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FAKTOR - FAKTOR YANG MEMPENGARUHI KEBERHASILAN MANAJEMEN PENGETAHUAN DALAM INSTITUSI PERGURUAN TINGGI Calandra Alencia Haryani; Suryasari Suryasari
IJNMT (International Journal of New Media Technology) Vol 7 No 2 (2020): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v7i2.1761

Knowledge Management (KM) has become an integral factor for many organizations to achieve their goals. Currently, Higher Education Institutions (HEI), have also joined the practices of KM, to enhance the institution’s quality, boosting the effectiveness, developing human resource, and constructing the knowledge culture within the institution. The aim of this study is to observe the constructed Critical Success Factor (CSF) model affecting the implementation of KM. This study follows Kitchenham’s Systematic Literature Review (SLR) procedures from 15 eligible journal articles obtained through KM related literature. The results, analysis, and discussions are elaborated in the paper.

EVALUATION OF QUALITY MANAGEMENT SYSTEM USING ISO 9001:2015 AT STARTUP COMPANY Luqyana Fitri Anggraeni; Wella Wella; Suryasari Suryasari
Ultima Management : Jurnal Ilmu Manajemen Vol 14 No 2 (2022): Ultima Management : Jurnal Ilmu Manajemen
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/manajemen.v14i2.2850

Abstract- The Company requires international standards to be recognized by large companies. The international standard that can be used is ISO 9001:2015, were before implementing directly to formal institutions, companies need to conduct a self-assessment on the achievement of ISO 9001:2015 implementation. The ISO 9001:2015 international quality management system is based on ten principles. These principles are used by senior management as a framework that guides the organization towards improving performance. The rating scale consists of Very Poor, Poor, Moderate, Good, and Very Good, which is converted into a maturity level. The implementation of ISO 9001:2015 has been going well, where the average maturity level obtained by The Company is at levels 3 to 4. Keywords: ISO 9001:2015; Maturity Level; Quality Management

The Development of Web-based Sales Reporting Information Systems using Rapid Application Development Method Suryasari Suryasari; Jansen Wiratama; Ririn Ikana Desanti
Ultima InfoSys : Jurnal Ilmu Sistem Informasi Vol 13 No 2 (2022): Ultima InfoSys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v13i2.3005

Many studies can be used as references regarding the application of information systems in companies that can optimize business processes. Information Systems can be designed according to the needs of each company. Concerning the company, PT Artindo Pratama Sejahtera is a private company engaged in paint production. One of the problems in business processes at the company is when the sales report activity from the salesperson to the warehouse admin section needs to be recorded in the database because it still uses the short message/chat application media. In addition, the short messages/chats often pile up. Based on the literature review that has been done from previous research, these problems can be overcome by implementing an information system. The purpose and contribution of this research are to optimize business processes in reporting activities so that they become integrated. The output of this research is a website-based Sales Reporting Information System. Before the Information System was implemented at PT Artindo Pratama Sejahtera, functionality testing was carried out by system users using the User Acceptance Test (UAT) method with the black box testing model and obtaining results according to company needs so that they could overcome the problems described earlier.

Illuminance Color Independent in Remote Photoplethysmography for Pulse Rate Variability and Respiration Rate Measurement Suryasari Suryasari; Aminuddin Rizal; Sri Kusumastuti; Taufiqqurrachman Taufiqqurrachman
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1176

Remote photoplethysmography (rPPG) is now becoming a new trend method to measure human physiological parameters. Especially due to it noncontact measurement which safe dan suitable to use in this new era condition. Pulse rate variability (PRV) and respiration rate (RR) included as parameters can be measured by using rPPG. PRV and RR are used to measure both physical and psychological wellness of the subject. However, current performance challenges in rPPG algorithm in measuring PRV and RR are illuminance invariant and motion. Especially in different light condition which represent real-life environment, signal-to-noise ratio (SNR) will be affected and directly reduce the measurement accuracy. Therefore in this study, we develop rPPG algorithm and then investigate the performance rPPG in different illuminance scenarios. We perform PRV and RR measurement under each scenario. On this study, for the pulse signal extraction, we were using algorithm is based on the modification of plane orthogonal-to-skin (POS) algorithm. While, for respiration signal extraction is done in CIE Lab color space. Our experimental results show the mean absolute error (MAE) of each measured parameters are 3.25 BPM and 2 BPM for PRV and RR respectively compared with clinical apparatus. The proposed method proved to be more reliable to use in real environments measurement. However, limitation of our proposed algorithm is still running in offline mode, hence for the future we want try to make our algorithm run in real time.

