Jurnal Politeknik Negeri Batam (PoliBatam)
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Enhancing Storage Efficiency: Class-Based Warehouse Layout Design of Indonesian Manufacturing Company
Get PDFPT XYZ, a service firm focused on marine and offshore projects, encountered inefficient warehouse storage and the lack of an item code system. This made it harder to find and store items. The purpose of this study is to create a more efficient warehouse plan by utilizing the class-based storage strategy. The ABC analysis was used to classify 45 different sorts of items as fast, medium, or slow moving. The results revealed that this strategy boosted warehouse utility by 15.87%, reduced material movement distance by 19,586 meters, and saved cost by Rp49,454,650. Furthermore, picking and arranging things became more efficient, increasing by 0.38% and 0.22%, respectively
The Use of The Gapuro System as a Radio Frequency Identification (RFID) Tool with the Technology Acceptance Model (TAM) Method on Employee Productivity at PT. Epson Batam
Get PDFThis paper examines the impact of the Gapuro system, which is used as a Radio Frequency Identification tool, on employee productivity at PT Epson Batam through the Technology Acceptance Model (TAM). The TAM model assesses how perceived benefits, perceived ease of use, attitudes towards use, behavioral intentions to utilize, and actual systems related to the Gapuro system affect employee productivity. The writing design uses a quantitative methodology with a descriptive framework. The writing sample was obtained using a non-probability methodology using the purposive sampling method, with 102 respondents representing the entire workforce of the IK-Production molding department of PT. Epson Batam. The data collection method uses a questionnaire. In contrast, the data processing strategy involves instrument testing, correlation analysis, classical assumption testing, multiple linear regression analysis, and hypothesis testing. The results show that perceived ease of use and intention to use the system significantly affect employee productivity. Meanwhile, perceived usefulness, attitudes towards use, and actual use show no significant direct effect on productivity. This writing has an important impact on companies in increasing the effectiveness of technology implementation and developing better technology training and adoption strategies.Artikel ini mengkaji dampak dari sistem Gapuro, yang digunakan sebagai alat Radio Frequency Identification (RFID), terhadap produktivitas karyawan di PT Epson Batam melalui pendekatan Technology Acceptance Model (TAM). Model TAM menilai bagaimana persepsi manfaat, persepsi kemudahan penggunaan, sikap terhadap penggunaan, niat perilaku untuk menggunakan, dan penggunaan aktual sistem Gapuro memengaruhi produktivitas karyawan. Desain penulisan menggunakan metodologi kuantitatif dengan kerangka deskriptif. Sampel penelitian diperoleh dengan metode non-probability melalui teknik purposive sampling, dengan jumlah responden sebanyak 102 orang yang mewakili seluruh tenaga kerja di departemen IK-Production molding PT Epson Batam. Metode pengumpulan data menggunakan kuesioner, sementara strategi pengolahan data mencakup uji instrumen, analisis korelasi, uji asumsi klasik, analisis regresi linier berganda, dan pengujian hipotesis. Hasil penelitian menunjukkan bahwa persepsi kemudahan penggunaan dan niat untuk menggunakan sistem berpengaruh signifikan terhadap produktivitas karyawan. Sementara itu, persepsi manfaat, sikap terhadap penggunaan, dan penggunaan aktual tidak menunjukkan pengaruh langsung yang signifikan terhadap produktivitas. Tulisan ini memiliki dampak penting bagi perusahaan dalam meningkatkan efektivitas implementasi teknologi serta mengembangkan strategi pelatihan dan adopsi teknologi yang lebih baik
Knowledge Management dan Kinerja Karyawan: Tinjauan Sistematis dari Asia
