Jurnal Politeknik Negeri Batam (PoliBatam)
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The Role of External Auditors in Mediating the Relationship Between Internal Audit and Sustainable Procurement
Full text linkThe vulnerability of state owned enterprise ( BUMN)’s procurement of goods and services from fraud is still high despite of heavy implementation of internal control function. This research aims to examine the influence of the effectiveness of internal audit on the sustainable procurement of goods and services with the role of external auditors as moderating. This research uses a quantitative method with a survey approach to internal auditors and procurement officer within BUMN. The number of participants involved were 130 participants consisting of auditors, managers and procurement experts in BUMN. PLS SEM software was utilized to analyze the survey results. The research finds that the competence and the independence of internal audit and management support with the effectiveness of internal audit as a mediating variable and the role of external auditors as a moderating variables are able to influence the sustainable procurement of goods and services within the BUM
:Pengaruh Gaya Kepemimpinan, Motivasi, dan Disiplin Kerja terhadap Kinerja Karyawan (Studi Kasus: Karyawan PT PLN BATAM: (Case Study: Employees of PT PLN BATAM)
No full textIn the context of an institution or organization, Human Resources, both as members and leaders, play a crucial role in achieving organizational goals. This study aims to assess the impact of leadership style, motivation, and work discipline on employee performance at PT PLN Batam. The research method employed is quantitative, involving 100 PT PLN Batam employees at the Corporate Office as the population and sample. Data analysis was conducted using SPSS 26. The results of hypothesis testing indicate that leadership style, motivation, and work discipline significantly influence employee performance at PT PLN Batam. These findings are further supported by simultaneous hypothesis testing results. Recommendations from the researcher include the implementation of suitable leadership styles to motivate employees, increasing motivation through financial incentives, and improving discipline through clear policies and sanctions. Further research is suggested to explore additional variables and apply research findings in the workplace.
Keywords: Leadership style, motivation, work discipline, employee performanceDalam konteks lembaga atau organisasi, sumber daya manusia baik sebagai anggota maupun pemimpin, memegang peran krusial dalam mencapai tujuan organisasional. Tujuan dari penelitian ini adalah untuk mengetahui bagaimana gaya kepemimpinan, motivasi, dan disiplin kerja berdampak pada kinerja karyawan PT PLN Batam. Seratus karyawan yang bekerja di kantor perusahaan digunakan sebagai populasi dan sampel dalam penelitian kuantitatif ini. Hasil pengujian hipotesis menunjukkan bahwa gaya kepemimpinan, motivasi, dan disiplin kerja secara signifikan memengaruhi kinerja karyawan PT PLN Batam. Analisis data dilakukan menggunakan SPSS 26. Temuan ini diperkuat oleh hasil pengujian hipotesis secara bersamaan. Rekomendasi peneliti mencakup penerapan gaya kepemimpinan yang sesuai untuk memotivasi karyawan, peningkatan motivasi melalui insentif finansial, dan perbaikan disiplin melalui kebijakan dan sanksi yang jelas. Penelitian selanjutnya disarankan untuk mengeksplorasi variabel tambahan dan menerapkan hasil penelitian di lingkungan kerja.
