Article Per Year (5 Year)
p-Index From 2021 - 2026
5.192
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) FORUM STATISTIKA DAN KOMPUTASI Media Statistika SAINSMAT CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Teknologi Informasi dan Ilmu Komputer JUITA : Jurnal Informatika JOIN (Jurnal Online Informatika) Padjadjaran Journal of Dentistry Jurnal Matematika Sains dan Teknologi Journal of Regional and Rural Development Planning BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jambura Journal of Mathematics ComTech: Computer, Mathematics and Engineering Applications Inferensi International Journal of Advances in Data and Information Systems Jurnal Statistika dan Aplikasinya Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam Jurnal Natural Xplore: Journal of Statistics Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Statistics and Its Applications
Yenni Angraini
Department Of Statistics And Data Science, Faculty Of Mathematics And Natural Science, IPB University, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Transportation Other

Published : 41 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 41 Documents
Search

 1   2   3   4   5 
Comparison of the Symmetric and Asymmetric Generalized Autoregressive Conditional Heteroscedasticity (GARCH) Models in Forecasting the 2018-2023 Jakarta Composite Index Yenni Angraini; Adelia Putri Pangestika; I Made Sumertajaya
ComTech: Computer, Mathematics and Engineering Applications Vol. 15 No. 1 (2024): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v15i1.10610

Abstract

The Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) method assumes a homogeneous residual variance, but data with high volatility can cause violations of this assumption. Hence, it is interesting to compare the forecasting accuracy of symmetric and asymmetric Autoregressive Conditional Heteroskedasticity (ARCH) models in various data conditions. The research aimed to compare the accuracy of the symmetric ARCH/ Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and asymmetric TGARCH models in forecasting weekly Jakarta Composite Index (JCI) data on January 1st, 2018, to April 24th, 2023, by involving the influence of COVID-19 as a covariate variable and applying several validation scenario models to training and testing data. Based on the best-selected model, forecasting was carried out from May 1st, 2023, to July 3rd, 2023. The data used were weekly JCI opening data from January 1st, 2018, to April 24th, 2023, with the COVID-19 period as a covariate variable. The analysis results show that symmetric and asymmetric methods can handle violations of the heteroscedasticity assumption in the ARIMAX model. The best model produced based on four data validation scenarios is the asymmetric ARIMAX(3,1,3)-TGARCH(1,2) model with an average MAPE value of 3.158%. In this model, the COVID-19 variable significantly influences the JCI movement. Forecasting is done with forecasting results that are stable with confidence intervals that widen in each period.
Penerapan Geographically Weighted Panel Regression dan Data Envelopment Analysis dalam Pemodelan Kemiskinan di Kalimantan Timur Azkiya, Azka Al; Angraini, Yenni; Anisa, Rahma
Journal of Regional and Rural Development Planning (Jurnal Perencanaan Pembangunan Wilayah dan Perdesaan) Vol. 8 No. 1 (2024): Journal of Regional and Rural Development Planning (Jurnal Perencanaan Pembangu
Publisher : P4W IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jp2wd.2024.8.1.41-53

