cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Jeffry
Contact Email
jeffry@unpacti.ac.id
Phone
+6285285111435
Journal Mail Official
jsce@unpacti.ac.id
Editorial Address
Jl. Andi Mangerangi No.73, Mamajang Dalam, Mamajang, Kota Makassar, Sulawesi Selatan 90132
Location
Kota makassar,
Sulawesi selatan
INDONESIA
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
 10   11   12   13   14 
Penerapan Tesseract OCR untuk Validasi Pembayaran Otomatis dalam E-Commerce Annisa Salsabila Apriliya Wijaya; A Inayah Auliyah; Jeffry Jeffry; Firman Aziz; Syahrul Usman
Journal of System and Computer Engineering Vol 7 No 2 (2026): JSCE: April 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i2.2625

Abstract

The rapid expansion of e-commerce in Indonesia has resulted in a significant increase in digital transactions, necessitating expedited and precise payment verification. Administrators at the SweetJab hijab e-commerce platform must manually verify bank transfer receipts, a process that is time-consuming and susceptible to errors. This study utilises Optical Character Recognition (OCR) with the Tesseract engine as a supplementary approach for verifying transfer payments on the SweetJab website. The methodology encompasses image preprocessing (resizing to 200%, converting to greyscale, and enhancing contrast), employing Tesseract OCR with PSM 6 and an LSTM model for character recognition, and utilising regular expressions (regex) to extract structured transaction data. We employed Black Box Testing and Character Error Rate (CER) computations on 40 preliminary test samples and 40 post-implementation samples to assess the system. The initial test demonstrated an accuracy of 89.5%, which increased to 92.5% upon complete system integration. This study demonstrates that OCR is an effective method for extracting information from payment receipts, while maintaining security through a final manual verification by the administrator.
Design and Implementation of a Shrimp Pond Monitoring Information System Using Internet of Things and Android Application ROZALINA AMRAN ROZALINA AMRAN
Journal of System and Computer Engineering Vol 7 No 2 (2026): JSCE: April 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i2.2633

Abstract

Shrimp ponds are a place built to cultivate shrimp, with a tropical climate that makes Indonesia one of the largest shrimp producers, both saltwater, freshwater and brackish water shrimp. For shrimp farming business people, it is one of the profitable business opportunities because it is easy to maintain and produces fantastic profits. Maintenance for shrimp pond cultivation can be done by checking water quality using litmus paper, a digital pH meter or a chlorine test kit. However, this method is still considered less efficient because water is taken from the pond repeatedly over time, which wastes time and energy, in addition, changes in water quality in the pond can occur at any time, causing shrimp death and causing farmers to fail to harvest. In this study, water temperature, water pH, and water current sensors were used to monitor water quality. Then the data from the water quality detected by the sensor will be received by the Esp32 microcontroller and then send the data to firebase. Firebase plays a role in storing and sending data to Android so that it can be displayed on a smartphone. Poor data values will cause a notification to appear on the farmer's smartphone so that the farmer does not need to check the pond location repeatedly.
Performance Evaluation of IoT-Based AC Control Using Multi-Modal Fuzzy Sensors Amiruddin A; Abdul Latief Arda; Abdul Jalil; Andani Achmad; Supriadi Sahibu; Yuyun Yuyun
Journal of System and Computer Engineering Vol 7 No 2 (2026): JSCE: April 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i2.2649

