Jurnal Politeknik Negeri Batam (PoliBatam)
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Match Outcome Prediction in Draft Pick and In-game Phases of MSC 2025 Mobile Legends using Random Forest and XGBoost
No full textMobile Legends: Bang Bang is a widely played Multiplayer Online Battle Arena game in Southeast Asia, and its competitive ecosystem has driven the need for accurate match outcome prediction. Most existing studies analyze either the draft pick phase or the in game phase in isolation, limiting their ability to capture the full progression of a match. To address this limitation, this study evaluates the performance of Random Forest and Extreme Gradient Boosting (XGBoost) in predicting match outcomes across both phases using data from the MSC 2025 tournament. The dataset was collected from Liquipedia’s official API and match replay recordings. Draft pick features represent team composition factors such as synergy, hero strength, and patch impact, while in game features consist of statistical indicators including gold, kills, turrets, and objectives extracted from multiple time based snapshots. Both models were trained using qualification stage matches and tested on the main event. A phase separated hybrid feature engineering approach was employed to represent strategic differences between the draft pick and in game phases. Evaluation metrics include accuracy, precision, recall, F1 score, and ROC AUC. Results show that the draft pick models achieved a maximum accuracy of 57%, whereas the in game models reached 88% for Random Forest and 84% for XGBoost, with both achieving a ROC AUC of 0.94. These findings indicate that snapshot based in game features provide stronger predictive signals than draft pick composition features, which reflect only the initial strategic potential rather than actual match conditions.Mobile Legends: Bang Bang is a widely played Multiplayer Online Battle Arena game in Southeast Asia, and its competitive ecosystem has driven the need for accurate match outcome prediction. Most existing studies analyze either the draft pick phase or the in game phase in isolation, limiting their ability to capture the full progression of a match. To address this limitation, this study evaluates the performance of Random Forest and Extreme Gradient Boosting (XGBoost) in predicting match outcomes across both phases using data from the MSC 2025 tournament. The dataset was collected from Liquipedia’s official API and match replay recordings. Draft pick features represent team composition factors such as synergy, hero strength, and patch impact, while in game features consist of statistical indicators including gold, kills, turrets, and objectives extracted from multiple time based snapshots. Both models were trained using qualification stage matches and tested on the main event. A phase separated hybrid feature engineering approach was employed to represent strategic differences between the draft pick and in game phases. Evaluation metrics include accuracy, precision, recall, F1 score, and ROC AUC. Results show that the draft pick models achieved a maximum accuracy of 57%, whereas the in game models reached 88% for Random Forest and 84% for XGBoost, with both achieving a ROC AUC of 0.94. These findings indicate that snapshot based in game features provide stronger predictive signals than draft pick composition features, which reflect only the initial strategic potential rather than actual match conditions
Moderating Role of Values in Religiosity and Brand Switching from Israel-Affiliated Brands among Gen Z
No full textThis study examines the influence of religiosity on Indonesian consumers\u27 brand switching behavior, particularly regarding brands affiliated with the Israeli-Palestinian issue. Using SEM-PLS analysis with 178 respondents obtained through snowball sampling, the results reveal that religiosity positively and significantly affects consumers\u27 tendency to switch from Israeli-affiliated brands to local alternatives. Furthermore, functional, emotional, social, and epistemic values significantly strengthen this relationship. These findings highlight that consumer behavior is influenced not only by economic considerations but also by spiritual, social, and cognitive factors. In today\u27s value-conscious consumer landscape, purchasing decisions increasingly reflect personal and collective beliefs. For marketers, this emphasizes the importance of developing ethical, value-driven branding strategies that resonate with consumers\u27 social and moral awareness. Brands that align with consumer values and demonstrate social responsibility are more likely to foster loyalty and trust in an era where consumers are buying not just products, but also meaning and identity
Does the Role of Corporate Governance can Improve Financial Performance? Bibliometric Literature Review
No full textThe objective of this study is to examine research trends on the role of corporate governance in financial performance across various countries, published in the Scopus database from 2019 to 2024. Data collection was conducted on May 24, 2024. The research method used was qualitative with bibliometric analysis using the R-Packages software and the Biblioshiny Web Interface for processing and visualizing data. For conciseness and professionalism, the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were used. The PRISMA method and bibliometric analysis are combined to strengthen the quality and scope of the literature review. PRISMA is used in the initial stage to systematically screen the literature. The bibliometric analysis is based on preset inclusion and exclusion criteria, applied at the end of the screening process to analyze and visualize data from the selected publications. There are five stages in this research: using keywords relevant to the research topic, searching for data matching the keywords, selecting articles, validating the data, and analyzing the data. One of the most influential publications is Tan Y\u27s research, which has been cited 194 times since its 2022 publication. The affiliate with the most publications is the University of Delhi, with 15, while China is the most productive country, with the highest number of citations at 154
