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All Journal Journal of Information Systems Engineering and Business Intelligence Jurnal CoreIT Jurnal Otomasi Kontrol dan Instrumentasi Journal of Innovation, Business and Entrepreneurship International Journal of Advances in Data and Information Systems Jurnal Ilmiah MEA (Manajemen, Ekonomi, dan Akuntansi) Journal of Consumer Study and Applied Marketing Jurnal Bisnis dan Pemasaran Digital Journal of Multidisciplinary Academic Business Studies
Wasesa, Meditya
School of Business and Management, Institut Teknologi Bandung
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences

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Business Process Improvement of Fixed Wing UAV Assembly Process: Imperia Dirgantara Case Ajibaskoro, Akbar; Wasesa, Meditya
Journal of Innovation, Business and Entrepreneurship Vol 4, No 2 (2019)
Publisher : Journal of Innovation, Business and Entrepreneurship

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Abstract. Imperia Dirgantara is a company engaged in providing aerial survey solutions using their own university-based research Unmanned Aerial Vehicle (UAV) technology and data processing systems. To anticipate an increasing demand of the aerial survey, the UAV has to be prepared in a short preparation time. Currently, Imperia Dirgantara is facing an issue of lengthy assembly process of the UAV which may lead to lost sales opportunity. In response, this research focuses on providing proposition improvements for the UAV assembly business process, All of data source directly from Imperia Dirgantara internal archives. This business process improvement research is initiated with thorough analysis of bottleneck process in the existing UAV assembly process. Following the bottleneck process identification, we conduct root cause analyses and propose the corresponding business process improvement propositions. Finally we assess the impact of the solution propositions, define the required resources and provide a plan to implement the solution proposition. This research resulting time reduce in processing time as much 12.66% or equal to 10 days. The suggestion of keep improving the current business process instead picking up another option is stated in this research’s report.Keywords: Business Process Improvement; Business Process Model; Bottleneck Analysis; Unmanned Aerial Vehicle; UAV Assembly Process.
Application of Predictive Analytics To Improve The Hiring Process In A Telecommunications Company Jayanti, Luh Putu Saraswati Devia; Wasesa, Meditya
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 8, No 1 (2022): June 2022
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (341.211 KB) | DOI: 10.24014/coreit.v8i1.16915

Abstract

Industry 4.0 refers to the increasing tendency towards automation and data exchange in technologies like Big Data and AI. The existence of technology means telecommunication companies have to adapt. Therefore, it takes great people so that the company can continue to survive. The problem that companies often face in hiring great people is that it costs a lot and takes a long time to recruit. Predictive analysis can assist in identifying system issues and solutions. This study aims to develop predictive analytics that can improve recruitment screening based on CVs and find the best predictive model for the company to reduce costs and long recruitment cycles using technology. The authors built an analytical prediction model in four stages: data collection, data preprocessing, model building, and model evaluation. This technique uses Random Forest and Naive Bayes classification algorithms. Both systems properly predicted more data sets with 70% accuracy, 70% precision, and a recall rate above 80%. Compared between the two techniques, Random Forest outperforms Naive Bayes for this predictive model. A lot of people are talking about predictive analytics for hiring, but there aren't many data mining frameworks that can help to find rules based on the CVs of people who have worked for companies before.Keywords: Recruitment, Human Resource, HR Analytic, Predictive Analytic, Random Forest, Naïve Bayes
The Impact of Mixue's Halal Announcement on Company's Brand Reputation: a Naive Bayes Sentiment Analysis Approach Tandiawan, Gilbert; Wasesa, Meditya
Journal of Consumer Studies and Applied Marketing Vol. 1 No. 1 (2023)
Publisher : Integrasi Sains Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58229/jcsam.v1i1.77

Abstract

With the rise of beverage and ice cream market entry through franchising, Halal certification is more concerned than ever, especially in a country where Muslims take a huge percentage of the whole populations, like Indonesia. The hinderance of business operation could potentially occur if customers felt dissatisfied and unwelcome toward the market entry, that had not had Halal certification, ultimately affecting the brand reputation based on customer satisfaction above all. Hence, this research aims to find out whether Halal Announcement can turn a brand into having a better brand reputation, by analyzing comments retrieved through Instagram, as well as to evaluate the prediction model of Naïve Bayes by using TF-IDF machine learning classification. The data for this research was separated into two posts, which have 3861 and 3128 unfiltered comments for occurrences before and after Halal Announcement respectively. These comments then are processed to validate that the company was able to rebound the positive sentiments after the Halal Announcement, in which the hypothesis was accepted. Furthermore, the developed Naïve Bayes model is evaluated on the After Halal Announcement dataset, achieving an average accuracy of 65.99%, showing that model is able to predict the sentiment quite fair with several key takeaways that are noted.
Adoption Drivers of Digital Platform for Coal Production Planning: an Extended UTAUT Model Using PLS-SEM Analysis Nugroho, Eko P.; Wasesa, Meditya
International Journal of Advances in Data and Information Systems Vol. 5 No. 2 (2024): October 2024 - International Journal of Advances in Data and Information System
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v5i2.1321

