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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

37 38 39 40 41

COMPARISON OF ARIMA, LSTM, AND GRU MODELS FOR FORECASTING SALES OF HIT AEROSOL PRODUCTS Sunendar, Nendi; Rianto, Yan
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.v21i2.6412

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.

APPLICATION OF ARTIFICIAL NEURAL NETWORK METHODS TO DETECT HEART ATTACKS Hamzah, Nasir; Rianto, Yan
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.v21i2.6413

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.

WASTEWISE: AI-POWERED WASTE EDUCATION FOR ELEMENTARY STUDENTS USING YOLOV8 AND ESP32-CAM Aldi, Kenny; Rianto, Yan
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.v21i2.6414

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.

KOPTIHUB: A WAREHOUSE APPLICATION PROTOTYPE FROM COOPERATIVE PERS PECTIVE Satyaninggrat, Luh Made Wisnu; Hamijaya, Prasis Damai Nursyam; Rachmawati, Isnaini Nur
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.v21i2.6456

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.

UTILIZING END USER DEVELOPMENT METHOD FOR DEVELOPING PENCAK SILAT ORGANIZATION INFORMATION SYSTEMS Setyadi, Heribertus Ary; Wahyuningsih, Hartati Dyah; Nurohim, Galih Setiawan; Sundari, Sundari
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.v21i2.6487

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.

IDENTIFICATION OF FOOD DIVERSIFICATION ON JAVA ISLAND USING ARCGIS Murtako, Amir; Hanifa, Faiqa Hadya; Effatha, Eidelwise Gloria; Nursari, Sri Rezeki Candra; Maspiyanti, Febri
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.v21i2.6570

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.

PERCEPTION AND BARRIERS TO MOOC ADOPTION: A CASE STUDY OF KARTU PRAKERJA RECIPIENTS Herdianto, Dendy; Hendrasto, Nur
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.v21i2.6623

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.

SENTIMENT ANALYSIS ON TRAINING IMPLEMENTATION’S FEEDBACK IN PT XYZ Rinarwastu, Fadilia; Yuadi, Imam
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.v21i2.6641

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.

ISOLATION FOREST PARAMETER TUNING FOR MOBILE APP ANOMALY DETECTION BASED ON PERMISSION REQUESTS Kaunang, Valencia Claudia Jennifer; Alamsyah, Nur; Nursyanti, Reni; Budiman, Budiman; Danestiara, Venia R; Setiana, Elia
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.v21i2.6647

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.

EVALUATING PREPROCESSING EFFECTS IN NAME RETRIEVAL USING CLASSICAL IR AND CNN-BASED MODELS Marcelly, Frizca Fellicita; Saputra, Irwansyah; Andra, Muhammad Bagus
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.v21i2.6884

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.

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