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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 14 Documents
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Search results for , issue "Vol. 9 No. 1 (2026)" : 14 Documents clear
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 Arifianto, Mada Jimmy Fonda; Nugroho, Waluyo; 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 Taufiq Subagio, Ridho; Fauzan, Ahmad Alif; Rafi`, Mohammad; Ramadhan, Bintang Trilanang; Surachman, Ade
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.

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