Kata kunci: Gaya kepemimpinan, motivasi, disiplin kerja, dan kinerja karyawa
Penerapan Metode Pose to Pose dalam Film Animasi 3D Edukasi Nura Sang Cahaya Harapan
Full text linkThis research aims to develop an educational 3D animation film titled "Nura the Light of Hope" by applying the Pose to Pose method as the main technique in character animation. The story focuses on the main character, Nura, a humble old lamp, which teaches moral values such as gratitude, humility, and the importance of appreciating differences. The animation production process is divided into three main stages, namely pre-production (script writing, character design, and storyboard), production (modeling, texturing, rigging, animating, rendering), and post-production (final rendering, dubbing, and editing). The results obtained show that the use of the Pose to Pose method is able to create expressive character movements and in accordance with the emotions of the story, thus supporting the delivery of educational messages effectively to children aged 7-12 years. This movie is expected to be an alternative learning media that is interesting and meaningful in the context of children\u27s education in the digital era.Penelitian ini bertujuan untuk mengembangkan film animasi pendidikan 3D berjudul "Nura Cahaya Harapan" dengan menerapkan metode Pose to Pose sebagai teknik utama dalam animasi karakter. Cerita ini berfokus pada karakter utama, Nura, sebuah lampu tua yang sederhana, yang mengajarkan nilai-nilai moral seperti rasa syukur, kerendahan hati, dan pentingnya menghargai perbedaan. Proses produksi animasi dibagi menjadi tiga tahap utama, yaitu pra-produksi (penulisan naskah, desain karakter, dan storyboard), produksi (pemodelan, teksturing, rigging, animasi, rendering), dan pasca-produksi (rendering akhir, pengisi suara, dan penyuntingan). Hasil yang diperoleh menunjukkan bahwa penggunaan metode Pose to Pose dapat menciptakan gerakan karakter yang ekspresif dan sesuai dengan emosi cerita, sehingga mendukung penyampaian pesan pendidikan secara efektif kepada anak-anak usia 7-12 tahun. Film ini diharapkan menjadi media pembelajaran alternatif yang menarik dan berarti dalam konteks pendidikan anak di era digital
Evaluasi User Experience Game Aaron Lost in the Jungle Menggunakan Metode Game Experience Questionnaire (GEQ)
Full text linkUser experience is a crucial indicator in triggering the quality of interaction in digital games. This study aims to provide user experience in the game Aaron Lost in the Jungle using the Game Experience Questionnaire (GEQ) method. The GEQ instrument consists of four modules: the Core Module, the In-game Module, the Post-game Module, and the Social Presence Module. However, due to the single-player nature of the game, the Social Presence module was not implemented in this study. This study uses a quantitative descriptive approach, with data collection through a questionnaire and structured observations of 30 participants who have completed the game. Analysis was conducted on the mean and standard deviation of each experience dimension measured by each GEQ module. The analysis results show that the Tension/Annoyance dimension (3.250) and Negative Experience (2.961) obtained the highest scores, while Flow consistently showed the lowest scores across the two modules, namely 2.540 and 2.300. These findings indicate that although the game successfully elicits emotional engagement, there are still obstacles in building an optimal gaming experience. Several factors are thought to have contributed to this, including the lack of interactive tutorials, linear level design, and a lack of narrative elements. Therefore, further development focused on improving the instructional aspects and storyline is recommended to enhance the overall user experience.Pengalaman pengguna (user experience) merupakan indikator krusial dalam mengevaluasi kualitas interaksi dalam permainan digital. Penelitian ini bertujuan untuk mengevaluasi pengalaman pengguna dalam permainan Aaron Lost in the Jungle dengan menggunakan metode Game Experience Questionnaire (GEQ). Instrumen GEQ terdiri atas empat modul, yakni Core Module, In-game Module, Post-game Module, dan Social Presence Module. Namun, sehubungan dengan karakteristik permainan yang bersifat single-player, modul Social Presence tidak diimplementasikan dalam penelitian ini. Penelitian ini menggunakan pendekatan kuantitatif deskriptif, dengan pengumpulan data melalui kuesioner daring dan observasi terstruktur terhadap 30 partisipan yang telah menyelesaikan permainan. Analisis dilakukan terhadap mean dan standard deviation dari setiap dimensi pengalaman yang diukur oleh masing-masing modul GEQ. Hasil analisis menunjukkan bahwa dimensi Tension/Annoyance (3,250) dan Negative Experience (2,961) memperoleh skor tertinggi, sedangkan Flow konsisten menunjukkan skor terendah pada dua modul, yakni 2,540 dan 2,300. Temuan ini mengindikasikan bahwa meskipun permainan berhasil memunculkan keterlibatan emosional, masih terdapat hambatan dalam membangun pengalaman bermain yang optimal. Beberapa faktor yang diduga berkontribusi terhadap hal tersebut mencakup absennya tutorial interaktif, desain level yang linier, dan minimnya elemen naratif. Oleh karena itu, disarankan adanya pengembangan lanjutan yang berfokus pada peningkatan aspek instruksional dan alur cerita guna meningkatkan kualitas pengalaman pengguna secara holistik
