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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 18 Documents
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Search results for , issue "Vol 6 No 1 (2025): JSCE: January 2025" : 18 Documents clear
Air Conditioner Control and Monitoring System based on Temperature Balance in Server Room using Fuzzy Logic and Internet of Things Methods Putu Rika Permana, I Gusti; Sahibu, Supriadi; Jalil, Abdul; Munawirah, Munawirah
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1623

Abstract

This research develops a temperature and humidity control system in the server room based on the Internet of Things and using fuzzy logic algorithms at AMIK Luwuk Banggai. The system is designed using NodeMCU ESP32, DHT11 sensor, Arduino IDE, and Blynk application, with objective of monitoring and controlling environmental conditions in real time. A series of quantitative experiments were conducted to evaluate the effectiveness of the sensor system. These experiments involved observations, measurements, and a comparison of the results with manual calculations. The results demonstrate that the DHT11 sensor exhibits a margin of error of 1.21% and a hardware accuracy rate of 98.79%. Furthermore, the integration of the Internet of Things (IoT) and the implementation of fuzzy logic in air conditioner control studies, as demonstrated in this study, has the potential to enhance the accuracy of temperature and humidity control within the room server to an accuracy rate of 90.91%. Furthermore, it can improve the responsiveness of the system in maintaining temperature stability. These findings were observed at AMIK Luwuk Banggai, where the application of IoT and fuzzy logic has been implemented. Fuzzy logic offers an effective and dependable approach to regulating temperature fluctuations in the server room, ensuring a stable environment that minimizes the likelihood of operational issues or hardware damage. The objective is to extend the lifespan of the hardware by preventing such complications.
Hill Cipher-Based Visual Cryptography for Copyright Protection of Images Using Flexible Matrix Keys Mafula, Veradella Yuelisa; Fauzan, Abd. Charis; Prabowo, Tito; Ramadhan, Muhammad Rizky
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1634

Abstract

The widespread distribution of digital images on the internet has diminished the copyright protection associated with them. In some cases, copyrighted and economically valuable digital images should not be modified or distributed without permission, as altering the original image can harm its owner. This violation is common, but many internet users are unaware of it. The goal of this research is to protect intellectual property rights of digital images using visual cryptography based on the Hill Cipher algorithm with matrix key flexibility. Hill Cipher is chosen for its ability to encrypt data in blocks, making it more secure than classical cryptographic algorithms that encrypt data individually. Visual cryptography is used to secure digital images through encryption and decryption. Encryption scrambles the image, while decryption restores it. The research method involves collecting digital image datasets, preprocessing, Hill Cipher encryption, and decryption. Key flexibility includes matrix keys of 2x2, 3x3, and 4x4 to enhance security. This research has demonstrated the effectiveness of the Hill Cipher algorithm in protecting digital images through encryption and decryption processes with flexible matrix keys of size 2x2 and 3x3. The results of the experiments, including encryption and decryption using both matrix sizes, have been thoroughly analyzed with respect to various cryptographic metrics: histogram analysis, energy, entropy, and running time.
Parameter Optimization Supports Vector Machine Using Genetic Algorithms to Improve the Efficiency of Data Transfer Prediction on Google Cloud Namruddin, Respaty; Mahendra, Ricky; Kunaefi, Aang; Bakar, Ramlah Abu
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1636

Abstract

Efisiensi transfer data merupakan elemen kunci dalam infrastruktur cloud seperti Google Cloud. Penelitian ini bertujuan untuk meningkatkan akurasi prediksi efisiensi transfer data menggunakan Support Vector Machine (SVM) yang dioptimasi dengan Algoritma Genetika (GA). Dataset berisi informasi tentang ukuran file, latensi jaringan, utilisasi server, dan waktu transfer data. Algoritma Genetika diterapkan untuk mencari parameter optimal, yaitu nilai C dan gamma. Hasil penelitian menunjukkan bahwa optimasi parameter menggunakan GA mampu meningkatkan akurasi prediksi hingga 90%, dibandingkan metode tradisional Grid Search yang mencapai akurasi maksimal sebesar 88%.
Performance Analysis of API in Google Cloud Storage Service Integration Namruddin, Respaty; Sam, Rafiqa Mulia Indah Sari; Syamsuddin, Rajul Waahid; A, Amiruddin; Kunaefi, Aang
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1637

