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All Journal Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Jurnal Kridatama Sains dan Teknologi TIERS Information Technology Journal
Candrawengi, Ni Luh Putu Ika
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Agriculture, Biological Sciences & Forestry Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Mechanical Engineering Public Health Social Sciences

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Penanaman TOGA dan Produksi Jamu untuk Peningkatan Kesehatan dan Ekonomi Desa Buahan Amritha, Yadhurani Dewi; Candrawengi, Ni Luh Putu Ika
Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Vol 7, No 3 (2024): Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstormin
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/japhb.v7i3.6750

Abstract

Meningkatnya harga obat-obatan modern menurunkan tingkat kesehatan pada masyarakat dan berdampak negatif terhadap kesejahteraan dan ketahanan negara secara keseluruhan. Pemerintah dan masyarakat harus mencari alternatifadengan memanfaatkan kembali potensi tanaman obat. Untuk meningkatkan kesehatan, pertumbuhan ekonomi, dan konservasi keanekaragaman hayati tanaman obat, penciptaan TOGA (tanaman obat rumah tangga) di desa merupakan bagian penting dari program pengelolaan keanekaragaman hayati tanaman obat. Kegiatan pengabdian masyarakat di desa Buahan dilakukan sebagai upaya untuk memberikan edukasi tentang TOGA kepada masyarakat sehingga dapat meningkatkan kemampuan masyarakat dalam pemanfaatan TOGA dalam kehidupan sehari-hari. Maka dari itu diperlukan upaya-upaya yang dapat meningkatkan kemampuan masyarakat dalam memanfaatkan TOGA dan selanjutnya dapat juga meningkatkan nilai ekonomi TOGA. Dengan pendekatan yang sistematis dan berkelanjutan, kegiatan penanaman TOGA dapat memberikan dampak positif yang signifikan bagi masyarakat desa, baik dari segi kesehatan, ekonomi, maupun sosial. Produk olahan TOGA nantinya dapat membantu perekonomian masyarakat. Memperoleh pengetahuan dan keterampilan dalam proses budidaya tanaman obat rumahan dapat dilihat sebanyak 85% peserta pelatihan dapat mengidentifikasi 10 jenis tanaman obat dan manfaatnya, tingkat partisipasi sangat tinggi yaitu 90% warga desa terlibat aktif dalam kegiatan penanaman dan perawatan kebun TOGA, pengolahan pasca panen menjadi produk jamu, dan pemasaran yang tepat akan membantu meningkatkan kuantitas produk jamu serta keberlangsungan pemasaran.
Performance Analysis and Traffic Flow Simulation of Tukad Pakerisan Road Segments Using VISSIM in South Denpasar Tapa, I Gede Fery Surya; Yuliadewi, Ni Putu Ary; Candrawengi, Ni Luh Putu Ika; Prakasa, I Made Panji Tirta; Zainordin, Nadzirah; Sutapa, I Ketut
TIERS Information Technology Journal Vol. 6 No. 1 (2025)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v6i1.6546

Abstract

The increasing ownership of motor vehicles has significantly contributed to heightened levels of traffic congestion. This study was conducted on Tukad Pakerisan Road in Denpasar City and aims to evaluate the current performance of the road segment by employing traffic modeling through Vissim software. The research adopts the Indonesian Highway Capacity Guidelines (PKJI) as the methodological framework. Data collection was carried out via a 12-hour on-site survey across two road segments of Tukad Pakerisan. The analysis revealed that the traffic flow volume reached 2025.45 Passenger Car Station/hour on Segment A and 1865.65 Passenger Car Station/hour on Segment B. The respective road capacities were 1877.669 Passenger Car Station/hour and 1671.583 Passenger Car Station/hour. The degree of saturation was found to be 1.08 on Segment A and 1.12 on Segment B, indicating Level of Service (LOS) F—characterized by severe traffic congestion. The simulation indicates significant future congestion, with projected saturation levels exceeding 1.5, underscoring the need for integrated mitigation strategies such as adaptive signal control and vehicle restriction policies. A five-year performance projection further suggests a continual increase in the degree of saturation, surpassing the acceptable limit of 0.85 as stipulated in PKJI 2023. These findings underscore the urgent need for capacity enhancement on Tukad Pakerisan Road. The study recommends the installation of additional traffic signage and the implementation of traffic engineering strategies to mitigate congestion along this critical corridor in South Denpasar.
Pendekatan Transformer Deep Learning dalam Meramalkan Harga Minyak Sumatran Light Crude Candrawengi, Ni Luh Putu Ika; Amritha, Yadhurani Dewi; Dananjaya, Md. Wira Putra
Jurnal Kridatama Sains dan Teknologi Vol 7 No 02 (2025): Jurnal Kridatama Sains dan Teknologi
Publisher : Universitas Ma'arif Nahdlatul Ulama Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53863/kst.v7i02.1993