No full textThis study provides a comprehensive analysis of the role of Knowledge Management (KM) in enhancing employee performance through a Systematic Literature Review (SLR). In the context of a growing knowledge-based economy, organizations are increasingly required to manage information and experience in a structured manner to foster innovation, improve efficiency, and sustain competitive advantage. This review adopts the SLR protocol proposed by Williams et al, selecting 12 peer-reviewed articles from the Scopus database based on inclusion criteria including Asian geographic focus, Q1/Q2 journal rankings, and relevance to the research variables. The findings reveal that KM significantly contributes to employee performance, both directly and as a mediating variable. KM processes—such as knowledge creation, storage, sharing, and application—play a vital role in promoting productivity, innovation, and adaptability. This study contributes to the literature by mapping current trends and identifying research gaps, offering both theoretical insight and practical implications for future studies.Penelitian ini bertujuan untuk menganalisis secara komprehensif peran Knowledge Management (KM) dalam meningkatkan kinerja karyawan melalui pendekatan Systematic Literature Review (SLR). Dalam era pertumbuhan ekonomi berbasis pengetahuan, organisasi dituntut untuk mengelola informasi dan pengalaman secara sistematis guna mendorong inovasi, meningkatkan efisiensi, serta mempertahankan keunggulan kompetitif. Kajian ini mengacu pada protokol SLR dari Williams et al. dengan menyeleksi 12 artikel ilmiah terindeks Scopus yang telah melalui proses peer review. Kriteria inklusi yang digunakan meliputi fokus geografis Asia, publikasi dalam jurnal berperingkat Q1/Q2, serta relevansi dengan variabel penelitian, yaitu KM dan kinerja karyawan. Hasil tinjauan menunjukkan bahwa KM memberikan kontribusi signifikan terhadap peningkatan kinerja karyawan, baik secara langsung maupun sebagai variabel mediasi. Proses-proses dalam KM—seperti penciptaan, penyimpanan, pembagian, dan penerapan pengetahuan—berperan penting dalam meningkatkan produktivitas, inovasi, dan kemampuan adaptasi organisasi. Kajian ini memberikan kontribusi terhadap pengembangan literatur dengan memetakan tren terkini dan mengidentifikasi kesenjangan penelitian yang masih belum banyak dijelajahi. Temuan ini juga memberikan implikasi praktis bagi organisasi dan akademisi yang ingin memanfaatkan KM secara strategis untuk meningkatkan kinerja sumber daya manusia
IDENTIFIKASI FAKTOR KERUSAKAN MESIN BUBUT DI LABORATORIUM MANUFAKTUR MENGGUNAKAN METODE FAILURE MODE AND EFFECT ANALYSIS (FMEA): Studi Kasus: Laboratorium Manufaktur Politeknik Negeri Batam
No full textDalam kegiatan praktikum di Laboratorium Manufaktur Politeknik Negeri Batam terdapat beberapa mesin bubut konvensional yang beroperasi. Namun dalam pengoperasinnya, terkadang terjadi masalah dengan proses produksi karena kerusakan mesin yang signifikan sehingga menyebabkan proses belajar menjadi terganggu. Tujuan studi ini adalah untuk mengidentifikasi komponen utama penyebab kerusakan pada salah satu mesin bubut konvensional dengan menggunakan metode Failure Mode and Effect Analysis (FMEA) dengan cara menghitung Risk Priority Number (RPN) dan memberikan rekomendasi. Dengan menghitung nilai RPN dapat diidentifikasi risiko kerusakan tertinggi dari komponen mesin bubut tersebut sehingga prioritas perawatan yang dibutuhkan dapat diketahui. Berdasarkan analisis, nilai RPN terbesar diperoleh sebesar 192 oleh gearbox mesin bubut, kemudian eretan pemutar sumbu x sebesar 36 dan kopling transmisi roda gigi sebesar 32. Karena seluruh komponen tersebut memiliki nilai RPN < 200, maka mitigasi risiko yang perlu dilakukan adalah dengan melakukan corrective maintenance namun untuk mencegah kerusakan mesin bubut sebelum terjadi dan memperkecil risiko gangguan pada praktikum, diperlukan peningkatan strategi yang lebih tepat pada preventive dan predictive maintenance.
Dalam kegiatan praktikum di Laboratorium Manufaktur Politeknik Negeri Batam terdapat beberapa mesin bubut konvensional yang beroperasi. Namun dalam pengoperasinnya, terkadang terjadi masalah dengan proses produksi karena kerusakan mesin yang signifikan sehingga menyebabkan proses belajar menjadi terganggu. Tujuan studi ini adalah untuk mengidentifikasi faktor utama penyebab kerusakan pada salah satu mesin bubut konvensional dengan menggunakan metode Failure Mode and Effect Analysis (FMEA) dengan cara menghitung Risk Priority Number (RPN). Dengan menghitung nilai RPN dapat diidentifikasi risiko kerusakan tertinggi dari komponen mesin bubut tersebut sehingga prioritas perawatan yang dibutuhkan dapat diketahui. Berdasarkan analisis, nilai RPN terbesar diperoleh sebesar 192 oleh gearbox mesin bubut, kemudian eretan pemutar sumbu x sebesar 36 dan kopling transmisi roda gigi sebesar 32.