IoT-Based UPS Device Electricity Usage Monitoring System with MQTT Protocol
Full text linkThe continuity of network device operations heavily relies on stable power supply, especially in digital environments that demand uninterrupted connectivity. One commonly used solution to ensure power continuity is the Uninterruptible Power Supply (UPS). However, traditional UPS systems often lack real-time monitoring mechanisms, leaving users uninformed during the transition from main electricity to UPS power. To address this challenge, this study proposes the design of a UPS power consumption monitoring system based on the Internet of Things (IoT) using the Message Queuing Telemetry Transport (MQTT) communication protocol. The system integrates a PZEM-004T power sensor and ESP32 microcontroller to read electrical parameters such as voltage, current, and power in real-time, and displays the data through a digital dashboard built with Node-RED. The implementation results show that the system can automatically detect changes in power source status and record electrical parameters with an average error rate below 1%, both during normal grid operation and when switching to UPS power. This system is expected to serve as a practical and efficient solution for minimizing network downtime caused by power disruptions.The continuity of network device operations heavily relies on stable power supply, especially in digital environments that demand uninterrupted connectivity. One commonly used solution to ensure power continuity is the Uninterruptible Power Supply (UPS). However, traditional UPS systems often lack real-time monitoring mechanisms, leaving users uninformed during the transition from main electricity to UPS power. To address this challenge, this study proposes the design of a UPS power consumption monitoring system based on the Internet of Things (IoT) using the Message Queuing Telemetry Transport (MQTT) communication protocol. The system integrates a PZEM-004T power sensor and ESP32 microcontroller to read electrical parameters such as voltage, current, and power in real-time, and displays the data through a digital dashboard built with Node-RED. The implementation results show that the system can automatically detect changes in power source status and record electrical parameters with an average error rate below 1%, both during normal grid operation and when switching to UPS power. This system is expected to serve as a practical and efficient solution for minimizing network downtime caused by power disruptions
Layered Image Encryption Method Based on Combination of Logistic Map, Henon Map, and Sine Map to Enhance Digital Image Security
Full text linkIn today\u27s digital era, ensuring the confidentiality of image data is crucial due to the widespread use of images in fields such as medical imaging, military communication, and multimedia applications. This study proposes a layered image encryption method by integrating three chaotic systems: Logistic Map, Henon Map, and Sine Map. Each layer in the encryption process applies a different chaotic map to sequentially perform pixel permutation, XOR-based substitution, and modulus-based substitution. Key generation is carried out by producing pseudo-random number sequences derived from the iterations of each chaotic map: the Logistic Map (using specific initial and control parameters), the Henon Map (with two initial condition variables), and the Sine Map (based on a sine function), all of which are highly sensitive to initial conditions and control parameters. These sequences are then used as keys in each encryption stage. The proposed method strengthens the principles of confusion and diffusion, thereby enhancing the security and randomness of the encrypted images. Evaluation was conducted using metrics such as histogram analysis, entropy, chi-square, correlation coefficient, PSNR, and BER. The experimental results demonstrate that the method produces encrypted images with strong statistical characteristics and high resilience against common cryptographic attacks. Thus, this approach makes a significant contribution to the development of secure and efficient image encryption techniques based on chaos theory.In today\u27s digital era, ensuring the confidentiality of image data is crucial due to the widespread use of images in fields such as medical imaging, military communication, and multimedia applications. This study proposes a layered image encryption method by integrating three chaotic systems: Logistic Map, Henon Map, and Sine Map. Each layer in the encryption process applies a different chaotic map to sequentially perform pixel permutation, XOR-based substitution, and modulus-based substitution. Key generation is carried out by producing pseudo-random number sequences derived from the iterations of each chaotic map: the Logistic Map (using specific initial and control parameters), the Henon Map (with two initial condition variables), and the Sine Map (based on a sine function), all of which are highly sensitive to initial conditions and control parameters. These sequences are then used as keys in each encryption stage. The proposed method strengthens the principles of confusion and diffusion, thereby enhancing the security and randomness of the encrypted images. Evaluation was conducted using metrics such as histogram analysis, entropy, chi-square, correlation coefficient, PSNR, and BER. The experimental results demonstrate that the method produces encrypted images with strong statistical characteristics and high resilience against common cryptographic attacks. Thus, this approach makes a significant contribution to the development of secure and efficient image encryption techniques based on chaos theory
