Article Per Year (5 Year)
p-Index From 2020 - 2025
0.983
P-Index
This Author published in this journals
All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI JURNAL ILMIAH MATEMATIKA DAN TERAPAN BAREKENG: Jurnal Ilmu Matematika dan Terapan Mathvision : Jurnal Matematika Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Iman Al Fajri
Hasanuddin University
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Penyelesaian Persamaan Diferensial Biasa Menggunakan Metode Runge-Kutta Orde Keempat Paralel dengan Tiga Prosesor Iman Al Fajri; Hendra Mesra; Jeffry Kusuma
Jurnal Matematika, Statistika dan Komputasi Vol. 17 No. 3 (2021): May, 2021
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v17i3.12490

Abstract

This paper presents a derivation of the Runge-Kutta or fourth method with six stages suitable for parallel implementation. Development of a parallel model based on the sparsity structure of the fourth type Runge-Kutta which is divided into three processors. The calculation of the parallel computation model and the sequential model from the accurate side shows that the sequential model is better. However, generally, the parallel method will end the analytic solution by increasing the number of iterations. In terms of execution time, parallel method has advantages over sequential method.
APLIKASI SISTEM PERSEDIAAN BARANG GUDANG PT. BERKAH PENA ILMU MENGGUNAKAN ANDROID STUDIO DAN METODE RAPID APPLICATION DEVELOPMENT (RAD) Iman Alfajri; NM Faizah; Ryan Rakryan WP
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 1 (2023): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i1.106

Abstract

The purpose of the research is to make it easier for staff to record and report warehouse inventory, online or offline stores with this application where the application is designed so that it can be held in the hand and running, so the admin can monitor if there is stock taking in the warehouse. This inventory data application makes it very easy for staff in terms of appearance, use, and available features so that there is no need for employees to carry inventory paper when stock taking, no need to manually copy data during monthly inventory reports, and easy to see remaining inventory if anyone asks. UML (Unified Modeling Language) is used as an approach for modeling and in the application development process using the Rapid Application Development (RAD) method. The results show that a low level of accuracy in recording inventory can be overcome by using an Android-based barcode that replaces manual recording, providing a solution in the form of convenience in recording warehouse inventory data, online or offline stores, inventory applications with barcodes can be applied to applications based Android makes it easy for admins and users so that the process of recording the availability of goods will be faster and more accurate and this application can run well on various smartphones with the Android operating system.
Analisis Komparasi Algoritma Clustering Berbasis Partisi Untuk Data Numerik Dan Data Kategorikal Lusiyanti, Desy; Fajri, Iman Al; Andri; Fajri, Mohammad
JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 20 No. 2 (2023)
Publisher : Program Studi Matematika, Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/2540766X.2023.v20.i2.16871

Abstract

Dalam prakteknya, tidak selalu semua fitur data bertipe numerik ataupun bertipe kategorik. Perbedaan fitur pada suatu data menjadi permasalahan dalam menentukan metode yang akan digunakan. Salah satu cara yang sering digunakan untuk mengatasi permasalahan tersebut yaitu mengubah salah-satu dari nilai fitur dengan menyesuaikan metode yang akan digunakan. Misalkan dalam analisis cluster, terdapat beberapa algoritma yang sering digunakan diantaranya adalah K-Means dan K-Modes. Kedua metode ini memiliki perbedaan dari fitur yang digunakan. K-Means menggunakan tipe data numerik sedangkan K-Modes menggunakan tipe data kategorik. Dalam penelitian ini dilakukan komprasi antara metode K-Means dan K-Modes untuk mengclusterkan pasien penyakit jantung. Dataset yang digunakan dalam penelitian ini adalah data rekam medis pasien penyakit jantung RSUD Undata palu. Hasil penelitian menunjukkan bahwa dari kedua metode yang dibandingkaan memiliki tingkat akurasi yang baik, yaitu 84.47% (untuk metode K-Means), dan 83.85% (untuk metode K-Modes).
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.
Co-Authors Agusman Sahari Andri Desy Lusiyanti Hendra Mesra Jeffry Kusuma Mohammad Fajri NM Faizah Ryan Rakryan WP Selvy Musdalifah