 
Public Sentiment Analysis of the Free Meal Program: A Comparison of Naive Bayes and Support Vector Machine Methods on the Twitter (X) Social Media Platform
Get PDFThe problems of nutrition, including stunting, remain a challenge in Indonesia. Therefore, Prabowo and Gibran launched the 2024 Free Meal Program, which provides free lunch to every school child as well as pregnant mothers. This research analyzes public sentiment towards this program using data from X with Naïve Bayes and Support Vector Machine (SVM) methods. The data was analyzed through crawling, preprocessing, labeling, and feature extraction using TF-IDF. The results showed a predominance of positive sentiment towards the program, with SVM performing better in sentiment classification, achieving 86.42% accuracy compared to Naïve Bayes with 67.9%. The findings can guide policymakers in improving the communication strategy and implementation of the Free Meal Program to make it more impactful for Indonesians.The problems of nutrition, including stunting, remain a challenge in Indonesia. Therefore, Prabowo and Gibran launched the 2024 Free Meal Program, which provides free lunch to every school child as well as pregnant mothers. This research analyzes public sentiment towards this program using data from X with Naïve Bayes and Support Vector Machine (SVM) methods. The data was analyzed through crawling, preprocessing, labeling, and feature extraction using TF-IDF. The results showed a predominance of positive sentiment towards the program, with SVM performing better in sentiment classification, achieving 86.42% accuracy compared to Naïve Bayes with 67.9%. The findings can guide policymakers in improving the communication strategy and implementation of the Free Meal Program to make it more impactful for Indonesians
Coffee Beans Classification Using Convolutional Neural Networks Based On Extraction Value Analysis In Grayscale Color Space
Get PDFCoffee is a vital agricultural commodity, and precise classification of coffee beans is crucial for quality assessment and agricultural practices. In this study, we propose a methodology utilizing Convolutional Neural Networks (CNN) based on ResNet-101 architecture for coffee bean classification. The novelty of our approach lies in the integration of comprehensive feature extraction from grayscale coffee bean images, including mean, standard deviation, skewness, energy, entropy, and smoothness, with the transfer learning capabilities of CNN. Through this integration, we achieved exceptional classification performance, with the CNN model attaining accuracy, recall, precision, and F1-score metrics of 99.44% and 100% on the training data, and 100% on the testing data. These results underscore the robustness and generalization capability of our methodology in accurately classifying coffee bean types. While the dataset used in this study is experimental, the comprehensive feature extraction and the effectiveness of the CNN architecture suggest the potential for accurate classification of coffee bean types beyond the experimental data, provided the new data shares similar characteristics to the collected samples. For future research, we recommend exploring the integration of two transfer learning techniques within CNN architectures to further enhance coffee bean classification systems. Specifically, leveraging pre-trained CNN models as a foundation for feature extraction, while simultaneously fine-tuning specific layers to adapt to the nuances of coffee bean classification tasks, could offer improved model performance and scalability.Coffee is a vital agricultural commodity, and precise classification of coffee beans is crucial for quality assessment and agricultural practices. In this study, we propose a methodology utilizing Convolutional Neural Networks (CNN) based on ResNet-101 architecture for coffee bean classification. The novelty of our approach lies in the integration of comprehensive feature extraction from grayscale coffee bean images, including mean, standard deviation, skewness, energy, entropy, and smoothness, with the transfer learning capabilities of CNN. Through this integration, we achieved exceptional classification performance, with the CNN model attaining accuracy, recall, precision, and F1-score metrics of 99.44% and 100% on the training data, and 100% on the testing data. These results underscore the robustness and generalization capability of our methodology in accurately classifying coffee bean types. While the dataset used in this study is experimental, the comprehensive feature extraction and the effectiveness of the CNN architecture suggest the potential for accurate classification of coffee bean types beyond the experimental data, provided the new data shares similar characteristics to the collected samples. For future research, we recommend exploring the integration of two transfer learning techniques within CNN architectures to further enhance coffee bean classification systems. Specifically, leveraging pre-trained CNN models as a foundation for feature extraction, while simultaneously fine-tuning specific layers to adapt to the nuances of coffee bean classification tasks, could offer improved model performance and scalability
Static Analysis-Based Security Enhancement for Mobile Applications Using Mobile Security Framework (MOBSF)