Understanding the "Fear of Missing Out" (FOMO) Phenomenon Among Retail Investors in the Indonesian Capital Market: A Literature Review
No full textThe study examining the Fear of Missing Out (FOMO) phenomenon among Indonesian retail investors found that it is a significant driver of impulsive and herding investment behaviors. Using a qualitative literature review of studies from 2019 to 2025, the research identified several key characteristics and triggers of FOMO. The study revealed that FOMO is often characterized by investors abandoning fundamental analysis in favor of following the crowd. Its primary triggers are the rapid spread of information on social media, the bandwagon effect, and low financial literacy. These factors collectively lead to suboptimal decisions, increased risk, and psychological stress for investors. The research concludes that mitigating FOMO is crucial for a stable capital market. Effective strategies include improving financial literacy, encouraging long-term investment planning, and promoting self-discipline. These educational and regulatory measures are essential to fostering a more rational investment environment
Green Accounting as a Sustainability Mechanism Moderating Financial Statement Fraud in the Fraud Pentagon Context: A Study from Indonesia
No full textFinancial statement fraud remains a major threat to corporate transparency and sustainable business practices. Traditional frameworks, such as the Fraud Triangle and Fraud Pentagon, focus on psychological and organizational factors behind fraudulent behavior. However, they often overlook the influence of environmental accountability on corporate ethics. This study examines the effects of pressure, opportunity, rationalization, capability, arrogance, and collusion on financial statement fraud. Green accounting is introduced as a moderating factor linking fraudulent behavior and corporate sustainability. The study uses a quantitative approach, drawing on data from manufacturing companies listed on the Indonesia Stock Exchange from 2021 to 2024. Data were collected through financial report analysis and questionnaires. Analysis used Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess both measurement and structural models. Descriptive statistics, validity, and reliability tests were performed before path analysis. The findings show that the six Fraud Pentagon elements significantly influence the risk of financial statement fraud, though their effects differ in magnitude. Green accounting moderates these relationships by reducing fraudulent tendencies through the integration of sustainability principles in financial reporting. Firms with strong environmental accounting practices show higher transparency and accountability toward both financial and ecological stakeholders. This study concludes that embedding green accounting in corporate governance enhances financial integrity and promotes sustainable business conduct. By extending the Fraud Pentagon through a sustainability lens, the research contributes to fraud theory and corporate environmental responsibility. This has practical implications for regulators, auditors, and managers, who should strengthen the adoption of green accounting as a strategic measure to counter fraudulent financial reporting
Comparison of Sentinel-1A Ascending and Descending Image Processing Results on the Tukul Dam Using SNAP Software
No full textThis study analysed and compared the accuracy of the results of Sentinel 1A satellite image processing in the ascending and descending orbit directions with SNAP software. The research is located at Tukul Dam, Karanggede Village, Arjosari District, Pacitan Regency, East Java Province, with a range of observation data for one year in 2022-2023. Sentinel 1A satellite image processing uses the Differential Interferometry Synthetic Aperture Radar (DInSAR) method. The results of Sentinel 1A image processing were validated using measurement data from 38 dam surface measurement points that had been measured terrestrially.
The accuracy calculation uses the Root Mean Square error (RMSe) to measure the vertical movement of coordinates (Z) from the results of Sentinel 1A image processing in the ascending and descending orbit directions with the actual position in the field measured terrestrially. The result is the RMSe value of vertical movement from the Sentinel 1A image processing in the ascending direction is 0.015m. In comparison, the result of Sentinel lA image processing in the descending orbit direction is 0.234m. Based on the calculation results of the RMSe value of vertical movement, the results of Sentinel 1A image processing in the ascending direction are better used for calculating vertical movement at Tukul Dam
Optimalisasi Manajemen Downtime pada SIMRS dan REM di RSUD dr. R. Goeteng Taroenadibrata Purbalingga
No full textGuidance in managing downtime in the Hospital Management Information System (SIMRS) and Electronic Medical Records (RME) is a strategic step to maintain operational continuity and the quality of healthcare services at RSUD dr. R. Goeteng Taroenadibrata Purbalingga. Recurrent downtime negatively impacts administrative processes and medical services, necessitating systematic and continuous management. This study aims to provide technical guidance in managing downtime and to enhance the understanding and skills of human resources, particularly the Information Technology (IT) team and users of SIMRS and RME. The methods employed include technical training, identification of downtime causes, development of system maintenance procedures, and capacity building for users in operating the information systems. The results indicate that structured training and technical support can reduce downtime frequency and accelerate responses to technical disruptions. These findings highlight the importance of strengthening the IT team through ongoing training, more systematic system maintenance planning, and the establishment of clear, user-friendly operational procedures. Continuous guidance is expected to improve the