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All Journal EKSAKTA: Journal of Sciences and Data Analysis 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 REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat International Journal of Research in Community Services
Triyani Hendrawati
Statistics Department, Faculty Of Mathematics And Natural Sciences, Universitas Padjadjaran, Indonesia
Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Measuring the Water Surface Speed of the Cikidang River as a Supporting Facility for the Development of Ecotourism Areas Subiyanto Subiyanto; Sudradjat Supian; Yuyun Hidayat; Triyani Hendrawati
International Journal of Research in Community Services Vol 3, No 3 (2022)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijrcs.v3i3.333

Abstract

Pangandaran Regency is famous for its natural beauty, especially the beauty of its beaches. In this district there are many famous beaches. Besides the beach, there are also rivers that offer beautiful views. However, not many tourists have made the rivers in Pangandaran as tourist destinations, especially the Cikidang River. Cikidang River has the potential to be used as ecotourism. In building an ecotourism area on the banks of a river, it is necessary to carry out an in-depth analysis of the damage that might occur due to erosion. One of the factors that can increase erosion is the speed of water on the surface. Therefore, in this paper, measurement of river surface velocity is carried out in a simple way and analyzed using the velocity formula. From the results of this measurement, a recommendation was made to direct all parties involved in measuring the surface velocity of the Cikidang River as a means of supporting the development of ecotourism areas.
Parents' Understanding of the Safety and Comfort in Using Gadgets for Children Anindya Apriliyanti Pravitasari; Mulya Nurmansyah Ardisasmita; Fajar Indrayatna; Intan Nurma Yulita; Triyani Hendrawati; Gumgum Darmawan
REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Vol 4, No 2 (2023): REKA ELKOMIKA
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/rekaelkomika.v4i2.151-160

Abstract

The utilization of technology among children has significantly increased since the outbreak of the Covid 19 pandemic. Therefore, the use of gadgets among children requires special attention from parents, since under incorrect ergonomic circumstances, it could endanger the health of children. This webinar was designed with parents in mind, giving them valuable information on how to use kid-friendly technology. Additionally, a pre- and post-test was assigned to evaluate parents’ knowledge about ergonomic conditions (safety and comfort) when using gadgets, both before and after the webinar. The results indicated a substantial increasement in parental knowledge among the webinar participants as well as the heightened desire and willingness to apply the right ergonomic conditions for their children’s gadget use at home.
POLA PENYEBARAN PENYAKIT MENULAR BERDASARKAN KABUPATEN/KOTA DI JAWA TIMUR MENGGUNAKAN ANALISIS KORESPONDENSI Triyani Hendrawati; Riska Tiana; Soffy Mulyani; Silma Minnatika
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 4 No. 2 (2023): 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.v4i2.355

Abstract

Infectious diseases are a problem that is still a challenge and has not been resolved in Indonesia. The number of infectious disease cases continues to increase every year in Indonesia. Infectious diseases that are still a problem in Indonesia include tuberculosis (TB), pneumonia and leprosy (Leprosy). Identification of infectious disease endemic areas is an important issue in the health sector, the average rate of people with physical disabilities and deaths originating from infectious diseases. Indonesia, as a country consisting of 34 provinces, including East Java as one of the provinces that has a high rate of infectious disease cases. Therefore, research was conducted using correspondence analysis which aimed to determine the pattern of infectious disease trends and grouping districts / cities in East Java Province based on similarities between the spread of disease sufferers in each district / city. This type of research uses quantitative methods with secondary data obtained from the Central Bureau of Statistics of East Java Province. The results of correspondence analysis show that  the spread of pneumonia and tuberculosis in East Java  Province has a relative tendency to almost all regencies/cities in East Java, while the spread of leprosy is closer to Sampang Regency. From this research, it can be used to supervise and control infectious diseases in districts / cities in East Java Province, so that the government can adjust policy formulation and actions to prevent an increase in infectious disease cases in East Java
ANALISIS INDEKS PEMBANGUNAN MANUSIA PROVINSI JAWA TENGAH MENGGUNAKAN ANALISIS REGRESI Fawwaz Ziddan Azis; Triyani Hendrawati; Azmi Muhammad Nafis; Dimas Fattah
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 4 No. 2 (2023): 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.v4i2.374

Abstract

The Human Development Index is one approach to measuring the success rate of human development. Central Java Province is one of the provinces that experienced an increase in the human development index in 2022. Therefore, this study was conducted to determine the regression model used and the factors that affect the human development index in Central Java Province in 2022. Some of the factors used in this study are life expectancy, average years of schooling, expected years of schooling and adjusted per capita expenditure. The method used in this research is multiple linear analysis, parameter significance test, and classical assumption test. By using the human development index as the response variable (Y), life expectancy (X1), average years of schooling (X2), expected years of schooling (X3) and adjusted per capita expenditure (X4) as predictor variables. From the results of the analysis that has been done, the equation Y = 6.55 + 0.4626X₁ + 1.341X₂ + 0.8971X₃ + 0.0008329X₄ +e is obtained. This shows that there is a relationship between the human development index and life expectancy, average years of schooling, expected years of schooling and adjusted per capita expenditure. The classical assumption test, namely the normality test, multicollinearity test, autocorrelation test and heteroscedasticity test, shows that the regression model can be used
FAKTOR-FAKTOR YANG MEMENGARUHI INDEKS ARTIFICIAL INTELLIGENCE GLOBAL Yanuar Ichwan Satria Nugroho; Triyani Hendrawati; Kennedy Marthendra; Brian Riski Jayama Simanjuntak
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 4 No. 3 (2023): 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.v4i3.406

Abstract

The Global AI (Artificial Intelligence) Index is a value that aims to measure the progress of artificial intelligence (AI) around the world. Currently, technology is increasingly sophisticated and of course makes humans compete to create technology to make life easier. The purpose of this study is to analyse the effect of human resources, infrastructure, and government policies on the global AI index. The method used to determine the relationship between human resources, infrastructure, and government policies with the global AI index is the multiple linear regression method. From the results of data processing, a linear regression  = - 7,54675 + 0,65972  + 0,25096  + 0,07672 . Based on this model, the influence of human resources, infrastructure, and government policies has a significant positive effect on the Global AI Index. The coefficient of determination of the model is 0.8833, in other words, human resources (), infrastructure (), and government policy () are able to explain the value of the global AI index (Y) by 88.33% and the remaining 11.67% is explained by other variables
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.
Co-Authors Al Ghifari, Abdurrahman Al Ghifari Anindya Apriliyanti Pravitasari Azmi Muhammad Nafis Brian Riski Jayama Simanjuntak Defi Yusti Faidah Defi Yusti Faidah Dimas Fattah Fajar Indrayatna Fawwaz Ziddan Azis Fikriya, Aufa Gumgum Darmawan Intan Nurma Yulita Irlandia Ginanjar Karin, Nabila Kennedy Marthendra Marthendra, Kennedy Mulya Nurmansyah Ardisasmita Munawwaroh, Ihksa Pravitasari, Anindya Aprilianti Puspitasari, Priscilla Ardine Putri Samsi, Haana Lahanda Putri, Nisa Akbarilah Rachman, Hallen Naafi Aliya Raisah Zharifah Labibah Riska Tiana Shalfa Salsabilla Silma Minnatika Simanjuntak, Brian Riski Jayama Soffy Mulyani Sri Winarni Subiyanto Subiyanto Sudradjat Supian Toharodin, Toni Yanuar Ichwan Satria Nugroho Yuyun Hidayat