International Journal of Informatics, Economics, Management and Science

Sistem Pendukung Keputusan Pemilihan Mitra Jasa Pengiriman Barang Pada PT OCBC Menggunakan Metode Simple Additive Weighting (SAW)

Dewi Kartika — 2026-08-24

Pemanfaatan sistem penunjang keputusan dengan metode simple additive weighting pada PT OCBC mengenai pemilihan mitra jasa pengiriman dengan alternatif yaitu PT Satria Antaran Prima (SAPX), PT Tri Adi Bersama (Anteraja), PT Lion Express (Lion Parcel), PT Paxel Algorita Unggul (Paxel), dan Advantage. Perhitungan ini memiliki 4 kriteria yaitu harga; waktu pengiriman; layanan jangkauan; dan jenis layanan. berdasarkan dari penilaian PT OCBC dan dirumuskan ke dalam perhitungan Simple Additive Weighting diperoleh bahwa PT Lion Express (Lion Parcel) mendapatkan nilai tertinggi yaitu 0,87 dan menjadikannya sebagai solusi optimal untuk PT OCBC.

The Effect of Career Development, Non-Financial Compensation, and Transformational Leadership on Organizational Citizenship Behavior (OCB) through Job Satisfaction among Government Employees with Work Agreements (PPPK) at Dinas Bina Marga Cipta Karya dan

Ferna Kaswanti — 2026-08-24

This study aims to analyze the effects of career development, non-financial compensation, and transformational leadership on Organizational Citizenship Behavior (OCB) through job satisfaction as an intervening variable among Government Employees with Work Agreements (PPPK) at the Department of Highways, Human Settlements and Spatial Planning of West Sumatra Province. This quantitative study involved 104 PPPK employees selected using saturated sampling. Data were collected through questionnaires and analyzed using Structural Equation Modeling-Partial Least Square (SEM-PLS) with SmartPLS 4. The results showed that career development, non-financial compensation, and transformational leadership had positive and significant effects on job satisfaction. Career development and non-financial compensation did not have significant direct effects on OCB, while transformational leadership and job satisfaction had positive and significant effects on OCB. Job satisfaction did not mediate the effect of career development on OCB, but positively and significantly mediated the effects of non-financial compensation and transformational leadership on OCB. The R-square values were 0.648 for job satisfaction and 0.558 for OCB. These findings indicate that job satisfaction is an important mechanism for strengthening OCB among PPPK employees.

CEO Turnover, Earnings Management, And Family Ownership (Empirical Study On Property And Real Estate Companies Listed On The Indonesia Stock Exchange, 2020-2022)

Titi Aslah — 2026-03-30

This study aims to analyze and provide empirical evidence of the effect of CEO turnover on earnings management, with family ownership as a moderating variable, in property and real estate companies in Indonesia. The hypotheses tested are: (1) CEO turnover is positively related to earnings management, and (2) family ownership strengthens the relationship between CEO turnover and earnings management. This study uses data from property and real estate companies listed on the Indonesia Stock Exchange (IDX) during 2020-2022, selected on the criteria that they published audited financial statements denominated in rupiah, accessible on the IDX website. Samples were obtained through purposive sampling, and the data were analyzed using multiple regression analysis. The results show that (1) CEO turnover is related to earnings management, so the first hypothesis is accepted, and (2) family ownership significantly strengthens the relationship between CEO turnover and earnings management, so the second hypothesis is accepted.

Internal Control over Financial Reporting (ICoFR) in Indonesia: Regulatory Development, Implementation Challenges, and Organizational Readiness Framework

Adrian Adrian — 2026-07-29

Reliable financial reporting is essential for investor confidence, corporate governance, and sustainable organizational performance. The increasing complexity of business operations and regulatory expectations has heightened the importance of Internal Control over Financial Reporting (ICoFR). Indonesia recently introduced a mandatory ICoFR framework through Financial Services Authority Regulation (POJK) No. 15 of 2024 and the Minister of State-Owned Enterprises Decree No. SK-5/DKU.MBU/11/2024. This study examines the development of ICoFR regulations in Indonesia, identifies key implementation challenges, and proposes an Integrated Organizational Readiness Framework for Internal Control over Financial Reporting (IORF-ICoFR). Using an integrative literature review and regulatory analysis, the study synthesizes international governance frameworks, Indonesian regulations, and professional implementation guidance. The findings indicate that effective ICoFR implementation depends not only on regulatory compliance but also on organizational readiness, including governance, leadership commitment, human capital, business process maturity, technology governance, risk management integration, and continuous monitoring. The proposed framework provides a practical reference for organizations seeking to strengthen internal controls and enhance the reliability of financial reporting. This study contributes to the literature by integrating regulatory and organizational readiness perspectives within the Indonesian context and offers practical implications for regulators, financial institutions, state-owned enterprises, audit committees, internal auditors, and corporate management.