Abstract

In 2022, the coal production industry encountered unprecedented challenges accompanied by a substantial global commodity price surge. The operational impact of this situation surpasses current technological capabilities of coal companies, particularly in optimizing coal blending scenarios. A pivotal aspect of digital transformation involves integration of new digital platform for production planning. This study employs the Unified Theory of Acceptance and Use of Technology in conjunction with decision theory to identify key factors influencing the platform adoption at a coal mining company. Structured questionnaires were utilized, followed by analysis using the SmartPLS 4.0.9.9 software. Findings reveal that both Performance Expectancy and Effort Expectancy positively influence users’ behavioral intention to adopt digital platform for production planning. Behavioral Intention, in turn, significantly impacts actual usage behavior. Unanticipated situational factors and others' attitudes were found to have negligible mediating effects, while variables such as age and experience showed no moderating influence on the pathways from behavioral intention to usage behavior. Companies are advised to improve digital platform performance through functionalities enhancements and pilot testing to reduce perceived effort and stimulate behavioral intention. Additionally, fostering a positive organizational mindset through routine motivational communications can further stimulate usage behavior.
Industry 5.0 Research in the Sustainable Information Systems Sector: A Scoping Review Analysis Zulkifli, Ahmad; Wasesa, Meditya
International Journal of Advances in Data and Information Systems Vol. 5 No. 2 (2024): October 2024 - International Journal of Advances in Data and Information System
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v5i2.1336

Abstract

Industry 4.0, centered on cyber-physical production systems, has been criticized for prioritizing profit over social and environmental concerns. In contrast, Industry 5.0 emphasizes AI efficiency while promoting human-centric, resilient, and sustainable approaches, integrating economic, social, and environmental systems. Previous research has often focused solely on conceptual frameworks and technologies, overlooking Industry 5.0's sector-specific impacts. This study addresses that gap by conducting a scoping review to map research findings, identify trends, and highlight knowledge gaps and future research opportunities. By systematically analyzing literature from the Scopus database (2016-present), the study refined a large dataset to focus on Industry 5.0's relevance. The analysis revealed significant attention to sectors like Industry and Producer Services, while Agriculture and Retail, particularly natural resource-based sectors like agriculture and fisheries, are often neglected. Key findings indicate that Industry 5.0 is likely to be driven by the industrial sector, followed by product services and financial industries. The study also highlights the strong connection between IoT and AI in optimizing operations with real-time data and automation and identifies blockchain as a promising technology for enhancing transparency and security, despite existing implementation challenges. This research not only serves as a foundational record of Industry 5.0's implications across various sectors but also provides valuable insights into its role in Information Systems (IS). It lays the groundwork for future exploration of Industry 5.0 in diverse sectors and industries.
Improving Café Reputation: Machine Learning Analytics for Predicting Customer Engagement on Google Maps Anisah, Siti; Wasesa, Meditya
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 1 (2025): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.1.91-102

