The Pilar Nusa Mandiri : Journal of Computing and Information System Journal is a formation of the Information Systems study program, which was originally a medium for accommodating scientific writings of Universitas Nusa Mandiri Jakarta Information Systems lecturers. Along with the times, this journal has become a National journal that has P-ISSN: 1978-1946 and E-ISSN: 2527-6514. Pilar Nusa Mandiri : Journal of Computing and Information System has become a Rank 5 Accredited Journal and is trying to become a higher accredited journal. Pilar Nusa Mandiri : Journal of Computing and Information System is published 2 times in 1 year, namely in March and September. This journal is Rank 5 Accreditation Certificate (S5), Accreditation is valid for 5 years. Starting from Vol. 19, No. 2 the Year 2023 to Vol. 24, No. 1 the Year 2028 based on the Decree of the Minister of Research and Technology / National Research and Innovation Agency Number 0173 / C3 / DT.05.00 / 2025, March 21, 2025.
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CLUSTERING OF E-WALLET USAGE BASED ON TRANSACTION PATTERNS USING K-MEANS
The use of e-wallets has grown rapidly along with the increasing demand for fast and conven-ient digital financial transactions. This development requires service providers to better under-stand user behavior through transaction pattern analysis. This study aims to cluster e-wallet users based on their transaction patterns using the K-Means algorithm. The data analyzed in-clude several key variables, namely transaction frequency, transaction value, transaction type, and transaction time. The K-Means method is applied to group users with similar transaction characteristics through data normalization and optimal cluster determination. The results in-dicate that e-wallet users can be divided into several distinct segments with significantly dif-ferent transaction patterns. Each cluster represents specific user behavior characteristics, such as high-frequency users, high-value transactions, or time-based usage patterns. In conclusion, this study contributes to the growing body of research on digital payment analytics by demonstrating the applicability of the K-Means clustering algorithm for transaction-based user segmentation. The findings provide empirical evidence that behavioral transaction data can be systematically structured into meaningful user groups through unsupervised learning techniques. This research extends prior studies by integrating clustering evaluation methods to ensure optimal segmentation results, thereby offering a methodological reference for future studies in fintech data analysis.
Copyright (c) 2026 Ayu Hernita, Nouval Trezandy Lapatta, Sabaruddin Saputra, Muhammad Akbar http://creativecommons.org/licenses/by-nc/4.0
BALANCING SECRECY AND TRANSPARENCY IN SMART CITIES: A SYSTEMATIC REVIEW OF BLOCKCHAIN-BASED GOVERNMENT DATA MANAGEMENT
Blockchain offers a promising solution to recurring problems in centralized government data systems, including security vulnerabilities, inefficiencies, and declining public trust. Blockchain’s decentralized and tamper-resistant architecture provides a mechanism to balance transparency and confidentiality in the management of public data. This paper presents a systematic review of the application of blockchain technology in the government domain, evaluating its impact on data security, efficiency, and public participation. This systematic review was conducted following the PRISMA protocol and searched the Scopus database for publications from 2016 to 2025. Of the 592 publications retrieved, 49 met the eligibility and quality criteria. A thematic synthesis was conducted on the results with respect to transparency, confidentiality, governance, and scalability. The findings indicate that blockchain has the potential to enhance transparency and data security through the creation of tamper-resistant records that strengthen public oversight. Blockchain also has the potential to improve the efficiency of public service delivery through digital platforms such as land registry systems and health record management. However, challenges remain, particularly related to scalability and regulatory frameworks for the adoption and implementation of the technology. Therefore, adaptive regulatory frameworks and appropriate governance mechanisms are required to support fair and sustainable adoption of blockchain technology in government data management.
