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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 JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
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

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Latent Household Food Security in Raja Ampat Marine Protected Areas: A Binary CFA Approach Anggriyani, Indah Ratih; Sumertajaya, I Made; Notodiputro, Khairil Anwar; Angraini, Yenni
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.40979

Abstract

This study examines household food security in four marine protected areas in Raja Ampat using repeated cross-sectional household survey data. Data were collected between 2010 to 2024, grouped into five monitoring periods. This study aims to provide a measurement framework for household food security as a latent construct based on binary indicators representing dimensions of food access and to estimate latent household food security scores in the four analyzed areas. In addition to applying confirmatory factor analysis to new empirical data, this study also presents a systematic estimation framework for measuring the latent construct using binary household indicators in repeated cross-sectoral survey data. The framework includes indicator threshold estimation, tetrachoric correlation estimation, parameter estimation using the robust diagonally weighted least squares method, and derivation of latent scores based on posterior expectations using the Gauss–Hermite quadrature approach. The analysis results indicate that the one-factor model provides acceptable fit and adequate construct reliability across the analyzed area-period groups. Estimates of factor loadings and thresholds provide information on the relative contribution and severity of each indicator in representing variations in household food access conditions. Overall, the goodness-of-fit indices indicate that the one-factor structure provides a reasonable representation of the relationships among the observed indicators under the fitted measurement model.
Penerapan Pemodelan Konvensional dan Deep Learning pada Data Saham dengan Pencilan Maulana, Muhammad Firlan; Fayiza, Salsabila; Suhaeri, Bulan Cahyani; Febyan, Ardelia Rahma; Hambali, Thariq; Angraini, Yenni; Nurhambali, Muhammad Rizky
Jurnal Teknik Informatika dan Sistem Informasi Vol 12 No 1 (2026): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v12i1.10587

Abstract

Apple Inc. stock (AAPL), one of the leading technology companies, is one of the concerns of investors as it continues to see an increase in the number of users every year. Therefore, forecasting Apple's stock price is important to help investors mitigate risks and optimize investment decisions. This forecasting can be done using two main approaches, namely conventional approaches such as Autoregressive Integrated Moving Average (ARIMA) and deep learning-based approaches such as Long Short-term Memory Network (LSTM). This study aims to find the best model using both methods, as well as compare the accuracy of the models based on datasets with outliers and datasets with handled outliers. The dataset analyzed in this study comes from weekly AAPL stock closing price data for 500 periods, from January 26, 2015 to August 19, 2024 obtained from Yahoo Finance. This study obtained the ARIMA(1,1,1) model as the best model for both datasets, with the outlier-handled dataset producing better test MAPE, while the dataset with outliers had better training MAPE. The LSTM method produced smaller MAPE values than ARIMA, demonstrating its superiority in capturing the fluctuating patterns of the AAPL stock data. Outlier handling was shown to improve model accuracy, as seen in the outlier-handled dataset. This research provides insight into the effectiveness of statistical and deep learning methods in modeling stock prices, and emphasizes the importance of outlier handling in financial data analysis.
Technical Analysis of the Indonesian Stock Market with Gated Recurrent Unit and Temporal Convolutional Network Siti Aisyah; Yenni Angraini; Kusman Sadik; Bagus Sartono; Gerry Alfa Dito
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.23464

Abstract

Big data is essential in the age of 4.0 industry as it becomes the basis of decision making. Deep learning research in the last few years has been proven effective in understanding complex big data patterns, especially in the finance sector. The rapid growth of the Indonesian stock market in the last 20 years, which was driven by globalization, prompted fluctuation in the Bursa Efek Jakarta (JKSE) which was influenced by stock prices, commodity prices, and exchange rate. This study identifies the main indicators of Indonesian stock market crisis, applies and compares deep learning models, particularly Gated Recurrent Unit (GRU) and Temporal Convolutional Network (TCN), in predicting stock prices. This study identified 20 JKSE crisis points between the 2002-2023 period with average return value at around -6%. All variables correlated positively with JKSE, with SET.BK as the highest correlated variable in lag 0. The American and European stock market, commodity price, and exchange rate tend to show a pattern opposite to the JKSE crisis. Predictor variables such as STI, HIS, KLSE, KS11, SET.BK, PSEI.PS, RUT, and USDIDR are chosen based on significant cross correlation and average return plot. Hyperparameter tuning and cross validation within a 3 years window concluded that the GRU model is accurate and efficient, with RMSE value at 43.35568 and MAE value at 33.66909 in the validation data.
Performance Evaluation of ARIMA and GRU Models for Forecasting Chili Price in East Jawa Windi Pangesti; Nabila Syukri; Khairil Anwar Notodiputro; Yenni Angraini; Laily Nissa Atul Mualifah
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.26445

Abstract

Time series forecasting plays a crucial role in predicting future conditions based on historical data, particularly in the food sector, which is highly susceptible to price fluctuations. This study compares two approaches: the conventional ARIMA method and the deep learning method GRU, to forecast the price of red chillies in East Java. East Java was chosen because it is the largest national producer of chilies, thus the stability of its prices has a broad impact. The research results indicate that the GRU model outperforms the ARIMA model with a MAPE value of 19.80% compared to a MAPE of 27.63% for the latter. The benefit of this research is to contribute to the literature on developing agricultural commodity price forecasting models as a basis for enhancing food security policies and stabilizing commodity prices, particularly in East Java Province, Indonesia
Kajian Simulasi untuk Identifikasi Faktor yang Memengaruhi Kinerja LSTM dan XGBoost untuk Deteksi Anomali pada Data Deret Waktu yang Dilabelkan Muhammad Rizky Nurhambali; Yenni Angraini; Anwar Fitrianto
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.26604

