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Evaluating IT Capabilities in The Success of Pipe Manufacturing Company Holman, Jason Nathanael; Desanti, Ririn Ikana
ULTIMA InfoSys Vol 15 No 1 (2024): Ultima Infosys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v15i1.3475

Information technology (IT) management is a process that a corporation or organization must carry out, particularly in terms of data management. The research employs a case study of a pipe manufacturer that has implemented a human resources system to manage employee information. To avoid problems in the data management process that can impede company performance, the company must have good information technology governance capabilities in the data management process. This research will focus on measuring and evaluating the capability of information technology governance at the company, as well as making recommendations to improve the company's existing IT governance. The COBIT 2019 framework will be used to assess the company's IT governance capabilities using qualitative data collected from collaborative interviews with the company and supported by previous research literature. The measurement focus will be on IT infrastructure for data management and operations management to support the data management process. APO01 - Managed I&T Management Framework, APO14 - Managed Data, and DSS01 - Managed Operations are the COBIT 2019 processes to be monitored. The study's findings include the realization of IT governance capabilities in the APO01 domain and a lack of IT governance capabilities in the other two domains, namely APO14 and DSS01. The capability level is stopped at level 2 for the APO14 and DSS01 domains, which is one level below the declared aim of level 3. The recommendations will center on enhancing the IT governance skills of the two domains that fail to meet the company's capability targets.

PENGEMBANGAN E-LEARNING SYSTEM SD STRADA KARAWACI Desanti, Ririn Ikana; Suryasari; Wella; Johan, Monika Evelin; Faza, Ahmad
PROFICIO Vol. 5 No. 1 (2024): PROFICIO: Jurnal Abdimas FKIP UTP
Publisher : FKIP UNIVERSITAS TUNAS PEMBANGUNAN SURAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36728/jpf.v5i1.3037

Learning Management System (LMS) has become one of the media used to support online teaching learning activities. LMS has a wide range of functions such as administration, documentation, and reporting. The condition of the Covid-19 pandemic in 2020 has caused SD Strada Karawaci to find a way to keep providing school supplies to the students. The Information Systems Study Program – Universitas Multimedia Nusantara (IS-UMN) has a strong commitment to make a positive contribution to the community, one of which is through community service activities (PKM). IS- UMN organizes PKM activities with one of its goals to improve literacy of information technology of the community. In general, the activities of PKM IS-UMN are divided into two stages: system development and training. Therefore, in the first phase of the activities of PKM IS-UMN will design an e-learning system for elementary school of Strada Karawaci. The PKM team conducted in-depth research to understand the user needs (requirements) and conducted interviews with teachers and students. In addition, the development phase of the e-learning system is also carried out with consideration of the curriculum and school learning methods.

A Comparative Study of Machine Learning Approaches to Megathrust Earthquake Prediction in Subduction Zones Wella, Wella; Desanti, Ririn Ikana; 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.

Implementation of Decision Support System Method to Evaluate Posyandu Program In Tangerang Selatan Desanti, Ririn Ikana; Maximus, Jason; Sitorus, Budi Berlinton
IJNMT (International Journal of New Media Technology) Vol 10 No 2 (2023): IJNMT
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v10i2.3451

The goal of this study is to develop a decision support system (DSS) using the Technique for Order Preference by Similarity to Solution (TOPSIS) and Simple Multi-Attribute Rating Technique (SMART) approaches to evaluate the Posyandu Program. The case study focuses on Posyandu in Tangerang Selatan Regional. The local government runs Posyandu, an Indonesian public health service facility for women and children. This DSS system uses the TOPSIS approach to employ the ranking function and select the best alternative based on agreed-upon criteria with the local Posyandu. This approach calculates the distance between each option with a positive ideal solution and a negative ideal solution and then computes the relative preference value, which determines how to rank. In addition, the SMART approach assigns weights to each predefined criterion. Using the TOPSIS and SMART approaches to implement DSS at Posyandu helps people make decisions by providing them with the best ranking results based on predefined criteria. Thus, it is believed that the findings of this study would improve the quality of Posyandu services and contribute to the overall health of the community.

