Article Per Year (5 Year)
p-Index From 2021 - 2026
0.947
P-Index
This Author published in this journals
All Journal Jurnal Teknologi Perikanan dan Kelautan Jurnal Silvikultur Tropika (JST) Journal of Information Systems Engineering and Business Intelligence Jurnal Perlindungan Tanaman Indonesia Journal of Innovation Information Technology and Application (JINITA) Biodiversitas Journal of Biological Diversity
Yeni Herdiyeni
Department Of Computer Science, Faculty Of Mathematics And Natural Sciences, IPB University, Jalan Meranti, Bogor, West Java 16680 Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Energy Engineering Environmental Science Library & Information Science Social Sciences

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search

 1 
The Impact of Socioeconomic and Demographic Factors on COVID-19 Forecasting Model Siti Nur Hasanah; Yeni Herdiyeni; Medria Kusuma Dewi Hardhienata
Journal of Information Systems Engineering and Business Intelligence Vol. 9 No. 1 (2023): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.9.1.70-83

Abstract

Background: COVID-19 has become a primary public health issue in various countries across the world. The main difficulty in managing outbreaks of infectious diseases is due to the difference in geographical, demographic, economic inequalities and people's behavior in each region. The spread of disease acts like a series of diverse regional outbreaks; each part has its disease transmission pattern. Objective: This study aims to assess the association of socioeconomic and demographic factors to COVID-19 cases through cluster analysis and forecast the daily cases of COVID-19 in each cluster using a predictive modeling technique. Methods: This study applies a hierarchical clustering approach to group regencies and cities based on their socioeconomic and demographic similarities. After that, a time-series forecasting model, Facebook Prophet, is developed in each cluster to assess the transmissibility risk of COVID-19 over a short period of time. Results: A high incidence of COVID-19 was found in clusters with better socioeconomic conditions and densely populated. The Prophet model forecasted the daily cases of COVID-19 in each cluster, with Mean Absolute Percentage Error (MAPE) of 0.0869; 0.1513; and 0.1040, respectively, for cluster 1, cluster 2, and cluster 3. Conclusion: Socioeconomic and demographic factors were associated with different COVID-19 waves in a region. From the study, we found that considering socioeconomic and demographic factors to forecast COVID-19 cases played a crucial role in determining the risk in that area.   Keywords: COVID-19, Facebook Prophet , Hierarchical clustering, Socioeconomic and demographic
HUBUNGAN PARAMETER OSEANOGRAFI PERAIRAN TERHADAP POLA MUSIM IKAN PELAGIS DI PERAIRAN PALABUHANRATU Gilar Budi Pratama; Tri Wiji Nurani; Mustaruddin Mustaruddin; Yeni Herdiyeni
Jurnal Teknologi Perikanan dan Kelautan Vol 13 No 1 (2022): MEI 2022
Publisher : Fakultas Perikanan dan Ilmu Kelautan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3135.795 KB) | DOI: 10.24319/jtpk.13.67-78

Abstract

Pola musim penangkapan dan kelimpahan ikan pelagis dipengaruhi oleh dinamika nilai klorofil-a dan suhu permukaan laut. Informasi pola musim penangkapan ikan dan hubungannya terhadap parameter oseanografi dapat membantu nelayan dalam meningkatkan efisiensi penangkapan. Oleh karenanya penelitian ini bertujuan menentukan pola musim penangkapan ikan pelagis di Perairan Palabuhanratu dan mengindentifikasi dinamika klorofil-a dan suhu permukaan laut serta hubungannya terhadap produksi perikanan pelagis. Data yang digunakan berupa data produksi perikanan pelagis tahun 2016-2020, dan data citra klorofil-a serta suhu permukaan laut. Data dianalisis menggunakan metode rata-rata bergerak dan korelasi pearson. Hasil penelitian ini menunjukkan musim penangkapan ikan cakalang, tongkol, tenggiri, dan layaran secara umum terjadi di musim timur. Terdapat hubungan yang erat antara produksi perikanan tongkol dan cakalang terhadap fluktuasi bulanan nilai suhu permukaan laut dan klorofil-a (p-value < 0,05). Kenaikan konsentrasi klorofil-a dan penurunan nilai suhu permukaan laut akan diikuti peningkatan produksi perikanan tongkol dan cakalang di perairan Palabuhanratu. Indeks korelasi ikan cakalang terhadap klorofil-a masuk pada kategori kuat (r = 0,697), sedangkan pada ikan tongkol masuk pada kategori sedang (r = 0,485). Indeks korelasi ikan cakalang dan tongkol terhadap suhu permukaan laut masuk pada kategori sedang (0,40 < r < 0,599).
Random Forest-Based Multiclass Classification of Pestalotiopsis sp. Disease Severity in Rubber Plants Using UAV Multispectral Data. Solikin; Yeni Herdiyeni; Annisa; Lilik Budi Prasetyo; Tri Rapani Febbiyanti; Imas Sukaesih Sitanggang; Sri Nurdiati
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.3310