Abstract

Background: Online reviews is a powerful tool in shaping customer decisions, as they significantly influence a business’s reputation and the ability to attract new customer. Given the growing reliance on digital platforms, understanding engagement levels is crucial for business that want to enhance online presence. By analyzing these customer activities, business owners can leverage Machine Learning (ML) analytics to predict engagement on Google Maps reviews. Objective: This study aimed to develop the most suitable ML model in order to predict customer engagement levels in café business on Google Maps, and determine the online review features that have the greatest impact on engagement. Additionally, the analysis aimed to provide actionable recommendations to help business owners improve online reputation and engagement strategies. Method: A total of 5,626 online reviews data were collected using web scraping methods during the analysis. The data was then preprocessed by extracting major review features, calculating engagement levels, and addressing class imbalance with SMOTE method. In the study, K-Means clustering was used to segment engagement levels, while sentiment analysis through VADER Lexicon was applied to measure sentiment content. Various ML models were trained and validated using a 10-fold cross-validation method. Finally, Analysis was conducted using Spearman's correlation to identify relationships among features derived from the best-performing ML models. Results: The result of the analysis showed that Random Forest model achieved the highest accuracy and PR AUC in predicting engagement levels. The four most influential factors were review length (16.23%), photos (15.57%), total rating (12.35%), and author review count (10.19%). Spearman's correlation analysis showed a positive relationship among review length, photos, and author review count, signifying the combined impact on engagement levels. Conclusion: This study described the effectiveness of Random Forest model in predicting engagement levels in Google Maps reviews. Specifically, the model identified review length, photos, total rating, and author review count as the key factors influencing engagement. These results would provide valuable guidance for business owners that desire to improve customer engagement and online reputation. Building on this, future studies should explore larger datasets, integrate additional features, and examine how the engagement contribute to long-term customer retention. Keywords: Online Reputation Management, Customer Engagement, Behavior, Machine Learning, Google Maps Review, Predictive Analytics
Frequency Migration Challenges and Strategic Decisions-Making in Broadband Wireless Access Networks Sulaeman, Maman; Wasesa, Meditya
Jurnal Bisnis dan Pemasaran Digital Vol. 3 No. 1 (2023): Juli
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jbpd.v3i1.4502

Abstract

Purpose: The impetus for this study lies in the pressing need for telecommunication companies to adapt to the rapidly evolving technological landscape and regulatory environment to maintain competitiveness and meet increasing consumer demands. Method: This study conducted a comprehensive analysis using SWOT analysis, Qualitative Data Analysis, and the Analytical Hierarchy Process (AHP) to determine the best course of action. Results: This study navigates through the initial stages of identifying the strategic, operational, and financial implications of PT. XYZ Frequency Migration. It delves into the background of Broadband Wireless Access (BWA) technology, the significance of the 3.3 GHz and 10 GHz bands in telecommunications, and the broader context of industry trends necessitating this shift. Methodologically, this research integrates SWOT, stakeholder, qualitative data analysis (QDA), and the Analytical Hierarchy Process (AHP) to provide a holistic understanding of the complexities of migration. Conclusions: The migration of PT. XYZ from the 3.3 GHz to the 10 GHz band represents a strategic move to enhance broadband capacity, service quality, and market competitiveness. Through a holistic evaluation using SWOT, stakeholder input, qualitative data, and the Analytical Hierarchy Process (AHP), the initiative is shown to be both viable and forward-looking despite its complexity. Success hinges on PT. XYZ's ability to synchronize technological upgrades with regulatory compliance, financial prudence, and stakeholder engagement. Limitations: This study is limited by its reliance on qualitative assessments and structured decision-making tools that may not fully capture dynamic market or regulatory shifts. Additionally, the scope did not include real-time performance data post-migration, which could further validate the strategic assumptions. Contributions: This study contributes to the academic and practical understanding of network migration within the telecommunications sector, offering nuanced insights into the complexities of transitioning to higher frequency bands.
Risk Identification and Decision-Making in Low-Rank Coal Handling: FMEA-AHP Prabawa, Ananta; Wasesa, Meditya
Jurnal Bisnis dan Pemasaran Digital Vol. 4 No. 1 (2024): Juli
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jbpd.v4i1.4523