Copyright (c) 2026 Nabil Abdallh Abbas Atmotawkel, Abdulrahman Mohammed Hussein Obaid http://creativecommons.org/licenses/by-nc/4.0
WEB-BASED DIGITAL DOCUMENT GOVERNANCE SYSTEM FOR OPTIMIZING MULTI-AGENCY COMPLIANCE MONITORING: AN EMPIRICAL STUDY AT TEGAL CITY PPID
Information and documentation management at the Public Information and Documentation Management Office of Tegal City faces operational challenges due to reliance on manual mechanisms for collecting files from 73 Regional Government Agencies. The main obstacles include slow recapitulation processes that can take up to four weeks, high risk of damage or loss of physical archives, and difficulty in monitoring document submission status in real time. This study aims to design and implement a web-based information system that optimizes the workflow of collecting, verifying, and monitoring public document compliance across government agencies within the digital governance infrastructure of Tegal City PPID. The system was developed using the Waterfall model which includes requirement analysis, system and data design, coding, testing, and maintenance. System modeling was carried out using Unified Modeling Language to visualize system functions and data structures. The resulting system integrates workflows between 73 Regional Government Agencies and the PPID through a centralized platform that allows operators to upload documents independently and administrators to conduct verification processes. The system also provides a monitoring dashboard to track document submission compliance in real time. System validation was conducted using Black-box Testing on 11 functional scenarios with a success rate of 100%, and User Acceptance Testing confirming system acceptance based on seven evaluation criteria. The implementation of this system improves efficiency in document management, reduces the risk of data loss, and supports more effective public information services.
public information and documentation management office
Copyright (c) 2026 Rahmat Afandi, Nurmalasari Nurmalasari http://creativecommons.org/licenses/by-nc/4.0
IMPLEMENTATION OF A SCHOOL INFORMATION SYSTEM AT SD AL ISTIQOMAH IN BANJAR CITY
Information technology facilitates human activities in the modern era. The need for information technology has influenced various institutions, including the education sector. SD Islam Al-Istiqomah is a formal elementary education institution located in Banjar District, Banjar City, West Java. The New Student Admission System (PPDB) still uses Google Forms, which can cause delays in validating applicant data. The school fee payment process, which must be made directly, also creates difficulties for parents due to the separate locations of the administrative office and the school, often resulting in late payments. Therefore, this research aims to assist both parents and the school in carrying out their tasks more easily, securely, and efficiently. The system designed is an integrated information system for New Student Admission (PPDB) and school fee payments within a single system. The integration of these two systems simplifies centralized student data management without the need to export and import data between systems. The method used in this research is Rapid Application Development (RAD), which was selected because it can accelerate application development. The results of testing using Black Box Testing indicate that the system operates as expected without any errors.
Copyright (c) 2026 Trisnawan Trisnawan, Kusnadia Kusnadia, Susy Rosyida http://creativecommons.org/licenses/by-nc/4.0
DESIGN AND BUILD A WEBSITE-BASED EMPLOYEE ATTENDANCE INFORMATION SYSTEM AT SATKER BEKANGDAM I/BUKIT BARISAN
The employee attendance process at Bekangdam I/Bukit Barisan was previously conducted manually, which created potential problems such as recording errors, data loss, and delays in generating attendance reports. This study aims to design and develop a website-based employee attendance information system that supports computerized and accurate attendance recording. The system was developed using the Laravel framework as the backend, HTML and JavaScript as the frontend, and MySQL as the database management system. The system also utilizes Global Positioning System (GPS) technology to validate employee locations during the attendance process. The development method used in this research is Rapid Application Development (RAD), which consists of planning, system modeling, construction, and implementation stages. Performance testing using WAPT Pro with a simulation of 20 concurrent users for 10 minutes showed that the system successfully handled 20 user sessions with 646 pages accessed and 3,039 server request hits, with an average page response time of 0.61 seconds. Security testing using OWASP ZAP indicated that no high-risk vulnerabilities were found. In addition, User Acceptance Testing (UAT) involving 20 respondents showed that the system functions according to user needs. The results indicate that the developed system improves attendance recording accuracy, accelerates attendance reporting, and increases the effectiveness of employee attendance management.