Abstract

Pestalotiopsis sp.-induced leaf-fall disease threatens rubber (Hevea brasiliensis) productivity by reducing canopy density, degrading chlorophyll, and impairing photosynthetic capacity. This study developed an exploratory multiclass framework to classify disease severity in rubber plantations using UAV multispectral data and a Random Forest (RF) algorithm. The dataset comprised 144 ground-truth canopy-level samples labeled into four ordinal severity classes, L1 (mild), L2 (moderate), L3 (severe), and L4 (very severe), and 1760 additional UAV-derived observations for spatial inference. Spectral and structural predictors included the Leaf Chlorophyll Index (LCI), Normalized Difference Red Edge (NDRE), density_LCI, and density_NDRE, collected across three rubber clones, BPM 24, GT 1, and RRIC 100. Model development used an 80:20 stratified train–test split, stratified and nested cross-validation, class weighting, and SMOTE applied only within training folds to reduce imbalance and leakage. Performance was evaluated using accuracy, class-wise precision–recall–F1, and the multiclass Matthews Correlation Coefficient (MCC). Hyperparameter tuning (n_estimators = 150, max_depth = 7, min_samples_split = 9) increased MCC from 0.143 to 0.547 on independent test data and 0.586 under the all-labeled-data scenario, indicating moderate agreement. LCI emerged as the dominant predictor, supporting the physiological relevance of chlorophyll-sensitive red-edge information. However, low recall for L2 and unstable L4 performance reflected class imbalance, limited labeled samples, and overlapping spectral responses. The framework provides a more informative severity stratification than binary detection, but further ground-truth expansion, ordinal or cost-sensitive modeling, comparative benchmarking, and multi-season validation are required before operational deployment.
Short Communication: Geometric morphometric analysis of leaf venation in four Shorea species for identification using Digital Image Processing Ishak Ariawan; YENI HERDIYENI; ISKANDAR ZULKARNAEN SIREGAR
Biodiversitas Journal of Biological Diversity Vol. 21 No. 7 (2020)
Publisher : Society for Indonesian Biodiversity

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/biodiv/d210754

Abstract

Abstract. Ariawan I, Herdiyeni Y, Siregar IZ. 2020. Short Communication: Geometric morphometric analysis of leaves venation in Shorea spp. for identification using Digital Image Processing. Biodiversitas 21: 3303-3309. Shorea is one of the genera of the Dipterocarpaceae family which consists of more than 190 species. Massive exploitation of forests has threatened the sustainability of Shorea in nature. A total of 156 species has been listed on the IUCN (International Union for Conservation of Nature) red list. From the 156 species, 59.6% are in the critically endangered category, so urgent conservation is needed. However, during collection of Shorea at the seedling phase for conservation &nbsp;purposes, it is often difficult to distinguish among them that can cause errors in their collection process. To avoid these errors, identification needs to be done, usually based on plant &nbsp;leaf and flower&nbsp; morphology. Leaves are easier because they have the main features that distinguish each plant species, one of which is the venation structure. Geometric morphometric techniques are a modern approach recognized as useful for the identification of species in many plants. Geometric morphometrics analyzes the position of the venation point using coordinate geometry values. This research was aimed to extract venation features of Shorea leaves using a geometric morphometric approach. The extraction process result in some features, such as straightness, different angle, length ratio, scale projection, and secondary nerves. On extracted features, an analysis was then performed to find out the best features in classifying species of Shorea spp. The results of this study indicated that the geometric morphometric approach could extract the value of the features of straightness, different angle, length ratio, scale projection, and secondary nerves. The secondary nerve feature is the best feature because it can distinguish between fourcommonly planted species of Shorea spp. (S. acuminata, S. leprosula, S. ovalis, and S. selanica). By using the support vector machine classification technique to identify species of Shorea spp., the classification results obtained an average accuracy of 84.46%.
The IDENTIFIKASI CENDAWAN PATOGEN PENYEBAB BERCAK DAN HAWAR DAUN JABON PUTIH (Anthocephalus cadamba) ASAL BOGOR Elis Nina Herliyana; Hasan Basri; Yeni Herdiyeni; Abdul Munif; Prayogo Probo Asmoro; Adisti Triani Wandayanti
Journal of Tropical Silviculture Vol. 17 No. 01 (2026): Jurnal Silvikultur Tropika
Publisher : Departemen Silvikultur, Fakultas Kehutanan dan Lingkungan, Institut Pertanian Bogor (IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/j-siltrop.17.01.%p

