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Journal of Soft Computing Exploration
Published by shm publisher
ISSN : 27467686     EISSN : 27460991     DOI : https://doi.org/10.52465/joscex
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering
The journal focuses on publishing high-quality, original research and review articles in the field of Soft Computing, Informatics and Computer Science, emphasizing the development, application, and rigorous evaluation of Advanced Computational Methods, Artificial Intelligence (AI), Machine Learning (ML), and Data Science to address complex real-world challenges. The scope of the journal includes, but is not limited to, innovative research in the following areas: 1. Artificial Intelligence and Machine Learning Novel Algorithms and Architectures: Development and comparison of ML/DL models for classification and prediction (including Logistic Regression, Ridge Classifier, SVM, k-NN, and Random Forest). Ensemble Learning: Evaluation and optimization of ensemble methods Balanced Random Forest, SMOTE-RF, SMOTEBoost, and RUSBoost for robust prediction. Data Challenges and Preprocessing: Techniques for mitigating issues like class imbalance (using methods like SMOTE and GAN) and feature extraction/dimension reduction techniques (including Principal Component Analysis (PCA) and Local Binary Pattern (LBP)). 2. Deep Learning and Computer Vision Convolutional Neural Networks (CNNs): Research on CNN architectures (VGG16, ResNet50, DenseNet121, EfficientNet, and MobileNetV2) and the impact of optimization functions (Adam, SGD, NAdam) on model performance. Hybrid and Concatenated Architectures: Proposing and evaluating hybrid models (MobileNetV2 combined with LBP) or concatenated architectures (MobileNetV2 and DenseNet201) to improve classification and feature representation. Image Analysis Tasks: Advanced techniques for image classification (specifically Diabetic Retinopathy), image similarity detection (using Siamese Networks and Test-Time Augmentation), and multi-object segmentation (using FCN with Squeeze-and-Excitation and Attention Mechanisms for palm oil images). 3. Data Science and Advanced Analytics Pattern Detection and Data Mining: Performance evaluation of data mining algorithms, including Biclustering (Cheng & Church and Spectral Biclustering), specifically under challenging structural conditions like collinearity and overlap. Time Series Analysis and Forecasting: Application of advanced decomposition and clustering methods (Ensemble Empirical Mode Decomposition (EEMD) and Time Series Clustering with DTW/ARIMA) for accurate economic or temporal prediction. 4. Applied Informatics (Domain-Specific Applications) Health and Medical Informatics: Classification models for disease diagnosis (including Heart Attack Disease and Diabetic Retinopathy). Agricultural Informatics: Automated detection and classification of plant diseases from leaf/crop images (including Mango Leaf Disease and Chili Plant Disease) and Palm Oil Segmentation. Business and Economic Informatics: Predictive modeling for crucial business metrics (Customer Churn Prediction in Telecommunications) and economic forecasting (Rice Price Forecasting).
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 22 Documents
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Predicting willingness to pay for urban rail transit using machine learning : Evidence from jakarta MRT Wisnu Wardana Kusuma; ADE IRFAN EFENDI EFENDI; Dandun Prakosa; M. Popik Montanasyah Montanasyah; adil wanadi; Yus Rizal
Journal of Soft Computing Exploration Vol. 7 No. 1 (2026): March 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i1.18

Abstract

The development of urban transportation requires an efficient, reliable and sustainable system, so fare determination is an important factor in the success of the Jakarta MRT service. In this context, understanding the user's Willingness to Pay (WTP) is crucial because it is not only influenced by economic ability, but also perception and preference for services. This study aims to analyze and predict the WTP of MRT users by integrating transportation economics approaches and machine learning methods. The research data is in the form of primary data from a survey of 296 MRT users which includes socio-economic characteristics, transportation costs, frequency of use and Ability to Pay (ATP). The methodology used includes descriptive analysis and regression modeling using various algorithms, namely Linear Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Regression (SVR) and XGBoost. Model evaluation was carried out using MAE, RMSE and determination coefficient (R²). The results showed that the value of WTP was relatively homogeneous compared to variations in income and transportation costs, which indicated that willingness to pay was not entirely determined by economic ability. The performance of the model shows that no algorithm is consistently superior, with R² values that tend to be low. The feature importance analysis identified income, transportation costs and ATP as the main factors. This research contributes through the application of a multi-model machine learning framework and policy implications that MRT fare determination needs to consider economic aspects and user preferences in a balanced manner.
Comparative of YOLOv5 and YOLOv8 for rice leaf disease detection on diverse image datasets Muhammad Nandaarjuna Fadhillah; Anindita Septiarini; Hamdani; Rajiansyah; Andi Tejawati
Journal of Soft Computing Exploration Vol. 7 No. 1 (2026): March 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i1.19

