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Muhammad Wali
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International Journal Software Engineering and Computer Science (IJSECS)
Published by Lembaga Komunitas Informasi Teknologi Aceh
ISSN : 27764869     EISSN : 27763242     DOI : https://doi.org/10.35870/ijsecs
Core Subject : Science,
Computer Science & IT
IJSECS is committed to bridge the theory and practice of information technology and computer science. From innovative ideas to specific algorithms and full system implementations, IJSECS publishes original, peer-reviewed, and high quality articles in the areas of information technology and computer science. IJSECS is a well-indexed scholarly journal and is indispensable reading and references for people working at the cutting edge of information technology and computer science applications..
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 387 Documents
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Increasing Product Promotion by Implementing Association Rules in The Web-Based Second_Oldshoes Store Sales System Wulandari, Maria Sri; Noveandini, Rahayu; Dahlan, Fuadi Ahmad
International Journal Software Engineering and Computer Science (IJSECS) Vol. 3 No. 3 (2023): DECEMBER 2023
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v3i3.1939

Abstract

Promotion is one of the efforts that can be made to maximize profits. Currently, there are various techniques and models for promoting products, both through social media and advertising systems. To get optimal promotional results, it is necessary to calculate the level of customer attraction to a product being offered. The second_oldshoes shop is an original second-hand shoe shop, currently the second_oldshoes shop does not have media to promote its products. The application of association rules in the system is used to display the associative value of each product that a customer buys so that a pattern is formed of the products that are often purchased by each customer. So, it can be used as a basis for decisions in determining suitable products to promote to each customer. The design of this web-based sales system uses Navigation Structure and Unified Modeling Language (UML). Creating a system using the HTML, PHP, CSS, JavaScript, MySQL, and Bootstrap programming languages as a framework.
Development of Front-End Web Applications Utilizing Single Page Application Framework and React.js Library Jonathan, Ricky; Suprihadi
International Journal Software Engineering and Computer Science (IJSECS) Vol. 3 No. 3 (2023): DECEMBER 2023
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v3i3.1943

Abstract

In an era marked by rapid technological advancements, the impact of these developments is profoundly significant, particularly in the context of the ubiquity and indispensability of the internet. Motivated by this modern landscape, this study focuses on employing the Single Page Application (SPA) technique for the development of the Mbantu website. Central to this research is the utilization of the React.js library. The methodology encompasses several key phases: a comprehensive literature review, thorough analysis of the problem domain, design and prototyping, and the implementation of React.js for front-end development. The application of the SPA technique is observed to offer notable benefits for developers, including enhanced efficiency and time savings in the web development process. The findings presented in the Results and Discussion section demonstrate that the incorporation of React.js substantially contributes to the development of Mbantu's front-end architecture. Rigorous unit testing is performed on each component to ensure robustness and error minimization. The study concludes with suggestions for future research aimed at expanding the Mbantu web application's feature set to further improve user experience.
Application of Scrum Methodology in The Design of Micro, Small, and Medium Enterprise Systems: A Case Study on Laundry Services Basri, Amat; Atmaja, Dewi Marini Umi; Hakim, Arif Rahman; Sanjaya, Andreas Rino
International Journal Software Engineering and Computer Science (IJSECS) Vol. 3 No. 3 (2023): DECEMBER 2023
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v3i3.1968

Abstract

This study investigates the application of the Scrum methodology in developing systems for Micro, Small, and Medium Enterprises (MSMEs), specifically focusing on laundry service operations. The case study centers on D'Laundry, a laundry service provider, which has traditionally operated with conventional transaction methods. The objective of this research is to develop an MSME system employing the Scrum framework. The system analysis was executed using an Object-Oriented approach, utilizing Unified Modeling Language (UML) for modeling. The development phase employed PHP for application creation, while MySQL was used for the system database. The implementation phase was conducted within a local network, with functional system testing carried out via Black Box testing techniques. Furthermore, the software quality was assessed through a User Acceptance Test, complemented by a questionnaire-based approach. The findings offer insights into the adoption of agile methodologies by MSMEs, emphasizing digital transformation strategies.
Utilizing Clustering Methods for Categorizing Delivery Requirements Based on Analysis of E-Commerce Product Data Sugiarto, Jumat Azzam; Suprapto; Fatchan, Muhamad
International Journal Software Engineering and Computer Science (IJSECS) Vol. 3 No. 3 (2023): DECEMBER 2023
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v3i3.1969