Abstract

In Indonesia, there are two types of jabon: one of which is white jabon (Anthocephalus cadamba Roxb.). White jabon is a type of jabon that is often cultivated. The prospect of developing jabon plants that continue to increase has led to an increase in the need for jabon seedling supplies. However, the development of jabon seedlings is hampered by infections of pathogens that cause leaf diseases in white jabon seedlings in nurseries. Information related to pathogenic fungi that cause leaf spot and blight symptoms in white jabon is still not widely known. This study aims to identify pathogenic fungi that cause leaf spot and blight symptoms in White Jabon (Anthocephalus cadamba Roxb.) from Bogor, West Java. Sampling was carried out at the Dramaga Permanent Nursery, IPB Dramaga Campus, Bogor. Pathogenic fungi were isolated from the part of the leaf that had symptoms of spots or blight and then tested for their pathogenicity against the leaves of white Jabon seedlings using the Koch Postulate method. The results of observations of symptoms on white Jabon leaves were obtained for four types of spot symptoms and two types of blight symptoms. The results of the isolation and identification of pathogenic fungi showed that four genera of fungi caused spot symptoms, namely Botrytis sp., Pestalotiopsis sp., Curvularia sp., and Colletotrichum sp.2, and two genera of fungi caused blight symptoms, namely Curvularia sp., and Colletotrichum sp.1. The Koch Postulate test showed that the six types of fungi isolated caused the leaves of white Jabon seedlings to become necrotic.
Applying Small Area Estimation to Optimize Sample Size for Brown Planthopper (Nilaparvata lugens Stal.) Density Farhan Alfian Nur; Ali Nurmansyah; I Wayan Winasa; Yeni Herdiyeni
Jurnal Perlindungan Tanaman Indonesia Vol 30, No 1 (2026)
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jpti.108155

Abstract

The brown planthopper (BPH), Nilaparvata lugens Stål, is an important rice pest requiring consistent monitoring to prevent population outbreaks. However, conventional monitoring relying on large sample sizes is labor-intensive and susceptible to non-sampling errors. This study aimed to evaluate the estimation precision of the Kernel-based Small Area Estimation (SAE) method for BPH population density under significantly reduced sample size conditions. The research utilized observation data from 81 paddy fields in Subang Regency, combined with synthetic data generated via Monte Carlo simulation and variance estimation using Taylor’s Power Law. Altitude was selected as the auxiliary variable for the Kernel-based SAE model based on Spearman’s correlation analysis. The estimation quality was evaluated by comparing Relative Efficiency (RE) and Coefficient of Variation (CV) against the direct estimation method. The results showed that the Kernel-based SAE method with a 20% sample size produced reliable estimates with an RE value of less than one. Statistical tests confirmed no significant difference between the SAE method at 20% sampling and direct estimation at 100% sampling, yet the SAE method demonstrated significantly higher precision with a lower average CV (3.03%) compared to direct estimation (12.72%). In conclusion, the Kernel-based SAE method allows for an 80% reduction in sample size while delivering unbiased and more precise BPH density estimates than conventional methods.
Co-Authors Abdul Munif Adisti Triani Wandayanti Ali Nurmansyah Annisa Elis Nina Herliyana Farhan Alfian Nur Gilar Budi Pratama Hardhienata, Medria Kusuma Dewi Hasan Basri I Wayan Winasa Imas Sukaesih Sitanggang Ishak Ariawan ISKANDAR ZULKARNAEN SIREGAR Lilik Budi Prasetyo Mustaruddin Prayogo Probo Asmoro Siti Nur Hasanah Solikin Sri Nurdiati Tri Rapani Febbiyanti Tri Wiji Nurani