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Pengaruh Pelatihan, Kompetensi, dan Penempatan Tenaga Pendidik Terhadap Kinerja Melalui Komitmen Organisasi di Institusi Pendidikan
Full text linkAnalisis ini mengkaji bagaimana program pelatihan, tingkat kompetensi, dan sistem penempatan tenaga pendidik berkontribusi terhadap peningkatan kinerja mereka, dengan komitmen organisasi berperan sebagai variabel perantara dalam hubungan tersebut. di institusi pendidikan yang berada di bawah naungan Perkumpulan Boen Tek Bio. Pendekatan yang digunakan adalah kuantitatif dengan metode Structural Equation Modelling (SEM). Data dikumpulkan melalui kuesioner yang melibatkan 150 tenaga pendidik di Universitas Buddhi Dharma dan institusi terkait. Hasil penelitian menunjukkan bahwa pelatihan, kompetensi, dan penempatan tenaga pendidik memiliki pengaruh langsung dan tidak langsung terhadap kinerja tenaga pendidik melalui komitmen organisasi. Pelatihan yang efektif dapat meningkatkan kompetensi tenaga pendidik, yang selanjutnya berkontribusi pada peningkatan kinerja. Penempatan tenaga pendidik yang tepat juga berperan penting dalam meningkatkan kinerja mereka. Komitmen organisasi terbukti memainkan peran sebagai penguat dalam hubungan antara pelatihan, kompetensi, penempatan, dan kinerja. Secara keseluruhan, hasil penelitian ini memberikan wawasan strategis mengenai pentingnya pengoptimalan program pelatihan, pengembangan kompetensi, dan penempatan tenaga pendidik yang disertai dengan peningkatan komitmen organisasi untuk mencapai kinerja yang optimal. Temuan ini diharapkan menjadi panduan bagi manajemen institusi pendidikan dalam penyusunan kebijakan yang mendukung peningkatan kualitas tenaga pendidik dan kinerja pendidikan secara keseluruhan
Dampak Influencer, Marketing Viral dan Online Costumer Rating Terhadap Keputusan Pembelian Produk secara Online di Kota Langsa
Full text linkPenelitian ini menganalisis dampak influencer, marketing viral, dan online customer rating terhadap keputusan pembelian produk secara online di Kota Langsa. Dalam penelitian ini, sampel yang digunakan terdiri dari 100 responden. Metode analisis data yang diterapkan adalah analisis regresi linier berganda. Dari pengujian memberikan menghasilkan persamaan regresi Y = 3,144 + 0,032X1 + 0,297X2 + 0,512X3. Secara parsial variabel marketing viral dan online customer rating menunjukkan dampak signifikan terhadap keputusan pembelian produk secara online di Kota Langsa, sedangkan variabel influencer tidak menunjukkan pengaruh signifikan terhadap keputusan pembelian. Selain itu, analisis mengindikasi influencer, viral marketing, dan online customer rating secara bersamaan berdampak signifikan terhadap keputusan pembelian produk secara online di Kota Langsa. Dari uji koefisien determinasi diketahui bahwa pengaruh ketiga variabel tersebut terhadap keputusan pembelian produk secara online di Kota Langsa bernilai 57,1%, sementara 42,9% berasal dari komponen yang berada di luar cakupan penelitian ini
Pengaruh Komunikasi, Disiplin Kerja, Dan Lingkungan Kerja Terhadap Kinerja Karyawan Pada SMA Negeri 13 Kabupaten Tangerang
Full text linkTujuan dari penelitian ini yaitu untuk memahami besarnya pengaruh variabel komunikasi, disiplin kerja, dan lingkungan kerja karyawan di SMA Negeri 13 Kabupaten Tangerang yang berpengaruh pada kinerja mereka, dengan melalui pendekatan kuantitatif. Penelitian ini menggunakan instrumen koesioner dan berhasil dikumpulkan sebanyak 53 orang. Pengolahan data menggunakan SPSS versi 25 untuk menganalisis data dan diperoleh hasil persamaan linear berganda Hasil koefisien regresi 0,301 untuk variabel komunikasi artinya memiliki korelasi yang kuat; koefisien regresi untuk variabel disiplin kerja yaitu 0,528, memiliki korelasi yang kuat; koefisien regresi 0,265 untuk variabel lingkungan kerja artinya memiliki korelasi yang sedang. Berdasarkan uji hipotesis, hasil uji t pada variabel komunikasi memiliki nilai signifikan 0,035 0,05 dan t hitung 2.754 t tabel 1.67528, Sehingga dapat disimpulkan bahwa Ho ditolak dan Ha diterima, bahwa artinya berpengaruh signifikan terhadap kinerja karyawan. Nilai signifikan untuk pengaruh variabel disiplin kerja adalah 0,001 0,05 dan t hitung 3.478 t tabel 1.67528, Sehingga dapat disimpulkan bahwa Ho ditolak dan Ha diterima, berarti berpengaruh signifikan terhadap kinerja karyawan. Nilai signifikan untuk pengaruh variabel lingkungan kerja (X3) adalah 0,132 0,05 dan nilai t hitung 1.533 t tabel 1.67528. Sehingga dapat disimpulkan bahwa Ho diterima dan Ha ditolak, berarti tidak berpengaruh signifikan terhadap kinerja karyawan. Berdasarkan hasil Uji F bahwa nilai Fhitung (26,604) Ftabel (3,179) dan signifikansi 0,000 0,05, maka dapat disimpulkan Ho ditolak dan Ha diterima, yang berarti terdapat pengaruh secara simultan antara variabel independent dengan variabel dependent.Tujuan