Abstract

Rice (Oryza sativa) is Indonesia’s primary food crop, yet its productivity is often threatened by leaf diseases such as Brownspot, Hispa, and Sheath Blight. To address the limitations of manual inspection, this study proposes an automated detection and classification framework based on deep learning, with a comparative evaluation of the YOLOv5 and YOLOv8 models. This study is novel in that it assesses the robustness of models across a variety of data sources, such as a public dataset collected under controlled conditions and a private dataset collected in the field that replicates real-world agricultural contexts. The experimental results suggest that YOLOv8 consistently outperforms YOLOv5 in a variety of evaluation metrics. YOLOv8 performed best on the private dataset, with a precision of 0.907, recall of 0.886, F1-score of 0.896, Intersection over Union (IoU) of 0.71, and mAP50 of 0.924 under the 90:5:5 data split configuration. It shows that it can detect things well even in difficult field conditions. Both models performed about the same on the public dataset; however, YOLOv8 was better at finding objects, as shown by higher mAP50–95 values. Both models also did a great job of classifying; however, YOLOv8 was better at generalising across different dataset distributions. These results demonstrate that YOLOv8, which operates without anchors, is a superior and more dependable method for the real-time detection of rice leaf disease. This study offers pragmatic insights for implementing advanced computer vision models in precision agriculture systems, particularly in resource-constrained, dynamic agricultural environments.
Benchmarking deep transfer learning for imbalanced skin cancer classification: Integrating focal loss, explainable AI, and web deployment Yazid Aufar; Muhammad Daffa Abiyyu Rahman; M. Fadli Ridhani
Journal of Soft Computing Exploration Vol. 7 No. 1 (2026): March 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i1.20

Abstract

Non-melanoma skin cancer (NMSC) classification faces challenges like severe data imbalance and the "black-box" nature of AI, limiting clinical trust. This study benchmarks four pre-trained convolutional models (ConvNeXt-Tiny, EfficientNetV2-S, DenseNet121, MobileNetV3-Large) for the imbalanced multi-class classification of Squamous Cell Carcinoma, Actinic Keratosis, and benign Nevus. Images were preprocessed using morphological hair removal and inpainting. The methodology integrated a 5-fold Stratified Group-KFold cross-validation, Focal Loss to address class imbalance, and Grad-CAM for Explainable AI (XAI) transparency. Results showed ConvNeXt-Tiny achieved the highest and most stable performance with a Balanced Accuracy of 76.98% (± 0.31 standard deviation) and a Macro F1-Score of 0.7513, significantly outperforming the other architectures. Grad-CAM confirmed the model's precise focus on pathological lesion borders. Ultimately, the optimal model was deployed as a real-time Streamlit web application, establishing a robust and practical clinical decision-support system.
Automatic identification system big data‑driven maritime traffic density prediction in surabaya port using PCA and k‑means clustering Afif Zuhri Arfianto; Muhammad Izzul Haj; Muhammad Khoirul Hasin; Noorman Rinanto; Imam Sutrisno; Dimas Pristovani Riananda; Dwi Sasmita Aji Pambudi
Journal of Soft Computing Exploration Vol. 7 No. 1 (2026): March 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i1.22

Abstract

The management of maritime traffic directly determines the level of operational efficiency and safety achievable at major ports, including Tanjung Perak in Surabaya, which serves as a critical logistics node for eastern Indonesia. This study presents a comprehensive analysis of maritime traffic density prediction using Automatic Identification System (AIS) big data combined with Principal Component Analysis (PCA) and K-Means clustering techniques. The dataset comprises 1,173 vessel movements recorded in December 2025, encompassing various vessel types, port operations, and voyage characteristics. Through dimensionality reduction using PCA and unsupervised clustering with K-Means, we identified 10 distinct traffic patterns representing different operational profiles. The analysis revealed significant temporal patterns, with peak traffic occurring at 14:00 (79 vessels) and lowest traffic at 02:00 (18 vessels). The clustering results achieved a silhouette score of 0.3863, effectively segmenting vessels based on voyage distance, capacity, speed, draught, and temporal features. The results of this research offer practical guidance for port authorities seeking to improve resource allocation, traffic management, and operational efficiency based on empirical evidence.
Integration of blockchain and cryptographic algorithms for education certification and verification: a systematic literature Roy Mubarak; Imam Riadi; Tole Sutikno
Journal of Soft Computing Exploration Vol. 7 No. 1 (2026): March 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i1.23

