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 105 Documents
 7   8   9   10   11 
Crop Recommendation Based on Soil and Weather Conditions Using the K-Nearest Neighbors Algorithm Yuliyanto, Yuliyanto; Sahibu, Supriadi; Imran, Taufik; Arisha, Andriansyah Oktafiandi; Munawirah, Munawirah
Journal of System and Computer Engineering Vol 6 No 3 (2025): JSCE: July 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i3.1955

Abstract

The national food self-sufficiency program demands innovation in optimizing the selection of agricultural commodities based on environmental and weather conditions. This challenge is rooted in a fundamental problem faced by farmers—achieving harmony among soil characteristics, weather patterns, and suitable crops. In support of this initiative, it is necessary to develop a crop recommendation system based on machine learning that utilizes key soil and weather condition parameters. This study employs the K-Nearest Neighbors (KNN) algorithm, which functions by identifying the optimal value of ‘K’ to maximize classification accuracy. The KNN algorithm is implemented in a crop recommendation system to classify 1,100 datasets representing ideal growing conditions for 11 crop types. These datasets were generated using a normal distribution approach with a 5% variation from the mean values, and were validated using a clipping function to ensure the data remained within ideal ranges. The results of this study demonstrate that the KNN algorithm achieves high accuracy 96,67% in utilizing soil and weather parameters to generate crop recommendations. The average probability score for the recommended crops was 83.33%. Based on experimental testing, rice was recommended during the rainy and extreme rainy seasons, soybeans were recommended during the dry season, and mung beans were most suitable during extreme dry conditions.
Graph-Based Fraud Detection with Optimized Features and Class Balance Azizah, Anisa Nur; Ritonga, Alven Safik; Atmojo, Suryo; Widhiyanta, Nurwahyudi; Dewi, Suzana; Murdani, M Harist; Sari, Mamik Usniyah
Journal of System and Computer Engineering Vol 6 No 3 (2025): JSCE: July 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i3.2001

Abstract

The increasing use of digital transactions also elevates the risk of fraud, particularly in credit card transactions. Fraud detection poses a challenge due to the highly imbalanced nature of the data and the complexity of relationships among entities. This study proposes a GNN-based approach, integrated with feature selection techniques and class imbalance handling through class weighting based on data distribution. Feature selection was performed using two methods: Correlation-based Feature Selection (CFS) and Random Forest Feature Importance, to obtain the most relevant features. Experimental results show that the combination of Random Forest feature selection and class weighting yielded the highest F1 Score, despite a slight decrease in accuracy. This indicates that feature selection and class weighting strategies can improve the model's ability to detect rare fraudulent transactions. This approach contributes to the development of more accurate and adaptive fraud detection systems in digital transaction environments.
Performance Exploration of Tree-Based Ensemble Classifiers for Liver Cirrhosis: Integrating Boosting, Bagging, and RUS Techniques Aziz, Firman; Jeffry, Jeffry; Wungo, Supriyadi La; Rijal, Muhammad; Usman, Syahrul
Journal of System and Computer Engineering Vol 6 No 3 (2025): JSCE: July 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i3.2031

Abstract

Liver cirrhosis, as a significant chronic liver disease, exhibits a rising global prevalence, demanding more effective preventive approaches. In an effort to enhance early detection and patient management, this research proposes the development of a liver cirrhosis risk prediction model using machine learning technology, specifically comparing the performance of three ensemble tree models: Ensemble Boosted Tree, Ensemble Bagged Tree, and Ensemble RUSBoosted Tree. Utilizing clinical and laboratory data from adults with a history or risk of cirrhosis, the study reveals that Ensemble Bagged Tree achieved the highest accuracy at 71%, followed by Ensemble Boosted Tree (67.2%) and Ensemble RUSBoosted Tree (66%). Analysis of clinical and laboratory variables provides further insights into the most significant contributors to risk prediction. The findings lay the groundwork for the advancement of a more sophisticated liver cirrhosis risk prediction tool, supporting a vision of more personalized and effective preventive strategies in liver disease management
Enhancing Intrusion Detection Using Random Forest and SMOTE on the NSL‑KDD Dataset Saputra, Febri Hidayat; Ilham, Ilham; Rizal, Muhammad; Wisda, Wisda; Wanita, First; Mursalim, Mursalim; Fadillah, Arif
Journal of System and Computer Engineering Vol 6 No 3 (2025): JSCE: July 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i3.2056