Copyright (c) 2026 Prawira Maulana Ilham, Dinih Rachmasari, Hikmatulloh Hikmatulloh http://creativecommons.org/licenses/by-nc/4.0
APPLICATION OF THE RANDOM FOREST ALGORITHM IN PREDICTING IMPULSE BUYING BY CONSUMERS ON INDONESIAN MARKETPLACE APPLICATIONS
The development of e-commerce in Indonesia has driven an increase in impulsive buying behavior influenced by emotional and situational factors. This study aims to determine the important factors that influence impulsive buying by applying the Random Forest machine learning algorithm. Data were obtained from 628 online shopping application users aged 18-40 years in Indonesia. The analysis results indicated that the majority of respondents fall into the medium impulsive buying category (67.9%), with a model accuracy of 86.5%. The Hedonic Browsing (HB) variable had the greatest influence on impulsive buying behavior, followed by Utilitarian Browsing (UB) and Time Perspective (TP). These findings confirm that emotional aspects and situational conditions play a more dominant role than rational considerations in driving spontaneous shopping decisions.
Copyright (c) 2026 Ni Nyoman Cory Villdina, Monica Julia Efata, Etania Frederika, Muhammad Sony Maulana http://creativecommons.org/licenses/by-nc/4.0
DESIGNING A VTUBER CHARACTER BASED ON INDONESIAN FOLKLORE TO ENHANCE YOUTH INTEREST IN LOCAL CULTURE
Indonesian folklore contains rich cultural values, yet its relevance to Generation Z is declining due to their preference for digital content consumption. Modern media such as animations or games are no longer effective to capture their attention, creating a gap in how folklore is introduced to younger audiences but not for older audiences. This study explores the use of Virtual YouTubers (VTubers) as a medium to adapt and present folklore in ways that align with Gen Z’s digital lifestyle. Dewi Sekartaji, a character from the Panji folklore of East Java, is reimagined as a VTuber whose personality emphasizes patience and perseverance. The objective is to preserve folklore while fostering cultural pride and introducing new perspectives from side characters to Gen Z. User testing showed an average satisfaction score of 4.8/5, and the Character livestream attracted around 500 viewers, indicating that VTubers are an effective approach for bridging traditional culture with modern digital engagement.
Copyright (c) 2026 Christopher Owen Setiawan, Aristarchus Pranayama, Rebecca Milka Natalia Basuki http://creativecommons.org/licenses/by-nc/4.0
PADANG FOOD IMAGE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK (CNN)
The recognition of Padang traditional foods presents a challenge because of their high visual similarity, which makes manual classification difficult. This study aims to develop an automatic image classification model for Padang foods using the Convolutional Neural Network (CNN) algorithm. The dataset consisted of 1350 images across nine classes of Padang dishes including omelet, chili egg, cow tendon curry, stuffed intestine curry, fish curry, dendeng batokok, rendang, ayam pop, and fried chicken. The CNN architecture was trained for twenty epochs and evaluated using accuracy, loss, confusion matrix, and testing with new images. The results show that the model reached a final training accuracy of 70.2 percent and a validation accuracy of 65 percent, while testing with unseen images produced correct predictions with moderate confidence levels. These findings suggest that CNN is effective for classifying Padang traditional foods and can be applied in culinary promotion, digital food catalogs, and technology based ordering platforms.
Copyright (c) 2025 Nabilah Putri Permana, Syafri Arlis http://creativecommons.org/licenses/by-nc/4.0
ANALYSIS OF THE NEED FOR AN INFORMATION SYSTEM ON PRICES AND AVAILABILITY OF BASIC MATERIALS
The development of information technology has driven digital transformation in various sectors, including the economic sector. Managing data on the prices and availability of basic commodities is crucial for maintaining community economic resilience. This study applies a design thinking approach to analyze the need for an information system on the prices and availability of basic commodities in Yogyakarta City, with a testing plan prepared using black box, white box, and security methods. The analysis produced three main findings: the need for Single Sign-On (SSO) with role-based access, real-time monitoring of commodity prices, and cross-agency integration in agenda and program management. The proposed system design consists of four main modules: administration, agenda, services, and programs/activities. Since this study is limited to the needs analysis and prototype design stage, empirical test results are not yet available. Nevertheless, the study provides an initial framework and foundation for cross-agency integration in the Yogyakarta City Government to support transparency, coordination, and control of basic commodity prices.