Abstract

Indonesia currently still needs to focus on achieving sustainable development goals agreed by all countries in the world. Indonesia presently ranks 82nd out of 163 nations in terms of SDG accomplishment, indicating that there is still plenty of potential for improvement. One of the goals that hasn't been accomplished is ‘no poverty’. Regarding the poverty cases, among all province in Indonesia, East Kalimantan is important to be analyzed, because Penajam Paser Utara and Kutai Kartanegara in East Kalimantan are scheduled to become Indonesia's next capital, Nusantara. The goal of this research is to investigate the variables that influence poverty in East Kalimantan and determine the effectiveness of poverty alleviation in the regencies/cities in East Kalimantan. This research used indicator data of poverty from 2019-2021 retrieved from Statistics Indonesia. This research use spatial panel data analysis regression method or Geographically Weighted Panel Regression (GWPR) and Data Envelopment Analysis (DEA). In GWPR model, this research compared adaptive gaussian, adaptive bisquare, adaptive exponential, fixed gaussian, fixed bisquare, and fixed exponential kernel. The findings of this investigation revealed that fixed exponential is the kernel that has lowest AIC and the highest adj-?2. The variables that determine poverty of regencies/cities in East Kalimantan are expenditure per capita, life expectancy, and number of village with higher education facilities. Furthermore, according to DEA, only three cities were effective in addressing poverty: Mahakam Ulu, Paser, and Penajam Paser Utara.
Perbandingan Kinerja Metode Arima, Multi-Layer Perceptron, dan Random Forest dalam Peramalan Harga Logam Mulia Berjangka yang Mengandung Pencilan Prasetyo, Teguh; Putri, Rizki Alifah; Ramadhani, Dini; Angraini, Yenni; Notodiputro, Khairil Anwar
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 2: April 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20241127392

Abstract

Akurasi peramalan sebagai tolok ukur kinerja metode deret waktu bergantung beberapa hal, antara lain karakteristik data, pemilihan metode, dan jangka waktu, di samping fluktuasi data dan keberadaaan pencilan dalam data. Keberadaan pencilan dalam data sering kali tidak dapat dihindari sehingga dapat mengganggu akurasi dan presisi dari peramalan. Berdasarkan hal tersebut dalam artikel ini dibahas tentang hasil kajian perbandingan kinerja metode ARIMA, Multi-Layer Perceptron (MLP), dan Random Forest (RF) dalam peramalan data deret waktu yang mengandung pencilan, khususnya untuk data harga logam mulia berjangka (emas, perak, dan platina) berdasarkan nilai Mean Absolute Percentage Error (MAPE). Ditunjukkan bahwa kinerja metode ARIMA dengan Interpolasi Linier mampu menekan pengaruh pencilan lebih baik dibanding ARIMA dengan Winsorized Mean dan ARIMA tanpa penanganan data pencilan Dalam hal ini diperoleh nilai MAPE rata-rata berturut-turut sebesar 10,67% dibanding 12,33% dan 11,79% ketika dievaluasi menggunakan data uji. Selain itu, metode MLP memiliki kinerja yang tidak lebih baik dibanding ARIMA dengan Interpolasi Linier dengan nilai MAPE rata-rata sebesar 11,13% ketika dievaluasi menggunakan data uji. Secara keseluruhan kinerja terbaik dihasilkan oleh metode RF, yang memiliki nilai MAPE rata-rata jauh lebih kecil dibanding metode lainnya, yakni 2,85% ketika dievaluasi menggunakan data uji. Dalam kajian ini nampak bahwa Metode RF memiliki kinerja terbaik dibandingkan semua metode dalam peramalan data deret waktu yang dicobakan menggunakan data empiris yaitu harga loga mulia berjangka.
Antioxidant activity of roots, stems, and leaves Spatholobus littoralis Hassk.: an experimental study Ariesanti, Yessy; Wahyudina, Salsa Putri; Poedjiastoeti, Wiwiek; Angraini, Yenni
Padjadjaran Journal of Dentistry Vol 35, No 3 (2023): November 2023
Publisher : Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/pjd.vol35no3.50423