Abstract

This study presents the implementation of the K-Means algorithm model, revealing novel insights into risk categorization in the delivery process. Two distinct clusters were identified: Cluster 1 (C0) indicating high risk, comprising 53 data points out of a dataset of 360, and Cluster 2 (C1) indicating low risk, encompassing 307 data points from the same dataset. Analysis conducted using RapidMiner Studio corroborated these findings, further delineating the cluster membership: C0 with 53 data points and C1 with 307 data points. Each cluster was characterized by optimal centroid values, recorded at 131.717 & 385.075 for C0, and 119.932 & 111.414 for C1. The model's effectiveness was assessed using the Davies-Bouldin Index, yielding a value of 0.626.
Design and Development of An Information System for Indemnity Claim Box Recapitulation Using SDLC Method at Mandiri Inhealth Insurance Yumansyah, Qori; Fatchan, Muhamad; Turmudi Zy, Ahmad
International Journal Software Engineering and Computer Science (IJSECS) Vol. 3 No. 3 (2023): DECEMBER 2023
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v3i3.1970

Abstract

The recapitulation of indemnity claim box system is an ongoing procedural development activity aimed at producing a new system. This activity is undertaken once the system analysis phase has been completed. Based on the results of the current system analysis discussed in the previous section, this paper presents the outcomes of the new system. The performance of the new system is expected to address several issues related to claim data recapitulation. The design of the Indemnity Claim Box Recapitulation system can be applied to reduce the potential for missing documents, simplify reporting, and ensure easy, fast, and accurate access. Implementation testing can assist users and leaders in the claim data recapitulation process.
Interactive Media Application for Banana Cultivation Among Farming Groups at Rajabasa Lama 2 Village Tama, Excellino Egi Yoga; Frimansyah, Ada Udi
International Journal Software Engineering and Computer Science (IJSECS) Vol. 3 No. 3 (2023): DECEMBER 2023
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v3i3.1974

Abstract

This research project aims to develop an interactive media application to enhance the knowledge and skills of banana cultivation among farming groups in Rajabasa Lama 2 Village. The development approach utilized the MDLC (Multimedia Development Life Cycle) method, with a specific focus on the implementation phase. The application has been meticulously designed to facilitate effective learning and tailored to the specific needs of the local farming community. The results of the implementation phase have demonstrated a positive impact on both productivity and the understanding of banana cultivation within the farming groups. Consequently, this application makes a valuable contribution to the advancement of sustainable agriculture at the village level.
Integration of Geographic Information Systems and Spatial Data Analysis in Location Decision Making for Manufacturing Industries Nofirman; Ahmada, Naufal Haidar; Fauzan, Tribowo Rachmat
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 1 (2024): APRIL 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i1.2027

Abstract

-This research analyzes potential locations for the manufacturing industry studied using a GIS approach and data analysis. Researchers combine statistical and spatial analysis methods and unique techniques such as TOPSIS and MOORA to evaluate the most suitable locations based on predefined criteria. Key findings show that Purbalingga Regency is the optimal location, supported by high labor availability, developed logistics infrastructure, and supportive environmental regulations. Sumedang Regency also shows good potential, especially regarding vital market accessibility and strict environmental regulations. However, Bengkulu City faces challenges in several aspects, such as underdeveloped logistics infrastructure and suboptimal ecological regulations. The implications of these findings for manufacturing location decision-making practices, the advantages and disadvantages of GIS approaches and data analysis, and the research contributions to science and the manufacturing industry are also discussed in depth. Thus, this research provides valuable insights for decision-makers in allocating resources and planning investments in the manufacturing industry.
Logistics Efficiency in Product Distribution with Genetic Algorithms for Optimal Routes Trisolvena, Muhammad Nana; Wattimena, Fegie Yoanti; Untajana, Paulus Perey
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 1 (2024): APRIL 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i1.2045

