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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
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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.
Application of the C45 Decision Tree Method in Evaluating the Potential and Contribution of Retribution to Pad: Case Study of Barru Regency Arfiansyah, Wahyu; Zainal, Muhammad; Wahyuddin, Wahyuddin; Masnur, Masnur
Journal of System and Computer Engineering Vol 6 No 2 (2025): JSCE: April 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i2.1834

Abstract

Local Original Revenue (PAD) is a vital source for financing regional development, with levies as the main component. However, the main challenge faced is the inability of conventional evaluation methods to identify factors that influence the contribution of levies to PAD effectively. This study aims to evaluate the potential and contribution of levies to PAD by applying the Decision Tree C4.5 method. This research method uses a quantitative data-based approach, by analyzing tax data from various sectors, including Hotel Tax, Parking Tax, Entertainment Tax, and Advertising Tax. The results of the study indicate that the C4.5 method successfully identified more complex contribution patterns and provided a deeper understanding of the influence of seasonal and external factors on tax contributions. Entertainment Tax and Hotel Tax showed the largest contributions in certain periods, while Parking Tax showed greater stability throughout the year. The implications of this study indicate that the application of C4.5 can improve the effectiveness of PAD management, by providing a basis for tax policies that are more data-based and responsive to economic and seasonal fluctuations.
Optimizing Car Wash Services with Web-Based Ordering System Mattola, Andi Alvian As.; Selao, Ahmad; Masnur, Masnur; Alam, Syahirun
Journal of System and Computer Engineering Vol 6 No 2 (2025): JSCE: April 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i2.1835

Abstract

The car wash service industry faces various operational challenges such as long queues, difficulty in finding a reliable location, and unclear information about prices and services. This study aims to optimize car wash services through a web-based booking system. This system allows customers to book services online, choose a convenient time, and make electronic payments, which can reduce waiting times and increase convenience. The research method used is a mixed approach, with data collection through surveys, interviews, and observations at several car wash locations. The results show that the implementation of a web-based booking system improves operational efficiency and customer satisfaction, especially in terms of ease of booking, speed of service, and quality of information provided. However, there is still room for improvement in terms of the timeliness of the wash and the quality of service results. This study also identifies factors that influence customer adoption of web-based systems, such as ease of use, perceived benefits, and social influence. In conclusion, the implementation of a web-based booking system has a positive impact on the performance of the car wash service business by improving operational efficiency, customer satisfaction, and service quality.
Classification of Chocolate Consumption Using Support Vector Machine Algorithm Aziz, Firman; Jeffry, Jeffry; Ayu Asrhi, Nur; La Wungo, Supriyadi
Journal of System and Computer Engineering Vol 6 No 2 (2025): JSCE: April 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i2.1860

Abstract

Chocolate, derived from the processing of cocoa beans (Theobroma cacao), is a widely consumed product with potential health risks when consumed excessively. This study investigates the classification of chocolate consumption behaviors using the Support Vector Machine (SVM) algorithm and evaluates its classification performance. A benchmark dataset on chocolate consumption was employed, partitioned into nine folds for training and testing purposes. To mitigate issues related to data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The experimental findings indicate that SVM, enhanced by SMOTE, demonstrates a reliable capacity for classifying chocolate consumption categories. Performance evaluation across multiple experiments revealed variations in Accuracy, Precision, Recall, and F1-Score, with overall accuracies ranging from 50% to 60%, suggesting moderate but consistent classification performance.
Sentiment Analysis in Indonesian’s Presidential Election 2024 Using Transfomer (Distilbert-Base-Uncased) Aljabar, Andi; Karomah, Binti Mamluatul; Tarisafitri, Nahla; Jeffry, Jeffry
Journal of System and Computer Engineering Vol 6 No 2 (2025): JSCE: April 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i2.1867

Abstract

Utilizing a transformer-based natural language processing model called DistilBERT-base-uncased, this study investigates the use of sentiment analysis in relation to Indonesia's 2024 presidential election. Particularly during political events, sentiment analysis is a potent tool for gaining insight into public opinion. The program divides public posts' sentiment into positive and negative categories by examining social media data (twitter). In order to assure consistency and correctness, the dataset used in the research has been carefully selected. DistilBERT is then used to train the model. The result shows from 19920 row of data only 4.47% of Indonesia’s citizen left positive comment.
Decision Support System for Aren Sugar Aid Using SMART Method anas, anas; Amran, Rozalina; Yunendar, Wakhid
Journal of System and Computer Engineering Vol 6 No 2 (2025): JSCE: April 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i2.1876

Abstract

Sugar palm (Arenga pinnata Merr) is a type of palm plant that is widely found in Indonesia. This plant is able to produce sap liquid from its cut flower bunches. Chemically, sugar palm sap contains 87.2% water, 12.7% carbohydrates, 0.24% ash, 0.2% protein, and 0.02% fat. Simple Multi Attribute Rating Technique is a multi-attribute decision-making method used to assist decision makers in determining several alternative choices. Each alternative is arranged based on a number of attributes, where each attribute has a certain value that is assessed on a certain scale. In addition, each attribute is given a weight that indicates its level of importance compared to other attributes. The application of a Decision Support System with this method can produce more effective, fast, and accurate decisions in the initial selection process for recipients of palm sugar production assistance. The reliability of this system is proven through testing using the White Box Testing and Basis Path Testing methods, which produce a V(G) value of 9.

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