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PERANCANGAN APLIKASI TIKET IT HELPDESK UNTUK MENINGKATKAN EFEKTIVITAS LAYANAN TEKNOLOGI INFORMASI
Full text linkEffective and responsive Information Technology (IT) services play a crucial role in supporting organizational operations. However, in practice, ASPAN Company still faces challenges in recording, monitoring, and resolving IT incidents due to the absence of an integrated system. This study aims to design an IT Helpdesk ticketing application that enhances the effectiveness of IT services through a systematic, fast, and measurable incident management process. The research adopts the waterfall methodology and qualitative data collection using a case study approach through direct observation involving IT staff and service users. The result of this study is a web-based application that enables users to create incident report tickets, track ticket status, and provide feedback on the services received. The system also provides performance reports that are useful for service evaluation. The implementation results demonstrate a significant improvement in IT service performance, where the average incident resolution time decreased from 2.5 hours to 1.5 hours. Furthermore, the system facilitates real-time ticket tracking, historical issue management, and structured performance reporting to support IT service evaluation. With the implementation of the IT Helpdesk ticketing application, the IT issue-handling process becomes more efficient, well-documented, and ultimately improves the overall quality of Information Technology services within ASPAN compan
UTILIZING END USER DEVELOPMENT METHOD FOR DEVELOPING PENCAK SILAT ORGANIZATION INFORMATION SYSTEMS
Full text linkGondang 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
EVALUATING PREPROCESSING EFFECTS IN NAME RETRIEVAL USING CLASSICAL IR AND CNN-BASED MODELS
Full text linkInformation 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
NAVIGATING ETHICAL LONG-TERM PERSONAL DATA STORAGE: PRIVACY, SECURITY, REGULATORY CHALLENGES, AND SOCIETAL IMPACT REVIEW
Full text linkDigital data storage is now fundamental to modern society, facilitating the collection and utilization of vast information resources. Yet, the exponential growth of stored data has intensified ethical concerns, particularly regarding long-term retention. This multidisciplinary review synthesizes literature on the ethical implications of long-term data storage, focusing on privacy, security, data ownership, accessibility, sustainability, and societal impact. The study analyzes ethical frameworks encompassing deontology, privacy by design, fair information practices, data minimization, and accountability while examining the responsibilities of organizations, individuals, regulators, technology providers, and ethics committees in upholding ethical standards. Key findings reveal that the ethical landscape of long-term data storage is highly complex and demands a collaborative, multi-stakeholder approach. The review concludes by recommending the integration of robust security measures, enhanced user control and consent mechanisms, sustainable storage technologies, and governance frameworks prioritizing fairness, non-discrimination, and the public good. These recommendations aim to guide stakeholders toward responsible and ethical data stewardship, ensuring that digital data storage practices remain trustworthy and sustainable amid ongoing technological and societal chang
CRYPTOGRAPHIC FRAMEWORK FOR CLOUD-BASED DOCUMENT STORAGE USING AES-256 AND SHA-256 HYBRID SYSTEMS
Full text linkCloud-based document storage offers significant flexibility but faces security challenges such as the risk of data leaks and illegal modifications. The study proposes a cryptographic framework using a combination of Advanced Encryption Standard (AES)-256 for confidential encryption and Secure Hash Algorithm (SHA)-256 for cloud storage-based document integrity verification. The system was developed with an experimental approach, implemented in application prototypes, and tested on a wide range of file sizes from as small as < 1 mb, 10 mb to 100 mb showing greater efficiency than Rivest-Shamir-Adleman (RSA) and elliptical curve cryptography (ECC). To improve security, a distributed key management scheme and password-based user authentication were added. The encryption system will be tested on Google Drive, One Drive, and mega cloud platforms and evaluated through a series of performance and security tests combined with on-premises personal computer (PC) systems. This framework provides a practical solution for secure document storage in the cloud with a balance between security, performance, and ease of use. This research reinforces the urgency of applying modern cryptography in dealing with the risk of data leakage in public cloud services, and can be adopted as a security and efficiency model and solution for individuals, as well as government and private offices that use cloud storage as a storage base for important documents such as Decrees, Securities, certificates, diplomas and other important dat
SENTIMENT ANALYSIS OF IT WORKERS ON NO CODE AND LOW CODE TRENDS: COMPARISON OF LSTM AND SVM MODELS
Full text linkThis research explores the sentiment of IT professionals toward the growing trend of No Code and Low Code technologies by comparing the performance of Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) algorithms. Using the SEMMA methodology and automatic labeling with ChatGPT, a total of 4,238 comments were collected from Reddit and Twitter and categorized into positive, neutral, and negative sentiments. The analysis showed that neutral sentiment dominates on both platforms (47.9% on Reddit and 48.8% on Twitter), followed by positive sentiment (41.3% and 43.1%, respectively), indicating cautious but optimistic attitudes toward LCDPs. In terms of model performance, SVM outperformed LSTM with 87% accuracy and a weighted F1-score of 0.87, compared to LSTM’s 80% accuracy and a weighted F1-score of 0.80. These findings confirm that classical machine learning methods remain highly effective for short-text sentiment analysis in social media, particularly when combined with TF-IDF feature representation, SMOTE balancing, and LLM-based automatic labeling, while also offering new insights into IT community perceptions of disruptive technologie