Whatsapp to The Rescue: Automated Billing Reminders Daffa Harastha Musyaffa; Ririn Ikana Desanti; Suryasari Suryasari
G-Tech: Jurnal Teknologi Terapan Vol 8 No 2 (2024): G-Tech, Vol. 8 No. 2 April 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i2.3989

This research takes a case study approach to a company that provides digital marketing services. The company still relies on a manual process to record transactions on a spreadsheet. Their admin keeps track of clients' hosting and domain information in a Microsoft Excel file, and customers are not notified of their lease payment deadline in advance. There are also certain billing issues for service and subscription costs that are issued to customers. The proposed system is built utilizing the rapid application development (RAD) methodology. This method enables rapid system development. Furthermore, the RAD technique requires user participation during system development. The system was created using the PHP programming language, the CodeIgniter 3 framework, and MySQL as the database. It also utilizes the PHP WhatsApp Gateway API. The final outcome is a web-based billing tracking system that will send billing information to consumers via WhatsApp and email, as well as a notification of payment.

ITIL V3: Evaluation of IT Governance Gasindo Group Nurul Lia Ocativa Devi Ariska; Wella Wella; Suryasari Suryasari
G-Tech: Jurnal Teknologi Terapan Vol 8 No 4 (2024): G-Tech, Vol. 8 No. 4 Oktober 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v8i4.4977

This research was conducted at the Gasindo Group, especially in the enterprise resource planning system or commonly abbreviated as ERP. Researchers conducted research by measuring IT governance services using the ITIL V3 framework. The Department of Gasindo Group experienced data mismatches in controlling assets and trading activities due to errors in tube numbering registrations from various Departments. The research was conducted using the ITIL V3 framework in the Service Operation domain with 5 processes in it, namely Incident Management, Request Fulfillment, Event Management, Access Management and Problem Management. in this study only used three processes, namely problem management, access management and event management. The limitation of the distributed questionnaire adapts to the conditions that exist in the Gasindo Group. The percentage results obtained from problem management (71,1), access management (74,3) and event management stopped at level 1.

Optimizing XGBoost with Optuna for Attendance-Based Prediction of Student Academic Success Marcello Roy; Ririn Ikana Desanti; Iwan Prasetiawan; Suryasari Suryasari
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1116

The growth of data analytics in higher education has increased the need for predictive models that identify students’ academic potential and support data-driven decision-making. Academic institutions are able to enhance student outcomes by implementing suitable interventions that are based on an accurate early prediction of Grade Point Average (GPA). This study aims to develop a practical and accurate GPA prediction model based on Extreme Gradient Boosting (XGBoost), enhanced through Optuna-based hyperparameter tuning, to support academic monitoring systems in higher education. This model is intended to assist academic monitoring systems in higher education. Using academic performance data, attendance records, and course load information obtained from 961 undergraduate students, a quantitative predictive modeling approach was implemented. The CRISP-DM framework was implemented during the modelling process, which included the following stages: data understanding, data preparation, modelling, evaluation, and deployment. To ensure the stability and relevance of the model, exploratory analysis and correlation assessments were implemented to determine feature selection. Optuna was employed to optimize hyperparameters, utilizing Bayesian optimization with adaptive trial pruning to efficiently examine the parameter space. Experimental results demonstrate that the Optuna-tuned XGBoost model achieved superior predictive performance compared to baseline XGBoost models and models optimized using Grid Search and Random Search. The proposed model attained a coefficient of determination (R²) of 0.8637 and a Root Mean Square Error (RMSE) of 0.1165, indicating improved accuracy and robustness in handling large prediction errors. To enhance practical applicability, the final model was deployed in a Streamlit-based web application that enables real-time GPA prediction and supports academic advisors. Overall, the findings confirm that Optuna-based hyperparameter tuning significantly enhances XGBoost performance and provides a solution for data-driven academic monitoring in higher education institutions.

Hybrid Deep Learning for Image Authenticity: Distinguishing Between Real and AI-Generated Images Wella Wella; Suryasari Suryasari; Ririn Ikana Desanti
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.991

