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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 Jurnal Pengabdian kepada Masyarakat Nusantara 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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Analisis Regresi Binomial Negatif terhadap Faktor-Faktor yang Memengaruhi Kasus Tuberkulosis di Jawa Barat, Indonesia Fikriya, Aufa; Shalfa Salsabilla; Raisah Zharifah Labibah; Sri Winarni; Defi Yusti Faidah; Anindya Apriliyanti Pravitasari; Triyani Hendrawati; Irlandia Ginanjar
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 7, ISSUE 1, April 2026
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol7.iss1.art3

Abstract

Tuberculosis (TB) remains a major public health problem globally, with West Java reporting the highest number of TB cases among all provinces in Indonesia in 2023. This study aims to identify key factors influencing TB incidence across districts and cities in West Java in 2024. The analysis focuses on healthy living behaviors, proper sanitation, HIV cases, and AIDS cases using a Negative Binomial Regression approach to address overdispersion in count data. The results show that proper sanitation has a significant negative association with TB incidence, while HIV and AIDS cases exhibit significant positive associations. The best-performing model includes these three variables, yielding a residual deviance of 27.615. These findings highlight the importance of integrated public health interventions that simultaneously improve sanitation and strengthen HIV/AIDS control programs to effectively reduce TB incidence in high-burden regions.
LSTM AND GRU IN RICE PREDICTION FOR FOOD SECURITY IN INDONESIA Triyani Hendrawati; Kennedy Marthendra; Brian Riski Jayama Simanjuntak; Anindya Aprilianti Pravitasari
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.
Real-time Emotion Recognition Using the MobileNetV2 Architecture Triyani Hendrawati; Anindya Apriliyanti Pravitasari
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.
Storytelling dan Permainan Edukatif dalam Menumbuhkan Kepedulian Lingkungan di SDN Babakancianjur Defi Yusti Faidah; Gumgum Darmawan; Bertho Tantular; Triyani Hendrawati; Nisrina Khoirunnisa; Anangga Arkan Thirafi; Nayadiva Shafinka; Muhammad Rafli Ramadhan; Zahra Hana Dwi Pasha
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 7 No. 2 (2026): Edisi Mei - Agustus
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v7i2.8914

Abstract

Pencemaran Sungai Citarum akibat limbah domestik dan industri menunjukkan pentingnya peningkatan kesadaran lingkungan sejak dini. Berdasarkan hasil observasi awal di SDN Babakancianjur, sebagian siswa masih memiliki pemahaman yang terbatas mengenai pelestarian sungai dan pengelolaan sampah. Kegiatan pengabdian ini bertujuan untuk meningkatkan pemahaman dan kepedulian lingkungan siswa melalui program edukasi pelestarian sungai di SDN Babakancianjur. Program dilaksanakan pada 47 siswa kelas IV SDN Babakancianjur melalui pendekatan storytelling, permainan edukatif memilah sampah, serta aktivitas partisipatif berupa melukis pot dan menanam bibit tanaman, yang dievaluasi menggunakan desain pre-test dan post-test. Analisis data dilakukan menggunakan uji paired t-test. Hasil kegiatan menunjukkan adanya peningkatan pemahaman siswa mengenai pelestarian sungai dan pengelolaan sampah setelah mengikuti program edukasi. Siswa menjadi lebih mampu membedakan sampah organik dan anorganik, memahami dampak pencemaran sungai, serta menunjukkan kepedulian yang lebih baik terhadap lingkungan. Pendekatan pembelajaran interaktif juga meningkatkan partisipasi aktif dan antusiasme siswa selama kegiatan berlangsung. Persentase siswa yang memperoleh nilai 100 meningkat dari 42% pada pre-test menjadi 68% pada post-test, sedangkan hasil uji paired t-test menghasilkan p-value sebesar 0,001 yang menunjukkan adanya peningkatan pemahaman yang signifikan. Program edukasi pelestarian sungai dinilai efektif dalam mendukung penguatan kepedulian lingkungan pada siswa sekolah dasar serta mendukung pencapaian Sustainable Development Goals (SDGs).
Co-Authors Al Ghifari, Abdurrahman Al Ghifari Anangga Arkan Thirafi Anindya Aprilianti Pravitasari Anindya Apriliyanti Pravitasari Azmi Muhammad Nafis Bertho Tantular Brian Riski Jayama Simanjuntak Brian Riski Jayama Simanjuntak Defi Yusti Faidah Dimas Fattah Fajar Indrayatna Fawwaz Ziddan Azis Fikriya, Aufa Gumgum Darmawan Intan Nurma Yulita Irlandia Ginanjar Karin, Nabila Kennedy Marthendra Kennedy Marthendra Muhammad Rafli Ramadhan Mulya Nurmansyah Ardisasmita Munawwaroh, Ihksa Nayadiva Shafinka Nisrina Khoirunnisa Puspitasari, Priscilla Ardine Putri Samsi, Haana Lahanda Putri, Nisa Akbarilah Rachman, Hallen Naafi Aliya Raisah Zharifah Labibah Riska Tiana Shalfa Salsabilla Silma Minnatika Soffy Mulyani Sri Winarni Subiyanto Subiyanto Sudradjat Supian Toharodin, Toni Yanuar Ichwan Satria Nugroho Yuyun Hidayat Zahra Hana Dwi Pasha