Copyright (c) 2025 Andriyan Dwi Putra, Diana Rohmaniah http://creativecommons.org/licenses/by-nc/4.0
EVALUATING SISDM EFFECTIVENESS USING THE EUCS METHOD: A CASE STUDY AT SSDM POLRI
The Human Resource Information System (SiSDM) application of the Indonesian National Police (Polri) serves as a digital tool to support personnel management. The success of its implementation strongly depends on user satisfaction. This study aims to analyze the effectiveness of the SiSDM system using the End-User Computing Satisfaction (EUCS) method, which consists of five key dimensions: content, accuracy, format, ease of use, and timeliness. A quantitative research method was applied by distributing questionnaires to 100 personnel at SSDM Polri who actively use the SiSDM application. The collected data were analyzed using the Partial Least Square-Structural Equation Modeling (PLS-SEM) technique with the help of SmartPLS software. The results indicate that format, accuracy, and timeliness have a significant influence on user satisfaction, while content and ease of use do not show a significant effect. The R² value of 0.810 reveals that 81% of the variance in user satisfaction can be explained by the five EUCS dimensions, with f² values indicating that most dimensions contribute substantially to user satisfaction. This research confirms that the EUCS method is effective for evaluating information system quality in government institutions such as Polri. In practical terms, these insights provide input for the Indonesian National Police to prioritize improvements in format, accuracy, and timeliness, as well as to improve content and ease of use so that the system better meets the needs of personnel. As a result, the ongoing development of SiSDM can support organizational performance improvement, information transparency, and HR management efficiency within the Indonesian National Police.
Copyright (c) 2026 Ahmadin Cetta Subagyo, Nurul Khasanah http://creativecommons.org/licenses/by-nc/4.0
COMPARATIVE ANALYSIS OF RANDOM FOREST AND SUPPORT VECTOR CLASSIFIER FOR PREDICTING STUDENTS’ ON-TIME GRADUATION
On-time graduation is one of the key indicators of educational quality in higher education. The influencing factors range from students’ internal issues and academic abilities to institutional policies. However, academic management has not yet been able to classify the data and analyze the underlying factors contributing to delayed graduation. By identifying these factors, management can formulate appropriate academic solutions or policies. The purpose of this study is to build a prediction model for on-time graduation using machine learning algorithms. This study compares the classification performance of the Random Forest algorithm and the Support Vector Classifier (SVC). The dataset, consisting of 1,298 student records, includes academic data such as study program, GPA, TOEFL score, cohort year, and study duration. Model performance was evaluated using accuracy, F1 score, and ROC-AUC metrics, followed by a confusion matrix analysis. The final evaluation revealed that the Random Forest algorithm achieved the best performance, with an accuracy of 91.86%, an F1 score of 91.86%, and a ROC-AUC of 97.39%. Meanwhile, the SVC model obtained an accuracy of 81.12% and an F1 score of 81.09%. Based on these results, it can be concluded that the Random Forest algorithm is more reliable as a prediction model in the academic domain. The main contribution of this study is the development of an early detection system for students at risk of delayed graduation. Furthermore, the findings can serve as a basis for designing more solution-oriented academic policies in accordance with the conditions at STIMIK Tunas Bangsa Banjarnegara.
Copyright (c) 2025 Nurus Sarifatul Ngaeni http://creativecommons.org/licenses/by-nc/4.0
ADDRESSING DIGITAL STARTUP FAILURE THROUGH THE AGILE METHODOLOGY APPROACH: A SYSTEMATIC LITERATURE REVIEW
Startups are recognized as emerging enterprises that contribute to job creation, economic stabilization, and national development. Digital startups are formed to address challenges within their environments. This study aims to provide solutions and preventive measures for digital startup failures, given the persistently high global failure rate of 90%. A systematic literature review (SLR) was conducted to identify Agile-based Critical Success Factors (CSFs), which were then mapped as solutions to mitigate digital startup failures. Based on the findings, the most significant contributing factor to the failure of digital startups is insufficient funding (i.e., running out of capital or financial resources). To address this issue, the agile method offers relevant solutions that can be mapped to the problem, namely the adoption of “Iterative Budget Management,” “Accurate Effort Estimation,” and “Risk Management Strategies.” This study provides practitioners with valuable insights, knowledge, and reference points regarding the critical success factors (CSFs) derived from agile practices, which can serve as strategic mechanisms for mitigating failure in early-stage startups. Moreover, the research is expected to contribute new theoretical understanding that informs potential solutions to prevent digital startup failure.
