Perkembangan teknologi otomasi industri menuntut adanya sistem kontrol yang andal, efisien, dan mudah dioperasikan untuk mendukung proses produksi. Salah satu penerapan otomasi tersebut adalah penggunaan Programmable Logic Controller (PLC) dan inverter pada mesin vacuum industri. PLC berfungsi sebagai pengendali utama yang mengatur logika kerja sistem, sedangkan inverter berperan dalam mengatur kecepatan dan torsi motor listrik agar sesuai dengan kebutuhan proses. Laporan kerja praktik ini membahas sistem kontrol PLC dan inverter pada vacuum mesin industri di PT. DNP Indonesia. Metode yang digunakan dalam penyusunan laporan ini meliputi observasi langsung di lapangan, studi literatur, serta konsultasi dan bimbingan dengan pembimbing lapangan dan dosen pembimbing. Pembahasan difokuskan pada prinsip kerja PLC, prinsip kerja inverter, sistem mesin vacuum, serta alur kerja sistem kontrol yang diterapkan. Hasil dari kegiatan kerja praktik ini menunjukkan bahwa penerapan sistem kontrol PLC dan inverter mampu meningkatkan efisiensi, kestabilan, dan keandalan pengoperasian mesin vacuum industri. Selain itu, penggunaan inverter memungkinkan pengaturan kecepatan motor secara fleksibel sehingga dapat menghemat energi dan memperpanjang umur peralatan. Dengan demikian, sistem kontrol PLC dan inverter sangat berperan penting dalam menunjang kelancaran dan efektivitas proses produksi di PT. DNP Indonesia

Regional revenue is one of the indicators of regional financial ability that needs to be analyzed to determine the realization of revenue. Analyses that are still descriptive have not been able to group data based on similar characteristics. This study aims to apply the K-Means algorithm to classify the regional income pattern of Kudus Regency based on monthly income realization data. The research uses a quantitative approach with secondary data in the form of the realization of regional revenue in Kudus Regency in 2020–2024 obtained from the Regional Revenue, Finance, and Asset Management Agency (BPPKAD) of Kudus Regency. Data processing is carried out using the RapidMiner application through the Read Excel, Set Role, Normalize, K-Means Clustering, and Performance stages, The number of clusters is set to three (k = 3). The results of the study showed that the K-Means algorithm succeeded in grouping data into three Cluster 0 clusters consisting of 4 data, namely September, October, November, and December. Cluster 1 consists of 1 data, namely August, while Cluster 2 consists of 7 data, namely January, February, March, April, May, June, and July. Centroid analysis showed that each cluster had different characteristics, while evaluation using Performance Vector yielded a Davies-Bouldin Index value of -0.444 which showed good grouping results based on the RapidMiner evaluation. The results of this study are expected to be supporting information in the evaluation and planning of regional revenue management in Kudus Regency

Proyek konstruksi dengan tingkat kompleksitas tinggi membutuhkan sistem pengendalian yang efektif untuk memastikan pencapaian sasaran waktu dan biaya sesuai dengan rencana pelaksanaan. Ketidaksesuaian antara progres aktual dengan rencana dapat menyebabkan keterlambatan penyelesaian proyek serta peningkatan biaya pelaksanaan. Oleh karena itu, diperlukan metode pengendalian yang mampu mengevaluasi kinerja proyek secara komprehensif. Penelitian ini bertujuan untuk menganalisis pengendalian proyek pembangunan Gedung NICE di PIK 2 menggunakan metode Critical Path Method (CPM) dan Earned Value Management (EVM). Metode CPM digunakan untuk mengidentifikasi aktivitas yang berada pada lintasan kritis, sedangkan metode EVM digunakan untuk mengevaluasi kinerja biaya dan waktu melalui indikator Cost Variance (CV), Cost Performance Index (CPI), Schedule Variance (SV), dan Schedule Performance Index (SPI). Hasil analisis CPM menunjukkan bahwa dari 84 aktivitas pekerjaan terdapat 14 aktivitas yang termasuk dalam lintasan kritis dan memerlukan pengendalian lebih intensif karena berpengaruh langsung terhadap durasi penyelesaian proyek. Hasil evaluasi EVM menunjukkan bahwa kinerja biaya proyek berada dalam kondisi efisien, ditunjukkan dengan nilai Cost Variance (CV) positif dan Cost Performance Index (CPI) lebih besar dari satu selama periode pengamatan. Sementara itu, kinerja waktu mengalami fluktuasi dengan adanya keterlambatan sementara pada minggu ke-16 hingga minggu ke-25 yang ditunjukkan melalui nilai Schedule Variance (SV) negatif dan Schedule Performance Index (SPI) kurang dari satu. Berdasarkan hasil proyeksi EVM, proyek diperkirakan dapat diselesaikan dalam durasi 49,99 minggu dengan estimasi biaya sebesar Rp49.854.436.514,00. Hasil penelitian menunjukkan bahwa integrasi CPM dan EVM dapat digunakan sebagai metode pengendalian proyek untuk mengidentifikasi aktivitas kritis serta memantau kinerja waktu dan biaya secara terpadu

