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Jurnal ULTIMA InfoSys
Published by Universitas Multimedia Nusantara
ISSN : 20854579     EISSN : 25811851     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal ULTIMA InfoSys merupakan Jurnal Program Studi Sistem Informasi Universitas Multimedia Nusantara yang menyajikan artikel-artikel penelitian ilmiah dalam bidang Sistem Informasi, serta isu-isu teoritis dan praktis yang terkini, mencakup sistem basis data, sistem informasi manajemen, analisis dan pengembangan sistem, manajemen proyek sistem informasi, programming, mobile information system, dan topik lainnya terkait Sistem Informasi. Jurnal ULTIMA InfoSys terbit secara berkala dua kali dalam setahun (Juni dan Desember) dan dikelola oleh Program Studi Sistem Informasi Universitas Multimedia Nusantara bekerjasama dengan UMN Press.
Arjuna Subject : -
Articles 236 Documents
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An Integrated Web-Based Waste Management Platform for Urban Collection Schedulling and Fee Payment Services Pekanbaru City Ibnu Surya; Juni Nurma Sari; Satria Perdana Arifin; Thesa Nabila Balqis Pardede
ULTIMA InfoSys Vol 17 No 1 (2026): Ultima InfoSys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v17i1.4550

Abstract

Waste management has become a global issue, encompassing various challenges such as the separation of organic and inorganic waste, waste collection scheduling, fee collection, and the accumulation of waste at final disposal sites (TPA). In Pekanbaru City, the waste management mechanism involves empowering Waste Collection Agencies (LPS) across 83 sub-districts. LPS is responsible for collecting household waste and transporting it to the TPA, as well as managing the waste collection fees paid by residents. Since the fee management process is still carried out manually, an information system is needed to manage resident data and monitor fee payments effectively. This study developed the SilepasPKU Waste Management Information System using the prototype method, featuring waste collection management and fee management modules. The system was tested through three iterations with LPS UmbanSari users: the first iteration focused on design testing, while the second tested system functionality. Several improvements were made based on user feedback. In the third iteration, all system functionalities operated successfully. Currently, SilepasPKU is being used by LPS UmbanSari in their waste management operations
A Cloud-Integrated Business Process Redesign Framework for Digital Transformation in Traditional SMEs Yulyanty Chandra; Lorio Purnomo
ULTIMA InfoSys Vol 17 No 1 (2026): Ultima InfoSys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v17i1.4612

Abstract

Rapid development of digital technologies has forced small and medium sized businesses to rethink their business processes to remain competitive. Yet many traditional SMEs - particularly food and beverage - still operate in manual and fragmented ways and are thus limiting their operational effectiveness and scalability. In this study a cloud-embedded business process design framework for traditional Bakery enterprise is proposed using Suisse bakery Jakarta as case study. Core and supporting business activities were identified qualitatively through direct observation and semi structured interviews with operational stakeholders. Findings were translated into value chain analysis, use case modelling and cloud-based system architecture to align business processes with digital infrastructure. Results show that cloud computing in bakery management systems enables data interoperability, process flexibility and operational efficiency without compromising physical infrastructure dependency. This work contributes to the literature regarding digital transformation within traditional SMEs by systematically modelling business process redesign before cloud adoption providing theoretical as well as practical insights regarding SME digitalization strategies
Depression Risk Classification Using Machine Learning: A Model Performance Study Marcelinus Jonathan Salim; Tegar Anugrah Firdaus; Carens Chanda Claudhyta Hasan; Yuri Pamungkas
ULTIMA InfoSys Vol 17 No 1 (2026): Ultima InfoSys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v17i1.4670

Abstract

This study presents a comparative evaluation of multiple machine learning algorithms for depression risk classification using a publicly available mental health survey dataset. Rather than predicting clinical depression, the target variable is formulated as a risk proxy derived from social weakness indicators to support screening-oriented analysis. A quantitative experimental framework is employed to compare Logistic Regression, Random Forest, Support Vector Machine, and Extreme Gradient Boosting under consistent preprocessing and data partitioning conditions. Model performance is evaluated using complementary metrics, including accuracy, recall for High-risk cases, and the area under the receiver operating characteristic curve (ROC-AUC). Threshold optimization based on ROC analysis is applied to align model outputs with screening objectives that prioritize sensitivity. The results demonstrate that Logistic Regression and Support Vector Machine consistently achieve superior or comparable performance across all evaluation dimensions, including high overall accuracy, near-perfect sensitivity for High-risk detection, and strong discriminative capability. In contrast, more complex ensemble and distance-based models show mixed outcomes, indicating diminishing performance gains from increased algorithmic complexity. These findings highlight that simple and interpretable models can effectively support depression risk screening using survey-based data, offering a practical balance between predictive performance, transparency, and computational efficiency.
Redundancy-Aware Feature Selection using mRMR and F-Test for EEG Emotion Classification Ira Febrianti; Carens Chanda Claudhyta Hasan; Nadzifatu Chomtsa; Hanifa Khairunisa; Deandra Faysa Mardatila; Yuri Pamungkas
ULTIMA InfoSys Vol 17 No 1 (2026): Ultima InfoSys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v17i1.4672

