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Jurnal Pilar Nusa Mandiri

pilar
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Published by STMIK Nusa Mandiri

ISSN : 19781946 EISSN : 25276514 DOI : -

Core Subject : Science,

Computer Science & IT

Jurnal Pilar merupakan jurnal ilmiah yang diterbitkan oleh program studi sistem informasi STMIK Nusa Mandiri. Jurnal ini berisi tentang karya ilmiah yang bertemakan: Rekayasa Perangkat Lunak, Sistem Pakar, Sistem Penunjang, Keputusan, Perancangan Sistem Informasi, Data Mining, Pengolahan Citra.

Arjuna Subject : -

Articles 418 Documents

36 37 38 39 40

ONLINE DELIVERY, DINE-IN, AND RESERVATION SYSTEM USING THROW-AWAY PROTOTYPING AT JONG JAVA RESTAURANT Rahmawati, Titasari; Trianto, Edwin Meinardi; Tjipta, William Eka
Jurnal Pilar Nusa Mandiri Vol. 21 No. 1 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i1.6139

The culinary industry continues to grow amidst the increasing purchasing power and consumption of society. Many restaurants still rely on direct sales (dine-in) without utilizing technology to enhance services and expand their market. This study aims to design and build a web-based information system for Restoran Jong Java, which includes dine-in, online delivery, and table reservation features. The system is expected to simplify transaction processes, improve operational efficiency, and provide added value for the restaurant in the industry competition. The system is developed using the throw-away prototyping method. This method allows for iterative system development with direct input from users. Testing is conducted using black-box testing to ensure the system functions as per the specifications, and user acceptance testing is performed through questionnaires with five main types of users: customers, admins, cashiers, waiters, and waitresses. The designed system is capable of supporting the integrated management of dine-in services, reservations, and online deliveries. The tests show that the system meets user needs, with a high level of satisfaction from the respondents. The user acceptance testing in this study shows positive results across different user groups. For the customer group, the average score obtained was 77.25%, the waiter group gave an average score of 83.6%, and for the managers, the system was also well received by them. This system has successfully improved the restaurant's operational efficiency and provided convenience for customers in making orders. It also serves as a technological solution that can help Jong Java expand its market reach and increase competitiveness.

DETERMINATION OF POTENTIAL BUSINESS LOCATIONS USING DATA MINING CLUSTERING Erdiansyah, Dian; Abdullah, Indra Nugraha; Tallo, Amandus Jong
Jurnal Pilar Nusa Mandiri Vol. 21 No. 1 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i1.6295

Potential locations for businesses are highly sought after by business people to set up, expand their business, or establish a new business. Limited information on potential business locations is still a problem faced by many business people in making business decisions. The purpose of this research is to overcome the limitations of potential business location information. The approach used is the K-Means data mining clustering method which is compared to the Gaussian Mixture Model. The dataset used is residential, road access data and business points that already exist around the location. Both clustering methods are compared to the model evaluation method to determine the model with the best performance. The results show that the clustering method with the K-Means algorithm is the clustering model with the best performance. The results of the clustering resulted in 2 clusters, one of which is a cluster of potential business locations of 1041 locations. The conclusion of this study is that data mining clustering can be used to determine the optimal business location cluster. The results of this study can be recommended for business people to look for potential business locations, and for local governments to publicize potential business locations in order to attract investors from outside.

FINE-TUNING RESNET50V2 WITH ADAMW AND ADAPTIVE TRANSFER LEARNING FOR SONGKET CLASSIFICATION IN LOMBOK Wahyudi, Erfan; Imran, Bahtiar; Zaeniah; Erniwati, Surni; Karim, Muh Nasirudin; Muahidin, Zumratul
Jurnal Pilar Nusa Mandiri Vol. 21 No. 1 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i1.6485

This study aims to develop a classification system for traditional Lombok songket fabric patterns using the ResNet50V2 architecture, optimized through fine-tuning and the AdamW optimizer. The data were collected directly from songket artisans in Lombok and categorized into three groups based on the origin of the patterns: Sade, Sukarara, and Pringgasela. The model was trained with data augmentation techniques, including rotation, shifting, and zooming, to increase data diversity. During the training process, fine-tuning was applied to the last layer of ResNet50V2, and optimization was performed using AdamW with a learning rate of 0.0001. The model was evaluated using a confusion matrix, classification report, and analysis of accuracy and loss. The experimental results showed that the model achieved 100% accuracy at the 15th epoch. Furthermore, experiments with different parameters (epochs, batch size, and learning rate) demonstrated that the 15th epoch provided the best results with 100% accuracy, while using higher epochs (30 and 40) did not necessarily yield better outcomes. This model is effective in identifying songket fabric patterns with good classification results for each class. Although the results are excellent, increasing the dataset size and exploring more complex model architectures could further enhance performance. Overall, this study demonstrates the significant potential of deep learning technology in classifying songket patterns with reliable accuracy in real-world applications.