Abstract

ABSTRACTIntroduction: Tooth extraction is an action that will leave scars where the procedure is conducted. Post-extraction wounds will heal after going through 4 complex healing phases. Reactive Oxygen Species (ROS) play a role in wound healing, where antioxidants become substances that can control ROS levels in the body. Spatholobus littoralis Hassk., which comes from Central Kalimantan, has benefits in the wound healing phase. The aim of study was to analyze the effectiveness of Spatholobus littoralis Hassk., extracts on antioxidant activity. Methods: The type of research was an in vitro laboratory experimental study. Extracts of roots, stems, and leaves of Spatholobus littoralis Hassk. DPPH 2, 2-Diphenyl-1-picrylhydrazylradical as negative control, and Vitamin C as positive control were tested for antioxidants by using DPPH solution. Tests using a spectrophotometer with a wavelength of 517 nm were conducted after each sample was incubated for 30 minutes. Then, It would be calculated to determine the percentage of inhibition and IC50 of each sample. Data analysis used in this research was One-way ANOVA. Results: One-way ANOVA test showed no significant differences in the root extract, DPPH, and vitamin C groups; besides, there were significant differences in the stem and leaf extract groups. In the post hoc Tukey test, a concentration of 2500 ppm in stem extract was the most effective concentration, and a concentration of 2000 ppm in leaf extract was the most effective, with IC50 values from lowest to highest: stem extract (9.46), vitamin C (11.52), root extract (23.86), leaf extract (47.71), and DPPH (1660710) Conclusion: Extract of Spatholobus littoralis Hassk. has antioxidant activity, with the highest antioxidant activity in Spatholobus littoralis Hassk. stem extract, the most effective concentration is at 2500 ppm.Keywords: DPPH, reactive oxygen species, spatholobus littoralis Hassk.
Enhancing interpretability in random forest: Leveraging inTrees for association rule extraction insights Hilali Moh’d, Fatma; Anwar Notodiputro, Khairil; Angraini, Yenni
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4054-4061

Abstract

The random forest model is a powerful supervised learner, recognized for its ability to learn the pattern within data with superior predictive accuracy. However, it is a black box model because it lacks interpretability. This study addressed the interpretable challenge by employing the inTree framework. The rules were extracted from each decision tree in a random forest model, and the association rules were determined through measured matrix support and confidence to reveal the frequent variable interactions for predicting unemployment. This approach provided insight into the relationships between specific variables and unemployment outcomes. The developed method used data set from the integrated labor force survey (ILFS) 2020/2021 in Zanzibar. Zanzibar’s unemployment rate consistently increased across surveys conducted in 2006, 2014, and 2020/2021. Results have shown that the rules that most predict unemployment for individuals are female and lack of health insurance and secondary education level, female and youth age group and lack of health insurance and secondary education level with a high confidence level. This study provides practical insights for Zanzibar’s government to develop effective interventions, programs, and policies. Improving the interpretability of the random forest model enhances decision-making to address unemployment challenges.
Comparative Analysis of ARIMA and LSTM for Forecasting Maximum Wind Speed in Kupang City, East Nusa Tenggara Magfirrah, Indah; Ilma, Meisyatul; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
Jambura Journal of Mathematics Vol 6, No 2: August 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i2.25834

Abstract

This study compares the Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models for predicting maximum wind speed based on accuracy measured by Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Based on the results of the research, the LSTM model is better than the ARIMA model in predicting maximum wind speed in Kupang City, East Nusa Tenggara Province. The best LSTM model has hyperparameters of 200 epochs; batch size of 32; learning rate of 0,001; and 8 neurons. Based on the evaluation results of predicted data against actual data, the MAPE value of the LSTM model is 19,40%. The benefit of this research is that it can contribute to the literature on the development of wind utilization as a basis for building power plants on small islands as a renewable resource, particularly in Kupang City, East Nusa Tenggara.
Comparison of Seasonal ARIMA and Support Vector Machine Forecasting Method for International Arrival in Lombok MY, Hadyanti Utami; Setyowati, Silfiana Lis; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
Jambura Journal of Mathematics Vol 6, No 2: August 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i2.26478

