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INDONESIA
Jurnal Sistem Cerdas
Published by Asosiasi Prakarsa Indonesia Cerdas
ISSN : -     EISSN : 26228254     DOI : -
Core Subject : Science, Education, Engineering,
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering
Jurnal Sistem Cerdas dengan eISSN : 2622-8254 adalah media publikasi hasil penelitian yang mendukung penelitian dan pengembangan kota, desa, sektor dan kesistemam lainnya. Jurnal ini diterbitkan oleh Asosiasi Prakarsa Indonesia Cerdas (APIC) dan terbit setiap empat bulan sekali.
Arjuna Subject : Umum - Umum
Articles 192 Documents
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Excessive Permissions Investigation with Data-Driven Account Security with Classification Setiawan, Heri Satria; Pamuji, Agus; Suparman, Rudi
Jurnal Sistem Cerdas Vol. 8 No. 2 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i2.397

Abstract

Many companies lack configuration systems due to the need to protect assets from unauthorized access by individuals or groups. Data mining can help by securing the configuration system to identify accounts in the database. Given the sensitivity of activities on the database system, access permissions are a major concern, especially with unauthorized users. Excessive permissions can compromise database security, making it important to group users into authorized and unauthorized classes. This study uses the decision tree method to extract and investigate factors that affect excessive permissions, and validates the dataset with 10-fold cross-validation to ensure data quality. The final result identifies two classes for user access, showing that the decision tree method performs well with significant values on the AUC curve and the Confusion Matrix
Anxiety Anxiety Detection Based on EEG Signal using 1-D Convolutional Neural Network Classifier Fadhilah Qalbi Annisa
Jurnal Sistem Cerdas Vol. 8 No. 2 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.506

Abstract

Anxiety is defined as fear and symptoms of somatic tension experienced when a threat or danger is anticipated. In recent years, biological markers have been explored to detect anxiety noninvasively, one method is Electroencephalography (EEG). Detecting state anxiety using EEG is an intriguing area of research. This study detects the state of anxiety based on an EEG signal using a 1-Dimensional Convolutional Neural Network (1-D CNN). The dataset is provided by the Database for Anxious States based on Psychological Stimulation (DASPS). DASPS is an EEG recording obtained from twenty-three participants for this investigation. The data were analyzed for statistical features, and then a 1-D CNN was employed to classify anxiety levels. The results show that 95.1% of mild and severe anxious conditions can be accurately detected. Furthermore, 94.8% of detection accuracy is achieved when anxiety is classified as normal, mild, moderate, or severe. Overall, this study provides a solid foundation for multi-level anxiety detection by improving the accuracy and selecting better features.
Hand Gesture Detection Implemented based on Long Short-Term Memory (LSTM) Method I Gede Wiryawan; Taufiq Rizaldi; Pramuditha Shinta Dewi Puspitasari; Arvita Agus Kurniasari
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025): In progress (December)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.526

Abstract

The Indonesian government encourages accessibility of information that is friendly to people with disabilities, one of which is through the development of information and communication technology. Efforts to increase accessibility of information and encourage independence of people with disabilities need to be supported by the right solutions. According to the Central Statistics Agency, there were 0.68% of the total population of Indonesia in 2019, this data shows that deafness is one of the highest disabilities in Indonesia. Efforts to increase accessibility of information and encourage independence of people with disabilities need to be supported by the right solutions. One potential solution is the development of a self-service system that is friendly to the deaf. This study aims to develop a self-service system that is friendly to the deaf and helps in obtaining information and services independently. The results achieved in this study are in the application of hand signal detection using the Long Short-Term Memory method which can overcome the problem of long-distance dependency and improve performance in recognizing complex hand signal patterns. The hand signal recognition feature can be improved by overcoming the problem of long-distance dependency with a maximum user distance of 1.25 meters, the system can still recognize hand signals well. It is hoped that in the future, more in-depth studies can be carried out on long-distance dependency for variations of other hand signal recognition methods, so that people with disabilities can more easily use the self-service system.
The Effect of Lighting Variations on the Accuracy of Formalin Detection in Milkfish Using HSV Color Space and k-Nearest Neighbors (kNN) Algorithm Falih, Noor; Wadu, Ruth Mariana Bunga; Indarso, Andhika Octa; Audytra, Hastie; Dani, Ahmad Ali Hakam
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025): In progress (December)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.564

