cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Saluky
Contact Email
luke4line@yahoo.com
Phone
-
Journal Mail Official
jurnalsistemcerdas@gmail.com
Editorial Address
-
Location
Kota bekasi,
Jawa barat
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 209 Documents
 17   18   19   20   21 
Analysis of Diabetes Classification Performance Improvement Using Ensemble Bagging and K-Fold Mawardi Kudin; Abd Salam At Taqwa; Angga Kurniawan; Chairi Nur Insani
Jurnal Sistem Cerdas Vol. 9 No. 1 (2026)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v9i1.630

Abstract

Diabetes mellitus represents a long-term metabolic disorder whose global incidence continues to rise, making precise early identification essential to minimize severe complications. Machine learning techniques have been extensively utilized for diabetes classification; however, single-model approaches often suffer from performance constraints, such as susceptibility to overfitting and high variability in prediction outcomes. To address these challenges, this research introduces a bagging-based ensemble learning strategy integrated with K-Fold Cross Validation to enhance both predictive accuracy and model robustness. The study employs the Pima Indians Diabetes Dataset, which contains 768 patient records described by eight clinical features and one outcome variable. Eight classification methods—Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Naïve Bayes, Gradient Boosting, and XGBoost—were assessed individually and within the proposed ensemble framework. Model effectiveness was measured using accuracy, precision, recall, and F1-score derived from the confusion matrix. The findings indicate that the ensemble bagging approach generally strengthens model stability and yields improvements in accuracy and precision across most algorithms. Notably, K-Nearest Neighbors and XGBoost demonstrated the most stable gains following ensemble integration. Nevertheless, enhancements in precision were frequently associated with a reduction in recall, reflecting a trade-off in identifying positive cases. In summary, the integration of bagging and K-Fold Cross Validation provides a more resilient and dependable classification model, offering strong potential for supporting clinical decision-making in early diabetes detection.
Implementation of Content-Based Cosine Similarity Algorithm with TF-IDF and SBERT for Movie Recommendation Eliata Zefanya Irabela; Norhikmah
Jurnal Sistem Cerdas Vol. 9 No. 1 (2026)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v9i1.633

Abstract

The number of films continues to increase on streaming platforms often makes users confused in deciding which film to watch. To overcome this research develops content-based movie recommendation system. Representation of the film information obtained through the application of TF-IDF and SBERT to genre and synopsis data. Cosine similarity is used to calculate the closeness between representations. The performance system is then evaluated through the Precision@K, MAP@K, and Recall@K metrics. From the test results, hybrid approach shows better performance more stable than single method. With a MAP value reaching 0.95 Recall 0.95 dan Precission 0.71 . In the future, the development system will still possible by utilizing other types of data, including user interaction data.
A Machine Vision–Based Automated Wheel Leak Detection System Using Real-Time Object Detection in the Water Leak Testing Process Susetyo Bagas Bhaskoro; Sarosa Castrena Abadi; Aris Budiyarto; Inkreswari Retno Hardini; M. Pribadi Lukman
Jurnal Sistem Cerdas Vol. 9 No. 1 (2026)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v9i1.637

Abstract

Water leak testing in automotive wheel manufacturing has traditionally relied on manual visual inspection of bubble formation, introducing subjectivity and limiting repeatability in quality assurance processes. This study developed and experimentally validated a real-time leak detection system based on machine vision, directly integrated with an industrial water leak tester platform. A dataset comprising 686 annotated images was constructed from recorded operational testing sequences and partitioned into 80% training and 20% validation subsets. The network was trained for 150 epochs and deployed within an integrated framework incorporating temporal decision logic and automated event logging to ensure deterministic classification under continuous video streaming. Experimental validation was conducted across five scenarios (A–E), including high-leak, low-leak, no-leak, and in-situ operational testing conditions, totaling 100 trials. The aggregated confusion matrix yielded 60 true positives and 40 true negatives with zero false positives and false negatives, resulting in accuracy, sensitivity, specificity, precision, and F1-score values of 1.0 within the evaluated domain. Receiver operating characteristic and precision–recall analyses confirmed strong class separability and stable decision boundaries. Although the results demonstrated high discriminative performance under controlled and operational settings, further large-scale validation under heterogeneous industrial environments is required to fully assess long-term robustness. The proposed framework provided an automated, objective, and real-time inspection solution aligned with Industry 4.0 principles for intelligent manufacturing systems.
Real-Time Telemetry Based Monitoring System for Energy Efficiency Evaluation of Scheduled Dimming in Office Corridor Lighting Muhammad Risqi Nuryana; Veronica Windha Mahyastuty; Ridwan Satrio Hadikusuma
Jurnal Sistem Cerdas Vol. 9 No. 1 (2026)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v9i1.640