dari penelitian ini yaitu untuk memahami besarnya pengaruh variabel komunikasi, disiplin kerja, dan lingkungan kerja karyawan di SMA Negeri 13 Kabupaten Tangerang yang berpengaruh pada kinerja mereka, dengan melalui pendekatan kuantitatif. Penelitian ini menggunakan instrumen koesioner dan berhasil dikumpulkan sebanyak 53 orang. Pengolahan data menggunakan SPSS versi 25 untuk menganalisis data dan diperoleh hasil persamaan linear berganda Hasil koefisien regresi 0,301 untuk variabel komunikasi artinya memiliki korelasi yang kuat; koefisien regresi untuk variabel disiplin kerja yaitu 0,528, memiliki korelasi yang kuat; koefisien regresi 0,265 untuk variabel lingkungan kerja artinya memiliki korelasi yang sedang. Berdasarkan uji hipotesis, hasil uji t pada variabel komunikasi memiliki nilai signifikan 0,035 0,05 dan t hitung 2.754 t tabel 1.67528, Sehingga dapat disimpulkan bahwa Ho ditolak dan Ha diterima, bahwa artinya berpengaruh signifikan terhadap kinerja karyawan. Nilai signifikan untuk pengaruh variabel disiplin kerja adalah 0,001 0,05 dan t hitung 3.478 t tabel 1.67528, Sehingga dapat disimpulkan bahwa Ho ditolak dan Ha diterima, berarti berpengaruh signifikan terhadap kinerja karyawan. Nilai signifikan untuk pengaruh variabel lingkungan kerja (X3) adalah 0,132 0,05 dan nilai t hitung 1.533 t tabel 1.67528. Sehingga dapat disimpulkan bahwa Ho diterima dan Ha ditolak, berarti tidak berpengaruh signifikan terhadap kinerja karyawan. Berdasarkan hasil Uji F bahwa nilai Fhitung (26,604) Ftabel (3,179) dan signifikansi 0,000 0,05, maka dapat disimpulkan Ho ditolak dan Ha diterima, yang berarti terdapat pengaruh secara simultan antara variabel independent dengan variabel dependent
Analysis and Evaluation Web-Based Sales System Using the ISO 9126 Quality Model
Full text linkThis study aimed to evaluate the quality of the TR Car Dealership web-based sales system by addressing specific challenges in its functionality, reliability, usability, efficiency, maintainability, and portability. The primary objective was to assess whether the system effectively supports user needs and provides a seamless experience for account registration, product browsing, purchase transactions, and administrative tasks. The ISO 9126 model was utilized as the assessment framework, focusing on six quality attributes that are critical to user satisfaction and system performance. Testing was conducted using black-box testing for functionality assessment and survey-based evaluations for usability and user satisfaction. Automated tools, such as JMeter, Google Lighthouse, and SonarQube, were employed to measure reliability, efficiency, and maintainability under various conditions. The results revealed high scores across several attributes: Functionality achieved an average of 90%, affirming the system’s operational capabilities; Usability scored 91.3%, highlighting ease of use; and Efficiency reached 87%, reflecting effective performance under normal load. However, the Reliability attribute scored 86%, indicating room for improvement, particularly in handling high traffic and unexpected inputs. These findings underscore the TR Car Dealership system’s strengths in user-friendliness and feature completeness while suggesting targeted enhancements for reliability and stability. By addressing these aspects, the system can further enhance user trust and deliver a more resilient and consistent performance. This research demonstrates the effectiveness of the ISO 9126 model in identifying actionable improvements for web-based sales systems
Identifikasi Pemilahan Sampah Berbasis Algoritma Transfer Learning CNN Menggunakan MobileNetV2 dan EfficientNetB0
Full text linkMasalah pengelolaan sampah menjadi tantangan lingkungan yang tidak ada habisnya di kalangan masyarakat. Pemilahan jenis sampah yang benar dapat mendukung upaya daur ulang dan pengelolaan limbah serta mengatasi masalah sampah tersebut. Karena itu, penelitian ini bertujuan untuk mengembangkan sistem yang memanfaatkan teknologi transfer learning CNN (Convolutional Neural Network) untuk membantu pengidentifikasian jenis sampah. Sistem ini diharapkan dapat membantu masyarakat dalam memilah, mengelola dan mendaur ulang sampah. Ada dua arsitektur model yang digunakan dalam penelitian ini, yaitu MobileNetV2 dan EfficientNetB0. Dengan kedua model pre-trained tersebut, penelitian ini juga bertujuan untuk membandingkan performa masing-masing model dalam mengidentifikasi sampah. Dataset yang digunakan untuk melatih kedua model ini diambil dari platform Kaggle dan data yang diambil secara mandiri, dengan total data berjumlah 2527 gambar. Proses penelitian mencakup pencarian data, pembersihan data (pre-processing), augmentasi, pelathan model, serta evaluasi menggunakan confusion matrix. Hasil penelitian menunjukkan MobileNetV2 mencapai akurasi 87,31%, sementara EfficientNetB0 memperoleh akurasi 82,21%. Analisis lebih lanjut menunjukkan bahwa MobileNetV2 unggul dari segi akurasi, presisi, recall, serta efisiensi waktu pelatihan disbanding EfficientNetB0. Meskipun hasil pelatihan menunjukkan performa yang cukup baik, nilai loss pada kedua model masih relatif tinggi. Hal ini kemungkinan disebabkan oleh kurangnya jumlah data yang digunakan untuk melatih model. Penelitian ini diharapkan dapat menjadi langkah awal dalam penerapan teknologi transfer learning untuk mendukung pengelolaan sampah secara efektif, sekaligus menjadi acuan untuk pengembangan sistem dengan dataset yang lebih besar di masa depan