Copyright (c) 2025 Kenedi Binowo http://creativecommons.org/licenses/by-nc/4.0
EVALUATING PREPROCESSING EFFECTS IN NAME RETRIEVAL USING CLASSICAL IR AND CNN-BASED MODELS
Information Retrieval (IR) systems are pivotal for efficient data management, particularly in tasks involving name searches and entity identification. This study evaluates text preprocessing techniques, including case folding, phonetic normalization, and gender tagging, that affect the performance of classical (TF-IDF, LSI) and CNN-based retrieval models for multilingual name matching. Using a dataset of 365,468 globally diverse names, this study implements a preprocessing pipeline featuring: Double Metaphone phonetic preprocessing (92% validation accuracy), gender disambiguation for unisex names (92% accuracy), and optimized n-gram tokenization for short names. Evaluation metrics include precision, recall, F1-score, and our novel Name Similarity Score (NSS), combining orthographic and phonetic preprocessing. Results show our full pipeline improves recall to 1.00 and F1-score by 37% while reducing false negatives by 63%. Key findings reveal: TF-IDF achieves superior recall (0.98 vs CNN’s 0.85), LSI handles cultural variants effectively, and CNNs deliver the highest precision (0.91 vs TF-IDF’s 0.70), particularly for unisex names. This work contributes both a scalable multilingual preprocessing framework and the NSS evaluation metric for robust name retrieval systems.
Copyright (c) 2025 Frizca Fellicita Marcelly, Irwansyah Saputra, Muhammad Bagus Andra http://creativecommons.org/licenses/by-nc/4.0
COMPARATIVE PERFORMANCE OF TRANSFORMER AND LSTM MODELS FOR INDONESIAN INFORMATION RETRIEVAL WITH INDOBERT
Neural network-based Information Retrieval (IR), particularly with Transformer models, has gained prominence in information search technology. However, the application of this technology in Indonesian, a low-resource language, remains limited. This study aims to compare the performance of the LSTM model and IndoBERT for IR tasks in Indonesian. The dataset consists of 5,000 query–document pairs collected via scraping from three Indonesian news portals: CNN Indonesia, Kompas, and Detik. Evaluation was performed using MAP, MRR, Precision@5, and Recall@5 metrics. The results show that IndoBERT outperforms LSTM in all metrics with a MAP of 0.82 and MRR of 0.84, while LSTM only reached a MAP of 0.63 and MRR of 0.65. These findings confirm that Transformer models like IndoBERT are more effective at capturing semantic relevance between queries and documents, even with limited datasets.
Copyright (c) 2025 Nendi Sunendar Sunendar, Irwansyah Saputra http://creativecommons.org/licenses/by-nc/4.0
IDENTIFICATION OF FOOD DIVERSIFICATION ON JAVA ISLAND USING ARCGIS
Indonesia is addressing the challenges of food security and consumer preference also known as Food diversification. The research aims to analyze the potential of various local food sources as alternatives to rice, which is the dominant staple food in Indonesia, with a particular focus on geographic implications. Although local carbohydrate sources like corn, potatoes, and tubers are available, their adoption is limited and understudied in relation to geographic distribution and consumer behavior. This study integrates survey data and GIS-based spatial analysis to evaluate local food diversification potential. Findings show that while 100% of respondents consume rice, 48.7% have tried alternatives, with limited availability (41.03%) and higher costs (17.95%) as key barriers. With 94.7% expressing willingness to adopt new staples, the results suggest GIS-based decision support systems can guide effective, region-specific food policy interventions.