Abstract

Milkfish (Chanos chanos) is a widely consumed fish commodity in Indonesia, often subject to preservation using formalin, a chemical with serious health risks when misused. This study proposes a non-destructive formalin detection method using HSV (Hue, Saturation, Value) color features extracted from eye images of milkfish, classified via the k-Nearest Neighbor (kNN) algorithm. The research investigates the impact of varying illumination levels low, medium, and high on the consistency of HSV features and the accuracy of kNN classification. Results show that medium lighting conditions yield the highest classification accuracy, suggesting an optimal illumination range for field deployment. The system's simplicity and potential for real-time implementation on mobile or embedded platforms make it suitable for use by non-technical personnel in traditional markets. Challenges such as environmental temperature, image angle, and surface reflectivity are addressed through calibration strategies and operational guidelines. This study contributes practical insights into lighting control and feature stability, enhancing the reliability of image-based formalin detection systems.
Comparison of SVM and Naive Bayes in Public Sentiment Analysis on Budget Efficiency Yusmita; Farid Wajidi; Muh.Rafli Rasyid
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025): In progress (December)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.576

Abstract

Kebijakan efisiensi anggaran melalui Instruksi Presiden Nomor 1 Tahun 2025 memicu beragam respons publik di media sosial, khususnya X. Penelitian ini mengklasifikasikan sentimen publik menggunakan algoritma Naïve Bayes dan SVM dengan 6.596 twit setelah tahap praproses, menggunakan pelabelan Lexicon InSet, dan ekstraksi fitur TF-IDF. Hasilnya menunjukkan bahwa SVM-LinearSVC mencapai akurasi tertinggi sebesar 94%, sementara Naïve Bayes mencapai 86% tetapi lebih cepat dalam pelatihan dan prediksi. Temuan ini menegaskan bahwa algoritma pembelajaran mesin efektif untuk memetakan opini publik terkait kebijakan, sekaligus menjadi referensi penelitian analisis sentimen berbahasa Indonesia.
Automated Waste Classification for Sustainable Cities Using YOLO Based CNN Integrated IoT Nugroho, Waluyo; Alfattah, Adnan; Arifianto, Mada Jimmy Fonda; Hadi, Aswan
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025): In progress (December)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.582

Abstract

Sustainable waste management is a vital component of smart city development, directly impacting environmental quality and recycling efficiency. This study presents an IoT-enabled waste classification system that utilizes a Convolutional Neural Network (CNN) for accurate, real-time identification of organic and non-organic waste. The model, implemented using the YOLO architecture, was trained on a diverse dataset of waste images captured under various environmental conditions to ensure robustness in practical scenarios. Classification results are automatically stored in a MySQL database and visualized via an Internet of Things (IoT) based Node-RED dashboard, enabling municipal operators to monitor waste categories and quantities remotely. Field evaluations demonstrate that the system achieves an accuracy of 94%, precision of 94.5%, recall of 93.2%, and an F1-score of 93.85%, indicating high detection reliability and consistent performance, even in challenging urban environments. By integrating CNN-based deep learning with IoT visualization tools, this approach offers a scalable and efficient solution that supports sustainable waste management initiatives within smart city frameworks.
The Role of Artificial Intelligence in Building a Culture of Knowledge Sharing among Students and University Students Suarni Norawati; Akmal Andri Yantama; M. Zacky
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025): In progress (December)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.583

Abstract

This study examines the role of Artificial Intelligence (AI) in fostering a culture of knowledge sharing and enhancing knowledge management among students and university learners in the digital learning environment. Using a quantitative explanatory approach, data were collected from 100 respondents who actively used AI-based applications such as ChatGPT, Grammarly, and Copilot in their academic activities. The data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. The results reveal that AI has a significant direct effect on knowledge management (β = 0.316, p = 0.000) and a strong positive influence on knowledge sharing behavior (β = 0.851, p = 0.000). Furthermore, knowledge sharing significantly mediates the relationship between AI and knowledge management (β = 0.611, p = 0.000), indicating that AI’s greatest impact occurs through the enhancement of collaborative knowledge exchange among learners. The model explains 80.3% of the variance in knowledge management and 72.3% in knowledge sharing, demonstrating strong predictive power. These findings highlight AI’s potential as a collaborative catalyst that strengthens human-centered learning ecosystems. The study contributes both theoretically by extending the understanding of AI-mediated knowledge processes—and practically by providing insights for educators to integrate AI ethically and effectively into knowledge-based learning systems
Elite-Refined Genetic Algorithm with Hill Climbing Local Search for University Course Scheduling Heru Purnomo Kurniawan; Lia Farhatuaini; Nurul Bahiyah; Ardi Susanto; Muhammad Iszul Wilsa; Gina Khayatun Nufus
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025): In progress (December)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.584