Abstract

Personal data has become a highly valuable asset in the digital era. The background of this research is the urgent need to raise awareness in the digital era about the importance of protecting digital education certificate. The purpose of this research is to analyze the integration of blockchain technology with cryptographic algorithms for the secure storage and verification of digital education certificates. The methodology follows the Prisma framework, which is carried out through four systematic stages: formulating research question, preparation research protocol, identification of records from four major databases (Google Scholar, IEEE, Springer, and MDPI) published between 2023 and 2026, screening of titles and abstracts to eliminate duplicate and irrelevant studies, eligibility assessment through full-text review based on predetermined inclusion and exclusion criteria, data extraction and validation for qualitative synthesis, and finally of 21 papers for analysis result, conclusion, limitation of research, research gap and novelty. The findings demonstrate that storage efficiency, verification speed, encryption diversity, fraud prevention and identity standards. The conclusion of this study is that a collaborative framework integrating blockchain with cryptographic techniques for the security and verification of digital education certificates, with a robust process, can create a robust and adaptive system against certificate forgery. This study provides conceptual and practical knowledge for developing a more comprehensive education certificate security framework.
Integrated aspect extraction and sentiment classification for aspect-based sentiment analysis using fine-tuned indoBERT on indonesian e-commerce reviews Feliks Victor Parningotan Samosir; Gavrila Louise Tumanggor
Journal of Soft Computing Exploration Vol. 7 No. 1 (2026): March 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i1.26

Abstract

The rapid growth of Indonesian e-commerce has generated vast volumes of consumer reviews, yet extracting actionable aspect-level sentiment from informal Indonesian-language texts remains challenging due to the limited availability of domain-specific Aspect-Based Sentiment Analysis (ABSA) models. This study aimed to develop and evaluate an integrated IndoBERT-based ABSA model that combines aspect extraction and aspect sentiment classification within a single framework, applied to Indonesian beauty product reviews. A corpus of 500 beauty product reviews was processed through aspect extraction, yielding approximately 10,000 aspect-level data points labeled as positive or negative. The IndoBERT model was fine-tuned with optimized hyperparameters. The model achieved 86% accuracy, 85.71% F1-score, and 88% balanced accuracy. Aspect-level evaluation revealed F1-scores of 100% for seller, 98% for product, and 86% for shipping. Inference throughput of 33,173 samples per second confirmed real-world deployment feasibility. These results demonstrate the effectiveness of integrated IndoBERT fine-tuning for ABSA on Indonesian e-commerce reviews and provide a foundation for enhancing data-driven marketing strategies in the beauty product sector.
Benchmarking mobileNetV3 and efficientNet-B0 for corn leaf disease classification with imbalanced dataset using stratified cross-validation Muhammad Shandy Alfarizal; Muhamad Kelvin Saputra; Ade Fajar Kurniawan; Khanahaya Adriano Fadhil; Anindita Septiarini; Novianti Puspitasari
Journal of Soft Computing Exploration Vol. 7 No. 2 (2026): June 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i2.30

Abstract

Corn leaf diseases pose a serious threat to crop productivity, yet most publicly available datasets for this task exhibit severe class imbalance that can mislead conventional accuracy-based evaluation. This study benchmarks two lightweight transfer learning architectures, MobileNetV3-Large and EfficientNet-B0, for multi-class corn leaf disease classification on the Seasonal Corn Leaf Disease Dataset from Mendeley Data 2025 containing 2,943 images across five imbalanced classes. Evaluation was conducted using Stratified 5-Fold Cross-Validation with Macro-F1 as the primary metric, complemented by per-class analysis through aggregated out-of-fold predictions. Class weights were applied to the CrossEntropyLoss function as a fixed experimental control for class imbalance, with the primary objective being the benchmarking of the two architectures rather than the comparison of imbalance-handling strategies. The experimental results revealed that EfficientNet-B0 consistently outperformed MobileNetV3, achieving a Macro-F1 of 0.9778 and an accuracy of 0.9796 with lower variance across folds. Error analysis through the OOF confusion matrix and a misclassification gallery confirmed that persistent errors predominantly occurred between Gray Leaf Spot and Healthy classes, particularly on early-symptom images captured under inconsistent lighting conditions.
The application of IndoBERT in tourist sentiment analysis:a comparative evaluation with SVM and LSTM Yoannes Romando Sipayung; Sri Mujiyono; Anni Malihatul Hawa
Journal of Soft Computing Exploration Vol. 7 No. 2 (2026): June 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i2.42