The effect of the fraud hexagon on Financial statement fraud in the finance sector companies Listed on the indonesian stock exchange from 2020 -2023

Bertha Elvy Napitupulu — 2026-07-29

Financial statements are documents that present a companyâ€™s financial information and serve as the basis for evaluating the companyâ€™s performance and financial stability. Financial statements contain information regarding the companyâ€™s management performance and reflect the companyâ€™s condition over a given business period. Every company strives to produce financial statements that are accurate, relevant, and reliable so as to demonstrate that its operations are running smoothly. Fraud is committed to manipulate financial statements so that they appear sound, relevant, and reliable. Financial statement fraud can be caused by various factors. Theories regarding the causes of financial statement fraud have evolved from the fraud triangle, fraud diamond, fraud pentagon, and fraud hexagon, where the factors are pressure, opportunity, rationalization, capability, arrogance, and collusion. This study aims to analyze the influence of the fraud hexagonâ€”comprising pressure, opportunity, rationalization, capability, arrogance, and collusionâ€”on financial statement fraud in financing sector companies listed on the Indonesia Stock Exchange during the 2020â€“2023 period. The results of the study indicate that none of the factors in the fraud hexagon had an effect on financial statement fraud at financing sector companies listed on the Indonesia Stock Exchange during the 2020â€“2023 period.

Blockchain For Halal Certification Integrity and Consumer Trust: A Systematic Literature Review

Marhaeni Marhaeni — 2026-07-29

This study synthesizes current developments concerning the application of blockchain technology to reinforce halal certification integrity and strengthen consumer trust. A systematic literature review (SLR) was carried out following the PRISMA protocol, using Scopus and Web of Science databases covering 2019- 2025. From an initial 108 records, 48 studies met the eligibility criteria and were analyzed thematically. The review reveals four interconnected domains traceability and transparency, governance and regulatory structures, organizational readiness, and technological orchestration which jointly determine blockchain readiness within halal ecosystems. The findings demonstrate that blockchain, particularly when combined with IoT, AI, and smart contracts, facilitates secure verification, improves data integrity, and enhances auditability. A conceptual framework is proposed to illustrate how institutional capacity, technological integration, and regulatory harmonization collectively shape consumer trust

Student Dropout Prediction Using XGBoost and Explainable AI (SHAP): A Case Study of Portuguese Student Dataset

Chandra Kesuma — 2026-08-24

Student dropout is one of the major challenges in higher education, as it can affect studentsâ€™ academic continuity as well as the efficiency of institutional resource management. The availability of academic, administrative, demographic, and socioeconomic data provides opportunities to develop machine learning models capable of identifying students based on their dropout status. However, models with high predictive performance often have limited interpretability, making them difficult to use as an informative basis for higher education decision-makers. This study aims to develop a dropout status classification model using Extreme Gradient Boosting (XGBoost) and to explain the contribution of features to the modelâ€™s decisions using Explainable AI through the SHapley Additive exPlanations (SHAP) method. The dataset consists of 4,424 students from higher education institutions in Portugal, with 36 predictor variables. The original three-class target, consisting of Dropout, Enrolled, and Graduate, was transformed into a binary classification problem, with Dropout as the positive class and Graduate and Enrolled as the Non-Dropout class. The study compares Logistic Regression, Random Forest, Support Vector Machine (SVM), and XGBoost using accuracy, precision, recall, F1-score, and ROC-AUC. The results of stratified 5-fold cross-validation show that XGBoost achieves the best overall performance, with an accuracy of 88.07%, recall of 77.90%, F1-score of 80.76%, and ROC-AUC of 92.64%. On the testing data, XGBoost achieves an accuracy of 88.14%, precision of 83.03%, recall of 79.23%, F1-score of 81.08%, and ROC-AUC of 93.40%. SHAP analysis indicates that Curricular units 2nd sem (approved) is the feature with the greatest contribution to the modelâ€™s decisions, followed by Tuition fees up to date, Curricular units 1st sem (approved), Course, and Age at enrollment. Additional experiments removing first- and second-semester academic features resulted in a substantial decrease in model performance. These findings indicate that the model has strong predictive capability but is more appropriately used to predict dropout status based on studentsâ€™ academic trajectories rather than being claimed as an early warning system from the beginning of their studies.

