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INDONESIA
EXPLORER
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27744647     DOI : https://doi.org/10.47065/explorer.v2i1.148
Core Subject : Science,
Computer Science & IT
EXPLORER Journal of Computer Science and Information Technology is a scientific journal published by the FKPT (Forum Kerjasama Pendidikan Tinggi). This journal contains scientific papers from Academics, Researchers, and Practitioners about research on Computer Science and Information Technology. EXPLORER Journal of Computer Science and Information Technology is published twice a year in January and July. The paper is an original script and has a research base on Computer Science and Information Technology. The scope of the paper includes several studies but is not limited to the study Artificial Intelligence, Computer Graphics and Animation, Image Processing, Cryptography, Computer Network Security, Modelling and Simulation, Multimedia, Computer Architecture Design, Computer Vision and Robotics, Parallel and Distributed Computing, Operating System, Information System, Mobile Computing, Natural Language Processing, Data Mining, Machine Learning, Expert System and Geographical Information System. Thus, we invite Academics, Researchers, and Practitioners to participate in submitting their work to this journal.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 97 Documents
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Decision Support System for Smartphone Recommendations Based on Consumer Purchasing Power Using the AHP-PROMETHEE Method Ary Santri Yuanda; Muhammad Dedi Irawan
Explorer Vol 6 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i2.2793

Abstract

The selection of smartphones that match consumer preferences and purchasing power is often challenging due to the large number of alternatives with varying specifications and prices. This research aims to develop a Decision Support System for smartphone recommendations using the Analytical Hierarchy Process (AHP) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) methods at R Ponsel Store. The AHP method was used to determine the priority weights of criteria based on the results of consumer preference questionnaires, while the PROMETHEE method was applied to rank smartphone alternatives. Data collection was conducted through observation, interviews, and literature studies. The criteria used include price, RAM, ROM, camera, processor, battery, and other supporting specifications. The results showed that the Consistency Ratio (CR) value was 0.035, indicating that the criteria comparison matrix was consistent. Based on the ranking results using the PROMETHEE method, the Redmi Note 14 8/256 achieved the highest net flow value of 0.042, making it the best alternative. The developed system is capable of assisting consumers in obtaining smartphone recommendations that better match their preferences and purchasing power
Klasifikasi Sentimen Teks Code-Mixed Indonesia–Inggris Non-Formal Pada X Menggunakan Model Fine-Tuned DistilBERT Syifa Arifah Nurbayani; Dian Sa'adillah Maylawati; Aldy Rialdy Atmadja
Explorer Vol 6 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i2.2856

Abstract

The rapid growth of social media has increased the use of Indonesian–English code-mixed language in digital communication, particularly on social media X. The non-formal characteristics of social media text, such as slang, abbreviations, emojis, and language switching within a single sentence, make sentiment analysis more challenging than monolingual text. This study aims to perform sentiment classification on code-mixed text by evaluating the performance of the lightweight Transformer model DistilBERT and comparing it with IndoBERTweet. The study adopts the CRISP-DM methodology, which consists of Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment stages. The dataset comprises 1,108 primary data collected from social media X between 2022 and 2026 and 5,048 secondary data obtained from previous research. Three experimental scenarios were applied: translation into Indonesian, translation into English, and raw data without translation. The results show that DistilBERT achieved its best performance on the English translation scenario, with accuracies of 73.66% on the primary dataset and 72.00% on the secondary dataset. Meanwhile, IndoBERTweet obtained the highest performance on the Indonesian translation scenario, achieving an accuracy of 79.01%. These findings indicate that the alignment between the language of the input data and the pre-training characteristics of the model significantly affects sentiment classification performance on non-formal code-mixed text.
Penerapan Convolutional Neural Network (CNN) pada Klasifikasi Tanaman Apel Menggunakan Resnet-50 M. Fauzan Djafaar; Mohamad Jamil
Explorer Vol 6 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i2.2876

Abstract

Apple plant classification based on leaf images still faces challenges in the form of variations in shape, texture, lighting, and visual similarity with other plants, which can potentially reduce identification accuracy. This problem impacts agricultural monitoring and decision-making processes that require high accuracy. This research aims to apply the Convolutional Neural Network (CNN) method, a deep learning approach designed for automatically extracting visual features, to apple plant classification using a fifty-layer Residual Network (ResNet-50) architecture. ResNet-50 uses a residual mechanism to overcome performance degradation in very deep networks. The results showed that the CNN model based on the ResNet-50 architecture was able to classify healthy and rotten apple leaf images with the highest accuracy of 82.40% using the 80:20 dataset split scenario. Evaluation using a confusion matrix and classification report produced a precision value of 0.86 for the rotten class and 0.79 for the healthy class, while the recal values were 0.77 and 0.88, respectivelty. These rindings indicate that ResNet-50 has a good capability in recognizing visual characteristics of apple leaves and can be effectively utilized as an image-based plant classification solution.
Integrasi Metode AHP-SAW untuk Evaluasi Kinerja Mitra Bisnis pada Sistem Manajemen Kemitraan Berbasis Web Surya Darma Nasution; Nurhayati Nurhayati; Sri Wanti Nasution
Explorer Vol 6 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i2.2899