Abstract

Google Cloud Storage (GCS) is one of the leading cloud storage services that supports large-scale data management through API integration. APIs allow applications to upload, download, and manage data in real-time. This study aims to analyze the performance of APIs in integration with GCS using response time, throughput, and latency parameters. Tests were conducted on various scenarios, including massive data transfer, distributed data management, and caching usage. The results showed that the average API response time reached 120 ms under normal conditions and increased to 180 ms under high load. Throughput reached an average of 400 MB/s, but decreased when the number of simultaneous requests increased. The average server latency was recorded at 60 ms and can be optimized with caching technology. Implementation of strategies such as Content Delivery Network (CDN) and request header optimization can improve performance by up to 30%. This study provides practical guidance for developers to optimally utilize GCS APIs in large-scale data management.
ARIMA Method Implementation for Electricity Demand Forecasting with MAPE Evaluation Wungo, Supriyadi La; Aziz, Firman; Jeffry, Jeffry; Mardewi, Mardewi; Syam, Rahmat Fuady; Nasruddin, Nasruddin
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1666

Abstract

Electricity demand forecasting is critical for efficient energy management and planning. This study focuses on the development and implementation of the Autoregressive Integrated Moving Average (ARIMA) method for forecasting electricity demand in South Sulawesi's power system. The evaluation of forecasting accuracy was conducted using the Mean Absolute Percentage Error (MAPE), which measures the percentage error between predicted and actual values. Two experiments were conducted with different ARIMA models: ARIMA(5,1,0) and ARIMA(2,0,1). Results showed that the ARIMA(5,1,0) model achieved a MAPE of 2.15%, while the ARIMA(2,0,1) model performed slightly better with a MAPE of 1.91%, indicating highly accurate predictions. The findings highlight the effectiveness of the ARIMA method in forecasting electricity demand, providing a reliable tool for energy providers to optimize resource allocation and enhance operational efficiency. Future research may explore integrating ARIMA with other advanced methods to further improve forecasting performance.
Evaluating the Effectiveness of Online Learning Methods with a Probabilistic Naive Bayes Approach Butsiarah, Butsiarah; Rijal, Muhammad
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1669

Abstract

Online learning methods become an important element in supporting the flexibility and effectiveness of teaching and learning process, especially through approaches such as Video Tutorial, Virtual Discussion, and Self-paced Reading. This research aims to evaluate the effectiveness of the three methods in improving students' engagement, comprehension, and learning motivation by utilizing Naive Bayes algorithm. The dataset used includes student data taken through questionnaires and teacher evaluation results, with variables such as material suitability, engagement, ease of access, and student exam results. Through this approach, the research is able to predict the learning method that best suits students' needs based on the analyzed variables. The results show that Video Tutorial is the most effective method in supporting students' understanding and motivation. The implementation of this research is expected to help the development of a better online learning system in improving students' learning experience, and provide practical recommendations for educators in choosing the right learning method.
CNN Modeling for Classification of Bugis Traditional Cakes Iskandar, Imran; Jeffry, Jeffry; Fadliana, Nurul; Rimalia, Watty; Ahyana, Nurul
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1685

Abstract

Abstract This research aims to create a classification system that can recognize traditional Bugis cakes using the Convolutional Neural Network method. (CNN). Traditional Bugis cakes play an important role in Indonesia's culinary heritage, which is rich in diversity and flavor. However, the lack of documentation and sufficient recognition of these cakes could lead to the loss of cultural knowledge. In this study, a collection of images of traditional Bugis cakes was gathered and processed for training a CNN model. This model was created to recognize and classify various types of cakes based on their visual attributes. The evaluation results show that the CNN model can achieve a high level of accuracy in identifying these cakes, making it a useful tool in preserving and promoting traditional Bugis cakes. This research is expected to contribute to the development of image recognition technology and raise public awareness about the richness of local culinary heritage. Keywords : Convolutional Neural Network (CNN), Bugis Cake, Indonesian Cuisine
Detection of Persistent vs Non-Persistent Medications in Pharmacy Using Artificial Intelligence: Development of Intelligent Algorithms for Pharmaceutical Product Safety Abasa, Sustrin; Aziz, Firman; Ishak, Pertiwi; Jeffry, Jeffry
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1618

Abstract

The pharmaceutical industry requires an effective system to detect medications that are persistent and non-persistent, in order to improve safety and the efficiency of product management. This study aims to develop a system based on Artificial Intelligence (AI) using the Decision Tree algorithm to classify medications based on prescription data provided by doctors. The dataset used in this study includes prescription information, such as medication type, prescription quantity, frequency of use, and duration of medication use, which are used to determine whether the medication is persistent or non-persistent. The Decision Tree algorithm is applied to develop a reliable classification model, with the goal of detecting medications that are used continuously (persistent) and those that are not used on a continuous basis (non-persistent). This study applies AI technology in the pharmaceutical field, focusing on the use of doctor prescriptions and classifying medications based on usage characteristics. The results of the study show that the algorithm performs well with an accuracy of 78.33%, recall of 0.7804, precision of 0.7804, and an F1 score of 0.6934, indicating the model's ability to classify medications with reasonable accuracy.

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