DEVELOPMENT OF A FUNDRAISING WEBSITE WITH PAYMENT GATEWAY TO SUPPORT DIGITAL ECONOMY AT LAZISMU Pratama, Yudistira Bagus; Hevitria, Hevitria; Setiawan, Rifki Hanif
Jurnal Pilar Nusa Mandiri Vol. 21 No. 1 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i1.5716

This study discusses digital-based fundraising and financial services in the management of zakat, infaq, and shadaqah (ZIS). In this process, strategies and data related to distribution, calculation, management, and disbursement of funds often pose challenges. This issue is particularly evident in LAZISMU Bangka Belitung, which has yet to establish a website for large-scale online fundraising and for providing information about its available programs. Currently, the process of fundraising and ZIS management is still carried out conventionally. Therefore, this study proposes a digital system for LAZISMU that can provide information, manage ZIS, and facilitate digital payments through an integrated payment gateway. This innovation aims to make it easier for the public to fulfill their zakat obligations and access information about the fund distribution programs available at LAZISMU Bangka Belitung. The method used in developing this system is a prototype. The results of the study indicate that the developed system can simplify the process of fundraising and managing ZIS digitally. The implementation of this system is expected to enhance the efficiency of zakat management and have a broader positive impact in supporting communities in need.

EVALUATING FIKOM THESIS ADVISORY QUALITY WITH MANAGEMENT BY OBJECTIVES AT UNIVERSITAS MUSLIM INDONESIA Haris, Najwan Firdaus; Hayati, Lilis Nur; Widyawati, Dewi
Jurnal Pilar Nusa Mandiri Vol. 21 No. 1 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i1.5734

The Faculty of Computer Science (FIKOM) at Universitas Muslim Indonesia (UMI) faces significant challenges in enhancing the quality of thesis supervision due to an increasing student population. This study utilizes the Management by Objectives (MBO) approach to evaluate and improve faculty supervision quality. MBO involves setting clear goals, monitoring progress, providing feedback, and evaluating performance based on Key Performance Indicators (KPIs), Customer Satisfaction Scores (CSAT), and Customer Effort Scores (CES). Data was gathered from questionnaires distributed to 211 FIKOM students currently writing or who have completed their theses. The findings reveal that MBO implementation significantly enhances communication between faculty and students, clarifies supervision goals, and boosts student satisfaction. The structured and directed approach of MBO makes the supervision process more efficient, leading to higher quality thesis completions. Additionally, the research underscores the importance of aligning supervision schedules and methods to better fit both faculty and student needs, thus mitigating issues related to faculty workload and student guidance. The study concludes that adopting MBO in thesis supervision processes can substantially improve both the effectiveness and satisfaction of academic guidance at FIKOM UMI.

IMPROVING THE IMAGE OF A BANANA USING THE OPENING AND CLOSING METHOD Fauziah, Siti; Merlina, Nita; Mayangky, Nissa Almira; Hasan, Muhamad; Fahrurrozi4, Nabil Ali; Panjaitan, Yogi Yosua; Putra, Ananta Kusuma
Jurnal Pilar Nusa Mandiri Vol. 21 No. 1 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i1.5968

One significant technique in image processing is morphological image operations, which include methods such as opening and closing. This research explores the application of the opening and closing methods in improving the quality of banana images. The Opening process effectively reduces noise and eliminates small, unwanted details, improving the clarity of the image. However, the Closing process presents some challenges, particularly in altering the natural texture of the banana and blurring fine lines. Careful adjustments are necessary to avoid reducing the visual quality of the image. The study begins with pre-processing steps such as image cleaning and contrast adjustment to enhance the image clarity. The Opening operation, using mathematical morphology and a structural element, removes unwanted small elements from the image, making fine lines and textures more visible for further analysis. The Closing operation, applied after Opening, fills small gaps and connects separated parts of the banana image, restoring the original structure and maintaining image continuity. The combined application of opening and closing methods significantly enhances the quality of banana images by improving clarity, preserving structural integrity, and optimizing overall visual appearance.