Performance Analysis of Deep Learning Model Quantization on NPU for Real-Time Automatic License Plate Recognition Implementation
Full text linkNeural Processing Units (NPUs) are dedicated accelerators designed to perform efficient deep learning inference on edge devices with limited computational and power resources. In real-time applications such as automated parking systems, accurate and low-latency license plate recognition is critical. This study evaluates the effectiveness of quantization techniques, specifically Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT), in improving the performance of YOLOv8-based license plate detection models deployed on an Intel NPU integrated within the Core Ultra 7 155H processor. Three model configurations are compared: a full-precision float32 model, a PTQ model, and a QAT model. All models are converted to OpenVINO’s Intermediate Representation (IR) and benchmarked using the benchmark_app tool. Results show that PTQ and QAT significantly enhance inference efficiency. QAT achieves up to 39.9% improvement in throughput and 28.6% reduction in latency compared to the non-quantized model, while maintaining higher detection accuracy. Both quantized models also reduce model size by nearly 50 percent. Although PTQ is simpler to implement, QAT offers a better balance between accuracy and speed, making it more suitable for deployment in edge scenarios with real-time constraints. These findings highlight QAT as an optimal strategy for efficient and accurate license plate recognition on NPU-based edge platforms
Implementation of the Hybrid K-Nearest Neighbour Algorithm for Dangdut Music Sub-Genre Classification
Full text linkThis research focuses on the classification of dangdut sub-genres — classical, rock, and koplo — by collecting 136 songs from Ellya Khadam, Rhoma Irama, and Denny Caknan, each representing distinct eras of dangdut music. From these, 483 music segments of 30 seconds each were extracted and labelled with expert assistance to ensure accuracy. Six spectral features (centroid, skewness, rolloff, kurtosis, spread, and flatness) were computed and stored in a dataset divided into 70% training and 30% testing sets. The Hybrid K-NN algorithm, integrating Genetic Algorithm (GA) to optimize the k parameter, was applied and evaluated through 5-fold cross-validation. GA parameters were set to a population size of 10, 15 generations, 4-bit chromosome length, and 3-fold cross-validation during optimization. Hybrid K-NN achieved the highest accuracy of 74.31% at k=4 with a processing time of 4.86 seconds, outperforming conventional K-NN (68.75% at k=4, 0.04 seconds), Decision Tree (61.11%, 0.42 seconds), and SVM with ECOC (54.86%, 1.99 seconds). The Hybrid K-NN also demonstrated stable performance with an average accuracy of 72.04% and a standard deviation of 2.22 percent, while the average precision, recall, and F1-score were each around 0.72. Confusion matrix analysis revealed frequent misclassification of class 2 as class 1, highlighting a classification challenge. Overall, this research shows that Hybrid K-NN is more effective than the other methods in capturing data patterns, optimizing parameters, and generalizing to unseen data, though at the cost of longer computation time due to GA’s iterative optimization and validation processes.This research focuses on the classification of dangdut sub-genres — classical, rock, and koplo — by collecting 136 songs from Ellya Khadam, Rhoma Irama, and Denny Caknan, each representing distinct eras of dangdut music. From these, 483 music segments of 30 seconds each were extracted and labelled with expert assistance to ensure accuracy. Six spectral features (centroid, skewness, rolloff, kurtosis, spread, and flatness) were computed and stored in a dataset divided into 70% training and 30% testing sets. The Hybrid K-NN algorithm, integrating Genetic Algorithm (GA) to optimize the k parameter, was applied and evaluated through 5-fold cross-validation. GA parameters were set to a population size of 10, 15 generations, 4-bit chromosome length, and 3-fold cross-validation during optimization. Hybrid K-NN achieved the highest accuracy of 74.31% at k=4 with a processing time of 4.86 seconds, outperforming conventional K-NN (68.75% at k=4, 0.04 seconds), Decision Tree (61.11%, 0.42 seconds), and SVM with ECOC (54.86%, 1.99 seconds). The Hybrid K-NN also demonstrated stable performance with an average accuracy of 72.04% and a standard deviation of 2.22 percent, while the average precision, recall, and F1-score were each around 0.72. Confusion matrix analysis revealed frequent misclassification of class 2 as class 1, highlighting a classification challenge. Overall, this research shows that Hybrid K-NN is more effective than the other methods in capturing data patterns, optimizing parameters, and generalizing to unseen data, though at the cost of longer computation time due to GA’s iterative optimization and validation processes