Abstract

This research aims to optimize product distribution routes in logistics using computer simulation approaches and genetic algorithms. This research produces more efficient distribution routes by utilizing mathematical models that reflect actual distribution processes, including variables such as warehouse locations, distribution points, product types, customer demand, and vehicle availability. Genetic algorithms are used to design optimal solutions with implementation stages, which include solution representation, population initialization, fitness evaluation, selection, crossover, mutation, and stopping criteria. The visualization results show that the genetic algorithm can produce more structured and efficient distribution routes, reducing total travel distance, distribution costs, and delivery time. Statistical analysis supports significant improvements in distribution performance after implementing the genetic algorithm, with substantial reductions in total mileage, distribution costs, and delivery times and substantial improvements in customer satisfaction. Financial analysis shows significant cost savings and positive ROI from investing in genetic algorithms, while sensitivity analysis reveals the impact of critical factors on distribution costs. This research confirms the financial and operational benefits of applying genetic algorithms in product distribution optimization, with significant efficiency, cost savings, and customer satisfaction results.
Classification of Hoax News Using the Naïve Bayes Method Qubra, Rama; Saputra, Rizal Adi
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 1 (2024): APRIL 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i1.2068

Abstract

The rampant dissemination of false and unsourced information, commonly known as hoaxes, has become a pervasive issue in the era of internet media. In the digital age, the widespread dissemination of false and unverified information has emerged as a critical concern within the realm of internet media. Hoax news can be used to influence elections, sway public opinion, and create political instability. The rapid evolution of information technology has contributed to the uncontrollable proliferation of hoax content, necessitating the development of intelligent systems for effective classification. This research focuses on implementing a robust classification system for identifying hoax news circulating through internet media. The method used in this program is the Naive Bayes method, specifically Naive Bayes Multinomial, which works with the assumption that each feature (word) is considered independent from the others. Text vectorization using CountVectorizer converts text into a numeric vector, which can be used by classification algorithms. This program uses a trained model to make predictions on testing data and calculate evaluation metrics such as accuracy, confusion matrix, and classification reports. By leveraging these methodologies, the study aims to enhance the accuracy and efficiency of distinguishing genuine news from deceptive hoaxes. The highest accuracy value obtained in this research was 94.73% with a division of 20% test data and 80% training data. True Negative (TN): 4555, False Positive (FP): 178 and False Negative (FN): 295, True Positive (TP): 3952
Classification of Potential Tsunami Disaster Due to Earthquakes in Indonesia Based on Machine Learning Mardiani, Eri; Rahmansyah, Nur; Ningsih, Sari; Lantana, Dhieka Avrilia; Wulandana, Nabila Puspita; Lombu, Azzaleya Agashi; Budyarti, Sisca
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 1 (2024): APRIL 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i1.2084

Abstract

Earthquakes and tsunamis pose significant threats to Indonesia due to its unique geological positioning at the convergence of four tectonic plates. This study focuses on classifying the potential occurrence of tsunami disasters following earthquakes using various data mining methods, including k-Nearest Neighbor (kNN), Naïve Bayes, Decision Tree and Ensemble Method, and Linear Regression. The research employs a qualitative approach to systematically understand and describe the context of natural disasters, utilizing both primary and secondary data collection techniques. Performance evaluation metrics such as Area Under the Curve (AUC), Classification Accuracy (CA), F1 Score, Precision, and Recall are utilized to assess the effectiveness of each method in predicting potential tsunami events. The findings reveal that the kNN method exhibits the highest performance, with an AUC of 94.4% and a precision of 82.8%, indicating robust predictive capabilities. However, misclassifications were observed, emphasizing the need for further refinement. Naïve Bayes also shows promising results with an AUC of 84.5% and precision of 78.6%. Decision Tree and Ensemble Method models, such as Random Forest and AdaBoost, demonstrate reasonable performance, with Random Forest achieving the highest AUC of 71.9%. Linear Regression is employed to explore the correlation between earthquake attributes and tsunami occurrence, revealing a weak relationship. Further research integrating advanced modeling approaches and additional earthquake attributes is recommended to enhance the predictive capabilities of tsunami risk assessment models. The study underscores the importance of employing diverse machine learning techniques and evaluating their performance metrics to refine the accuracy of tsunami prediction models, ultimately contributing to practical disaster preparedness and mitigation strategies.

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