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JURIKOM (Jurnal Riset Komputer)
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma
ISSN : 2407389X     EISSN : 27157393     DOI : http://dx.doi.org/10.30865/jurikom.v7i1.1927
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
JURIKOM (Jurnal Riset Komputer) membahas ilmu dibidang Informatika, Sistem Informasi, Manajemen Informatika, DSS, AI, ES, Jaringan, sebagai wadah dalam menuangkan hasil penelitian baik secara konseptual maupun teknis yang berkaitan dengan Teknologi Informatika dan Komputer. Topik utama yang diterbitkan mencakup: 1. Teknik Informatika 2. Sistem Informasi 3. Sistem Pendukung Keputusan 4. Sistem Pakar 5. Kecerdasan Buatan 6. Manajemen Informasi 7. Data Mining 8. Big Data 9. Jaringan Komputer 10. Dan lain-lain (topik lainnya yang berhubungan dengan Teknologi Informati dan komputer)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,025 Documents
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Predicting AI Job Salary Classes Through a Comparative Study of Machine Learning Algorithms Vincent, Vincent; Robet, Robet; Edi Wijaya
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.8979

Abstract

The rapid growth of Artificial Intelligence (AI) has brought significant transformation to the global job market, particularly in salary structures across various AI-related professions. This study aims to classify AI job salaries into three categories—Low, Medium, and High—using supervised machine learning algorithms. The dataset, sourced from Kaggle, combines two real-world datasets featuring key attributes such as experience level, job type, education level, technical skills, remote work ratio, and salary in USD. Preprocessing techniques include One-Hot Encoding for categorical data, StandardScaler for normalization, and MultiLabelBinarizer to handle multi-skill entries. Four machine learning models—Logistic Regression, Random Forest, Gradient Boosting, and XGBoost—were trained and evaluated using consistent pipelines, with evaluation metrics including accuracy, precision, recall, and F1-score, applying macro-averaging to address class imbalance. Logistic Regression achieved the highest performance with 85.4% accuracy and 77.6% F1-score, followed by Gradient Boosting with 84.8% accuracy and 76.3% F1-score. High-salary classes were predicted with higher precision and recall than low-salary classes, indicating skewness in class distribution. Feature importance analysis shows that experience, remote work ratio, and key skills such as Python and SQL significantly affect prediction accuracy. This study demonstrates that traditional machine learning methods, when applied with appropriate preprocessing, can effectively support salary classification and labor market analysis in the AI domain.
Pemetaan Kepribadian RIASEC melalui Klasifikasi Multi-Task Fitur Grafologi Tulisan Tangan Menggunakan ResNeXt50 Hanif Arif, Abdullah; Samsuryadi
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9201

Abstract

Personality analysis based on graphology—the study of handwriting characteristics to identify individual personality traits—is an approach that has been increasingly developed in the fields of psychometrics and artificial intelligence. This research proposes a method for mapping Holland’s personality types (RIASEC) through graphology-based handwriting analysis using a deep learning approach. Conventional personality assessments generally rely on self-assessment questionnaires, which are highly subjective. To address this limitation, this study develops a Convolutional Neural Network (CNN) model with a ResNeXt50 architecture based on multi-task learning to classify five graphological features: letter size, writing slant, word spacing, line spacing, and pen pressure. The dataset used in this study was obtained from the IAM Handwriting Database, consisting of 1,533 handwriting images. The data underwent preprocessing steps—including resizing, conversion to tensor format, and normalization—before being trained using a multi-head CNN model with cross-entropy loss for each graphological feature and the Adam optimizer for optimization. After the training process, the model was evaluated using a testing set that had never been used during the training or validation stages to objectively assess its generalization capability. The evaluation results indicate that the proposed model can classify graphological features with an average accuracy above 80% and map the classification results to RIASEC personality types with up to three dominant types. These findings indicate that the ResNeXt50-based multi-task learning approach has the potential to serve as a more objective, efficient, and applicable alternative method for personality assessment in the contexts of career development and education.
Analisis Komparatif Model Regresi Machine Learning untuk Prediksi Prestasi Akademik Siswa dengan Optimasi Hyperparameter Hose, Fernando; Robet, Robet; Hendri, Hendri
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9240

Abstract

Low accuracy in the early identification of at-risk students often hinders timely academic intervention. This study analyzes and compares seven machine learning algorithms to predict student academic achievement, aiming to provide a foundation for a reliable early warning model. The dataset includes 2.392 students with 15 features covering demographics, learning behavior, and environmental support. Model training was performed using GridSearchCV optimization combined with stratified cross-validation to mitigate overfitting.Performance was evaluated using MAE, RMSE, and R². The results show CatBoost performed the best R² = 0,774; RMSE = 0,581; MAE = 0,306) followed by LightGBM (R² = 0,771) and Gradient Boosting (R² = 0,767), while MLP showed the lowest performance. Feature importance analysis placed GPA as the dominant predictor, followed by absenteeism and weekly study time. These findings affirm the superiority of boosting-based models in capturing complex nonlinear relationships and provide a practical framework for educational institutions to build data-driven early warning systems.
Optimasi Hyperparameter Algoritma Decision Tree dan Random Forest Menggunakan Particle Swarm Optimization Untuk Prediksi Risiko Obesitas Anak Andi Mulawati Mas Pratama; Utiarahman, Siti Andini; Satriadi D. Ali; Ishak Fardiansyah Mohamad
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9250