Abstract

his study addresses the challenge of controlling Air Conditioner (AC) temperature in enclosed spaces in tropical climates, where improper operation often leads to thermal discomfort and excessive energy consumption. The research aims to develop and implement an Internet of Things (IoT)-based system for monitoring and controlling AC temperature by integrating multi-modal sensors and applying a fuzzy logic approach. The proposed system employs a DHT22 sensor to measure temperature and humidity, a thermopile sensor to capture human body temperature, and a PIR sensor to detect occupancy and movement within the room. Sensor data are processed using an ESP32 microcontroller with FreeRTOS-based multitasking and transmitted to the Blynk platform for real-time monitoring. Decision-making is carried out using fuzzy logic based on the temperature difference (ΔT) between body temperature and ambient conditions to automatically regulate AC operation. Experimental results indicate that the system performs reliably and provides adaptive control, achieving a fuzzy logic accuracy of 64.34% under real-world conditions. Furthermore, the automated control mechanism reduces energy consumption by 35.7% compared to conventional manual operation. Overall, the findings confirm that the integration of multi-modal sensing, IoT technology, and fuzzy logic can effectively enhance energy efficiency while maintaining thermal comfort in indoor environments.
Attention-Driven Contrastive Learning for the Identification of Rare Partial Discharge Signal in GIS Muhaimin Hading; Herviana Herviana; Muh. Ikhsan Amar; A. Syahrinaldy Syahruddin; Muhammad Irsan; Aulia Salsabila R.H
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.v7i3.2664

Abstract

Gas-insulated switchgear (GIS) is a critical component in high-voltage power transmission systems, where partial discharge (PD) activity can indicate early-stage insulation defects. However, phase-resolved partial discharge (PRPD)-based fault diagnosis remains challenging due to noisy signals, perturbed measurement conditions, and severe class imbalance, particularly for rare floating-electrode defects. This study proposes an attention-driven contrastive learning framework for rare PD signal identification in GIS. PRPD data are represented as two-dimensional density matrices derived from phase angle, discharge magnitude, and occurrence count. The proposed framework applies PRPD-specific data augmentation, followed by ResUNet-based denoising, CBAM-based feature refinement, and supervised contrastive learning to improve feature separability among PD classes. The framework was evaluated using a public 550 kV GIS PRPD dataset containing corona-type, surface-type, floating-electrode-type, and noise classes. The results show that augmentation substantially improved robustness. When trained with raw data, the proposed model achieved 91.90% accuracy and 53.81% F1-score under the original test scenario, but decreased to 48.76% accuracy and 46.26% F1-score under IEC-perturbed testing. After augmentation, the model achieved 98.24% accuracy and 96.58% F1-score under the original scenario, and maintained 97.44% accuracy and 97.76% F1-score under IEC perturbation. These findings indicate that the proposed framework supports robust PRPD representation learning for GIS PD diagnosis under perturbed and imbalanced conditions.
Evaluasi Kinerja SIPETA : Integrasi EUCS dan Teknik Equivalence Partitioning Litafira Syahadiyanti; Pamudi Pamudi; Alda Raharja; Maulana Zidan Adriansyah
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.v7i3.2687

Abstract

The development of information systems in higher education institutions requires comprehensive performance evaluations to ensure that the systems are capable of supporting learning and administrative processes effectively, efficiently, and in accordance with user needs. This study aims to evaluate the performance of SIPETA in supporting academic processes and to identify factors that affect the quality of the system. The main problems found were suboptimal information quality, data accuracy, and several system functions that affected user trust. The study used an integrative approach with the End User Computing Satisfaction (EUCS) model to measure user satisfaction and the Equivalence Partitioning technique in Black Box testing to assess system functionality. Data was collected through questionnaires administered to 100 users and direct testing of the system's features. The results showed that the system had an effectiveness rate of 86 percent and was rated as good in terms of appearance, ease of use, and timeliness of service. However, the content and accuracy variables were still in the poor category due to several functional failures such as schedule validation, notifications, revision uploads, and guidance history. Overall, SIPETA is suitable for use but requires improvements in data quality and system logic to increase reliability and user satisfaction.
Comparison of SMOTE, Class Weighting, and Classical Machine Learning Models on the ID-SMSA Indonesian Stock Market Dataset I Komang Adyanata; I Gede Aris Gunadi; I Made Gede Sunarya
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.v7i3.2688