Abstract

Time series forecasting plays an important role in understanding the dynamics of volatile data that depends on long-term historical patterns, such as crude oil prices. Parametric statistical approaches often face limitations due to strict assumptions, making nonparametric deep learning methods a more flexible alternative. This study proposes the application of a Transformer-based deep learning model to predict the price of Sumatran Light Crude Oil (SLC), utilizing a self-attention mechanism to capture long-term dependencies in time series data. Experiments were conducted by evaluating various configurations of multi-head attention and number of layers, while keeping the model dimensions and input-output windows consistent. The results show that the Transformer configuration with 16 heads and 4 layers provides the best performance with a Root Mean Square Error (RMSE) value of 8.19818. These findings indicate that Transformer is capable of effectively modeling long-term trends in SLC prices, although its sensitivity to short-term fluctuations is still limited. The main contribution of this research lies in the use of Transformer as an alternative approach to forecasting crude oil prices in Indonesia, which was previously dominated by statistical methods and recurrent models. In practical terms, the results of this study provide a basis for the development of a more adaptive oil price forecasting system to support energy analysis and data-driven decision making
Model Machine Learning yang Dioptimalkan untuk Prediksi Penyakit Jantung Menggunakan R Shiny Amritha, Yadhurani Dewi; Candrawengi, Ni Luh Putu Ika; Dananjaya, Md Wira Putra; Dayanti, Made Ari Riska
Jurnal Kridatama Sains dan Teknologi Vol 8 No 01 (2026): Jurnal Kridatama Sains dan Teknologi (In Progress)
Publisher : Universitas Ma'arif Nahdlatul Ulama Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53863/kst.v8i01.1994

Abstract

Heart disease continues to be a major contributor to global mortality, highlighting the critical importance of early detection in enhancing patient outcomes. The increasing availability of structured clinical datasets has enabled the application of intelligent systems for risk prediction and diagnostic support. In this paper, the effectiveness of three supervised learning algo- rithms—Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT)—is evaluated for the task of heart disease prediction. This investigation is based on the Heart Failure Prediction dataset sourced from the Kaggle platform. The training process for each model involved a 10-fold cross- validation, with its hyperparameters later being tuned using grid search optimization. Model efficacy was measured against standard classification benchmarks, including accuracy, sensitivity, specificity, and the area under the ROC curve (AUC). The Random Forest model emerged as the most effective, demon- strating superior performance with an AUC of 0.9517, sensitivity of 81.18%, and specificity of 90.44%. To facilitate clinical use, this model was subsequently integrated into a user-friendly web tool built with the R Shiny framework. The interface allows users to input patient-level clinical data and obtain real-time predictions, along with visualizations of feature importance and risk probability. This implementation bridges the gap between algorithm development and practical application, offering a user- friendly decision support tool for early heart disease screening. The findings affirm that machine learning models, when properly tuned and validated, can serve as effective and interpretable tools in clinical decision-making. This work contributes to the advancement of e-health and the integration of AI-driven models into medical workflows
Co-Authors Amritha, Yadhurani Dewi Dananjaya, Md Wira Putra Dananjaya, Md. Wira Putra Dayanti, Made Ari Riska I Gede Fery Surya Tapa I Ketut Sutapa Prakasa, I Made Panji Tirta Yuliadewi, Ni Putu Ary Zainordin, Nadzirah