Abstract

Obesitas pada anak merupakan masalah kesehatan global yang mengalami peningkatan prevalensi signifikan di Indonesia dengan proyeksi mencapai 254 juta kasus pada tahun 2030. Penelitian ini bertujuan mengoptimasi model prediksi risiko obesitas anak menggunakan Particle Swarm Optimization (PSO) pada algoritma Decision Tree dan Random Forest untuk meningkatkan akurasi klasifikasi status gizi anak berdasarkan Permenkes No. 2 Tahun 2020. Metode penelitian menggunakan pendekatan ekperimental dengan dataset 64.506 anak dengan rentang usia 0-5 tahun dari Dinas Kesehatan Provinsi Gorontalo tahun 2024 yang kemudian di balancing menjadi 3.837 sampel. Optimasi PSO dilakukan dengan 40 partikel dan 80 iterasi untuk mencari hyperparameter optimal pada kedua algoritma. Hasil penelitian menunjukkan Decision Tree yang dioptimasi PSO menghasilkan akurasi terbaik sebesar 91.32% pada test set, meningkat 4.51% dari baseline, dengan precision 0.95, recall 0.95 dan F1-score 0.95. Random Forest teroptimasi mencapai akurasi 84.2%, meningkat 2.60% dari baseline. Model Decision Tree + PSO menunjukkan performa superior pada klasifikasi obesitas dengan precision 0.98 dan recall 0.96, serta berhasil mengurangi overfitting dari gap 3.47% menjadi 2.78%. model yang dikembangkan dapat diimplementasikan sebagai alat bantu deteksi dini risiko obesitas anak dalam layanan kesehatan masyarakat untuk mendukung pencapaian Indonesia Emas 2045.
Pengembangan Platform Digital Community-Based Tourism Menggunakan Model System Development Life Cycle untuk Pemberdayaan Masyarakat Wisata Bukit Lawang Matondang, Sondang; Tumini Sipayung; Andre Pasaribu; Swanra Simare-mare
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9283

Abstract

This study aims to develop a digital platform based on Community-Based Tourism (CBT) to support community empowerment in the Bukit Lawang tourism area, North Sumatra. Despite its rich natural beauty and cultural values, Bukit Lawang’s tourism potential has not yet provided significant economic benefits to local communities due to low digital literacy and limited online promotion access. The research employs the Research and Development (R&D) method using the System Development Life Cycle (SDLC) model, which includes needs analysis, system design, development, testing, and user evaluation. The results show that the developed CBT digital platform successfully integrates destination information, community-based tourism services, as well as reservation and local product promotion systems. User testing indicates a satisfaction rate of 89%, with a 40% increase in community engagement in tourism promotion and management. Furthermore, the platform strengthens networks among local entrepreneurs, visitors, and tourism managers through an effective digital communication system. This research provides a practical contribution to technology-based community economic empowerment and serves as a replicable model for other sustainable tourism destinations in Indonesia.
Implementasi Logika Fuzzy Mamdani pada Aplikasi Sistem Pakar Diagnosis Penyakit Ternak Babi Berbasis Web Sirait, Gebriella Wahyuni; Widodo, Tri
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9302

Abstract

Diseases in pig livestock are a major cause of economic losses for farmers, especially when treatment is delayed, leading to disease spread and increased mortality rates. In addition, high consultation costs and limited access to animal health service centers (puskeswan) often result in farmers performing manual diagnosis, which is inaccurate and may worsen the livestock’s condition. This study develops a web-based expert system for diagnosing pig diseases using the Mamdani fuzzy logic method as a solution to assist farmers in conducting independent and early diagnosis based on observable symptoms. The system is built using Python with the Flask framework, Bootstrap for the user interface, and Supabase as a cloud-based database. The diagnosis process consists of three main stages: symptom fuzzification, rule-based fuzzy inference, and defuzzification using the centroid method. Testing was conducted on 8 types of diseases and 45 symptoms using validated test data, resulting in an accuracy of 100%, with the system consistently producing diagnostic outcomes that match the symptom inputs. The system also includes an online consultation feature with experts, enhancing accessibility and effectiveness in identifying diseases in pig livestock.
Sistem Pengelolaan Inventori Real-Time untuk UMKM Berbasis Flutter dan QR Code Menggunakan Metode R&D Saputra, Faiz; Ujianto, Erik Iman Heri
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9309

