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All Journal TEKNIK INFORMATIKA JOIN (Jurnal Online Informatika) Jurnal Ilmiah KOMPUTASI EDUMATIC: Jurnal Pendidikan Informatika SENTRI: Jurnal Riset Ilmiah Intellect : Indonesian Journal of Learning and Technological Innovation Coreid Journal INTERNAL (Information System Journal) ISTEK
Cecep Nurul Alam, Cecep Nurul
Teknik Informatika Fakultas Sains dan Teknologi UIN SGD Bandung
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Other

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Klasifikasi Tulisan Tangan Huruf Hijaiyah Anak Usia 6-8 Tahun Menggunakan Metode Support Vector Machine Roofiad, Ahmad Maulidi; Alam, Cecep Nurul; Atmadja, Aldy Rialdy
SENTRI: Jurnal Riset Ilmiah Vol. 4 No. 12 (2025): SENTRI : Jurnal Riset Ilmiah, Desember 2025
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/sentri.v4i12.5077

Abstract

This study aims to develop a handwritten Hijaiyah letter classification system for children aged 6–8 years using the Support Vector Machine (SVM) algorithm. The main problem in elementary education is the difficulty children face in recognizing and writing Hijaiyah letters due to the similarity of their shapes and variations in handwriting. The research process uses the CRISP-DM stages, consisting of problem understanding, data collection and preparation, modeling with SVM (GridSearch for hyperparameter tuning), and evaluation using a confusion matrix and f1-score. The dataset used consists of 2,100 images of handwritten letters from elementary school students. The results show that the SVM model with RBF kernel, C=10, and gamma="scale" achieved the highest accuracy of 83.57%. This study demonstrates that an SVM-based machine learning approach can assist in recognizing Hijaiyah letters, making it a practical solution for teachers in teaching Hijaiyah writing.
Analysis of Critical Factors Influencing Online Motorcycle Taxi Driver's Income Per Transaction Using Random Forest Regressor And Feature Importance Wahana, Agung; Alam, Cecep Nurul
ISTEK Vol. 14 No. 2 (2025)
Publisher : Fakultas Sains dan Teknologi UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/istek.v14i2.2338

Abstract

This study aims to identify and measure the main factors that most significantly affect the Income of Online Motorcycle Taxi Drivers Per Transaction in the gig economy sector. The Machine Learning Random Forest Regressor algorithm was used on driver transaction data. This methodology was chosen for its ability to handle the data's non-linearity and to objectively measure Feature Importance. Traditional linear regression models have limitations in these areas. The main results show the Random Forest model is highly accurate (R2 = 0.9634). It confirms the absolute dominance of distance, which accounts for 94.98% of the total predictive importance of revenue. The Total Transaction Value factor (3.82%) is a secondary predictor. Demographic variables (Age and Gender) and temporal variables (Days and Hours) together had a minimal (less than 1%) influence on fare per trip. This research concludes that the rate per driver transaction is determined almost exclusively by the platform's distance-based pricing policy. It is neutral to the characteristics of the driver. These findings recommend that platforms focus on increasing order volume and optimizing operational costs, rather than modifying base rates.
Co-Authors Agung Wahana Aldy Rialdy Atmadja Andriyan, Acep Razif Chotib, Ahmad Sulhi Firdaus, Imam Iskandar, Ibrahim Juliansyah, Roby Jumadi Maylawati, Dian Sa’adillah Maylawati, Maylawati Mohamad Irfan, Mohamad Muhammad Hamdani, Muhammad Nur Lukman Pamungkas, Arba Adhy Raihan Raihan, Raihan Rio Guntur Utomo Rohmah, Siti Nur Roofiad, Ahmad Maulidi Sa’adillah, Dian Setiawan, Munazir Dzuana Sobri, Abdul Muis Undang Syaripudin, Undang Wildan Budiawan Zulfikar Yana Aditia Gerhana, Yana Aditia