ANALYZING CLIMATE IMPACTS ON RICE PRODUCTION IN SUMATRA THROUGH SPATIOTEMPORAL MACHINE LEARNING MODELS
Full text linkClimate variability poses a major challenge to rice production in Sumatra, a key contributor to Indonesia’s food security. This study aims to analyze spatiotemporal climate impacts on rice yields by integrating climatic, geographical, and agricultural datasets. Historical records from 1993–2024, including rainfall, temperature, humidity, and rice production statistics, were collected from BMKG, BPS, and the Ministry of Agriculture. After preprocessing and feature selection, six machine learning algorithms—Linear Regression, Random Forest, Gradient Boosting, Support Vector Regression, Decision Tree, and K-Nearest Neighbors—were evaluated for predictive performance. Results show significant spatial heterogeneity: rainfall strongly affects yields in Aceh and North Sumatra, while temperature stress is critical in southern provinces. Among the tested models, Random Forest achieved the best accuracy (R² = 0.985), outperforming other algorithms. These findings highlight the importance of localized adaptation strategies and demonstrate the potential of ensemble machine learning to support climate-resilient rice production
PENGARUH CITRA MEREK DAN DESAIN PRODUK TERHADAP KEPUTUSAN PEMBELIAN TAS WANITA DI KOTA DEPOK
Full text linkThe fashion industry has undergone substantial growth driven by evolving consumer preferences, wherein buyers increasingly prioritize not only product functionality but also aesthetic design and a strong brand image. This research seeks to examine how perceptions of brand identity and structural attributes of the product shape consumer buying behavior. decisions for women’s handbags in Depok City, offering scholarly contribution by assessing these variables both individually and collectively. The research employed a quantitative approach using non-probability targeted sampling, where information was obtained from individuals aligned with the predetermined qualifications related to handbag purchases. The findings reveal that 59.5% of purchasing decisions are explained by brand image and product design, with brand image contributing 3.496 and product design contributing 4.452, indicating that product design serves as the most influential determinant. These results underscore the importance for industry practitioners to prioritize design innovation aligned with prevailing market trends while simultaneously strengthening brand image to enhance consumer purchase decisions.Perkembangan industri fashion berlangsung pesat seiring perubahan preferensi konsumen yang kini tidak hanya menuntut fungsi produk, tetapi juga keindahan desain serta kekuatan citra merek. Situasi tersebut menuntut para pelaku usaha untuk memahami bagaimana citra merek serta desain produk dalam memengaruhi proses konsumen dalam menentukan keputusan pembelian. Penelitian ini berkontribusi pada kajian di sektor fashion, khususnya produk tas, dengan menganalisis pengaruh citra merek dan desain produk terhadap keputusan pembelian tas wanita di Kota Depok. Tujuan pada penelitian ini untuk mengukur citra merek dan desain produk dalam mempengaruhi keputusan pembelian, di mana hasil analisis menunjukkan sekitar 59,5% pembelian suatu produk tas dipengaruhi oleh kedua variabel tersebut dengan kontribusi citra merek sebesar 3,496 dan desain produk sebesar 4,452. Temuan ini mengidikasikan bahwa desain produk merupakan faktor dominan dalam mempengaruhi keputusan pembelian tas wanita di kota Depok. Oleh karena itu, pelaku bisnis disarankan untuk memfokuskan strategi pada inovasi desain yang sesuai tren sekaligus memperkuat citra merek secara konsisten
PREDICTING SOLAR POWER GENERATION: A MACHINE LEARNING APPROACH FOR GRID STABILITY AND EFFICIENCY
Full text linkIn countries with high levels of insolation, the demand for renewable energy sources has driven the rapid emergence and growth of solar power plants. Maintaining grid stability and efficient power management in response to weather variations that affect solar radiation intensity and battery consumption limits remains a major challenge. This study aims to develop a machine learning-based prediction model to estimate the electricity generated by solar power plants using weather data. Four algorithms are utilized: Linear Regression, Random Forest Regressor, Decision Tree Regressor, and Gradient Boosting Regressor. The results show that the Random Forest algorithm produces the best model, with MAE and RMSE values of 0.1114281 and 0.3187232, respectively. This research contributes to the literature, particularly on the relatively unexplored topic of using multiple machine learning models to predict energy output from photovoltaic systems. The findings have the potential to inform more efficient energy policies and improve energy integration technologies for grid-connected solar power systems
APPLICATION OF NON-PREEMPTIVE PRIORITY SCHEDULING METHOD FOR WORK ORDER SCHEDULING SYSTEM
Full text linkWork order allocation is one of the problems experienced by PT Indomobil Trada Nasional. Companies need tools to make it easier to allocate work orders effectively, namely an optimal work order scheduling system. Work order allocation data for the last three months was 3,817, with 15 technicians. This work order exceeds the company's target, namely to have a difference of 1.2 work orders per technician daily. These work orders have a priority order in their processing. The work order scheduling method used in this research is the non-preemptive priority scheduling method. The non-preemptive priority scheduling method is used because it can determine which work orders are in the queue and ready to be allocated according to the priority order without disturbing work orders that are being worked on when new work orders arrive. The work order scheduling system that was built provides adequate scheduling time and produces a smaller average waiting time, namely 12.97 minutes. The average waiting time in the scheduling system without priority non-preemptive scheduling is 52.18, and the difference in average waiting time for the 34 existing work orders is 39.12 minutes. Applying the non-preemptive priority scheduling method helps companies allocate work orders optimally