Abstract

Intrusion Detection Systems (IDS) play a crucial role in identifying suspicious activities on computer networks. However, a major challenge in developing machine learning-based IDS is the issue of class imbalance, where attacks—being minority classes—are often overlooked by classification models. This study aims to construct an intrusion detection system based on the Random Forest algorithm integrated with the Synthetic Minority Over-sampling Technique (SMOTE) to address this problem. The NSL-KDD dataset is used for evaluation, with the data split into 80% for training and 30% for testing. Experiments include Random Forest-based feature selection and performance evaluation using accuracy, precision, recall, and F1-score metrics. The results show that the Random Forest–SMOTE combination achieves an accuracy of 99.78%, precision of 99.70%, recall of 99.88%, and an F1-score of 99.79%. The confusion matrix indicates a very low rate of false positives and false negatives. Additionally, selecting the most influential features such as src_bytes and dst_bytes improves model efficiency. Thus, the integration of Random Forest and SMOTE proves to be effective in enhancing detection sensitivity toward attacks without compromising model precision. This approach offers a significant contribution to the development of adaptive, accurate, and deployable IDS in real-world network environments.
Perbandingan SVM dan IndoBERT untuk Deteksi Intent Chatbot Lembur dalam Bahasa Indonesia Santosa, Rahmad; Nusantara, Adetiya Bagus; Imron, Syaiful
Journal of System and Computer Engineering Vol 6 No 3 (2025): JSCE: July 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i3.2058

Abstract

Digital transformation in higher education requires the development of intelligent and adaptive information systems, including services such as overtime submission for university staff. Chatbots offer a promising solution to enhance user interaction with the E-LEMBUR system. However, developing chatbots in academic settings poses challenges, including limited training data, complex overtime policies, and diverse institutional terminology. This study compares two intent classification approaches: Support Vector Machine (SVM), a traditional machine learning method, and IndoBERT, a transformer-based model designed for the Indonesian language. The dataset comprises 250 real user queries from the overtime system at Institut Teknologi Sepuluh Nopember (ITS). Experimental results show IndoBERT achieves 87% accuracy, slightly outperforming SVM at 85%. While IndoBERT offers better accuracy, it demands higher computational resources, presenting a trade-off between performance and efficiency. This study contributes by validating IndoBERT’s effectiveness on a limited dataset, establishing an initial benchmark for intent classification in overtime chatbots, and offering implementation recommendations aligned with university IT infrastructure. These findings lay the groundwork for developing context-aware information systems for staff services in Indonesian higher education.
Implementation of the K-Means Algorithm for Clustering Students’ Web Programming Course Grades Using Silhouette Score Limbong, Josua Josen A.; Likumahwa, Fervin Mayos
Journal of System and Computer Engineering Vol 6 No 3 (2025): JSCE: July 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i3.2100

Abstract

The development of information technology requires students majoring in informatics engineering to master web programming as one of the core competencies of the study program. Variations in students' ability to understand the material are reflected in significant differences in grades, so an objective analysis approach is needed to determine the ability of students. This study aims to group students based on academic grades in Web Programming courses using the K-Means algorithm. The data analyzed includes 1-3 assignment grades, attendance, UTS, and UAS from 32 students in the Department of Informatics Engineering, University of Papua. The research stages include preprocessing, data normalization, and clustering process using Orange Data Mining tools. Determination of the optimal number of clusters is done using the Silhouette Score method, and the best results are obtained at K = 4 with a Silhouette Score value of 0.513 which indicates a good cluster structure. The clustering results show that Cluster 1 has the highest score with a final score ranging from 0.93-1 with an Excellent score category consisting of 8 students, Cluster 2 with a Poor score category consists of 10 students with a final score range of 0.23-0.61, then Cluster 3 with a Good score category consists of 10 students with a Final score of 0.78-0.87 and Cluster 4 with a Fair score category consists of 4 students with a score range of 0.64-0.75. The results of this study provide information about the distribution of student abilities and can be the basis for improving learning strategies in the future.
Decision Support System for Selecting Used Cars Using the Analytical Hierarchy Process (AHP) Method Based on a Website at CV Auto Mobil Manokwari Marhaba, Melvi; Mardewi, Mardewi; Sangka, Yuliana; Hasbi, Hasbi; Wungo, Supriyadi La
Journal of System and Computer Engineering Vol 6 No 3 (2025): JSCE: July 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i3.2106