Abstract

Time series analysis has evolved to include forecasting and anomaly detection, which can be applied in various fields. Machine learning methods, such as long short-term memory (LSTM) and extreme gradient boosting (XGBoost), are widely developed because they are considered superior to conventional methods. Both use a forecasting approach for anomaly detection. However, the limitations of both methods on anomalies, such as data length, labeling method, and number of anomalies have not been explored. Therefore, this study aims to identify factors that affect the performance of LSTM and XGBoost in forecasting and anomaly detection through various scenarios and compare their metrics evaluation. The study utilizes Jakarta's air quality index data for 2018–2023, which was preprocessed and augmented for simulation purposes. The study shows that the LSTM method is superior to XGBoost, as shown by the lower MAPE (14.7024%), lower RMSE (13.9909), and higher balanced accuracy (0.9935). These results are reinforced by the significant Mann-Whitney test between the two methods, indicating a difference in the method's accuracy. In addition, the Kruskal-Wallis test for each combination of method and treatment showed significant results. These results indicate that data length, labeling method, and number of anomalies affect the method's accuracy
Comparison of The SARIMA Model and Intervention in Forecasting The Number of Domestic Passengers at Soekarno-Hatta International Airport Iswari, Anistia; Angraini, Yenni; Masjkur, Mohammad
Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i1p132-146

Abstract

The Covid-19 pandemic has had a massive effect on the air transportation sector. Soekarno-Hatta International Airport (Soetta) skilled a lower variety of passengers because of the Covid-19 pandemic, even though Soetta Airport persisted to perform normally. Forecasting the number of passengers needs to be done by the airport to decide the proper policy. Therefore, the airport wishes to estimate the range of passengers to determine the right coverage and prepare the facilities provided if there may be a boom withinside the range of passengers throughout the Covid-19 pandemic. Forecasting the number of domestic passengers at Soetta Airport on this examination makes use of the SARIMA model and intervention. This examination compares the SARIMA model and the intervention in forecasting the number of domestic passengers at Soetta Airport. The effects confirmed that the best SARIMA model became ARIMA ARIMA(0,1,0)(1,0,0)12 with MAPE and RMSE of 55,18% and 588887.4, respectively. The best intervention model  became ARIMA0,1,1) (1,0,0)12 b = 0, s = 5, r = 1  with MAPE of 35,25% and RMSE of 238563,4. The MAPE and RMSE values acquired suggest that the intervention model is better than the SARIMA model in forecasting the number of domestic passengers at Soetta Airport throughout the Covid-19 pandemic.
Machine learning approaches for anomaly detection of Jakarta air quality index Muhammad Rizky Nurhambali; Yenni Angraini; Anwar Fitrianto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2543-2553

Abstract

Anomalies in time series data are observations that deviate markedly from surrounding values or overall patterns. Air quality index (AQI) data, which vary over time, provide a suitable context for anomaly detection. Time series anomaly detection can be done with machine learning approaches like long short-term memory (LSTM) and extreme gradient boosting (XGBoost). These methods have advantages over conventional methods in handling nonlinearity and large data dimensions. This study compares LSTM and XGBoost methods for detecting anomalies in Jakarta's hourly AQI data. The dataset was obtained from the AirNow website and covers the period from January 1, 2018, to December 31, 2023. Anomalies in the observed data were labeled using moving range (MR) (2) and (3) approaches with three and four-sigma thresholds, and feature engineering (FE) was applied to improve model performance. The results indicate that LSTM is more suitable than XGBoost for forecasting and classification tasks in AQI data. LSTM achieved an average mean absolute percentage error (MAPE) of 10.3840%, a root mean square error (RMSE) of 10.5913, and a balanced accuracy (BACC) of 0.9424 under MR (2) labeling with the four-sigma rule. The anomalies detected mostly occurred between 21:00 and 09:00 and during the rainy season.
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 Anggriyani, Indah Ratih 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 Fayiza, Salsabila Febyan, Ardelia Rahma Fira Nurahmah Al Aminy Fitri, Zafira Ilma Fitrianti, Dwi Fitrianto, Anwar Gerry Alfa Dito Ghiffary, Ghardapaty Ghaly Gunawan, Windi Hakim, Bashir Ammar Hambali, Thariq Hari Wijayanto Hasanah, Mauizatun Hilali Moh’d, Fatma I Made Sumertajaya Ilma, Meisyatul Ilmani, Erdanisa Aghnia Indahwati Isnaini, Mardatunnisa Iswari, Anistia Itasia Dina Sulvianti Jamaluddin Rabbani Harahap Kenia Maulidia Kurnadipare, Aleytha Ilahnugrah Kusman Sadik Laily Nissa Atul Mualifah Lia Ratih Kusuma Dewi Magfirrah, Indah Maghfiroh, Firda Aulia Mahesa Ahmad Rahmawan Mahesa, Hakim Zoelva Maulana, Muhammad Firlan Maulidiyah, Wildatul Moh'd, Fatma Hilali Mohammad Abror Gustiansyah Mohammad Masjkur Mualifah, Laily Nissa Atul Mualifah, Laily Nissa Atul  Muhammad Rizky Nurhambali MY, Hadyanti Utami Nabila Ghoni Trisno Hidayatulloh Nabila Ghoni Trisno Hidayatulloh Nabila Syukri 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 Siti Aisyah Steven Kurniawan Suci Pujiani Prahesti Suhaeri, Bulan Cahyani 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 Windi Pangesti Wiwiek Poedjiastoeti, Wiwiek Wiwik Andriyani Lestari Ningsih Wiwik Andriyani Lestari Ningsih Yanuari, Eka Dicky Darmawan Yully Sofyah Waode Zulhijrah, Zulhijrah