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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 27 Documents
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Search results for , issue "Vol. 12 No. 6 (2025): Desember 2025" : 27 Documents clear
Development of a Smart Coffee Model Based on the Internet of Things (IoT) in West Java Province Nastiti, Tashia Indah; Sintha Wahjusaputri; Bunyamin, Bunyamin; Johan, Johan
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.v12i6.8786

Abstract

Coffee cultivation in fertile regions such as Ciniru District, Kuningan Regency, is still predominantly carried out manually, lacking adequate technological support. However, coffee plants require intensive monitoring of environmental conditions to ensure optimal productivity and quality. The absence of technology adoption in farming practices results in inefficiencies and makes the cultivation process vulnerable to pests and climate-related disruptions. This study aims to develop a smart monitoring system for coffee plantations in Gunung Manik Village, utilizing an Internet of Things (IoT)-based Smart Coffee model. The system is designed to assist coffee farmers in Kuningan in improving yields and providing guidance for crop management through real-time monitoring of soil moisture, environmental temperature, and pest activity. An experimental method was employed in this research, where data is transmitted via radio frequency to a gateway and subsequently forwarded to middleware for further processing. The processed data is then visualized through a dashboard and a mobile-based application. Currently, the system is focused on a single variety of coffee plants. The results demonstrate that the system provides accurate data for irrigation and fertilization scheduling, as well as notifications regarding plant conditions, growth stages, and cultivation status. In conclusion, the coffee plant monitoring system offers a practical digital solution for farmers and is expected to enhance agricultural productivity through the integration of information technology.
Redesign Of BUMDes And MSMEs Marketplace Platform Using User Centered Design Method Tirta Cahyatama, Muhammad Widya; Wardhana, Ariq Cahya
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.v12i6.8829

Abstract

This research centers on the redesign of Waroeng Kita, a digital marketplace platform developed to facilitate transactions for Village-Owned Enterprises (BUMDes) and Micro, Small, and Medium Enterprises (MSMEs) in rural Indonesia. An initial usability assessment conducted for this study revealed a low System Usability Scale (SUS) score of 49.16, classifying the platform as "Not Acceptable" and highlighting significant usability challenges. To address these issues, the study systematically applied the User-Centered Design (UCD) methodology, following an iterative process aligned with the ISO 9241-210:2019 standard. Across two design iterations , critical usability flaws, including a static cart, unclear input forms, and unfamiliar terminology —were systematically resolved based on direct user feedback. The final evaluation after the second iteration demonstrated a substantial improvement, with the average SUS score increasing to 70. This score elevates the system to a "Marginally Acceptable" status (Grade C), confirming that the UCD approach is highly effective for enhancing system usability, particularly for users in rural settings with limited digital literacy. The study provides practical insights for creating more inclusive digital solutions in similar socio-economic contexts.
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.

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