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All Journal JURNAL ILMIAH MATEMATIKA DAN TERAPAN BAREKENG: Jurnal Ilmu Matematika dan Terapan Mathvision : Jurnal Matematika Parameter: Journal of Statistics Jurnal Matematika Integratif Jurnal Pengabdian Farmasi dan Sains
Desy Lusiyanti
Universitas Tadulako
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Immunology & microbiology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Transportation

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Evaluasi Kinerja Algoritma Machine learning pada Dataset Skala Besar Lusiyanti, Desy; Musdalifah, Selvy; Sahari, Agusman; Fajri, Iman Al
MathVisioN Vol 7 No 1 (2025): Maret 2025
Publisher : Prodi Matematika FMIPA Unirow Tuban

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55719/mv.v7i1.1661

Abstract

Di era digital, volume data yang dihasilkan terus meningkat secara eksponensial, menuntut pengembangan metode analisis yang lebih efisien dan akurat. Machine Learning (ML) telah menjadi pendekatan utama dalam pengolahan dataset skala besar, termasuk dalam analisis kualitas air. Penelitian ini bertujuan untuk mengevaluasi performa beberapa algoritma ML dalam mengklasifikasikan kualitas air berdasarkan dataset berskala besar yang diperoleh dari sumber daring. Model yang diuji mencakup Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), XGBoost (XGB), K-Nearest Neighbors (KNN), dan Support Vector Machine (SVM). Metodologi yang diterapkan mencakup preprocessing data dengan imputasi median untuk menangani missing values serta K-Fold Cross Validation (k=10) untuk memastikan hasil yang lebih generalizable. Evaluasi model dilakukan berdasarkan metrik akurasi, precision, recall, F1-score, dan waktu komputasi. Hasil penelitian menunjukkan bahwa XGBoost memiliki performa terbaik dengan akurasi 67%, diikuti oleh Decision Tree, KNN, dan SVM dengan akurasi 65%, sedangkan Logistic Regression dan Random Forest memiliki akurasi 63%. Temuan ini mengonfirmasi bahwa model berbasis gradient boosting seperti XGBoost lebih unggul dalam menangani kompleksitas dataset skala besar dibandingkan model berbasis regresi atau pohon keputusan tunggal. Penelitian ini berkontribusi dalam memberikan rekomendasi bagi akademisi dan praktisi dalam memilih algoritma ML yang paling efisien dan optimal untuk analisis kualitas air. Rekomendasi untuk penelitian selanjutnya mencakup eksplorasi optimasi hyperparameter, balancing dataset, serta pengujian dengan dataset real-time untuk validasi lebih lanjut.
COMPARATIVE ANALYSIS OF MACHINE LEARNING METHODS IN CLASSIFYING THE QUALITY OF PALU SHALLOTS Lusiyanti, Desy; Musdalifah, Selvy; Sahari, Agusman; Fajri, Iman Al
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1853-1864

Abstract

This study conducts a comparative analysis of various machine learning methods for classifying the quality of Palu shallots based on the Indonesian National Standard (SNI). The dataset consists of 1,500 samples of Palu shallots, each characterized by 10 key features, including size, color, texture, and moisture content. Five machine learning models—Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine (SVM), and Logistic Regression—were evaluated using accuracy, precision, recall, and F1 score as performance metrics. The results indicate that Random Forest achieved the best performance with an accuracy of 95.4%, followed by Decision Tree (90.7%) and SVM (90.2%). Random Forest also excelled in precision (93.6%) and F1 Score (93.5%), making it the most reliable model for shallot quality classification. Meanwhile, SVM demonstrated a good balance between recall and precision, making it a strong alternative. Implementing machine learning models has the potential to enhance the efficiency and accuracy of agricultural product quality assurance. The findings of this study provide valuable insights for farmers, agribusiness practitioners, and researchers adopting artificial intelligence technology for more precise and efficient agricultural quality assessment.
Forecasting Of Crude Palm Oil By Using Fuzzy Time Series Method (Study Case : PT. Buana Mudantara Plantation) Rasna; Sudarsana, I Wayan; Lusiyanti, Desy
Parameter: Journal of Statistics Vol. 1 No. 1 (2021)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (765.159 KB) | DOI: 10.22487/27765660.2021.v1.i1.15442

Abstract

PT. Buana Mudantara is a company engaged in palm oil production. The production of oil palm at this company varies every period, so the problem that often occurs is insufficient supply and demand. Therefore, it is necessary to forecast future oil palm production. The method used in this research is the Fuzzy Time Series method which has advantages, among others, that the calculation process does not require a complicated system, so it is easier to develop and can solve the problem of forecasting historical data in the form of linguistic values. This method provides a level of accuracy calculated using the MAPE (Mean Absolute Percentage Error) of . The results show that the forecasting of the amount of oil palm production in November 2019 - March 2020 is respectively ton, ton, ton, tons and tons
Co-Authors Agusman Sahari Andri Andri Anwar, C Ayu Lestari Darmawanti, Y Fajri, M Gunawan, I I Wayan Sudarsana Iman Al Fajri Iman Al Fajri Jafaar, I M Jaya, A I Khairiah, J M Rifai Mohammad Fajri Muhammad Alkifar Masdin Musdalifah, S Mustakim, J R Nasria Nacong Natalia, Y Nur' eni Nur'Eni Oganis, C Oktavia, S Ondja, T N Peole, I N Prasetyo, W F Pratiwi, A M Rasna Rina Ratianingsih Rivaldi, Rivaldi Safaat, M Safir, M Sahari, A Selvy Musdalifah Sudarsana, I W Sutrisno, D Tandi, Andri Tuadi, A A Turilah, N H Usman, R Wardani, N M E