Environmental Scanning and Early Warning Against Counterfeit Heritage Textiles: Systematic Review and Conceptual Framework

Hajar Yuwono — 2026-08-24

Counterfeit and imitation goods have posed a persistent commercial threat to heritage textile industries, batik and ikat among a wider family of regionally distinctive crafts. Environmental scanning (ES) and early warning systems (EWS) would seem the natural instruments for meeting that threat, embedded as both constructs are within strategic management and supply chain risk scholarship. The difficulty lay elsewhere. Work applying them to counterfeit detection in heritage textiles had accumulated in scattered pockets across management, law and engineering, with little sustained exchange between them. This study accordingly undertook a systematic literature review pursuing four lines of inquiry, namely how ES and EWS were operationalised to surface counterfeit risk, which technological and procedural components sustained systems that produced usable alerts, what systematic scanning yielded relative to unsystematic monitoring, and which barriers obstructed deployment among small and medium sized enterprises (SMEs) in developing economies. A fifth aim drew the four together into a conceptual framework. Retrieval proceeded through the Consensus academic search engine, which aggregates Semantic Scholar, Scopus, PubMed and arXiv. Nine thematic clusters returned 179 matched records, of which 90 unique records were screened in detail and 54 retained for narrative synthesis. Four patterns emerged. No system built expressly for heritage textile settings was found; the architectures encountered belonged to generic supply chain risk and brand protection frameworks, leaving the provenance concerns peculiar to heritage products unaddressed. Enabling technologies, among them RFID, blockchain, anomaly detection through artificial intelligence, and optical or fluorescent security materials, matured at considerable speed, yet their cost structures often placed them beyond the reach of artisan scale producers. Technological availability was less often the limiting factor than affordability and the capacity to implement and act on these technologies. The reviewed evidence consistently favoured systematic monitoring over ad hoc alternatives, although returns on protective investment were nonlinear. Adoption among SMEs stalled less on technical grounds than on the familiar triad of constrained finance, uneven digital literacy and thin institutional support, a pattern intensifying sharply in the Indonesian batik industry, where geographical indication and copyright registration among artisans remained sparse. The review advances a cooperative level conceptual framework that integrates scanning, early warning, affordability tiered technology, moderating conditions, and collective response pathways for heritage textile protection.

Implementation Of A Bandwidth Monitoring System Using Observium At PT XYZ

Dwi Putro Bagus Sugianto — 2026-08-24

The increasing demand for fast, stable, and reliable internet services requires Internet Service Providers (ISPs) to have an effective network monitoring system. PT XYZ, as an Internet Service Provider, requires network monitoring to support bandwidth management and network performance supervision. Based on observations and interviews conducted during the Community Service Program, the existing network monitoring process had several limitations, including the absence of centralized monitoring, limited real-time bandwidth monitoring, lack of informative network visualization, and difficulties in identifying network disturbances. This activity aimed to implement a web-based network monitoring system using Observium to improve the effectiveness of bandwidth monitoring and assist the Network Operation Center (NOC) team in network supervision and troubleshooting. The implementation method used the Waterfall approach, consisting of requirements analysis, system design, implementation, testing, and maintenance. The system was implemented using Ubuntu Server 22.04, Nginx, MariaDB, PHP 8.1, Observium, and Simple Network Management Protocol (SNMP). Black Box Testing was conducted to verify the functionality of the implemented system. The results showed that the system successfully performed login, displayed the dashboard, monitored bandwidth, displayed network devices, and presented traffic graphs. The implementation centralized network monitoring, provided more understandable bandwidth information, and supported faster identification of network problems. Thus, Observium can support the effectiveness and efficiency of network monitoring activities at PT XYZ.

Systems Engineering Process Methodology for Machine Learning and Multi-Criteria Decision Making in Early Detection of Neurological Disorders in Infants

Muhammad Syaukani — 2026-08-25

Early detection of risk abnormalities in infants requires integration of clinical evaluation, multimodal data analysis, and structured data retrieval for decision-making. Conventional approaches rely on manual observation, which is subjective, time-consuming, and difficult to implement at scale. Although Machine Learning (ML) has potential for analyzing movement and clinical data, ML outputs are probabilistic and do not directly represent clinical decisions involving various criteria. This research proposes the System Engineering Process Methodology (SEPM) to integrate ML and Multi-Criteria Decision Making (MCDM) to develop a system that detects early-risk abnormalities in neurological disorders in infants. The proposed methodology consists of six stages, namely Requirements Analysis, System Design, Implementation, Testing, Deployment, and Maintenance. The resulting architecture integrates General Movements (GMs) videos and clinical data from the Electronic Health Record (EHR) and neurological inspection. A 3D Convolutional Neural Network (3D-CNN) is used for video analysis, while XGBoost is used for processing tabular clinical data. The predictive results are further integrated into an AHPâ€“TOPSIS-based MCDM layer for risk stratification. The research results are presented as a mapping of systematic ML and MCDM functions in the SEPM cycle, with SEPM as the system engineering backbone, ML as the analytical engine, and MCDM as the decision engine. This framework provides a methodological runway for developing a Clinical Decision Support tool to support early detection of neurological risk abnormalities in infants.