Get PDFMobile application security is crucial to protect users’ personal data and maintain trust in the application. Without proper security testing, an app becomes vulnerable to threats such as data theft and cyber attacks. This study aims to identify and fix security vulnerabilities in the XYZ mobile application, a social platform used to report domestic violence and child sexual abuse cases. The analysis was conducted using static analysis with the Mobile Security Framework (MOBSF). The XYZ app was developed using Flutter and falls under the hybrid application category. Since it handles sensitive information from victims and reporters, ensuring its security is essential. The analysis revealed four major vulnerabilities with high risk levels, mainly related to misconfiguration and weak security settings. After addressing these issues, the app’s security score improved from 37/100 (high risk) to 61/100 (low risk). These improvements were implemented in the final development phase before the app was released to users. MOBSF helped developers detect potential vulnerabilities early through static analysis, serving as a security baseline. This approach ensured the app no longer contained risks such as debug certificates, enabled debug mode, or support for outdated Android versions. The findings show that MOBSF-based security analysis is effective in detecting and reducing application security weaknesses, making the XYZ app more secure in protecting user data.Mobile application security is crucial to protect users’ personal data and maintain trust in the application. Without proper security testing, an app becomes vulnerable to threats such as data theft and cyber attacks. This study aims to identify and fix security vulnerabilities in the XYZ mobile application, a social platform used to report domestic violence and child sexual abuse cases. The analysis was conducted using static analysis with the Mobile Security Framework (MOBSF). The XYZ app was developed using Flutter and falls under the hybrid application category. Since it handles sensitive information from victims and reporters, ensuring its security is essential. The analysis revealed four major vulnerabilities with high risk levels, mainly related to misconfiguration and weak security settings. After addressing these issues, the app’s security score improved from 37/100 (high risk) to 61/100 (low risk). These improvements were implemented in the final development phase before the app was released to users. MOBSF helped developers detect potential vulnerabilities early through static analysis, serving as a security baseline. This approach ensured the app no longer contained risks such as debug certificates, enabled debug mode, or support for outdated Android versions. The findings show that MOBSF-based security analysis is effective in detecting and reducing application security weaknesses, making the XYZ app more secure in protecting user data
Esscore: An OCR-Based Android App for Scoring Short Handwritten Answer Using Levenshtein Distance
Get PDFManual evaluation of short answer tests is time-consuming and prone to subjectivity. This study presents Esscore, an Android-based application that automates the scoring of handwritten short answers using EasyOCR and the Levenshtein Distance algorithm. EasyOCR extracts text from student answers image, while Levenshtein Distance measures similarity against predefined answer keys, allowing tolerance for varied correct responses. The system was tested on 350 student’s handwritten answers, achieving 95.7% accuracy. Functional testing using 14 black box scenarios showed all features operated correctly without failure. A usability test conducted with the SUS method produced a score of 76.5, rated “Good” with a grade “B” and an “Acceptable” acceptance level. The Net Promoter Score (NPS) placed the application in the “Passive” category. These results confirm Esscore as a functional, accurate, and user-friendly solution for automated answer scoring in educational environments
Real-Time Detection of Coffee Cherry Ripeness Using YOLOv11
Get PDFThis study aims to develop a real-time coffee fruit ripeness detection system using the YOLOv11 algorithm to assist farmers in determining the optimal harvest time. The dataset comprises 302 images categorized into three ripeness levels: ripe, semi-ripe, and unripe. Model training was conducted on Google Colab with data augmentation to enhance dataset variability and prevent overfitting. After 20 epochs, the model demonstrated strong performance in the ripe category (mAP50: 0.774, Precision: 0.645, Recall: 0.812) and satisfactory results for semi-ripe fruits (mAP50: 0.695, Precision: 0.624, Recall: 0.679). However, detection performance for unripe fruits was lower (mAP50: 0.4). The system achieved an inference time of 183.4 ms per image, with fast preprocessing and postprocessing (0.5 ms each), indicating its suitability for real-time applications. While the model performs well overall, further improvement is needed in detecting unripe coffee fruits for enhanced system effectiveness
Hierarchical Clustering of Education Indicators in Papua Island: A Ward’s Method Approach
Get PDFEducation development aims to ensure inclusive, equitable education and increase learning opportunities for all Indonesian citizens. Papua Island is still not an island with a high education level; data on education indicators indicate this in each Regency / City on the island of Papua, with a value below the national average. Identifying districts/cities is needed to improve education, so clustering is carried out using the Ward method. This research aims to group and map regencies/cities on the island of Papua based on education indicators. The results of this study are expected to be a consideration and benchmark for the government in making decisions regarding education in districts/cities on the island of Papua, considering the region\u27s characteristics. This is an applied research with the data type used, namely secondary data on education indicators in Papua Island in 2022. Data sources are obtained from the official website of the Central Bureau of Statistics of each province on the island of Papua. Four education indicators are taken into account in this research, namely the School Participation Rate (SPR), the Gross Enrollment Rate (GER), the Net Enrollment Ratio (NER), and the Average Years of Schooling (AYS), which are then detailed into 10 variables. The cluster analysis process uses Euclidean distance and cluster validation using the Dunn Index. The results showed that 3 clusters formed. Cluster 1 consists of 27 districts/cities; this first group is classified as a high level of education. Cluster 2 consists of 7 districts/cities with a medium level of education, and Cluster 3 has eight districts/cities with a low level of education—cluster results based on the highest Dunn Index validation value of 0.414