Investment decision-making in the capital market requires accurate analysis to minimize risk and maximize returns. This study focuses on PT Unilever Indonesia Tbk, a leading Fast-Moving Consumer Goods (FMCG) company listed on the Indonesia Stock Exchange, to evaluate its stock price prediction using the linear regression method supported by RapidMiner software. Historical stock data were collected from January 2, 2018, to June 27, 2023, including attributes such as opening price, closing price, lowest price, highest price, and trading volume. The dataset was processed using data screening and modeling techniques to construct a linear regression model for prediction. Various scenarios with different proportions of training and testing data (70/30, 80/20, 60/40, and 90/10) were tested to analyze the impact of data distribution on model performance. The evaluation results showed that the 80% training and 20% testing scenario provided the lowest Root Mean Squared Error (RMSE) of 56.699, indicating better predictive accuracy compared to other scenarios. Nevertheless, the linear regression model still produced a relatively high error rate, with the best RMSE value suggesting limitations of this approach for complex market prediction. This study concludes that while linear regression can provide a basic framework for stock price forecasting, incorporating additional economic, fundamental, and external factors could significantly improve predictive reliability. The findings offer practical insights for investors and researchers in understanding the potential and limitations of linear regression in stock market analysis

Rainfall plays a vital role in various sectors such as transportation, agriculture, and industry. Having accurate rainfall information enables stakeholders in these fields to take proper measures and minimize potential losses caused by inaccurate data. This study focuses on identifying an effective method for rainfall forecasting by examining imputation techniques in data preprocessing and parameter settings within Support Vector Regression (SVR). The experimental findings indicate that the most effective imputation method for SVR is determined using the Mean Squared Error (MSE) and Mean Absolute Error (MAE) evaluation metrics. Based on MSE, the k-nearest neighbor method proves to be the most reliable approach for data imputation preprocessing. The preprocessing results were then applied to Polynomial SVR with parameters C = 1000, tolerance = 0.001, epsilon = 0.01, and unlimited iterations. Conversely, MAE results highlight Artificial Neural Network (ANN) as the optimal imputation method. ANN, when combined with a radial basis function kernel, gamma = 0.001, C = 1000, tolerance = 0.001, and unlimited iterations, was further tested using RBF SVR under the same parameter settings.

COVID-19 pandemic had caused a global crisis in the education sector due to the urgent closure of schools to prevent the spread of the virus. After so much effort to curb the transmission of Coronavirus, the Philippine government has finally approved the re-opening of face-to-face classes in colleges and universities. However, responding to educational needs might pose COVID risks to the academic community. Thus, this study used the Fuzzy Logic to develop a model to measure the risk associated with COVID under the Northeastern Mindanao State University – Tagbina campus condition. Results showed that developed model using Fuzzy Logic algorithm produced satisfying result after expert validation in assessing Covid-19 risk transmission. Further, using the developed model, the campus has 38.5% risk, classified as "Low”. Despite challenges in opinions of multiple experts, the model was able to draw conclusion in support to campus management’s decision-making pertaining to campus risk of covid transmission.

Diabetes mellitus is a chronic disease that occurs due to excessively high blood glucose levels resulting in the absence of insulin. In the period of data at the Siti Khadijah Islamic Hospital in Palembang, which is influenced by the number of patients undergoing health checks such as diabetes mellitus, it affects the classification of data that will complicate the hospital. So by utilizing data mining, classification to determine patients who have undergone examinations including diabetes sufferers or not. With these problems, the author conducted a comparative analysis of two algorithms, namely the naïve Bayes algorithm and the support vector machine algorithm for the classification of diabetes by using the WEKA tool with the Cross Validation and Confusion Matrix options tools with the highest accuracy results, namely the support vector machine algorithm with a polynomial kernel, the results of which are 96.2704% and an error rate of 3.7296%, it can be concluded that the most accurate algorithm in the classification of diabetes is the support vector machine algorithm with a polynomial kernel.

Dengue fever remains a significant public health issue in many countries. Its high prevalence highlights the need for effective tools like Geographic Information Systems (GIS) to help predict and manage the spread of the disease. This study aims to examine and summarize the role of GIS in mapping and communicating dengue transmission patterns. A bibliometric approach was used to collect relevant literature from databases such as Google Scholar, Scopus, and PubMed. Out of 440 identified articles, only 11 met the inclusion criteria. Data extracted included publication years (2013–2023), journal titles, study designs, populations, interventions, outcomes, and reported benefits of GIS in dengue-related research. Qualitative analysis was conducted by organizing and presenting key findings. The results show that GIS is valuable in identifying current outbreak areas, detecting high-risk zones through spatial clustering, improving the accuracy of case predictions, and supporting ongoing surveillance efforts. Additionally, GIS contributes to more informed decision-making in dengue prevention and control programs. Overall, GIS plays an essential role in understanding disease dynamics, enhancing early warning systems, and guiding public health responses to dengue outbreaks.