Copyright (c) 2025 Amir Murtako, Faiqa Hadya Hanifa, Eidelwise Gloria Effatha, Sri Rezeki Candra Nursari, Febri Maspiyanti http://creativecommons.org/licenses/by-nc/4.0
APPLICATION OF ARTIFICIAL NEURAL NETWORK METHODS TO DETECT HEART ATTACKS
A heart attack is a medical emergency caused by restricted blood flow to the heart, commonly leading to myocardial infarction due to blood clots or fat accumulation. Early detection of heart disease is crucial to support prevention efforts and assist healthcare professionals in timely diagnosis and treatment. This study applies the Backpropagation Neural Network (BPNN) algorithm as an intelligent computing method for heart attack detection. Experimental results demonstrate a prediction accuracy of 96.47%, confirming the effectiveness of artificial neural networks in identifying heart attacks in patients. These findings highlight the potential of BPNN as a reliable and precise early detection system, which can support more accurate clinical decision-making and improve the effectiveness of heart attack prevention and treatment.
Copyright (c) 2025 Nasir Hamzah, Yan Rianto http://creativecommons.org/licenses/by-nc/4.0
SENTIMENT ANALYSIS ON TRAINING IMPLEMENTATION’S FEEDBACK IN PT XYZ
Customer satisfaction is an important aspect in building a company's image, both for employees and external parties. In order to improve employee satisfaction and performance, training that organized by the company needs to receive feedback so that the training organizers can continue to provide the best service to employees who participate in the training. The large volume of feedback that must be processed in text form, leads to prolonged identification of comments and the omission of certain training programs from further analysis. This study applies text mining using sentiment analysis and Word Cloud visualization to evaluate the effectiveness of training methods and identify areas for improvement based on employee feedback on training programs at PT XYZ. The amount of data used after preprocessing was 48,910 open feedback responses from 4,314 training sessions consisting of three forms: classroom training, digital learning, and hybrid learning. The evaluation for clustering used the K-Means method, which turned out to use two optimal clusters based on the silhouette. Overall satisfaction with the training was determined through key points such as stable internet connection, overlapping of training schedule, and poor learning environment. Issues frequently that identified in the Word Cloud analysis revealed keywords describing positive and negative aspects of the situation that are requiring further improvement. This identification is useful for developing recommendations to enhance the implementation of the training and participants' experience. Further research may also involve advanced sentiment analysis and more accurate classification methods.
Copyright (c) 2025 Fadilia Rinarwastu, Imam Yuadi http://creativecommons.org/licenses/by-nc/4.0
MACHINE LEARNING FOR EMPLOYMENT POSITION MAPPING
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.
Copyright (c) 2025 Sena Aditia Apriadi, Hilman Ferdinandus Pardede http://creativecommons.org/licenses/by-nc/4.0
ISOLATION FOREST PARAMETER TUNING FOR MOBILE APP ANOMALY DETECTION BASED ON PERMISSION REQUESTS
Ensuring mobile app security needs the capability to detect apps that request excessive or inappropriate permissions. This research proposes an anomaly detection approach using Isolation Forest, enhanced through hyperparameter tuning, to identify suspect apps based on permission request patterns. The dataset is processed into binary features, followed by exploratory data analysis (EDA) to examine the distribution and highlight sensitive permissions. The Isolation Forest model is then optimized by tuning parameters such as contamination level, number of estimators, and sample size. The fine-tuned model achieved a more accurate separation between normal and anomaly applications, detecting 10 anomalies out of 200 applications, with anomaly applications averaging 125.10 permits compared to 42.76 in normal applications. These anomalies often requested permissions related to network, storage, contacts and microphone, indicating potential privacy risks. The results show that parameter tuning improves the detection performance of Isolation Forest, providing a practical solution for mobile security monitoring. After tuning, the number of false positives decreased by 50%, and the model successfully reduced detected anomalies from 20 to 10, increasing the precision of anomaly detection from 70% to 90%. Future work could include improving feature selection and integration into real-time detection systems.