Abstract

Emotions play an essential role in human interaction, driving the development of reliable automatic emotion recognition systems. Electroencephalography (EEG) offers a noninvasive method to record neural activity related to emotional states; however, many existing studies focus on limited feature configurations or binary classification problems. This research examines the influence of feature dimensionality and classifier selection on three-class EEG-based emotion recognition involving positive, neutral, and negative categories. The primary contribution of this study is a systematic assessment of feature and classifier compatibility across 28 experimental scenarios within a unified evaluation framework. Using a publicly available EEG dataset containing statistical and spectral features, selection was conducted using F-test and Minimum Redundancy Maximum Relevance (mRMR) methods, isolating the top 5, 10, and 15 features alongside the complete set. Four classifiers (Random Forest, Support Vector Machine, K-Nearest Neighbors, and Neural Networks) were evaluated via a 70/30 hold-out validation scheme using accuracy, F1-score, and Area Under the Curve (AUC). Results indicate that Random Forest trained with the full feature set achieved the highest performance, reaching 99.53% accuracy and 0.9994 AUC. These findings suggest that ensemble-based models demonstrate greater robustness when handling high-dimensional EEG features in multi-class emotion recognition.
Enterprise Architecture Design Using TOGAF ADM for Digital Transformation at PT Profito Inovasi Kreatif Allegra Aretha Putri; Nadine Aurelia; Vera Veronika; Fransiska Eka Putri Wiriady; Afifah Trista Ayunda
ULTIMA InfoSys Vol 17 No 1 (2026): Ultima InfoSys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v17i1.4701

Abstract

The rapid development of information technology encourages companies to adopt digital transformation to improve operational efficiency and business competitiveness. PT Profito Inovasi Kreatif, a distribution company, has implemented information systems such as ERP, CRM, and cloud-based services to support its operations. However, the company still faces several challenges, including declining ERP performance due to increasing data volume, complex data management with more than 6,000 SKUs, and inefficient distribution processes. This study aims to design an Enterprise Architecture using the TOGAF ADM framework. A qualitative case study approach was employed, with data collected through interviews and analysis of operational activities. The analysis identifies current (as-is) and proposed (to-be) conditions across business, application, and technology architectures. The results propose the development of an internal ERP system, implementation of a Transportation Management System (TMS) for automated route planning, and enhanced system integration. These solutions are expected to improve operational efficiency, optimize data management, and support real-time decision-making in the organization.
ERP Human Resource Management Readiness Framework Integrating Prioritization, Roadmaps, Risk Mitigation, McKinsey 7S Safires Atalla Zaraski; Raymond Sunardi Oetama
ULTIMA InfoSys Vol 17 No 1 (2026): Ultima InfoSys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v17i1.4781

Abstract

Enterprise Resource Planning (ERP) for Human Resource Management is widely adopted to improve operational efficiency, data accuracy, and decision-making. However, successful implementation depends on organizational readiness. This study assesses ERP readiness using the McKinsey 7S Framework, which evaluates Strategy, Structure, Systems, Style, Staff, Skills, and Shared Values. A mixed-methods approach was employed through questionnaires and interviews. Data were collected from 30 employees using 22 indicators measured on a five-point Likert scale. Validity testing confirmed that all indicators were valid, and the overall Cronbach’s alpha of 0.942 indicated excellent reliability. The readiness assessment showed that all seven dimensions were classified as Ready, with average scores ranging from 4.51 to 4.62. Structure achieved the highest readiness score (4.62), while Skills obtained the lowest score (4.51). Readiness profile analysis further revealed that Strategy achieved the highest profile score (18.23), reflecting strong organizational direction and planning for ERP adoption. Interview findings identified challenges related to manual attendance recording, payroll processing, and fragmented employee data management. The results indicate that the organization is ready for ERP HRM implementation. Recommendations, an implementation roadmap, and risk mitigation strategies are proposed to support successful ERP adoption.

Page 24 of 24 | Total Record : 236

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