Abstract

Abstract— This paper proposes a hybrid optimization approach combining Genetic Algorithm (GA) and Hill Climbing (HC) to address the university course scheduling problem in the Informatics Study Program at Universitas Islam Negeri Siber Syekh Nurjati Cirebon. The hybrid GA-HC model integrates GA’s global exploration capability with HC's local refinement strategy to minimize hard and soft constraint violations while achieving balanced timetables. The dataset includes 56 course classes, 18 lecturers, and three rooms, with scheduling over five working days and 11 time slots per day. Experimental results demonstrate that GA-HC outperforms pure GA and pure HC in convergence speed, average fitness, and stability of feasible solutions. Parameter tuning analysis further shows that moderate mutation rates and limited HC iterations yield optimal trade-offs between runtime and solution quality. The proposed hybrid framework effectively enhances convergence, reduces conflicts, and improves overall timetable quality, confirming its robustness for large-scale academic scheduling problems.
Melanin-Aware and ArcFace Methods in Facial Recognition for Dark-Skinned Individuals Purba, Risa Tioria Marlini; Hadayat, Fadhil; Supangkat, Suhono Harso; Arman, Arry Akhmad
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025): In progress (December)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.587

Abstract

The facial recognition system employing the Melanin-Aware method in conjunction with ArcFace, trained on a dataset of 1,000 dark-skinned facial samples, demonstrates the ability to accurately recognize individuals with dark skin while maintaining performance for non-dark-skinned individuals. ArcFace is utilized as the primary feature extractor, leveraging the additive angular margin to enhance inter-class separability. The experiments were conducted using 1,000 dark-skinned facial samples for training and 100 samples for testing. Evaluation results indicate that melanin-aware preprocessing improves average accuracy by up to 17% compared to the absence of preprocessing, and by 7% compared to the standard aggressive CLAHE-based method. Furthermore, the True Acceptance Rate (TAR) increased from 88.15% to 93.33% at FAR = 1e−2, and from 83.7% to 86.67% at FAR = 1e−3, signifying enhanced system stability under stringent security conditions. The performance gains are supported by a more stable distribution of similarity scores and lower threshold values, reflecting improved separation between genuine and impostor pairs.
Real-Time Face Age Detection System Based on Deep Neural Networks with MediaPipe Optimization for Enhanced Accuracy iskandar, muhaimin; Azizah, Nur; Jaya, Firman
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025): In progress (December)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.593

Abstract

The transformation of machine learning and computer vision technology enables computers to automatically learn complex visual patterns, forming the foundation for biometric applications such as identity authentication, face detection, and demographic analytics. Face age estimation predicts age based on facial characteristics in digital images with high accuracy. Handcrafted feature-based approaches such as Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are less stable against variations in lighting, camera orientation, and facial expressions. Deep learning, particularly Deep Neural Networks (DNN), improves accuracy through automatic hierarchical feature extraction. However, raw image-based methods have high computational loads and require large GPUs, which are less than ideal for real-time use on limited devices. This research proposes a DNN-based age estimation system optimized through MediaPipe Face Mesh geometric features. The system consists of five layers: input, feature extraction (468 facial landmarks), optimization with Principal Component Analysis (PCA) for 64 features, DNN regression (three hidden layers), and output. A custom dataset of 1,235 facial images (ages 3–40 years) was divided into 80% training and 20% testing. The model was trained with the Adam optimizer (learning rate 0.001, epochs 500, loss MAE). Evaluation results: MAE 0.56 years, RMSE 1.94 years, R² 0.9726. Tolerance accuracy: 91% (±1 year), 96.7% (±2 years), 97.5% (±3 years), 99.2% (±5 years). An efficient system for real-time use on low-computing devices, supporting biometric applications such as security, content filtering, personalization, and health. This research contributes to accurate, lightweight, and adaptive age estimation systems.

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