Article Per Year (5 Year)
p-Index From 2021 - 2026
0.778
P-Index
This Author published in this journals
All Journal ILKOM Jurnal Ilmiah International Journal of Informatics Engineering and Computing TheraEdu: Journal of Therapy and Educational Psychology
Anjani, Sarah
Unknown Affiliation
Computer Science & IT Education Social Sciences Other

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Prediction and Analysis of Rice Production and Yields Using Ensemble Learning Techniques Sulistya, Yudha Islami; Musdholifah, Aina; Sapuletea, Chrissandy; Br Bangun, Elsi Titasari; Hamda, Hizbullah; Anjani, Sarah; Septiadi, Abednego Dwi
ILKOM Jurnal Ilmiah Vol 16, No 2 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i2.1948.115-124

Abstract

This research focuses on predicting and analyzing rice production and yield throughout the world using ensemble learning techniques. The study applies and compares three methods: linear regression, ARIMA, and ensemble learning, to predict rice harvest yields. The results show that ensemble learning techniques significantly improve prediction performance. For instance, the ensemble model for predicting area harvested, combining Model 6 (linear regression) and Model 10 (ARIMA), achieved  of coefficient of determination outperforming the individual models. Similarly, for predicting yield, the ensemble model combining Model 4 (linear regression) and Model 9 (ARIMA) achieved  of coefficient of determination indicating superior prediction accuracy. For predicting production, the ensemble model combining Model 2 (linear regression) and Model 8 (ARIMA) achieved  of coefficient of determination. These results demonstrate the effectiveness of ensemble learning in enhancing prediction accuracy with lower MSE and RMSE values. By analyzing various factors influencing rice yields, this research provides valuable insights for increasing rice production and yield, supporting efforts to improve the efficiency and effectiveness of rice farming, and contributing to achieving the United Nations Sustainable Development Goals (SDGs).
Detecting Acute Liver Diseases Using CNN Algorithm Anjani, Sarah; Maria Yohana Jawa Betan
International Journal of Informatics Engineering and Computing Vol. 1 No. 2 (2024): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v1.i2.45

Abstract

This study tackles the critical challenge of detecting Acute Liver Failure (ALF) using machine learning algorithms. The main goal is to assess the effectiveness of several algorithms, including Convolutional Neural Network (CNN), Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting, in accurately classifying cases of ALF. For this purpose, a comprehensive dataset with 8,785 records and 30 features from Kaggle is utilized, involving thorough preprocessing steps like feature selection, data cleaning, and normalization. The research emphasizes achieving high precision in ALF detection. Results show that CNN outperforms other algorithms, achieving a precision score of 1.00 for identifying ALF cases, demonstrating its high reliability. This study highlights the importance of algorithm selection in complex medical diagnoses, showcasing the potential of deep learning methods in healthcare and paving the way for more accurate and timely ALF detection to improve patient outcomes.
Kesehatan Mental Dalam Konteks Tekanan Ekonomi: Pendekatan Studi Kasus Leonida, Syifa; Anjani, Sarah; Sugara, Hendry
TheraEdu: Journal of Therapy and Educational Psychology Vol. 1 No. 1 (2025): TheraEdu: Journal of Therapy and Educational Psychology
Publisher : Asosiasi Asesmen Pendidikan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63203/021817700

Abstract

Penelitian ini mengeksplorasi pengaruh tekanan ekonomi terhadap kesehatan mental anak muda, yang merupakan masalah penting karena masa remaja merupakan periode perkembangan dengan banyak perubahan. Tujuan dari studi ini adalah untuk mengkaji keterkaitan antara situasi ekonomi keluarga dan kesehatan mental remaja, serta menemukan faktor-faktor yang dapat melindungi. Dengan menggunakan pendekatan kualitatif berbasis studi kasus, data diperoleh melalui wawancara dengan dua informan dari MTs Hidayatut Tholibin, masing-masing ditanya dengan dua belas pertanyaan utama mengenai tekanan ekonomi dan kesehatan mental. Temuan menunjukkan bahwa tekanan ekonomi dapat menyebabkan kelelahan emosional, kecemasan, dan penurunan semangat belajar pada remaja. Strategi koping yang efektif, seperti mencari sumber penghasilan dan menabung, serta dukungan sosial dan spiritual, sangat diperlukan. Namun, terdapat pula strategi koping yang tidak efektif, seperti melarikan diri dari masalah atau menangis secara diam-diam. Kesimpulan studi ini menyatakan bahwa masalah ekonomi menghalangi pemenuhan kebutuhan dasar remaja, yang selanjutnya memengaruhi kebutuhan sosial, penghargaan, dan aktualisasi diri menurut Teori Hierarki Kebutuhan Maslow.
Weather Forecasting Using Stacked-LSTM Hidayatulloh, M. Riyan; Anjani, Sarah
International Journal of Informatics Engineering and Computing Vol. 2 No. 2 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/408j8q02

Abstract

This study proposes a Stacked Long Short-Term Memory (Stacked LSTM) model for multivariate weather forecasting using historical meteorological data from Denpasar City. The dataset consists of 264,924 records collected between 1990 and 2020, including four key weather variables: temperature, humidity, pressure, and wind speed. The model is designed to capture temporal dependencies in time-series weather data through multiple LSTM layers. A sliding window technique is used to construct input sequences, and the model is trained for 50 epochs with a batch size of 64, incorporating dropout regularization to improve generalization. The dataset is divided using a train–test split, where 20% of the data is reserved for performance evaluation. Experimental results demonstrate that the proposed model achieves strong predictive performance across all weather variables. The evaluation on the test dataset yields an average Mean Absolute Error (MAE) of 1.08, Mean Absolute Percentage Error (MAPE) of 10.22%, Root Mean Squared Error (RMSE) of 1.93, and a Coefficient of Determination (R²) of 0.86. Among the predicted variables, humidity and temperature show the highest accuracy with R² values of 0.9537 and 0.9031, respectively. The findings indicate that the Stacked LSTM architecture successfully captures both short-term and long-term temporal relationships within multivariate weather datasets. The proposed approach demonstrates strong potential for improving automated weather forecasting systems, particularly in tropical urban environments characterized by complex climatic dynamics. Future work may focus on integrating real-time weather data sources and adaptive retraining mechanisms to further enhance prediction accuracy and operational applicability.
Co-Authors Abednego Dwi Septiadi Aina Musdholifah Br Bangun, Elsi Titasari Hamda, Hizbullah Hendry Sugara Hidayatulloh, M. Riyan Leonida, Syifa Maria Yohana Jawa Betan Sapuletea, Chrissandy Yudha Islami Sulistya