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Journal of System and Computer Engineering
Published by Universitas Pancasakti Makassar
ISSN : -     EISSN : 27231240     DOI : -
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Programming Languages Algorithms and Theory Computer Architecture and Systems Artificial Intelligence Computer Vision Machine Learning Systems Analysis Data Communications Cloud Computing Object Oriented Systems Analysis and Design Computer and Network Security Data Mining
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 134 Documents
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Explainable Machine Learning for Long-Term Monthly Hydroclimatic Forecasting and Extreme-Event Detection Muhammad Rizal
Journal of System and Computer Engineering Vol 7 No 3 (2026): JSCE: July 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i3.2741

Abstract

Long-term hydroclimatic prediction in arid urban environments remains methodologically demanding because monthly records are often intermittent, highly seasonal, zero-inflated, and dominated by rare but consequential extreme events. Using a 121-year monthly hydroclimatic record for Makkah, Saudi Arabia, spanning January 1901 to December 2021, this study develops an explainable hybrid machine-learning framework for monthly forecasting, seasonal diagnostics, and extreme-event detection. The dataset contains 1,452 monthly observations with a mean value of 6.19, median of 3.00, standard deviation of 8.05, and maximum of 52.00, indicating a strongly skewed distribution. Exploratory analysis reveals pronounced seasonality: November, December, and January exhibit the highest hydroclimatic values, whereas June is consistently dry across the full record. A temporal feature set was constructed using lag variables, rolling statistics, annual seasonal memory, cyclical month encodings, and trend indicators. Several predictive models were evaluated, including Random Forest, Extra Trees, Histogram Gradient Boosting, XGBoost, and a hybrid SARIMA–Random Forest residual-correction model. Extra Trees achieved the best forecasting performance on the holdout period, with MAE = 2.997, RMSE = 5.603, sMAPE = 57.669%, and R² = 0.518. Extreme-event detection was performed using a 90th-percentile threshold of 17.68, identifying 146 extreme months over the full record. The best classification trade-off was obtained by Histogram Gradient Boosting, while Random Forest produced the highest ROC-AUC. SHAP-based interpretation demonstrates that seasonal phase variables and annual memory features dominate model behaviour, especially month_cos, month_sin, same_month_last_year, and lag_12. The findings show that interpretable ensemble learning can provide a more transparent and operationally relevant framework than accuracy-only forecasting for arid-region hydroclimatic risk assessment.
Public Approval Rating Updates for President Prabowo Using a Bayesian Dirichlet-Multinomial Hybrid Model anwar anwar anwar; Andries Riesfandhy
Journal of System and Computer Engineering Vol 7 No 3 (2026): JSCE: July 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i3.2748

Abstract

Conventional estimation of presidential approval ratings relies on face-to-face surveys that excel in population representativeness (probability sampling) but suffer from significant time lag between data collection and publication. Conversely, social media monitoring (SMM) provides real-time data but is often biased toward active digital platform demographics. This study proposes a hybrid model based on Bayesian Dirichlet-Multinomial (BDM) to integrate survey data as prior belief with SMM data as likelihood to generate more dynamic and accurate posterior estimates. Prior data were drawn from the Indikator Politik Indonesia national survey of October 2025 (n=1,220, MoE ±2.9%), indicating a public approval rate of 77.7%. Update data were collected from the X (Twitter) platform during November 2025 through crawling and scraping techniques, yielding 2.5 million raw data points processed through Named Entity Recognition (NER), text normalization, and Transformer-based classification (IndoBERT v2.0). After deduplication, 310,000 unique accounts were retained with 120 million total impressions. Sentiment distribution showed 61% positive, 15% negative, and 24% neutral. Bayesian updating produced a corrected public approval estimate of 68.4% (a 9.3 percentage point decline from the survey prior), detecting pockets of dissatisfaction on food price issues underrepresented in conventional surveys. Model validation using Leave-One-Out Cross-Validation (LOO-CV) produced an Expected Log-Pointwise Predictive Density (ELPD) of -127.4, superior to the single-survey baseline model (-148.9). This model offers a new framework as a public opinion early warning system that is responsive to current issue dynamics without sacrificing the statistical validity of traditional survey methods.
Deteksi dan Estimasi Berat Sampah Plastik Berbasis Visi Komputer Andri Dwi Utomo; Mar’atuttahirah Mar’atuttahirah; A. Inayah Auliyah; Muhammad Nur
Journal of System and Computer Engineering Vol 7 No 3 (2026): JSCE: July 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i3.2755

Abstract

Plastic waste management is an increasingly critical environmental challenge due to the growing volume of waste and the limitations of conventional manual weighing methods, which are inefficient and prone to human error. This study proposes a computer vision-based system for automatic detection, classification, and weight estimation of plastic waste using the YOLOv8n model for object detection and Random Forest Regression for weight estimation. The YOLOv8n model is used to detect and classify seven types of plastic waste based on the Resin Identification Code (RIC), namely PET, HDPE, PVC, LDPE, PP, PS, and OTHER. Subsequently, weight estimation is performed using a Random Forest Regression model based on bounding box features, including width, height, area, aspect ratio, and perimeter. The proposed system is evaluated using an unseen test set to ensure unbiased performance measurement. Experimental results show that the YOLOv8n model achieves a mean Average Precision (mAP@0.5) of 91.93% and mAP@0.5:0.95 of 73.27%, while the Random Forest Regression model achieves an R² score of 95.5% with a Mean Absolute Error (MAE) of 4.28 grams. These results demonstrate that the integration of object detection and regression enables accurate and automatic estimation of plastic waste weight, thereby improving the efficiency and objectivity of waste management systems.
Perbandingan DNN dan CNN dalam Klasifikasi Kata-Kata Kasar pada YouTube Shorts Evan Febditya Pratama; Ryan Ari Setyawan; Jemmy Edwin Bororing
Journal of System and Computer Engineering Vol 7 No 3 (2026): JSCE: July 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i3.2759

Abstract

Abusive words in YouTube Shorts are difficult to moderate because harmful expressions may appear in captions, comments, or embedded text within visual frames. This study compares the performance of a Deep Neural Network (DNN) and a Convolutional Neural Network (CNN) for abusive-word classification using text and image features from YouTube Shorts content. The dataset consisted of 1,013 image-text samples labeled into two binary classes: abusive and non-abusive. Textual features were derived from the text column representing OCR/caption information and transformed into 2,500-dimensional features, while image features were obtained from RGB images resized to 96 by 96 pixels. The dataset was divided into training, validation, and testing subsets consisting of 658, 202, and 153 samples, respectively. Both models were trained using dropout, L2 regularization, early stopping, and image augmentation. The results show that CNN outperformed DNN, achieving test accuracy of 0.8627 and macro F1-score of 0.8527, compared with DNN accuracy of 0.8301 and macro F1-score of 0.8195. These findings indicate that CNN provides more effective multimodal classification performance for detecting abusive words in YouTube Shorts content.

Page 14 of 14 | Total Record : 134

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