Copyright (c) 2025 Valencia Claudia Jennifer Kaunang, Nur Alamsyah, Reni Nursyanti, Budiman Budiman, Venia R Danestiara, Elia Setiana http://creativecommons.org/licenses/by-nc/4.0
WASTEWISE: AI-POWERED WASTE EDUCATION FOR ELEMENTARY STUDENTS USING YOLOV8 AND ESP32-CAM
The growing volume of global waste poses a significant challenge for effective waste management, particularly in developing countries where awareness and practices around waste sorting remain limited. This study aims to enhance elementary school students' understanding and efficiency in sorting organic and inorganic waste using an interactive, AI-powered educational tool. The proposed system, WasteWise, integrates YOLOv8 for real-time object detection and ESP32-CAM for capturing waste images. A pre-test and post-test experimental design was conducted to assess students’ performance before and after using the system. The results showed a notable improvement in sorting accuracy, increasing from 60% with manual sorting to 90% using the WasteWise system, alongside reduced sorting time. These findings highlight the system's potential not only as an automated waste classification tool but also as a cost-effective and engaging platform for promoting environmental awareness and digital literacy among young learners.
Copyright (c) 2025 Kenny Aldi, Yan Rianto http://creativecommons.org/licenses/by-nc/4.0
PERCEPTION AND BARRIERS TO MOOC ADOPTION: A CASE STUDY OF KARTU PRAKERJA RECIPIENTS
The Indonesian government launched the Pre-Employment Card (Kartu Prakerja) program to enhance workforce skills and address economic challenges. This program provides training through online platforms, including Massive Open Online Courses (MOOCs). The UTAUT2 model was employed as a framework to understand the factors influencing the acceptance and use of educational technology in this context. This study examines the effects of UTAUT2 variables—performance expectancy, effort expectancy, habit, traditional barriers, platform content, access limitations, interaction limitations, facilitating conditions, hedonic value, price value, and social influence—on the intention and adoption of MOOCs among Pre-Employment Card participants. The sample consisted of 222 respondents who were users of the Prakerja platform. Data were collected using a questionnaire and analyzed through Structural Equation Modeling (SEM) with the support of PLS-SEM software. In addition, a sentiment analysis was conducted on comments posted on the official Instagram account @prakerja.go.id to explore public perceptions of the program. The findings reveal that 46.2 percent of public sentiment was negative, particularly related to the program implementation and the use of partner MOOC platforms. SEM analysis further indicates that hedonic value, habit, and social influence have positive and significant effects on the intention and adoption of MOOCs. The moderation analysis by gender shows that performance expectancy, hedonic value, and social influence are stronger among males, whereas effort expectancy, habit, and platform content are stronger among females.
Copyright (c) 2025 Dendy Herdianto, Nur Hendrasto http://creativecommons.org/licenses/by-nc/4.0
UTILIZING END USER DEVELOPMENT METHOD FOR DEVELOPING PENCAK SILAT ORGANIZATION INFORMATION SYSTEMS
Gondang is one of the PSHT sub-branches located in Sragen Regency, Central Java, Indonesia. In managing member data from recruitment to promotion, conventional methods are still used using office applications and information dissemination is still using brochures and social media. This research aims to develop an information system that can help manage data and disseminate information at PSHT Gondang. The system developed can manage the registration of prospective member to become a member and the process of promotion. Delivery of information in the form of organizational structures, announcements, activity schedules, services for member and community, activity galleries containing photos and videos can also be accessed through the system.EUD was chosen as a method in system development because time required is quite short with a relatively small cost allocation. The system is created using Laravel framework and Firebase as a database with a responsive display so that it can be accessed using a smartphone. By using the EUD method, users can modify the appearance and existing information if there is a change in data from the organization which was not available in previous research.