English Language Learning System Using Web Based Rapid Application Development Method
Full text linkThis study aims to develop an interactive and fun web-based English learning application for school Pupil. The Rapid Application Development (RAD) method is used to design this application, which combines media elements such as animation, text, audio, and images. This application is made to arouse Pupil' enthusiasm for learning, while making it easier for teachers to teach. There are various fun and interactive features in it, for example, English learning materials are packaged with an attractive appearance, as well as the ability to monitor each Pupil's learning progress. By using this application, teachers can provide initial knowledge to Pupil more effectively and monitor Pupil learning progress online. This study has succeeded in developing an effective and efficient web based English learning application in improving Pupil' abilities in learning English. The evaluation results showed that 93.77% of Pupil succeeded in improving their English learning skills, with 30 out of 32 Pupil showing increased skills and a satisfaction level of 89.2% from teachers and 89.4% from Pupil.This web-based English learning application can be an alternative in the learning process at school, improving the quality of English learning for Pupil. The RAD method is effective in developing effective and efficient learning applications. This application is expected to increase Pupil' interest in learning and be an example for other schools. This research can contribute to the development of effective and efficient web-based English learning applications, improve the quality of English learning in schools, and be a solution to improve Pupil' English learning abilities
Food Price Prediction Using the Vector Moving Average (VMA) Model in Surabaya and Malang
Full text linkPrice fluctuations of animal-based food are a significant issue in Indonesia, especially for low-income communities. In 2023, chicken prices increased by 4.55% and beef prices rose by 11%, contributing to inflation in East Java. Fluctuations in the prices of tuna and milkfish also affected purchasing power and the consumption of animal protein, which remains relatively low compared to other Asian countries. This study aims to predict the prices of animal-based food commodities in Surabaya City and Malang Regency using the Vector Moving Average (VMA) method, which is capable of capturing strong correlations among variables in multivariate time series data. The study covers daily prices per kilogram of beef, chicken, tuna, and milkfish throughout 2023. The 14-day price prediction at the beginning of 2024 shows that the best model for Surabaya is VMA(2), while for Malang Regency, it is VMA(3), selected based on ACF and PACF plots, low AIC values, MAPE values, and consistency of prediction results. The evaluation results using the Mean Absolute Percentage Error (MAPE) indicate that in Surabaya, beef (0.63%), chicken (2.51%), and tuna (4.76%) achieved high prediction accuracy, while milkfish (13.38%) falls into the “good” category. In Malang Regency, the VMA(3) model yielded more consistent prediction results, with all commodities showing MAPE values below 10%: beef (5.62%), chicken (2.43%), tuna (5.61%), and milkfish (2.18%). These results show that the VMA model performs well in capturing the price dynamics of food commodities, as evidenced by the low MAPE values.Price fluctuations of animal-based food are a significant issue in Indonesia, especially for low-income communities. In 2023, chicken prices increased by 4.55% and beef prices rose by 11%, contributing to inflation in East Java. Fluctuations in the prices of tuna and milkfish also affected purchasing power and the consumption of animal protein, which remains relatively low compared to other Asian countries. This study aims to predict the prices of animal-based food commodities in Surabaya City and Malang Regency using the Vector Moving Average (VMA) method, which is capable of capturing strong correlations among variables in multivariate time series data. The study covers daily prices per kilogram of beef, chicken, tuna, and milkfish