Abstract

YouTube comments provide valuable public opinions about tourist destinations, but their informal and unstructured nature makes sentiment analysis challenging. Therefore, an automatic sentiment classification approach is needed to support tourism evaluation and promotion strategies. This study aims to analyze tourist sentiment toward tourism in the Bangka Belitung Islands based on comments on the YouTube platform. The analysis was conducted using a comparative approach with three models: IndoBERT, SVM, and LSTM. The dataset consisted of 1,000 YouTube comments, which were reduced to 913 valid comments after preprocessing, including data cleaning, case folding, normalization, tokenization, and stopword removal. The sentiment distribution consisted of 434 neutral comments, 333 positive comments, and 146 negative comments, indicating an imbalanced class distribution. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics based on a confusion matrix. The results show that IndoBERT performed best with an accuracy of 0.71 and the highest F1-score compared to the other models. The SVM model demonstrated fairly stable performance with an accuracy of 0.69, while the LSTM achieved an accuracy of 0.68 with lower performance on the minority class. The results indicate that transformer-based models are more effective in understanding linguistic context than machine learning and deep learning models. This study is expected to contribute to the development of sentiment analysis based on social media data in the tourism sector.
Transformer-based multiclass classification for detecting cross-site scripting attacks using supervised feature representation learning with transformer encoder Arda Surya Editya; Ntivuguruzwa Jean De La Croix
Journal of Soft Computing Exploration Vol. 7 No. 2 (2026): June 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i2.44

Abstract

Cross-Site Scripting (XSS) remains one of the most prevalent web application attacks, allowing malicious scripts to be injected into web pages and executed in users’ browsers. The increasing diversity and structural complexity of XSS payloads make conventional rule-based and signature-based detection methods less adaptive, particularly in multiclass classification scenarios. This study proposes a Transformer-based multiclass classification approach for detecting XSS attacks using supervised feature representation learning with a Transformer encoder. Experimental results show that the proposed Transformer achieved an accuracy of 0.9904, a weighted F1-score of 0.9898, and a macro F1-score of 0.7301. However, the CNN model achieved the highest overall accuracy (0.9934), while Logistic Regression and SVM demonstrated stronger macro-level performance, indicating better class-wise balance under imbalanced data conditions. These findings show that Transformer-based sequence modeling is effective for capturing contextual payload patterns, but its performance on minority classes remains limited in the current experimental setting. Overall, this study highlights both the potential and the limitations of Transformer-based multiclass XSS detection and contributes to the development of more intelligent and practical web attack detection systems.
Hybrid approach for identifying strategic promotional locations using k-means clustering and support vector machine classification Anisya Anisya; Brestina Gultom; Sarjon Defit
Journal of Soft Computing Exploration Vol. 7 No. 2 (2026): June 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i2.45

Abstract

In the increasingly competitive landscape of higher education marketing, determining strategic promotional locations was essential to reaching prospective students effectively. This study proposed a hybrid machine learning framework combining K-Means clustering and Support Vector Machine (SVM) classification to identify high-potential areas for targeted promotional activities. The analysis used student enrolment data from 2021 to 2024, focusing on features such as city, province, and school origin. K-Means clustering was first applied to segment the data into three spatially and institutionally distinct clusters. These clusters were then used as pseudo-labels to train the SVM model, enabling the classification of new data points based on learned patterns. The model achieved a classification accuracy of 98%, with consistently high precision and recall across all clusters. Cluster interpretation revealed meaningful geographic and institutional differences that supported differentiated promotional strategies. Thematic map visualizations further enhanced the applicability of the model for geospatial decision-making. This study contributed to the development of data-driven, scalable, and interpretable solutions for location-based marketing. It also demonstrated the practical relevance of hybrid learning models in supporting strategic planning for educational institutions. Future work was suggested to incorporate additional socio-demographic variables and advanced ensemble methods to improve model robustness.

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