The increasing use of artificially generated images raises significant concerns about the authenticity of digital content. This study introduces a hybrid deep learning model for binary classification of real and generated images by combining spatial and relational features. The central idea is to integrate a convolutional backbone adapted from ResNet18 for visual feature extraction with a graph representation based on nearest-neighbor relations to capture inter-image similarities. The objective is to evaluate whether this dual-feature approach improves classification performance compared to single-feature baselines. Using a balanced dataset of 1,256 images (744 real and 512 generated), the model was trained on 70% of the data and tested on the remaining 30%. Experimental findings demonstrate that the model achieved an overall accuracy of 88%, with precision of 0.91 and recall of 0.89 for real images, and precision of 0.85 and recall of 0.87 for generated images. The corresponding F1 scores were 0.90 and 0.86, yielding a macro average F1 of 0.88. Confusion matrix analysis shows balanced misclassification across both classes, while stable performance across epochs indicates reliable learning behavior. Results confirm that the hybrid model achieves stronger classification effectiveness than convolution-only or graph-only baselines. The novelty of this work lies in demonstrating that the integration of spatial and relational learning provides a more robust framework for detecting synthetic images than single-modality approaches. The contribution of this research is both methodological, in proposing a hybrid architecture that unifies convolutional and graph-based learning, and practical, in providing empirical evidence that such integration enhances the reliability of image authenticity verification. While the absence of a validation set limited hyperparameter optimization and early stopping, the findings indicate that this hybrid design offers a promising direction for improving the robustness and generalizability of synthetic image detection.

A Comparative Study of Machine Learning Approaches to Megathrust Earthquake Prediction in Subduction Zones Wella Wella; Ririn Ikana Desanti; Suryasari Suryasari
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.904

Megathrust earthquakes are one of the most severe threats to countries situated along tectonic subduction zones, particularly Indonesia, where the movement of converging plates frequently triggers large-scale seismic events and tsunamis. Although recent developments in seismology have introduced various predictive tools, many of these models still face challenges, especially due to limitations in hydrogeological data quality. This study aims to investigate how three different machine learning algorithms perform in predicting megathrust earthquake events. The algorithms tested are Support Vector Machine, Random Forest, and Artificial Neural Network, applied to a dataset dominated by earthquake records from the Indonesian and Pacific regions. Each model was evaluated based on accuracy, precision, recall, and F1 score to provide a comprehensive performance analysis. The results show that Random Forest produced the highest accuracy, reaching 96%, followed closely by Support Vector Machine with 95%, while Artificial Neural Network achieved 83%. In terms of the F1 score, Random Forest led with a score of 0.95, indicating balanced performance in classification. However, recall, which is critical in disaster preparedness because it measures the model’s ability to detect high-risk events, Artificial Neural Network reached 92% for tsunami-related classifications. This suggests that while Random Forest is the most accurate overall, Artificial Neural Network could be more appropriate for early warning systems where the cost of missing a true event is much higher than issuing a false alarm. The contribution of this research is the direct comparison of multiple machine learning methods using real earthquake data, focusing not only on accuracy but also on practical disaster management considerations such as recall. This study also presents a novel perspective by analyzing the trade-off between model accuracy and disaster risk, emphasizing the need for probabilistic forecasts that can support timely public decision-making during seismic crises.

Optimizing XGBoost with Optuna for Attendance-Based Prediction of Student Academic Success Marcello Roy; Ririn Ikana Desanti; Iwan Prasetiawan; Suryasari Suryasari
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1116

The growth of data analytics in higher education has increased the need for predictive models that identify students’ academic potential and support data-driven decision-making. Academic institutions are able to enhance student outcomes by implementing suitable interventions that are based on an accurate early prediction of Grade Point Average (GPA). This study aims to develop a practical and accurate GPA prediction model based on Extreme Gradient Boosting (XGBoost), enhanced through Optuna-based hyperparameter tuning, to support academic monitoring systems in higher education. This model is intended to assist academic monitoring systems in higher education. Using academic performance data, attendance records, and course load information obtained from 961 undergraduate students, a quantitative predictive modeling approach was implemented. The CRISP-DM framework was implemented during the modelling process, which included the following stages: data understanding, data preparation, modelling, evaluation, and deployment. To ensure the stability and relevance of the model, exploratory analysis and correlation assessments were implemented to determine feature selection. Optuna was employed to optimize hyperparameters, utilizing Bayesian optimization with adaptive trial pruning to efficiently examine the parameter space. Experimental results demonstrate that the Optuna-tuned XGBoost model achieved superior predictive performance compared to baseline XGBoost models and models optimized using Grid Search and Random Search. The proposed model attained a coefficient of determination (R²) of 0.8637 and a Root Mean Square Error (RMSE) of 0.1165, indicating improved accuracy and robustness in handling large prediction errors. To enhance practical applicability, the final model was deployed in a Streamlit-based web application that enables real-time GPA prediction and supports academic advisors. Overall, the findings confirm that Optuna-based hyperparameter tuning significantly enhances XGBoost performance and provides a solution for data-driven academic monitoring in higher education institutions.

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