. The analysis results formed four groups with different people's welfare indicators characteristics. The group that needs top priority in improvement is group 2 because it has a low level of people's welfare. Cluster 1 contains regencies/cities with high people's welfare characteristics in the housing and employment indicators. Cluster 3 includes regencies/municipalities with high people's welfare characteristics in the consumption pattern level, poverty, employment, and health indicators. Cluster 4 contains cities with high people's welfare characteristics in education and population indicators.
ENHANCING 〖PM〗_(2.5) PREDICTION IN KEMAYORAN DISTRICT, DKI JAKARTA USING DEEP BILSTM METHOD Karin, Nabila; Darmawan, Gumgum; Hendrawati, Triyani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp185-198

Abstract

Worldwide air pollution is a concern, and this is especially true in Indonesia, where most people breathe air that is more contaminated than recommended by the WHO. The concentration of presents notable health hazards. The respiratory system is the primary route of absorption for , allowing it to enter the lung alveoli and enter the bloodstream. Given the significant health risks associated with exposure, accurate forecasting methods are crucial to anticipate and mitigate its effects. Traditional forecasting methods like ARIMA have limitations in handling non-linear and complex patterns. Therefore, an accurate machine learning method is needed to improve forecasting performance. This research employs Deep Bidirectional Long-Short Term Memory (BiLSTM), a deep learning model particularly suited for time series forecasting due to its ability to capture both past and future dependencies in sequential data. To achieve accurate and precise forecasts for predicting concentration levels in Kemayoran District in November , 2023 (24 hours), this research utilized hourly concentration data from May until October , 2023, using Deep BiLSTM. The outcomes demonstrated the efficiency of the model, attaining a Mean Absolute Percentage Error (MAPE) of 17.1540% (training) and 14.2862% (testing) with an 80:20 data split. The optimal parameters, which comprised 24 timesteps, Adam optimizers with a learning rate of 0.001, 16 batch sizes, 1000 epochs, and ReLU activation functions across multiple BiLSTM layers, showcased the model’s effectiveness in forecasting the concentration in Kemayoran District, DKI Jakarta, on November , 2023.
Multidimensional Scaling Analysis Based on Factors Affecting Under-Five Malnutrition Cases in West Java Rachman, Hallen Naafi Aliya; Putri, Nisa Akbarilah; Hendrawati, Triyani
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 7 No 1 (2025)
Publisher : Math Program, Math and Science faculty, Pamulang University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v7i1.46533

Abstract

Malnutrition is a condition where the body's nutrition is below the average standard. Nutritional issues, particularly among toddlers, remain a serious problem in various provinces in Indonesia, including West Java. In 2022, 3.3% of toddlers in West Java experienced undernutrition, and 0.4% suffered from severe malnutrition. This study aimed to map 27 regencies/cities in West Java Province based on factors influencing toddler malnutrition in 2022, highlighting similarities among these areas. A statistical method, Multidimensional Scaling (MDS), was used to classify objects based on similar characteristics. This method illustrated the dispersion of observational units based on measured variables, creating a two-dimensional map. Nearby regencies/cities indicated similar malnutrition conditions among toddlers, suggesting that the same mitigation efforts could be applied in those areas. The analysis resulted in four quadrants. Red circles were used on the map to mark points that were very close. To test the validity, STRESS and R-Square values were calculated. The STRESS value of 0.012% indicates that the generated map is in the perfect category, demonstrating that this analysis has precise reliability and validity. The R-Square value of 99.76% shows that the variance of the data is well explained by the model. This indicates that the Multidimensional Scaling (MDS) model is acceptable for mapping purposes. The findings of this study serve as valuable information and a reference for the West Java provincial government to make more effective and targeted efforts in combating malnutrition.
FORMATION OF AN OPTIMAL PORTFOLIO OF LQ45 SHARES USING MARKOWITZ METHOD Al Ghifari, Abdurrahman Al Ghifari; Toharodin, Toni; Hendrawati, Triyani
JRAK Vol 16 No 1 (2024): April Edition
Publisher : Faculty of Economics and Business, Universitas Pasundan, Bandung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jrak.v16i1.8445

Abstract

Currently, there are still many investors who do not realize that stock management strategies are important. Therefore, as a practitioner, through this research, it is hoped that authors can help overcome this problem so that it can calculate maximum profits in-stock selection. This research aims at understanding stock management strategies by forming a Markowitz portfolio with the help of the K-Means grouping method. The population in this research included stock companies listed on the Indonesian Stock Exchange. The sample selection method used was the targeted sampling method. The sample data used was daily stock returns from LQ45 stock companies. The research results showed that based on data processing, stock grouping using the k-Means method and the Markowitz method was proven to produce maximum profits and low risk. Therefore, the method used in this research can be useful in the world of finance, especially to help investors.
LSTM AND GRU IN RICE PREDICTION FOR FOOD SECURITY IN INDONESIA Hendrawati, Triyani; Marthendra, Kennedy; Simanjuntak, Brian Riski Jayama; Pravitasari, Anindya Aprilianti
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0055-0068

Abstract

Hunger in Indonesia remains a serious challenge, especially in the face of food price instability, particularly rice as the main staple food. In order to achieve SDG 2 “Zero Hunger” by 2030, policies that support price stability and more effective food distribution are needed. This study aims to assess the predictive power of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models for Indonesian rice prices. The dataset, consisting of 1,424 observations from early 2021 to late 2024, was collected from official sources and preprocessed using normalization techniques. The data was then divided into training, validation, and testing sets. Each model was trained and evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) metrics. LSTM, a type of Recurrent Neural Network (RNN), uses three gates and cell memory to identify long-term patterns in time series data. GRU, with a simpler structure involving only two gates, is more efficient in modeling temporal relationships. The results show that the LSTM model achieved MAPE 3.49%, while the GRU model outperformed it with MAPE 1.08%. Overall, the GRU model demonstrated higher accuracy in forecasting rice prices.
Co-Authors Al Ghifari, Abdurrahman Al Ghifari Apriliyanti Pravitasari, Anindya Faidah, Defi Yusti Gumgum Darmawan Karin, Nabila Marthendra, Kennedy Munawwaroh, Ihksa Pravitasari, Anindya Aprilianti Puspitasari, Priscilla Ardine Putri Samsi, Haana Lahanda Putri, Nisa Akbarilah Rachman, Hallen Naafi Aliya Simanjuntak, Brian Riski Jayama Toharodin, Toni