throughout 2023. The 14-day price prediction at the beginning of 2024 shows that the best model for Surabaya is VMA(2), while for Malang Regency, it is VMA(3), selected based on ACF and PACF plots, low AIC values, MAPE values, and consistency of prediction results. The evaluation results using the Mean Absolute Percentage Error (MAPE) indicate that in Surabaya, beef (0.63%), chicken (2.51%), and tuna (4.76%) achieved high prediction accuracy, while milkfish (13.38%) falls into the “good” category. In Malang Regency, the VMA(3) model yielded more consistent prediction results, with all commodities showing MAPE values below 10%: beef (5.62%), chicken (2.43%), tuna (5.61%), and milkfish (2.18%). These results show that the VMA model performs well in capturing the price dynamics of food commodities, as evidenced by the low MAPE values
Information System Development for Dental Clinic Using ReactJS and Prisma ORM
Full text linkdrg. Irna Dental Clinic is a healthcare facility that still uses a semi-manual system for patient registration and medical recordkeeping, leading to various issues such as service delays, recording errors, and difficulties in managing patient data. This study aims to design and develop a web-based information system that integrates administrative processes, including patient registration, appointment scheduling, medical record management, and online consultation features. The system was developed using the Software Development Life Cycle (SDLC) Waterfall model, employing modern web technologies for the frontend, user interface design, and database management. System testing was conducted using the Black Box Testing method with 48 test cases, all of which were successfully executed as expected. This indicates that all the core features of the system function properly and consistently. The results show that the system can improve the clinic’s operational efficiency, simplify access to information, and support more organized and modern patient services. By transitioning from a semi-manual system to a fully digital one, the clinic can provide faster, more accurate, and easily accessible services, significantly enhancing the overall patient experience and helping the clinic keep up with technological advancements in healthcare service delivery. Additionally, the feedback from the clinic's staff (doctors and admins) confirmed that the system meets the operational goals and provides a more efficient solution compared to the previous manual processes.drg. Irna Dental Clinic is a healthcare facility that still uses a semi-manual system for patient registration and medical recordkeeping, leading to various issues such as service delays, recording errors, and difficulties in managing patient data. This study aims to design and develop a web-based information system that integrates administrative processes, including patient registration, appointment scheduling, medical record management, and online consultation features. The system was developed using the Software Development Life Cycle (SDLC) Waterfall model, employing modern web technologies for the frontend, user interface design, and database management. System testing was conducted using the Black Box Testing method with 48 test cases, all of which were successfully executed as expected. This indicates that all the core features of the system function properly and consistently. The results show that the system can improve the clinic’s operational efficiency, simplify access to information, and support more organized and modern patient services. By transitioning from a semi-manual system to a fully digital one, the clinic can provide faster, more accurate, and easily accessible services, significantly enhancing the overall patient experience and helping the clinic keep up with technological advancements in healthcare service delivery. Additionally, the feedback from the clinic's staff (doctors and admins) confirmed that the system meets the operational goals and provides a more efficient solution compared to the previous manual processes
Design and Construction of a Website-Based Water Apple Ordering and Management System