Abstract

In many office buildings, corridor lighting systems are commonly operated at full brightness continuously, regardless of occupancy conditions, resulting in unnecessary energy consumption. This study proposes and evaluates a real-time telemetry-based monitoring system to assess the energy efficiency of a scheduled dimming strategy for office corridor lighting. The developed system integrates dimmable LED luminaires with a telemetry unit capable of transmitting real-time illuminance and energy consumption data for monitoring and analysis. A time-based dimming schedule of 25%, 50%, and 75% output levels was implemented in an office corridor environment. Illuminance measurements were collected at five different points along the corridor, while electrical energy consumption was recorded continuously over a seven-day observation period through the telemetry monitoring platform. The results indicate that even at the lowest dimming level (25%), the corridor maintained an average illuminance of 100 lux, which remained within acceptable lighting standards for pedestrian circulation. Telemetry data further demonstrated that the scheduled dimming strategy reduced weekly energy consumption by approximately 64% compared to continuous full operation (0.711 kWh reduced to 0.253 kWh). These findings confirm that real-time telemetry monitoring enables accurate performance evaluation of lighting control strategies while ensuring compliance with visual comfort requirements. The study highlights the potential of telemetry-based lighting monitoring systems as an effective approach to optimize energy use, minimize over-lighting, and support data-driven energy management in office buildings.
ECRM Strategy In Improving Services And Sales Of Electronic Products Qevin Dwi Rafitrah; Dewi Anggraeni; Ruri Ashari Dalimunthe
Jurnal Sistem Cerdas Vol. 9 No. 1 (2026)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v9i1.648

Abstract

Business competition in the era of globalization demands companies to continuously adapt, one of which is through the implementation of Electronic Customer Relationship Management (E-CRM). Mega Cell Sei Piring Pulau Rakyat, a retail store for electronic products, faces challenges in the form of inconsistent sales fluctuations and customer management that is still manual. If this unstable sales pattern continues without improvement, it has the potential to disrupt profitability and create a risk of loss. This study aims to implement an E-CRM strategy as a strategic solution to improve customer service, optimize sales management, and maintain the company's revenue stability. This qualitative research produces a design and prototype of a web-based E-CRM system (PHP and MySQL) that enables transaction recording, product management, promotion management, and integrated sales reporting. The E-CRM system that has been built facilitates market segmentation and better customer interaction, thus it is expected to improve operational efficiency, reach a wider market through online information access, and encourage customer attraction and loyalty through data-based promotional offers
Logistic Regression Model for Predicting SNBP Admission Based on Academic Data Daffa Naufal Rahimi; Yusuf Ramadhan Nasution
Jurnal Sistem Cerdas Vol. 9 No. 1 (2026)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v9i1.649

Abstract

The National Selection Based on Achievement (SNBP) is a crucial pathway for prospective students to access higher education; however, the uncertainty surrounding admission outcomes often causes anxiety among prospective students. This study aims to develop an SNBP admission prediction model based on logistic regression using academic data from students at the Darul Arafah Raya Islamic Boarding School. The method used is binary logistic regression with parameter estimation via the Newton-Raphson method. The research data consists of 261 academic records of students from 2022 to 2025, divided into training and testing datasets. Model evaluation was conducted using accuracy, precision, recall, F1 score, and AUC-ROC metrics. The results show that the model achieved convergence at the seventh iteration with an accuracy rate of 81.25 percent. The precision and recall values were 82.35 percent, respectively, while the AUC-ROC value was 0.9049, which falls into the “good classification” category. It can be concluded that the logistic regression model is effective for predicting SNBP graduation based on average report card scores and is suitable for implementation as a decision support system for students in estimating their admission chances.
Random Forest Regression for Energy Consumption Prediction on Raspberry Pi Edge Computing Mada Jimmy Fonda Arifianto; Waluyo Nugroho; Afianto
Jurnal Sistem Cerdas Vol. 9 No. 1 (2026)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v9i1.632

Abstract

Efficient energy management in smart homes is critical for cost reduction and sustainability, yet conventional cloud-based monitoring systems often face challenges related to network latency, bandwidth consumption, and data privacy. This study proposes an Edge Computing architecture to predict electrical energy consumption locally using a Raspberry Pi, thereby eliminating the dependency on continuous cloud processing. The system integrates a PZEM-004T sensor to acquire real-time voltage, current, and power data, while the core intelligence is built upon the Random Forest Regression (RFR) algorithm trained and deployed directly on the Raspberry Pi to forecast short-term energy load based on historical usage patterns and Internet of Things (IoT). Experimental results demonstrate that the proposed edge system achieves high prediction accuracy with an R2 score of 0.94 and a Mean Absolute Percentage Error (MAPE) of 4.25%, and a Root Mean Square Error (RMSE) of 12.80 Watts using a model configuration of 100 estimators, confirming that Raspberry Pi based edge computing is a viable, low latency, and privacy preserving solution for intelligent energy management
AI Model for Detecting Depression Based on Sleep Pattern Analysis Using Sequence Models Gregorius Rizcy Orlando Pradana; Ida Nurhaida
Jurnal Sistem Cerdas Vol. 9 No. 1 (2026)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v9i1.651