Abstract

Purpose: This study addresses the challenges posed by the increasing proportion of lower-ranking coal through proactive risk management in the Coal Preparation Plant (CPP) area of the PT. XYZ. Previous failures in handling lower-ranking coal have led to production delays and have negatively impacted the coal handling process. The analysis focuses on identifying, assessing, and prioritizing risks, and formulating optimal solutions to minimize their impact. Method: The methodology incorporates FMEA for risk identification and ranking, and proposes the top three risks and alternative solutions. Then, it proceeds with The Analytic Hierarchy Process (AHP), guided by criteria from Value-Focused Thinking (VFT), to determine the most optimal solution. Results: The top three identified risks and their corresponding solutions are as follows: (1) sticky material adherence to the drawdown hopper and chute wall causing material flow blockage and overfill, addressed by installing a booster pump on the existing water suppression system. (2) The accumulation of fine coal in closed spaces causes equipment burning, mitigated through periodic clean-up using a new portable blower or water. (3) Burning coal, causing fires inside the tunnel, was resolved by installing a thermal camera monitoring system. Conclusions: The study concludes that the handling of lower-ranking coal within the CPP area presents several critical risks primarily associated with excessive airborne coal dust, sticky materials, and fine coal accumulation. Airborne dust reduces visibility, posing safety hazards and health risks, while also interfering with sensors and contaminating nearby equipment. Sticky materials adhering to chutes and conveyors cause blockages, overfills, belt drift, and trigger false sensor alerts, significantly reducing operational efficiency. Limitations: The scope of the research will be limited to the specific area of the Coal Processing Plant (CPP) in the company and will be based on the characteristics of the lower-ranking coal in the company. Contributions: This study provides valuable guidelines for CPP stakeholders to minimize disruptions and improve the overall effectiveness of coal-handling activities.
STRATEGIC DECISION MAKING FOR BUSINESS DEVELOPMENT AND PROFIT OPTIMIZATION USING THE ANALYTICAL HIERARCHY PROCESS: THE CASE OF KLINIK PRATAMA SINDANG SARI Fadillah, Reza Yudha; Wasesa, Meditya
Jurnal Ilmiah Manajemen, Ekonomi, & Akuntansi (MEA) Vol 9 No 2 (2025): Edisi Mei - Agustus 2025
Publisher : LPPM STIE Muhammadiah Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31955/mea.v9i2.5917

Abstract

Health is important for the well-being of the community, and basic clinics are important places for people in Indonesia to get health treatment. Klinik Pratama Sindang Sari has been committed to providing great service since it opened in 1997. In 2024, it received "Paripurna" (excellent) accreditation. Even though the clinic knows this, it still has trouble being financially stable, mostly because it relies heavily on BPJS capitation, which makes up 80% of its income. Rising costs of doing business and not enough variety make it even harder for it to earn profit. This study looks into ways for Klinik Pratama Sindang Sari to build its business in a smart way that will help its finances. The study uses Value-Focused Thinking (VFT), the Analytic Hierarchy Process (AHP), Stakeholder Analysis, Focus Group Discussions, and the Kepner-Tregoe Problem Analysis to find and rank alternative service innovations. The Analytic Hierarchy Process (AHP) is very important for judging four proposed strategies: Vaccine Service Implementation; Dental Aesthetic Care; Corporate Health Screening Cooperation; and BPJS Capitation Optimization. It does this by looking at factors like cost, regulation, market demand, profit potential, and implementation challenges.
XGBOOST MODEL FOR DEFAULT PREDICTION IN CREDIT SCORING OF CONVENTIONAL BANK Suftandar, Hilmi; Wasesa, Meditya; Putro, Utomo Sarjono
Jurnal Ilmiah Manajemen, Ekonomi, & Akuntansi (MEA) Vol 9 No 2 (2025): Edisi Mei - Agustus 2025
Publisher : LPPM STIE Muhammadiah Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31955/mea.v9i2.5920

Abstract

We develop an XGBoost-based classification model for predicting loan default in the context of credit scoring for a conventional commercial bank in Indonesia. The model aims to improve predictive performance in identifying high-risk borrowers using historical loan data. For this purpose, we use two years of consumer loan records, consisting of over forty thousand observations, including borrower demographic, credit score, and loan characteristics. To address the severe class imbalance within the dataset, we employ the Random OverSampling Examples (ROSE) technique on the training subset. The model is trained and evaluated using standard classification performance metrics, including precision, recall, F1 score, and area under both ROC and Precision-Recall curves. Our results show that the XGBoost model performs well in detecting non-defaults with high sensitivity and precision, particularly in the training set. However, performance on the test set indicates a significant drop in recall for the default class, suggesting model overfitting under imbalanced conditions. These findings highlight the potential and limitations of using ensemble learning methods such as XGBoost in real-world credit risk evaluation, especially when data imbalance remains a major concern.
Co-Authors Ajibaskoro, Akbar Arif M. Natawidjaja, Reyza Armanto Mangkuto, Rizki Edi Leksono Fadillah, Reza Yudha Friansa, Koko Jayanti, Luh Putu Saraswati Devia Justin Pradipta N. Haq, Irsyad Nanda, Rezky Mahesa Nihlahhania, Zulfa Raya Ningsih, Fauziah Firlita Nugroho, Eko P. Prabawa, Ananta Puspita, Yumna Simatupang, Kondar Paido Siti Anisah Suftandar, Hilmi Sulaeman, Maman Tandiawan, Gilbert Utomo Sarjono Putro Zulkifli, Ahmad