Copyright (c) 2025 Heribertus Ary Setyadi, Hartati Dyah Wahyuningsih, Doddy Satrya Perbawa, Sundari Sundari http://creativecommons.org/licenses/by-nc/4.0
KOPTIHUB: A WAREHOUSE APPLICATION PROTOTYPE FROM COOPERATIVE PERS PECTIVE
Effective warehouse management is crucial for ensuring the availability of raw materials and smooth product distribution, particularly at Sentra Industri Kecil Somber (SIKS) Balikpapan, which specializes in soybean-based industries. Manual record-keeping has presented significant challenges, leading to recording errors, stock discrepancies, and delays in raw material procurement. To address these issues, a digital warehouse management prototype, "KoptiHub," was developed using a User-Centered Design (UCD) approach. This approach aimed to enhance inventory tracking efficiency, streamline raw material ordering, and improve overall product distribution. The prototype was evaluated using the System Usability Scale (SUS) with 15 cooperative administrators at SIKS Balikpapan. The evaluation yielded an SUS score of 82.17, resulting in an "A" grade, which indicates high usability and strong alignment with user expectations. Compared to previous warehouse management solutions, KoptiHub demonstrates superior usability, particularly in cooperative settings. However, further improvements, such as a simplified user interface and an AI-driven inventory forecasting feature, could enhance efficiency and accessibility. The results suggest that KoptiHub could serve as a scalable model for digitizing warehouse management in MSMEs and cooperatives, aligning with emerging trends in smart inventory management and supply chain optimization.
Copyright (c) 2025 Luh Made Wisnu Satyaninggrat, Prasis Damai Nursyam Hamijaya, Isnaini Nur Rachmawati http://creativecommons.org/licenses/by-nc/4.0
STUDENT ATTENDANCE BASED ON FACE RECOGNITION USING THE CONVOLUTIONAL NEURAL NETWORK METHOD
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.
Copyright (c) 2025 Salman Salman, Hendri Ramdan http://creativecommons.org/licenses/by-nc/4.0
DEVELOPMENT OF A FUNDRAISING WEBSITE WITH PAYMENT GATEWAY TO SUPPORT DIGITAL ECONOMY AT LAZISMU
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.
Copyright (c) 2025 Yudistira Bagus Pratama, Hevitria Hevitria, Rifki Hanif Setiawan http://creativecommons.org/licenses/by-nc/4.0
COMPARISON OF ARIMA, LSTM, AND GRU MODELS FOR FORECASTING SALES OF HIT AEROSOL PRODUCTS
A more accurate forecasting model, such as LSTM, can significantly enhance business efficiency by providing more reliable predictions of future sales, allowing for better inventory management, optimized production schedules, and more precise distribution planning. This leads to reduced costs, minimized stockouts, and improved customer satisfaction. This study evaluates the forecasting performance of ARIMA, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models using sales data from 2021 to 2023. The models are assessed based on Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Results show that LSTM outperforms the other models with a MAPE of 10.76%, followed by ARIMA at 11.23% and GRU at 11.47%. These findings highlight the advantages of deep learning methods, particularly LSTM, in capturing complex patterns and trends in time series data. The study demonstrates the potential of these models to optimize sales forecasting, aiding decision-making processes in production and distribution planning.
Copyright (c) 2025 Nendi Sunendar, Dr. Yan Rianto, M. Eng http://creativecommons.org/licenses/by-nc/4.0
FINE-TUNING RESNET50V2 WITH ADAMW AND ADAPTIVE TRANSFER LEARNING FOR SONGKET CLASSIFICATION IN LOMBOK
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.
Copyright (c) 2025 Erfan Wahyudi, Bahtiar Imran, Zaeniah, Surni Erniwati, Muh Nasirudin Karim, Zumratul Muahidin http://creativecommons.org/licenses/by-nc/4.0
PROJECT MANAGEMENT OF STEEL PLATE WAREHOUSE INVENTORY INFORMATION SYSTEM
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.
Copyright (c) 2025 Samidi Samidi, Fitrah Romadhan http://creativecommons.org/licenses/by-nc/4.0
AGGLOMERATIVE HIERARCHICAL CLUSTERING FOR REGIONAL GROUPING IN CENTRAL JAVA BASED ON WELFARE INDICES
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.
Copyright (c) 2025 Raafi Kurnia Desita, Amiq Fahmi, Asih Rohmani, MY. Teguh Sulistyono http://creativecommons.org/licenses/by-nc/4.0
EVALUATING FIKOM THESIS ADVISORY QUALITY WITH MANAGEMENT BY OBJECTIVES AT UNIVERSITAS MUSLIM INDONESIA
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.
Copyright (c) 2025 Najwan Firdaus Haris, Lilis Nur Hayati, Dewi Widyawati http://creativecommons.org/licenses/by-nc/4.0