Abstract

Seasonal Autoregressive Integrated Moving Average is a statistical model designed to analyze and forecast data with that shows seasonal patterns and trends. Support Vector Machine (SVM) is a machine learning-based technique that can be used to forecast time series data. SVM uses the kernel tricks to overcome non-linearity problems, whereas The SARIMA model is well-suited for data that exhibit seasonal fluctuations that repeat over time. Lombok International Airport is the main gateway to West Nusa Tenggara and has become a symbol of tourism growth in the region. Time series analysis is a very useful tool in determining patterns and forecasting the number of international arrivals at Lombok International Airport within a certain period. This study aims to compare the SARIMA model and SVM which can read non-linear patterns in the number of international arrivals at Lombok International Airport. After obtaining the SARIMA and SVM models, the two models are evaluated using test data based on the smallest RMSE value. The SVM model with a linear kernel trick provides the smallest RMSE when compared to SARIMA with SVM RMSE is 238,655. While the best model in Seasonal ARIMA is SARIMA (3,1,0)(1,0,0)12, the forecasting results show SARIMA is better in the forecasting process for the next 10 months.
Long Short-Term Memory as a Rainfall Forecasting Model for Bogor City in 2025-2026 Fadhilah, Nur Anggraini; Dzulhij Rizki, Muhammad Abshor; Azahran, Muhammad Ryan; Arbaynah, Siti; Antique Yusuf, Rakesha Putra; Angraini, Yenni; Nurhambali, Muhammad Rizky
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9068

Abstract

Indonesia is a country with a tropical climate that has unique and changing weather patterns. Accurate rainfall prediction can help local governments, farmers, and the broader community plan activities that depend on rainfall patterns. This research aims to develop a rainfall prediction model for Bogor City using past rainfall data in Bogor City, which is known as an area with high rainfall levels and dynamic rainfall patterns. The analysis utilizes rainfall data recorded by the JAXA satellite from January 1, 2014, to December 31, 2024. The prediction method implemented in this research is the long short-term memory (LSTM). The LSTM modelling process evaluates various models by comparing RMSE, MAE, and correlation values through expanding window cross-validation, selecting the model with the lowest average RMSE and MAE with the highest correlation as the optimal choice. The best-performing model was achieved with 25 epochs and a batch size of 1, resulting in an average RMSE of 56.3340, MAE of 35.5223, and correlation of 0.3209. This best-performing model is then employed to predict rainfall for the next two years. The results show significant daily variations in the predicted rainfall but can capture existing seasonal patterns.
TIME SERIES MODEL FOR TRAIN PASSENGER FORECASTING Hakim, Bashir Ammar; Billy, Billy; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp755-766

Abstract

Trains as a means of public transportation have an important role in connecting various regions of Jabodetabek. Therefore, it is necessary to have a deep understanding of the trend of train passenger movements and predict the number of train passengers in the next period in order to optimize the management and service of train passengers properly. In this study, we examine two methods that can be used as forecasting methods for train passenger data sourced from the Central Statistics Agency (BPS), namely ARIMA and Prophet. This study demonstrates that the optimal ARIMA model is ARIMA (0,2,1), achieving a Mean Absolute Percentage Error (MAPE) of 4.91% and a Root Mean Square Error (RMSE) of 1754.970. In addition, the Prophet model, which is an additive regression model designed by Facebook for time series forecasting was also obtained with a MAPE of 0.04% and an RMSE of 1170.59. Considering the MAPE and RMSE values of the two models, the Prophet model emerges as the most suitable for forecasting the number of train passengers in the Jabodetabek region.
THE PERFOMANCE OF THE ARIMAX MODEL ON COOKING OIL PRICE DATA IN INDONESIA Ilmani, Erdanisa Aghnia; Amatullah, Fida Fariha; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp819-828