Abstract

The efficiency and accuracy of stock recording in MSMEs are enhanced through the development of a mobile-based inventory management application, addressing the challenges of manual systems, which are prone to human error and data delays. This study implements a real-time system by integrating three key technologies: Flutter as a cross-platform framework for efficient development, QR Codes for rapid item identification, and Firebase as a database for instant data synchronization. By applying the R&D method through the Waterfall model, the application was developed and tested in a case study at Warung Laras. The application is equipped with essential features for daily operations, including security verification using OTP via WhatsApp, unique QR code generation for each item, and the ability to automatically generate inventory reports in PDF format. The results from Black Box testing and user trials show a significant quantitative impact: data recording accuracy increased to 95%, while the average time per transaction was drastically reduced by 70%, from approximately 30 seconds to just 9 seconds. This finding proves that the technological integration has successfully created a reliable and effective solution to drive digital transformation in MSME-scale stock management
Implementasi Model Gpt-3.5 Turbo Untuk Otomatisasi Penilaian Esai Pada Sistem Pembelajaran Daring Ade Suryadi; Sandra Jamu Kuryanti; Cep Adiwihardja; Khaila Anjani; Meutya Febi Santoso
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9317

Abstract

Essay assessment in online learning requires significant time, effort, and consistency, which can be challenging to maintain when conducted manually. This study explores the use of the large language model GPT-3.5 Turbo as the core of an automated essay scoring system for online learning platforms. Employing a Research and Development (R&D) approach with the ADDIE development model—comprising Analysis, Design, Development, Implementation, and Evaluation phases—the research adopts the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework for its methodology. The automated essay scoring system utilizing Prompt 4 demonstrated exceptionally high accuracy and reliability. The model achieved an accuracy of 94.3%, an F1-Score of 0.955, and a Cohen’s Kappa value of 0.878. This high Kappa value indicates a very strong agreement between AI-generated assessments and the gold standard validated by educators, surpassing the initial inter-rater agreement among educators themselves, which was only 0.1157. The superior performance of Prompt 4 is also confirmed by the lowest Mean Absolute Error (MAE) of 30.54 and the highest Area Under the Curve (AUC) of 0.956.
Deteksi Stres Berbasis Teks pada Dreaddit Menggunakan Fine Tuning DeBERTa-v3 Ramadhan, Pramudia; Setiadi, De Rosal Ignatius Moses
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9318

Abstract

Mental health has become an important issue in the digital era, as psychological expressions are increasingly reflected through social media posts such as Reddit. This study uses the publicly available Dreaddit dataset containing Reddit user texts labeled with stress categories. The objective is to compare two text-based stress detection approaches: fine-tuning the transformer model DeBERTa-v3 and the classical TF-IDF LinearSVC method. Both approaches are implemented as binary classification systems to automatically distinguish stress and non-stress texts. The research workflow includes data preprocessing, tokenization, model training, validation, and evaluation using Accuracy, Precision, Recall, F1-score, and AUROC metrics. DeBERTa-v3 is fine-tuned using contextual representations with a self-attention mechanism, while TF-IDF LinearSVC relies on statistical n-gram weighting. Experimental results show that DeBERTa-v3 achieves superior performance with an Accuracy of 0.830, Precision of 0.802, Recall of 0.889, F1-score of 0.843, and AUROC of 0.918. Meanwhile, TF-IDF LinearSVC obtains an Accuracy of 0.732, Precision of 0.722, Recall of 0.783, F1-score of 0.751, and AUROC of 0.817. The experiments were conducted with consistent training configurations, data splits, and evaluation procedures to ensure a fair comparison. The confusion matrix analysis indicates that DeBERTa-v3 produces fewer false positives and false negatives, demonstrating stronger capability in recognizing implicit stress expressions. These findings highlight the advantages of transformer-based models in capturing emotional and semantic context and indicate the potential for real-time deployment in social-media-based mental health monitoring systems.
Prediksi Harga Mobil Global Menggunakan Machine Learning dengan Algoritma Naive Bayes Hts, Dedek Indra Gunawan; Firman Edi; Ratna Sri Hayati; Hendro Sutomo Ginting
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9320

Abstract

Determining car prices is one of the major challenges in the global automotive industry because it is influenced by various factors such as technical specifications, vehicle condition, and market dynamics. This issue becomes more complex as the volume of available data increases, requiring methods capable of performing fast and accurate analysis. This study aims to predict car price levels based on vehicle specifications using a Machine Learning approach, with the Naive Bayes algorithm selected as a solution to simplify the price classification process on large-scale data. The dataset used is the Global Car Sales Analysis from the Kaggle platform, which includes attributes such as Manufacturer, Model, Engine size, Fuel type, Year of manufacture, Mileage, and Price. The research methodology consists of data preprocessing, label encoding for categorical attributes, splitting the dataset into training and testing sets, and applying the Naive Bayes algorithm to classify car prices into three categories: Low, Medium, and High. The results indicate that Naive Bayes is capable of predicting car prices with very strong performance, achieving an accuracy of 96%, precision of 0.97, recall of 0.96, and an F1-score of 0.96. The model performs best on the Low category with an F1-score of 0.98, although performance decreases for the Medium and High categories due to imbalanced class distribution. Further analysis also reveals that Engine size, Year of manufacture, and Mileage are the most influential attributes in determining price. Overall, this study demonstrates that Naive Bayes is an effective method for predicting car prices using global automotive data.

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