Abstract

Buying a used car is often considered by the public as an alternative because it is more affordable than a new one. However, the process of choosing a used car is not easy because there are various factors that must be considered, such as engine condition, completeness of documents, physical condition, price, engine capacity, and year of manufacture. At CV Auto Mobil Manokwari, prospective buyers often have difficulty determining the choice of a used car that best suits their needs and budget. This research aims to design a website-based decision support system using the Analytical Hierarchy Process (AHP) method to assist buyers in choosing used cars objectively and systematically. The AHP method is used to compare each criterion in pairs and determine the priority weight of each criterion. The system was developed using the PHP programming language and MySQL database with a waterfall approach. With this system, the process of selecting used cars becomes more directed, accurate, and efficient, as well as helping users make decisions practically and quickly, and objectively.
Enhancing Flood Prediction Using Hybrid LSTM-Transformer Deep Learning Approach Fadillah, Arif; Rizal H, Muhammad; Mursalim, Mursalim
Journal of System and Computer Engineering Vol 6 No 3 (2025): JSCE: July 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i3.2083

Abstract

Flood prediction is crucial for effective disaster management, yet it remains a complex challenge due to the nonlinear nature of meteorological processes. This study develops and evaluates a novel hybrid model that integrates Long Short-Term Memory (LSTM) networks and Transformer attention mechanisms to enhance predictive accuracy for rainfall-based flood forecasting. Using extensive Australian weather data collected from 49 stations over a decade (2007-2017), the model incorporates comprehensive feature engineering, including derived meteorological indicators, rolling statistical measures, and temporal lag features. The hybrid LSTM-Transformer architecture achieved superior precision (77.69%) and high accuracy (84.57%) compared to a Random Forest baseline model. Confusion matrix analysis illustrated the hybrid model’s strength in reducing false alarms, indicating a conservative yet highly reliable predictive performance. Feature correlation analysis revealed important relationships among temperature, humidity, pressure, and rainfall, highlighting the complexity of meteorological interactions. The findings demonstrate the effectiveness of integrating sequential and global temporal modeling for flood prediction, providing valuable guidance for operational forecasting systems and disaster preparedness strategies. This research contributes significantly to existing flood forecasting methodologies and suggests promising directions for future enhancements.
Analisis Sentimen terhadap Ulasan Pengguna Aplikasi Gojek dengan Menggunakan Pemrosesan Bahasa Alami Yuliani, Silvia Putri; Muharani, Ari Ati Putri; Fatmawati, Riyana Qori; Fahmi, Faisal
Journal of System and Computer Engineering Vol 6 No 4 (2025): JSCE: October 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i4.2062

Abstract

Gojek, a leading proponent of on-demand services in Indonesia, has garnered a total of 142 million downloads. However, it has received the fewest reviews compared to other on-demand applications. The objective of this research is to identify sentiment in Gojek application user reviews on Google Playstore using Natural Language Processing (NLP) approaches and machine learning algorithms through the Orange platform. The reviews utilized in this study were collected in June 2025 and encompass a total of 3,615 data points, including 2,892 training data and 723 testing data. Sentiments are classified into two categories based on their ratings: positive (rating 4-5) and negative (rating 1-2). The research process is comprised of four primary stages: data collection and labeling, text pre-processing, feature transformation using TF-IDF, and testing five classification algorithms: neural network, naïve Bayes, random forest, decision tree, and k-nearest neighbors. The evaluation results indicate that the Neural Network model demonstrates optimal performance, exhibiting 93.20% accuracy, 93.00% F1-score, and 75.80% MCC. These findings suggest that the NLP approach can be utilized effectively to comprehend user perceptions of applications. It is anticipated that this research will assist Gojek developers in the monitoring and enhancement of service quality, with this enhancement being informed by user feedback.
A Deep Learning Approach to Respiratory Disease Classification Using Lung Sound Visualization for Telemedicine Applications Wahyudi, Andi Enal; Batau, Radus; Aziz, Firman; Jeffry, Jeffry
Journal of System and Computer Engineering Vol 6 No 4 (2025): JSCE: October 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i4.2144

Abstract

This study presents the development of an intelligent system for the classification of respiratory diseases using lung sound visualizations and deep learning. A hybrid Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN–BiLSTM) model was designed to classify four conditions: asthma, bronchitis, tuberculosis, and normal (healthy). Lung sound recordings were converted into time-frequency representations (e.g., mel-spectrograms), enabling spatial-temporal feature extraction. The system achieved an overall classification accuracy of 99.5%, with F1-scores above 0.93 for all classes. The confusion matrix revealed minimal misclassifications, primarily between asthma and bronchitis. These results suggest that the proposed model can effectively support real-time, non-invasive respiratory screening, particularly in telemedicine environments. Future work includes clinical validation, integration of patient metadata, and adoption of transformer-based models to further enhance diagnostic performance.

Page 10 of 11 | Total Record : 105

 7   8   9   10   11 

Filter by Year

2020 2025


Reset
Filter By Issues
All Issue 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