Comparative Analysis of the C5.0 Algorithm and Other Machine Learning Models for Early Detection of Multi-Class Heart Disease
Full text linkCardiovascular diseases represent the leading cause of mortality worldwide, making accurate and early detection a critical factor for effective medical intervention and improved patient prognosis. While machine learning (ML) offers promising tools for predictive diagnostics, many existing studies rely on single-algorithm approaches or less-than-robust validation methods, thereby limiting the generalizability and real-world applicability of their findings.This study aims to conduct a rigorous, head-to-head comparative evaluation of multiple machine learning algorithms for the multi-class classification of heart disease, with the goal of identifying the most effective and reliable model for this complex clinical task.We utilized a private dataset comprising 300 patient medical records, each described by 11 clinically relevant features. To ensure a robust and unbiased evaluation, a stratified 5-fold cross-validation methodology was employed. Five widely-used classification algorithms were evaluated: Naïve Bayes (NB), Logistic Regression (LR), Random Forest (RF), a C5.0-analog Decision Tree (DT), and Support Vector Machine (SVM). Model performance was assessed using standard metrics, including accuracy, precision, recall, and F1-score.The comparative analysis revealed that the Naïve Bayes algorithm delivered superior performance, achieving the highest mean accuracy of 43.33% (±4.22%). It also led in other key metrics with a mean precision of 43.40%, recall of 43.64%, and an F1-score of 41.26%. Other algorithms, such as Logistic Regression (40.67% accuracy) and Random Forest (39.33% accuracy), demonstrated competitive performance but were ultimately surpassed by the Naïve Bayes model in this specific multi-class classification context.This research underscores the critical importance of employing robust validation techniques and comprehensive comparative analyses to identify optimal models for clinical applications. The Naïve Bayes algorithm emerges as a strong candidate for developing a reliable clinical decision support system for the early differentiation of various heart conditions, providing a foundation for future data-driven diagnostic tools
Comparative Study of SVM and Decision Tree Algorithms on the Effect of SMOTE Technique on LinkAja Application
No full textThe widespread adoption of digital wallets like LinkAja in Indonesia has led to a surge in user-generated reviews, which are valuable for assessing service quality. This study compares the classification performance of Support Vector Machine (SVM) and Decision Tree algorithms on user reviews from the LinkAja application. 7.000 reviews were gathered through web scraping and processed with standard text cleaning, tokenization, stopword removal, and stemming, resulting in 6,261 usable entries. These were divided into training and testing sets in a 70:30 ratio. The performance of each algorithm was evaluated both before and after the application of Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Prior to SMOTE, SVM recorded an accuracy of 77.97%, precision of 0.74, recall of 0.33, and F1 score of 0.45, while Decision Tree reached 72.01% accuracy, 0.50 precision, 0.62 recall, and 0.55 F1 score. After SMOTE, SVM accuracy slightly improved to 78.29%, with notable increases in recall (0.74) and F1 score (0.60); Decision Tree also saw an accuracy rise to 74.56% but experienced a slight decline in F1 score to 0.52. These findings demonstrate that SVM, particularly when used with SMOTE, offers better overall performance and class balance in classifying reviews with imbalanced sentiment distribution, making it more suitable than Decision Tree for this application.The widespread adoption of digital wallets like LinkAja in Indonesia has led to a surge in user-generated reviews, which are valuable for assessing service quality. This study compares the classification performance of Support Vector Machine (SVM) and Decision Tree algorithms on user reviews from the LinkAja application. 7.000 reviews were gathered through web scraping and processed with standard text cleaning, tokenization, stopword removal, and stemming, resulting in 6,261 usable entries. These were divided into training and testing sets in a 70:30 ratio. The performance of each algorithm was evaluated both before and after the application of Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Prior to SMOTE, SVM recorded an accuracy of 77.97%, precision of 0.74, recall of 0.33, and F1 score of 0.45, while Decision Tree reached 72.01% accuracy, 0.50 precision, 0.62 recall, and 0.55 F1 score. After SMOTE, SVM accuracy slightly improved to 78.29%, with notable increases in recall (0.74) and F1 score (0.60); Decision Tree also saw an accuracy rise to 74.56% but experienced a slight decline in F1 score to 0.52. These findings demonstrate that SVM, particularly when used with SMOTE, offers better overall performance and class balance in classifying reviews with imbalanced sentiment distribution, making it more suitable than Decision Tree for this application