Abstract

Sentiment classification of social-media text related to the Indonesian stock market is a growing research area. The ID-SMSA dataset is the publicly available labelled corpus for this domain, yet class-imbalance handling strategies on this dataset have not been systematically compared across multiple classifiers. This paper evaluates Multinomial Naive Bayes, linear Support Vector Machine (SVM), and Random Forest under three imbalance-handling conditions: no handling, class weighting, and SMOTE. All experiments use the full 3,287-tweet dataset with an 80:20 stratified split and report macro F1 as the primary metric. SMOTE consistently improves macro F1 across all classifiers. The largest gain is on Naive Bayes (+0.137, from 0.589 to 0.726). The best configuration is SVM with SMOTE, achieving macro F1 of 0.752 and accuracy of 0.784. Class weighting benefits Random Forest (+0.011) but slightly reduces SVM, confirming that linear SVM on TF-IDF is robust to moderate imbalance at IR = 2.41. Per-issuer evaluation reveals macro F1 variation from 0.647 on TPIA to 0.881 on BBNI, shaped by vocabulary consistency, class dominance, and domain specificity. These results provide a transparent and reproducible classical baseline that situates transformer-based and deep-learning approaches on ID-SMSA within a well-defined reference frame.
Explainable Non-Organic Waste Classification: A Comparative Study of CNN with SHAP Interpretability Against Vision Transformer Approaches Setio Basuki; Lika Anjelina; Alfian Wahyu Juhar Putra; Yusuf Nur Muhammad
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.v7i3.2694

Abstract

This study develops a classification model based on Machine Learning (ML) and Computer Vision (CV) to automatically distinguish recyclable and non-recyclable waste. Waste management still faces the challenge of low manual sorting efficiency, thus reducing recycling potential. The dataset consists of 399 waste images divided with a ratio of 80:10:10 for training, validation, and testing. Three combinations of visual features were tested, namely Mean Color (RGB) + LBP + HOG, Histogram + HOG + Edge, and Mean Color (RGB) + Histogram + GLCM, each of which was evaluated using four conventional ML algorithms, namely SVM-RBF, SVM-Linear, Random Forest, and Gradient Boosting. Meanwhile, deep learning models namely CNN, ViT, and LoRA ViT were trained directly on raw images without manual feature extraction. Experimental results show that CNN achieved the highest testing accuracy of 82.50%, outperforming all conventional ML models that achieved a maximum accuracy of 75.00%, as well as ViT (72.50%) and LoRA ViT (70.00%). The application of SHAP-based Explainable AI (XAI) provides transparency to the model's decision-making process. These findings demonstrate that CNN with certain regularization settings are effective for distinguishing recyclable and non-recyclable waste, in supporting sustainable smart waste management systems.
Development Of Deep Learning in Diagnosing Pathology Januardi Nasir
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.v7i3.2697

Abstract

Skin cancer is one of the most common types of cancer worldwide, with a particularly high incidence in Indonesia. According to Globocan 2020 data, there were approximately 18,000 cases of skin cancer with nearly 3,000 deaths, with melanoma having the highest mortality rate. A study at Dr. Cipto Mangunkusumo General Hospital (2014–2017) showed that malignant melanoma accounted for 5.7% of all skin cancer cases, with the majority of patients presenting at an advanced stage. Pathological diagnosis remains the gold standard for confirming melanocyte lesions, but it is subjective with variability reaching 45.5%.The development of Whole Slide Imaging (WSI) and Deep Learning (DL) has enabled the implementation of more accurate and consistent computer-aided pathological diagnosis systems. Several previous studies, such as those by Hekler et al., Brinker et al., and Li et al., have demonstrated that Convolutional Neural Network (CNN)-based models can match or exceed the performance of human pathologists. However, two major challenges remain: the model's limitations in distinguishing atypical melanocytic lesions and decreased performance due to staining variations across medical centers. This study aims to develop a DL-based intelligent pathological diagnosis model using WSI images that can accurately distinguish benign, atypical, and malignant melanocytic lesions and is robust to staining variations, to improve the effectiveness of skin cancer diagnosis in Indonesia.
Perbandingan Kinerja YOLOv8 dan YOLOv11 untuk Deteksi Area Teks Manga Berdasarkan Metrik Intersection over Union M Ridwan Dwi Septian; Kautsar Hasby Dastien Fredila; Ericks Rachmat Swedia; Margi Cahyanti
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.v7i3.2712