Abstract

Forecasting is crucial for planning, particularly in addressing potential issues. While ARIMA models are commonly used for time series forecasting, they may need more accuracy by overlooking external factors. The ARIMAX model, which incorporates exogenous variables, is employed to enhance accuracy. This study applies the ARIMAX model to forecast cooking oil prices in Indonesia, known for its complex patterns. Using data from the Directorate General of Domestic Trade and Price Stability (2024), the research highlights fluctuating cooking oil prices from 2010 to 2023 every month. Both ARIMA and ARIMAX models are utilized, with domestic fresh fruit bunch (FFB) prices and the COVID-19 pandemic indicator as exogenous variables. Evaluation based on Mean Absolute Percentage Error (MAPE) shows that the ARIMAX model has a MAPE of 17.31%, compared to 17.69% for the ARIMA model. The lower MAPE value for ARIMAX indicates improved forecasting accuracy by incorporating external factors. Thus, the ARIMAX model is recommended for predicting cooking oil prices, offering better accuracy and valuable insights for policymakers and stakeholders.
Co-Authors Aam Alamudi Achmad Noerkhaerin Putra Adelia Putri Pangestika Akbar Rizki Akbar Rizki Al Maida, Mahda Amaliya, Sri Amanda, Nabila Tri Amatullah, Fida Fariha Anang Kurnia Andika Putri Ratnasari Anisa, Rahma Anistia Iswari Antique Yusuf, Rakesha Putra Arbaynah, Siti Ariesanti, Yessy ASEP SAEFUDDIN Azahran, Muhammad Ryan Azkiya, Azka Al Bagus Sartono Berliana Apriyanti Billy, Billy Cintani, Meavi Dian Kusumaningrum Dzulhij Rizki, Muhammad Abshor Eka Dewi Pertiwi Else Virdiani Fachry Abda El Rahman Fadhilah, Nur Anggraini Fadillah, Maulana Ahsan Fira Nurahmah Al Aminy Fitri, Zafira Ilma Fitrianti, Dwi Fitrianto, Anwar Ghiffary, Ghardapaty Ghaly Gunawan, Windi Hakim, Bashir Ammar Hari Wijayanto Hasanah, Mauizatun Hilali Moh’d, Fatma I Made Sumertajaya Ilma, Meisyatul Ilmani, Erdanisa Aghnia Indahwati Isnaini, Mardatunnisa Itasia Dina Sulvianti Jamaluddin Rabbani Harahap Kenia Maulidia Kurnadipare, Aleytha Ilahnugrah Kusman Sadik Lia Ratih Kusuma Dewi Magfirrah, Indah Maghfiroh, Firda Aulia Mahesa Ahmad Rahmawan Mahesa, Hakim Zoelva Maulidiyah, Wildatul Moh'd, Fatma Hilali Mohammad Abror Gustiansyah Mohammad Masjkur Mualifah, Laily Nissa Atul Mualifah, Laily Nissa Atul  MY, Hadyanti Utami Nabila Ghoni Trisno Hidayatulloh Nabila Ghoni Trisno Hidayatulloh Nensi, Andi Illa Erviani Nickyta Shavira Maharani Nizar, Yeky Abil Nugraha, Adhiyatma Nur Aziza, Vivin Nurhambali, Muhammad Rizky Oksi Al Hadi Oktaviani Aisyah Putri Pratiwi, Windy Ayu Putri Zainal Putri, Adelia Putri, Mega Ramatika Putri, Rizki Alifah Raffael Julio Roger Roa Rahmasari, Hazelita Dwi Rahmi, Salsabila Dwi Ramadhani, Dini Ramdani, Indri Riana Riskinandini Riska Yulianti, Riska Rizki, Akbar Rizki, Anwar Fajar Setyowati, Silfiana Lis Siregar, Indra Rivaldi Steven Kurniawan Suci Pujiani Prahesti Suwarso, Dhiya Khalishah Tsany Syam, Ummul Auliyah Tendi Ferdian Diputra Tias Amalia Safitri Tsabitah, Dhiya Tsabitah, Dhiya Ulayya Ulfia, Ratu Risha Utami Dyah Syafitri Wahyudina, Salsa Putri Wiwiek Poedjiastoeti, Wiwiek Wiwik Andriyani Lestari Ningsih Wiwik Andriyani Lestari Ningsih Yanuari, Eka Dicky Darmawan Yully Sofyah Waode Zulhijrah, Zulhijrah