Abstract

Depression is generally diagnosed through subjective clinical assessments, so objective biomarkers such as sleep patterns are needed. Unfortunately, conventional machine learning methods often ignore the temporal dynamics of sleep. This study aims to evaluate four Sequence Models architectures (LSTM, Bi-LSTM, GRU, Bi-GRU) to detect indications of depression from 7 days of sequential sleep data. The methodology processes data from 5,782 subjects using six physiological features (oxygen saturation, sleep efficiency, spindle microarchitecture) converted into a 3D matrix. Evaluation uses Precision, Recall, F1-Score, and ROC-AUC metrics to handle imbalanced data. The results prove that the Bidirectional model is more robust in capturing the temporal context holistically. Bi-GRU achieved the highest ROC-AUC score (0.9909), while Bi-LSTM produced the best F1-Score (0.85) and Recall (0.82). The standard GRU was validated as the most computationally efficient model (5 seconds/epoch). Explainable AI analysis confirmed that fast spindle percentage, REM duration, and spindle density are the strongest predictors of affective dysfunction. In conclusion, the Bidirectional architecture has proven reliable in identifying sleep anomalies, providing a solid foundation for real-time IoMT-based psychiatric screening systems.
Design and Usability Evaluation of an Android-Based Point of Sales Application Using Goal-Directed Design Ridho Taufiq Subagio; Ahmad Alif Fauzan; Mohammad Rafi`; Bintang Trilanang Ramadhan; Ade Surachman
Jurnal Sistem Cerdas Vol. 9 No. 1 (2026)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v9i1.660

Abstract

The implementation of Point of Sales (POS) systems in culinary MSMEs still faces a major problem in the form of low usability levels of available applications, thus hampering technology adoption in daily operations. This study aims to design and implement an Android-based POS application with a Goal-Directed Design (GDD) approach to improve ease of use and system effectiveness. The case study was conducted on a typical Cirebon culinary MSME, namely the Empal Gentong Mang Sumedi Restaurant. The research method includes Goal-Directed Design stages that include persona identification, user goal formulation, usage scenario design, and application implementation using the Flutter framework . System evaluation was carried out through functionality testing and usability testing using the System Usability Scale (SUS) on 20 respondents using observation, interviews, and questionnaires. The results showed that the developed POS application obtained an average SUS score of 77.25 which is in the Good and Acceptable Usability categories . The relatively homogeneous distribution of scores and the 95% Confidence Interval values which are all above the usability threshold indicate the consistency of user perceptions of the ease of use of the system. These findings indicate that the implementation of Goal-Directed Design contributes significantly to improving the quality of the interface and user experience in Android-based POS applications for culinary MSMEs.

Page 21 of 21 | Total Record : 209

 17   18   19   20   21 

Filter by Year

2018 2026


Reset
Filter By Issues
All Issue Vol. 9 No. 1 (2026) Vol. 8 No. 3 (2025) Vol. 8 No. 2 (2025) Vol. 8 No. 1 (2025) Vol. 7 No. 3 (2024) Vol. 7 No. 2 (2024) Vol. 7 No. 1 (2024) Vol. 6 No. 3 (2023) Vol. 6 No. 2 (2023) Vol. 6 No. 1 (2023) Vol. 5 No. 3 (2022) Vol. 5 No. 2 (2022) Vol. 5 No. 1 (2022) Vol. 4 No. 3 (2021) Vol. 4 No. 2 (2021) Vol. 4 No. 1 (2021) Vol. 3 No. 2 (2020): Riset dan Inovasi Sistem Cerdas pada Penanggulangan Wabah Covid-19 Vol. 3 No. 3 (2020): Kecerdasan Artifisial pada Rekayasa Biomedis Vol. 3 No. 1 (2020): Artificial Intelligence untuk Indonesia Vol. 2 No. 3 (2019): Transformasi dan Inovasi Kota Cerdas Vol. 2 No. 2 (2019): Smart Transportation Vol. 2 No. 1 (2019): Artificial Intelligence for Smart Society Vol. 1 No. 2 (2018): Internet of Things for Smart Society Vol. 1 No. 1 (2018): Smart System More Issue