effectiveness of downtime management and support the smooth delivery of healthcare services. This effort is an integral part of enhancing the quality of information systems and advancing digital transformation in healthcare services.Pendampingan dalam pengelolaan downtime pada Sistem Informasi Manajemen Rumah Sakit (SIMRS) dan Rekam Medis Elektronik (RME) merupakan langkah strategis untuk menjaga kontinuitas operasional serta mutu pelayanan kesehatan di RSUD dr. R. Goeteng Taroenadibrata Purbalingga. Downtime yang terjadi secara berulang berdampak negatif terhadap proses administratif dan layanan medis, sehingga memerlukan penanganan yang sistematis dan berkelanjutan. Pengabdian Kepada Masyarakat ini bertujuan memberikan pendampingan teknis dalam pengelolaan downtime serta meningkatkan pemahaman dan keterampilan sumber daya manusia, khususnya tim Teknologi Informasi (TI) dan pengguna SIMRS maupun RME. Metode yang digunakan meliputi pelatihan teknis, identifikasi penyebab downtime, penyusunan prosedur pemeliharaan sistem, serta peningkatan kapasitas pengguna dalam pengoperasian sistem informasi. Hasil kegiatan menunjukkan bahwa pelatihan dan dukungan teknis yang terstruktur dapat menurunkan frekuensi downtime serta mempercepat respons terhadap gangguan teknis. Temuan ini menekankan pentingnya penguatan tim TI melalui pelatihan berkelanjutan, perencanaan pemeliharaan sistem yang lebih sistematis, dan penyusunan prosedur operasional yang jelas dan mudah dipahami pengguna. Pendampingan berkelanjutan diharapkan dapat meningkatkan efektivitas manajemen downtime dan mendukung kelancaran pelayanan kesehatan secara menyeluruh. Upaya ini menjadi bagian integral dalam peningkatan mutu sistem informasi dan transformasi digital layanan kesehatan
REDUCTION OF LIFTED STITCH DEFECTS IN WIRE BONDING PROCESS THROUGH ROOT CAUSE ANALYSIS
No full textReliable connection inspection is crucial for the quality of semiconductor products. A frequent issue is stitch defects during wire bonding. To address this, an investigation was conducted using flow charts. Additionally, an analysis was performed by identifying root causes using the Fishbone Diagram method and the 5 Whys technique. After improvements, including targeted operator training, a significant reduction of 96.75% in stitch defects was achieved. This study demonstrates that the combination of root cause analysis methods and operator training effectively enhances the reliability of the wire bonding process and the quality of semiconductor products. This study did not account for environmental factors that might influence the wire bonding process, such as temperature and humidity variations. Therefore, the findings may be limited in settings with different environmental conditions
Rancang Bangun Kit Praktikum Pengendalian Temperatur Fluida Menggunakan Kontrol PID Bertingkat
No full textTemperatur adalah salah satu variabel yang perlu dikendalikan pada sistem industri proses. Beberapa contoh pengendalian temperatur adalah pada sistem boiler dan HVAC yang digunakan pada beberapa industri di batam. Oleh karena terdapat kebutuhan dari industri, maka pada penelitian ini dilakukan. Pada penelitian ini kit praktikum pengendalian temperatur fluida dirakit untuk digunakan pada laboratorium program studi instrumentasi sebagai alat praktikum. Selain membuat kit praktikum penelitian ini juga mencoba untuk mengatasi salah satu permasalahan pada pengendalian temperatur yaitu sering terjadinya fluktuasi yang tinggi pada proses variabel. Gangguan tersebut timbul ketika proses cairan masuk kedalam tabung, pemanas aktif dan air keluar dari tabung. Sistem pengendalian kemudian diperbaiki dengan menambahkan pengendalian pada pemanas dan pengendalian pada aliran air. Kedua kendali ini digabungkan menjadi sistem kendali PID bertingkat (control cascade). Hasil yang diperoleh dari penelitian ini adalah nilai overshoot sebelum mencapai steady state pada cascade control lebih rendah jika dibandingkan dengan single control. Maximum overshoot yang dihasilkan menggunakan single control mencapai 3°C diatas set point, namun jika menggunakan cascade control tidak terbentuk maximum overshoot. Pengambilan data antara cascade control dan single control dilakukan dengan nilai Kc, Ti dan Td yang sam
Effectiveness of AdaBoost and XGBoost Algorithms in Sentiment Analysis of Movie Reviews
Get PDFCurrently there are many entertainment platforms that provide various movies, TV shows, games, and other content. These platforms usually offer a variety of features, one of which is reviews. Review data written by viewers plays an important role in influencing public interest in the film. However, the increasing number of reviews makes it difficult to assess the sentiment of the film quickly and accurately. This highlights the need for a system that can analyze reviews based on sentiment, making it easier for viewers to evaluate the film and supporting the entertainment industry in understanding the needs of the audience. Therefore, this study develops a sentiment analysis model to identify whether a review contains positive or negative sentiment using machine learning algorithms. The data used to build the model is obtained from user reviews of a film on the IMDb platform. This dataset is available on Kaggle with 50,000 movie reviews in text format. The characteristics of the data include two columns: review_text and sentiment. The methods used to create the classification model are AdaBoost and XGBoost. The data preprocessing process includes several stages such as text cleaning, tokenization, stopword removal, lemmatization, and vectorization using TF-IDF to convert the review text into numeric form, as well as converting the positive and negative labels into 1 and 0. Based on the results of model training with cross-validation, the accuracy of the XGBoost model is 85% and AdaBoost is 77%. Feature selection showed an improvement in the XGBoost model\u27s accuracy from 85% to 86%, while the AdaBoost model\u27s performance remained stable at 77%. Thus, it can be concluded that the XGBoost model demonstrates better performance than the AdaBoost model in sentiment classification