Abstract

Manga has complex visual characteristics, such as variations in speech bubble shapes, diverse text orientations, and dense background illustrations, which complicate the automatic text area detection process. Detection errors in the form of false positives and false negatives can cause text areas to be localized inaccurately and affect the processing at subsequent stages. This study compares the performance of YOLOv8m and YOLOv11m in detecting text areas in Japanese manga using the Intersection over Union (IoU) metric. The dataset consists of 551 manga images annotated into three classes, namely clean_text, messy_text, and text_bubble. Both models were trained under the same parameter configuration for 60 epochs to ensure an objective comparison. The evaluation was performed on 50 test images covering 564 text objects. The test results show that YOLOv11m obtained an average IoU of 0.7598, which is higher than YOLOv8m (0.7196). In addition, YOLOv11m exhibited a faster inference time of 1158.72ms compared with 1276.23ms for YOLOv8m. Based on these results, YOLOv11m demonstrated superior performance over YOLOv8m in terms of both localization accuracy and computational efficiency for the Japanese manga text area detection task.
Analisis Sentimen Berbasis Aspek pada Komentar YouTube tentang CoreTax Menggunakan Support Vector Machine dan Random Forest Nur Vadila; Josua Josen A. Limbong; Ratna Juita
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.v7i3.2722

Abstract

The implementation of the CoreTax Administration System (CTAS) by the Directorate General of Taxes has received diverse responses from the public, which are reflected in YouTube comments. This study applies Aspect-Based Sentiment Analysis (ABSA) to identify user opinions regarding CoreTax and compares the performance of Support Vector Machine (SVM) and Random Forest for sentiment classification. Data were collected through web scraping from three Youtube videos, yielding 1.527 valid comments after preprocessing. A rule-based method was used to classify comments into five aspects, namely system, performance, user-friendliness, tax services, and policy. The results indicate that the system aspect was the most frequently discussed (56,12%), while negative sentiment dominated the dataset (59,2%). The highest proportion of negative sentiment was found in the user-friendliness aspect (81,29%), followed by performance (76,44%). In model evaluation, Random Forest achieved better results than SVM, obtaining 0.80 accuracy, 0.84 precision, 0.75 recall, and 0.79 F1-Score. Overall, ABSA provides deeper insights into user perceptions and issues related to CoreTax implementation.

Page 13 of 14 | Total Record : 134

 10   11   12   13   14 

Filter by Year

2020 2026


Reset
Filter By Issues
All Issue Vol 7 No 3 (2026): JSCE: July 2026 Vol 7 No 2 (2026): JSCE: April 2026 Vol 7 No 1 (2026): JSCE: January 2026 Vol 6 No 4 (2025): JSCE: October 2025 Vol 6 No 3 (2025): JSCE: July 2025 Vol 6 No 2 (2025): JSCE: April 2025 Vol 6 No 1 (2025): JSCE: January 2025 Vol 5 No 2 (2024): JSCE: Juli 2024 Vol 5 No 1 (2024): JSCE: Januari 2024 Vol 4 No 2 (2023): JSCE: Juli 2023 Vol 4 No 1 (2023): JSCE: Januari 2023 Vol 3 No 2 (2022): JSCE: Juli 2022 Vol 3 No 1 (2022): JSCE: JANUARI 2022 Vol 2 No 2 (2021): JSCE: JULI 2021 Vol 2 No 1 (2021): JSCE: JANUARI 2021 Vol 1 No 1 (2020): JSCE: Juli 2020 More Issue