Full text linkThis study aims to design and implement a website-based ordering and management system specifically for water apple commodities in Betokan Village, Demak. Farmers in this area previously relied on manual stock and transaction recording, which led to frequent issues such as data inaccuracy, difficulties in inventory tracking, and inefficiencies in sales operations. To address these challenges, a digital system was developed using the Waterfall method, comprising five stages: requirements analysis, system design, implementation, testing, and maintenance. The platform was built using the PHP programming language with the Laravel framework and a MySQL database. The system includes key features such as real-time stock updates, online ordering, transaction recording, and automated report generation. Evaluation of the system was conducted using black-box testing on eight core functions, of which 87.5% passed as expected. Post-implementation results showed a 60% reduction in inventory-related errors and a notable decrease in administrative workload. The system was also piloted with a sample group of local users, and feedback indicated increased efficiency in stock monitoring and transaction processing. This research contributes significantly to the digital transformation of local agricultural communities by offering a practical, scalable solution that improves business operations and customer service. Moreover, it enhances the ability of rural farmers to enter the digital market ecosystem and expand their market reach. The system demonstrates how localized digital tools can bridge gaps in rural agribusiness, increase productivity, and promote economic resilience through technology adoption
Comparative Analysis of K-Means and Gaussian Mixture Model in Clustering Global CO2 Emissions
Full text linkAs global CO2 emissions continue to rise, identifying meaningful patterns across countries has become increasingly vital for shaping effective climate policies. However, many existing approaches rely on uniform benchmarks that overlook national emission heterogeneity. To address this gap, this study applies two unsupervised machine learning techniques K-Means and Gaussian Mixture Model (GMM) to cluster countries based on CO2 emissions from both energy and industrial sectors. The dataset consists of six key indicators, including total emissions, growth rate, and sectoral shares. After handling missing values and applying Min-Max normalization, Principal Component Analysis (PCA) was used to reduce dimensionality and aid visualization. The core objective is to compare the effectiveness of K-Means and GMM in identifying emission-based country groupings. K-Means produced three distinct clusters with strong separation, including a unique cluster dominated solely by China due to its exceptional emission profile. GMM, by contrast, generated more flexible probabilistic clusters, better capturing overlapping patterns and internal variabilities among countries. Evaluation metrics showed that K-Means outperformed GMM in silhouette score and inertia, indicating clearer boundaries, while GMM was more adept at modeling complex, non-spherical distributions. These findings reveal the trade-offs between clarity and adaptability in clustering approaches. The study demonstrates how unsupervised learning can offer actionable insights for emission-based segmentation, enabling more nuanced and differentiated mitigation strategies. By highlighting algorithm-specific strengths, this research contributes to the advancement of machine learning applications in climate informatics and supports the development of targeted international environmental responses.As global CO2 emissions continue to rise, identifying meaningful patterns across countries has become increasingly vital for shaping effective climate policies. However, many existing approaches rely on uniform benchmarks that overlook national emission heterogeneity. To address this gap, this study applies two unsupervised machine learning techniques K-Means and Gaussian Mixture Model (GMM) to cluster countries based on CO2 emissions from both energy and industrial sectors. The dataset consists of six key indicators, including total emissions, growth rate, and sectoral shares. After handling missing values and applying Min-Max normalization, Principal Component Analysis (PCA) was used to reduce dimensionality and aid visualization. The core objective is to compare the effectiveness of K-Means and GMM in identifying emission-based country groupings. K-Means produced three distinct clusters with strong separation, including a unique cluster dominated solely by China due to its exceptional emission profile. GMM, by contrast, generated more flexible probabilistic clusters, better capturing overlapping patterns and internal variabilities among countries. Evaluation metrics showed that K-Means outperformed GMM in silhouette score and inertia, indicating clearer boundaries, while GMM was more adept at modeling complex, non-spherical distributions. These findings reveal the trade-offs between clarity and adaptability in clustering approaches. The study demonstrates how unsupervised learning can offer actionable insights for emission-based segmentation, enabling more nuanced and differentiated mitigation strategies. By highlighting algorithm-specific strengths, this research contributes to the advancement of machine learning applications in climate informatics and supports the development of targeted international environmental responses