Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal JURNAL ILMIAH INFOKOM
Dwi Robiul R
Universitas Potensi Utama
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
EXPLAINABLE MACHINE LEARNING UNTUK PREDIKSI HARGA MOBIL BEKAS DAN ANALISIS FAKTOR PENENTU HARGA Dwi Robiul R; M. Al-Adib; Romi Antoni; Diyo Mollana F; Rahmad S; Fauzan Hamdi R; Adil Setiawan
INFOKOM (Informatika & Komputer) Vol 13 No 1 (2025): JURNAL INFOKOM JUNI 2025
Publisher : POLITEKNIK PIKSI GANESHA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56689/infokom.v13i1.2322

Abstract

This research aims to predict used car prices and analyze the price determinants using an Explainable Machine Learning (XAI) approach. Used car price prediction presents a significant challenge in the automotive market, where pricing is influenced by various complex variables. The methodology involves comparing the performance of two machine learning models: linear regression (LR) and random forest (RF), trained on a dataset comprising 2,059 used car data points and 19 engineered features. The best-performing model is then interpreted using the SHAP (SHapley Additive exPlanations) method to identify the contribution of each feature. The evaluation results demonstrate that the Random Forest (RF) model exhibits superior performance compared to the Linear Regression model. The Random Forest model achieved a coefficient of determination (R2) of 0.819 and a Mean Absolute Error (MAE) of 294,591.0 . This performance is significantly better than the linear regression model, which yielded an R2 of 0.771 and an MAE of 716,221.3. The SHAP interpretive analysis identified the most significant price determinants. In sequential order, the five most dominant factors influencing price prediction are max power, car age, vehicle length (length_num), vehicle width (width_num), and kilometer (mileage). This finding provides transparent and justifiable insights into the key variables underlying price fluctuations in the used car market.
OPTIMASI PIPELINE KLASIFIKASI PENYAKIT PADI MENGGUNAKAN STRATEGI AUGMENTASI CITRA INTENSIF DAN TRANSFER LEARNING EFFICIENTNET-B0 Dwi Robiul R; Diyo Mollana F; Nanda S; Johan; Rahmad S; Satriyo W; Rika Rosnelly
INFOKOM (Informatika & Komputer) Vol 13 No 2 (2025): JURNAL INFOKOM DESEMBER 2025
Publisher : POLITEKNIK PIKSI GANESHA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56689/infokom.v13i2.2355

Abstract

Rice plant diseases represent a significant challenge to global food productivity. This study aims to optimize a rice disease classification pipeline using the EfficientNet-B0 architecture combined with intensive image augmentation strategies and transfer learning. The dataset comprises 10,407 rice leaf images categorized into 10 classes, including healthy conditions and nine types of diseases. Augmentation strategies such as random rotation, color jittering, and random resized cropping were implemented to enhance model robustness against diverse field conditions. Evaluation results demonstrate that the model achieved outstanding performance, with a Top-1 Accuracy of 96.25% and a Top-5 Accuracy of 99.90%. Grad-CAM++ analysis validated that the model accurately focuses feature extraction on pathological leaf areas. t-SNE visualization revealed clear feature cluster separation between classes, further supported by ROC curve AUC values reaching 1.00 for the majority of categories. This research confirms that the proposed pipeline is highly reliable for early rice disease detection and holds significant potential for mobile device implementation to assist farmers.
Co-Authors Diyo Mollana F Fauzan Hamdi R Johan M. Al-Adib Nanda S Rahmad S Rika Rosnelly Romi Antoni Satriyo W Setiawan, Adil