STUDENT ATTENDANCE BASED ON FACE RECOGNITION USING THE CONVOLUTIONAL NEURAL NETWORK METHOD Salman, Salman; Ramdan, Hendri
Jurnal Pilar Nusa Mandiri Vol. 21 No. 1 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i1.6157

Mataram University of Technology (UTM) still relies on a manual attendance process, such as signing paper-based attendance lists, which are prone to fraud and difficult to manage on a large scale. This study develops a face recognition-based attendance system using Convolutional Neural Network (CNN), which can automatically recognize visual patterns and unique facial features. CNN has advantages in extracting significant facial features, allowing it to recognize faces under various lighting conditions and viewing angles. The dataset used consists of 5,820 facial images from 97 students, with 60 augmented images per student. The results indicate that this system can be implemented in a lecture environment, achieving a validation accuracy of 98.5% at the 150th epoch. However, the model has some limitations, such as a relatively small dataset size and challenges in recognizing faces under extreme lighting conditions or unusual angles, which can affect accuracy in real-world applications. Additionally, although this system has the potential for real-time implementation, further optimization is required to ensure fast and accurate responses on a large scale. To overcome these limitations, future research can explore the use of direct camera input to enhance efficiency and user experience. Furthermore, improving dataset quality by incorporating variations in lighting and image angles, as well as exploring alternative deep learning architectures such as Vision Transformers (ViT) or Swin Transformer, can enhance model performance and generalization. By implementing these improvements, the facial recognition-based attendance system can be more optimal in enhancing accuracy and ease of use in academic environments.

PROJECT MANAGEMENT OF STEEL PLATE WAREHOUSE INVENTORY INFORMATION SYSTEM Samidi, Samidi; Romadhan, Fitrah
Jurnal Pilar Nusa Mandiri Vol. 21 No. 1 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i1.6258

Information system project management is an activity of available resources from an information system solution development project so that a system solution can be produced that meets predetermined objectives. From the findings in the field that the process of applying the inventory information system project management is still constrained because the previous business process lacks support and some still use ms. office excel in recording the process of entering and exiting goods becomes an obstacle if many transactions occur in a day, and inventory data tends to have differences from the warehouse and head office, so this study aims to apply the inventory information system project management that has been developed using the waterfall development method can function optimally and effectively by implementing the Project Management Body of Knowledge (PMBOK) method where the focus of discussion is work breakdown structure analysis, activity of arrow analysis, and project cost estimate analysis. The results of this study obtained the results of stage-based WBS analysis, activity of arrow analysis with 58 days, while project cost estimate analysis with 14% for the communication stage, 20% for the planning stage, 57% for the modeling stage, 4% for the construction stage, 5% deployment stage.

AGGLOMERATIVE HIERARCHICAL CLUSTERING FOR REGIONAL GROUPING IN CENTRAL JAVA BASED ON WELFARE INDICES Kurnia Desita, Raafi; Fahmi, Amiq; Rohmani, Asih; Sulistyono, MY. Teguh
Jurnal Pilar Nusa Mandiri Vol. 21 No. 1 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i1.6445

Central Java Province comprises 35 regencies/cities with diverse welfare characteristics. These variations present challenges for the government in formulating targeted development policies. This study aims to group regions in Central Java based on welfare indices to support more effective policy planning. The Agglomerative Hierarchical Clustering method with the Average Linkage approach is applied to cluster the regions based on three attributes: Human Development Index, Uninhabitable Houses, and Economic Growth Rate. Data were obtained from the Central Java Provincial Social Service and the official website of the Central Statistics Agency (BPS) and processed using the proposed method. Experimental results indicate three clusters with proportions: 32 regions in cluster 1 (91.4%), 2 regions in cluster 2 (5.7%), and 1 region in cluster 3 (2.9%). Regions with higher welfare dominate the first cluster, while the second and third clusters include regions facing more significant welfare challenges. Clustering results were evaluated using the Silhouette Score (0.535) and Davies-Bouldin Index Score (0.610), demonstrating that the applied method effectively grouped regions based on the specified attributes. The findings of this study are anticipated to lay the groundwork for more directed and effective development policies.

MACHINE LEARNING FOR EMPLOYMENT POSITION MAPPING Apriadi, Sena Aditia; Pardede, Hilman Ferdinandus
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.3028

Employee performance directly impacts organizational efficiency, yet traditional HR analytics often lack predictive precision. This study bridges HR theory and machine learning by evaluating tree-based algorithms for employee data analysis. Using a dataset of 15,227 employee records, we tested the Bagged Decision Tree algorithm, focusing on variables such as talent, career values, and aspirations. The Bagged Decision Tree achieved 98.65% accuracy, with talent and career values as key predictors. Excluding aspiration values reduced accuracy slightly to 98.57%, while excluding career values lowered it significantly to 92.13%. These findings highlight the robustness of the Bagged Decision Tree in HR analytics and emphasize the importance of variable selection, particularly career values and talent, in predicting performance outcomes. Future work should further explore real-world implementation challenges.

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