Abstract

Business partnership management in SMEs and companies is still largely conducted manually, making objective partner performance evaluation difficult. This study aims to integrate the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods to evaluate partner performance in a web-based partnership management system. AHP is used as the criteria weighting method through pairwise comparison validated by the Consistency Ratio (CR), while SAW is used for partner ranking. Four evaluation criteria were established: number of collaboration activities, active partnership duration, completeness of collaboration evidence, and number of expired MoUs without renewal. The AHP calculation produced criteria weights with a CR value of 0.0038 (< 0.1), indicating consistent judgment. Testing on five partners produced consistent rankings, with Partner C as the best-performing partner (preference value 1.0000). A comparative analysis between AHP-SAW and pure SAW (manual weighting) showed identical rankings (Spearman correlation = 1.000), yet AHP-SAW provided more objective and validated weighting justification. The developed system proved capable of producing objective, transparent, and measurable partner performance evaluations.
The Multimodal Medical Clustering of Chest X-Ray Images and Clinical Reports Using Deep Visual and Semantic Text Features Humasak Simanjuntak; Lenni Hutapea; Jonathan Simorangkir; Yolanda Saragih; Jaden Panggabean
Explorer Vol 6 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i2.2915

Abstract

The limited availability of labelled medical data is a real obstacle in building a supervised learning-based Radiology analysis system. This study offers an alternative approach, unlabeled multimodal clustering, that simultaneously combines clinical information sources: chest X-ray images and physician clinical reports. Visual features are extracted from 1,000 images using a pretrained ResNet152, and text features are constructed using a Dual TF-IDF Vectorizer that processed the findings and conclusions columns separately with explicit weightings (0.4 and 0.6), then enriched with 10 related topics from the Latent Dirichlet Allocation (LDA) model. These two modalities are combined through a weighted late fusion strategy after being normalized by L2, with a visual weight of 0.5 and a text weight of 0.5, respectively. Before clustering, dimensionality reduction is performed using UMAP with 50 components and a cosine metric. Determining the optimal K value using the Elbow and Silhouette Score methods in the range of K=2 to K=15 showed K=8 as the best choice with a Silhouette Score value of 0.7524. The clustering results reveal eight clinical groups reflecting diverse diagnostic patterns, ranging from normal to abnormal findings, such as cardiomegaly and pneumonia, that require clinical attention. This approach has the potential to serve as the basis for a medical data exploration system that relies on no label annotations.
Explainable Diabetes Classification Using Machine Learning Algorithm and SHAP Analysis on Clinical Laboratory Data Siti Agus Kartini; Muhammad Fauzi; Riki Winanjaya
Explorer Vol 6 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i2.2987

Abstract

Diabetes mellitus is a chronic metabolic disorder that continues to pose significant health challenges worldwide due to its increasing prevalence and potential complications. Early and accurate identification of diabetes is essential to support timely intervention and effective disease management. Recent advances in machine learning have enabled the development of intelligent classification systems; however, many predictive models still suffer from limited interpretability, reducing their applicability in clinical environments. Therefore, this study proposes an explainable diabetes classification framework using machine learning algorithms and SHapley Additive exPlanations (SHAP) analysis on clinical laboratory data. The dataset consists of 1,000 patient records containing demographic and laboratory attributes, including age, gender, glycated hemoglobin (HbA1c), cholesterol, triglycerides, lipoprotein levels, creatinine, urea, and body mass index (BMI). Four machine learning algorithms, namely Decision Tree, Random Forest, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), were developed and evaluated. The performance of each model was assessed using Accuracy, Precision, Recall, F1-Score, Classification Report, and Confusion Matrix. Experimental results indicate that Random Forest and XGBoost achieved the highest classification accuracy of 98.5%, outperforming Decision Tree (98.0%) and SVM (94.5%). Due to its strong predictive capability and compatibility with explainable artificial intelligence techniques, XGBoost was selected for further SHAP analysis. The SHAP results successfully identified the most influential features contributing to diabetes classification and provided transparent explanations regarding model predictions. The proposed framework demonstrates that combining machine learning algorithms with explainable artificial intelligence improves predictive accuracy and interpretability, supporting reliable clinical decision-making for diabetes diagnosis.
Prototipe Alat Pendeteksi Banjir Berbasis Mikrokontroler dengan Fuzzy Tsukamoto Alwi Andika Panggabean; M Fakhriza
Explorer Vol 6 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i2.3107

Abstract

Floods are among the most frequent natural disasters in Indonesia, causing significant material losses and casualties. Therefore, an early warning system capable of detecting flood potential quickly and accurately is required. This study aims to design and implement a flood detection prototype based on the ESP32 microcontroller using the Tsukamoto fuzzy logic method. The system employs an HC-SR04 ultrasonic sensor to measure water levels and an FC-37 rain sensor to detect rainfall intensity. Data collected from both sensors are processed using the Tsukamoto fuzzy logic approach through fuzzification, inference, and defuzzification stages to determine flood risk status categorized as safe, alert, and danger. The decision results are displayed through LED indicators and a buzzer, while real-time notifications are sent via the Telegram application using the ESP32 Wi-Fi connectivity. The testing results demonstrate that the system successfully classifies flood conditions according to the predefined fuzzy rules and automatically delivers notifications to users. The implementation of the Tsukamoto fuzzy logic method enhances decision-making capabilities in handling uncertain environmental conditions. Therefore, the developed prototype can serve as an effective, simple, and practical flood early warning system for flood-prone areas.

Page 10 of 10 | Total Record : 97

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