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All Journal ILKOM Jurnal Ilmiah JPM: JURNAL PENGABDIAN MASYARAKAT Journal of Software Engineering and Information System (SEIS)
Br Bangun, Elsi Titasari
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Aerospace Engineering Humanities Astronomy Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Medicine & Pharmacology Public Health Social Sciences Transportation Other

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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).
OPTIMISASI ALGORITMA K-MEANS DENGAN METODE REDUKSI DIMENSI UNTUK PENGELOMPOKAN BIG DATA DALAM ARSITEKTUR CLOUD COMPUTING Putra, Bayu Anugerah; Mukhtar, Harun; Br Bangun, Elsi Titasari; Gusnanda, Alris; Maisyarah, Adila; Kurniawan, Muhammad Irgi; Pradipa, Raditya; Ali, Zurrahman Muhammad
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 1 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i1.7616

Abstract

In the era of big data, data clustering becomes a major challenge due to the complexity and huge volume of data. The K-means algorithm is one of the clustering techniques that is often used due to its simplicity. However, K-means faces difficulties in handling high-dimensional and large-volume data. This study proposes an optimization of the K-means algorithm using the Principal Component Analysis (PCA) dimensionality reduction method to improve the efficiency and accuracy of big data clustering in cloud computing architecture. The KDD Cup 1999 dataset is used to test this method. The dataset undergoes pre-processing and dimensionality reduction using PCA, then K-means clustering is applied. The clustering results are evaluated using the Silhouette Score and Davies-Bouldin Index. The implementation is carried out in the Google Colab environment to utilize cloud computing resources. The results show that dimensionality reduction using PCA significantly reduces computational complexity and improves clustering quality. This method is effective in clustering big data, making it an efficient solution for data clustering in cloud computing architecture.
Pemanfaatan Kahoot Sebagai Media Pembelajaran Interaktif Kepada Guru Sekolah Dasar Setiawan Ardi Wijaya; Winarso, Doni; Arribe, Edo; Hafsari, Rizka; Syahril; Aryanto; Diansyah, Risnal; Mulyana, Wide; BR Bangun, Elsi Titasari
JPM: Jurnal Pengabdian Masyarakat Vol. 5 No. 3 (2025): Januari 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jpm.v5i3.2273

Abstract

In the rapidly evolving digital era, education in Indonesia faces new challenges and opportunities to enhance the quality of learning. Therefore, it is crucial for teachers to upgrade their knowledge of digital-based learning methods. One solution offered is the use of interactive learning methods. In this community service project, the interactive learning medium to be explored is Kahoot. This activity collaborates with SDN 011 Lenggadai Hilir and is scheduled to take place on Wednesday, August 14, 2024. The workshop aims to improve the understanding and skills of the teachers at SDN 011 Lenggadai Hilir in utilizing Kahoot as an interactive learning medium. Kahoot is a quiz-based platform capable of creating a fun and engaging learning atmosphere for students. The research method employed in this activity is Participatory Action Research (PAR), chosen because it involves active collaboration between researchers and participants to address real-world problems and create relevant solutions. This method consists of several stages: preparation, pre-test, training, post-test, and evaluation. A pre-test is conducted to measure participants' initial knowledge of using Kahoot, followed by training that includes material presentations and hands-on practice with Kahoot. A post-test is then conducted to assess the improvement in knowledge after the training. The results of the workshop showed a significant improvement in participants' understanding. The average post-test scores were higher than the pre-test scores, indicating an increase in participants' skills in using Kahoot. The workshop also received positive feedback from participants, who felt that Kahoot could enhance student engagement in learning. This training successfully enhanced the teachers' ability to use Kahoot and motivated them to actively incorporate technology into their teaching activities.
KLASIFIKASI MAKANAN BERDASARKAN NILAI GIZI MENGGUNAKAN ALGORITMA RANDOM FOREST DAN TEKNIK SMOTE Br Bangun, Elsi Titasari; Bayu Anugerah Putra; Aryanto
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 2 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i2.9725

Abstract

Classifying food based on nutritional content is essential for developing personalized dietary recommendation systems and promoting healthier eating habits. This study aims to construct a food classification model using the Random Forest algorithm combined with the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance in the dataset. The dataset includes various nutritional attributes such as calories, protein, fat, carbohydrates, fiber, sugar, sodium, and cholesterol, along with additional information such as food category and meal time. After preprocessing, the data were split into training and testing sets, with SMOTE applied to the training data to improve class representation. The model was trained using Random Forest and evaluated using accuracy, precision, recall, and F1-score. The results show that the model achieved an accuracy of 83.35% and an average F1-score above 0.80, with the best performance observed in majority classes. The confusion matrix analysis indicates that most predictions were accurate, although misclassifications occurred among classes with overlapping nutritional values. Protein, calories, and carbohydrates were identified as the most influential features in the classification process. These results show that combining Random Forest and SMOTE works well for creating food classification systems using nutritional data and could be useful in apps for diet recommendations and managing nutrition.
Co-Authors Abednego Dwi Septiadi Aina Musdholifah Ali, Zurrahman Muhammad Anjani, Sarah Aryanto Bayu Anugerah Putra Doni Winarso, Doni Edo Arribe, Edo Gusnanda, Alris Hafsari, Rizka Hamda, Hizbullah Harun Mukhtar Kurniawan, Muhammad Irgi Maisyarah, Adila Pradipa, Raditya Putra, Bayu Anugerah Risnal Diansyah, Risnal Sapuletea, Chrissandy Syahril Wide Mulyana Wijaya, Setiawan Ardi Yudha Islami Sulistya