Evaluating IT Capabilities in The Success of Pipe Manufacturing Company Holman, Jason Nathanael; Desanti, Ririn Ikana
ULTIMA InfoSys Vol 15 No 1 (2024): Ultima Infosys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v15i1.3475

Information technology (IT) management is a process that a corporation or organization must carry out, particularly in terms of data management. The research employs a case study of a pipe manufacturer that has implemented a human resources system to manage employee information. To avoid problems in the data management process that can impede company performance, the company must have good information technology governance capabilities in the data management process. This research will focus on measuring and evaluating the capability of information technology governance at the company, as well as making recommendations to improve the company's existing IT governance. The COBIT 2019 framework will be used to assess the company's IT governance capabilities using qualitative data collected from collaborative interviews with the company and supported by previous research literature. The measurement focus will be on IT infrastructure for data management and operations management to support the data management process. APO01 - Managed I&T Management Framework, APO14 - Managed Data, and DSS01 - Managed Operations are the COBIT 2019 processes to be monitored. The study's findings include the realization of IT governance capabilities in the APO01 domain and a lack of IT governance capabilities in the other two domains, namely APO14 and DSS01. The capability level is stopped at level 2 for the APO14 and DSS01 domains, which is one level below the declared aim of level 3. The recommendations will center on enhancing the IT governance skills of the two domains that fail to meet the company's capability targets.

PENGEMBANGAN SISTEM E-COMMERCE UNTUK UMAT PAROKI KARAWACI Desanti, Ririn Ikana; Suryasari; Wella; Wiratama, Jansen; Sanjaya, Samuel Ady
JP2N : Jurnal Pengembangan Dan Pengabdian Nusantara Vol. 2 No. 1 (2024): JP2N :September - Desember 2024
Publisher : Yayasan Pengembangan Dan Pemberdayaan Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62180/vxyvwz23

Paroki Karawaci memiliki sebuah divisi yang bernama Pengembangan Usaha Sosial dan Modal (PUSM) yang bertujuan untuk membantu menyejahterakan kehidupan umat gereja melalui beberapa kegiatan yang salah satunya yaitu kegiatan menjual berbagai macam produk makanan yang berasal dari para umat. Sebelumnya proses pemasaran dan penjualan dilakukan secara door-to-door ke rumah para umat, namun sayangnya hal tersebut tidak dapat dilakukan lagi pada saat pandemi melanda Indonesia sehingga menyebabkan omset penjualan menurun drastis bahkan hampir tidak ada. Berdasarkan permasalahan tersebut, tim pengabdian kepada masyarakat (PKM) dari program studi sistem informasi UMN mengusulkan cara pemasaran dan penjualan secara online menggunakan situs web e-commerce yang dirancang dan dipergunakan khusus untuk umat gereja paroki karawaci. Situs web e-commerce dirancang menggunakan metode prototyping dan bahasa pemodelan yang digunakan adalah UML diagram. Situs web e-commerce tersebut memiliki fitur utama pengelolaan produk, pengelolaan penjual, transaksi penjualan dan pengiriman barang. Hal yang perlu menjadi perhatian khusus oleh tim pengembang adalah sistem harus user friendly dan seluruh fiturnya juga harus dibuat sederhana karena calon pengguna sistem merupakan pengguna pemula (novice user). Kegiatan PKM telah berhasil terlaksana dengan baik dan untuk tahap selanjutnya situs web e-commerce akan mulai diimplementasi oleh paroki karawaci dan sebelumnya akan diberikan pelatihan kepada calon pengguna.

Unlocking Sales Insight through Business Intelligence and ERP Aulia, Azka; Desanti, Ririn Ikana; Amri, Mahfudz
ULTIMA InfoSys Vol 16 No 2 (2025): Ultima InfoSys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v16i2.4275

In recent years, companies have experienced growing competition, financial inefficiencies, and operational instability, which have contributed to declining profitability. Digital transformation is found to increase operational efficiency while minimizing risk of insolvency. PT Dwi Family Investama, an offset and printing company, experiences similar problems from its manual record of orders and invoices, which causes decreased efficiency, errors, and complicated handling of customer information. Such inefficiencies slow down business strategy formulation and decision-making. This research designs a web-based Enterprise Resource Planning (ERP) system integrated with business intelligence dashboard to facilitate better decision-making. The system automates business processes and delivers real-time operational insights through an interactive dashboard. Performance assessments record an increase in operational efficiency by 52.3%, proving the system’s effectiveness in improving business operations and decision-making.

Hybrid Deep Learning for Image Authenticity: Distinguishing Between Real and AI-Generated Images Wella, Wella; Suryasari, Suryasari; Desanti, Ririn Ikana
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.

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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