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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Ilmiah Wahana Pendidikan Multica Science and Technology Jurnal Malikussaleh Mengabdi Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Zahratul Fitri
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Journal : Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)

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Implementation of the Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) Algorithm for Rice Price Prediction Ezra Sasqia Syahna; Zara Yunizar; Zahratul Fitri
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstrak Studi ini mengimplementasikan model Seasonal Autoregressive Integrated Moving Average with Exogenous variables (SARIMAX) untuk memprediksi harga beras ( gabah ) berdasarkan data historis dari tahun 2020 hingga 2024. Dengan memanfaatkan data yang diperoleh dari Investing.com, penelitian ini mengintegrasikan variabel eksternal utama seperti suhu, harga pupuk, dan tingkat produksi untuk meningkatkan akurasi prediksi. Metodologi ini terdiri dari langkah-langkah sistematis, termasuk pengumpulan data, pemrosesan, dan evaluasi model, dengan menggunakan metrik seperti Mean Squared Error (MSE), Root Mean Squared Error (RMSE), dan Mean Absolute Percentage Error (MAPE) untuk menilai kinerja. Temuan tersebut mengungkapkan korelasi yang kuat antara harga pasar yang diprediksi dan aktual, khususnya dalam kategori harga penutupan, yang mencapai MAPE sebesar 1,354%. Metrik evaluasi selanjutnya mengonfirmasi kekokohan model, dengan harga penutupan menunjukkan MSE terendah sebesar 299.629,64 dan RMSE sebesar 547,38. Meskipun kategori harga tertinggi menunjukkan MAPE yang sedikit lebih tinggi, yaitu 2,007%, semua kategori tetap berada di bawah ambang batas yang dapat diterima, yaitu 2%, yang menunjukkan akurasi prediksi yang memuaskan. Sebagai kesimpulan, model SARIMAX menunjukkan efektivitas yang signifikan dalam peramalan harga beras, yang memberikan wawasan berharga bagi para pemangku kepentingan di pasar pertanian. Implementasi dalam aplikasi web memfasilitasi prediksi secara real-time, yang mendukung pengambilan keputusan yang tepat, dan meningkatkan strategi pasar. Kata kunci : SARIMAX; harga beras; model prediksi; MAPE; pasar pertanian; analisis deret waktu.
Implementation Of The Adaboost Method On Linear Kernel Svm For Classifying Pip Assistance Recipients At SMP Negeri 2 Kejuruan Muda Muhammad Fahri Al Fikri; Asrianda Asrianda; Zahratul Fitri
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract: This study examines the application of the AdaBoost algorithm to a Linear Kernel Support Vector Machine (SVM) for determining student eligibility for the Indonesian Smart Program (PIP) at SMP N 2 Kejuruan Muda. The main objective is to improve the accuracy and fairness of the PIP aid distribution using advanced machine learning techniques. The dataset used comprises 500 student records, which include demographic, academic, and economic factors. The dataset was divided into training and testing sets, with the AdaBoost algorithm applied to enhance the SVM model’s performance. The study found that the SVM model optimized with AdaBoost was able to classify 91 students as eligible for PIP aid, achieving an impressive accuracy rate of 97.85%. Only 2 students were classified as ineligible, representing 2.15% of the total sample. When compared to the standard SVM model, which also classified 91 students as eligible, the key advantage of AdaBoost lies in its ability to handle borderline data more effectively. AdaBoost improves the classification of students whose eligibility was less clear by reinforcing the importance of difficult-to-classify instances. The model’s higher precision on edge cases indicates that AdaBoost offers a significant improvement over traditional SVM models in handling complex classification tasks. This research concludes that incorporating AdaBoost into SVM models provides a more robust and accurate method for determining student eligibility for government aid programs such as PIP. Keywords: AdaBoost, SVM, Indonesian Smart Program, PIP aid, machine learning, student eligibility, classification.
Algorithm Implementation C4.5 For Classification Food Menu to Prevent Stunting in Children Rizki Fadhilah Ramadhani; Bustami; Zahratul Fitri
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Stunting in childhood is one of the most significant obstacles to human development and globally affects about 162 million children under five. One effort to prevent stunting is a program to increase the nutritional intake of the community, especially children under five, by providing supplementary food (PMT). Classification is one of the data processing techniques that can be used in this process. The results obtained from the study show that the designed system can input training data and data for classification so that the health centre and guardians can determine the good and bad food menus according to the existing data of toddlers. Based on the results of testing with training data and testing data with a ratio of 80:20 from a dataset of 200 data, namely 160 training data, and 40 test data using the C4.5 algorithm obtained in dataset 1 obtained an accuracy value of 82,5%, precision value of 0.96, recall value of 0,8 and F1-score of 0,87273, then in dataset 2 obtained an accuracy value of 72,5%, precision value 0,75, recall value 0,84 and F1-score value 0,79245.
Co-Authors Arief Rahman Arief Rahman Asrianda Asrianda Bambang Suhendra Bustami Eva Darnila Ezra Sasqia Syahna Indradewa, Rhian Irma Yurni Mellyzar, Mellyzar Muhammad Fahri Al Fikri Muhammad Fikry Munawwar Khalil, Munawwar Mustaqim Mutammimul Ula Nunsina, Nunsina Riska Imanda Rizki Fadhilah Ramadhani Silfa Maharani Br Padang Sirry Alvina Zara Yunizar