Pengaruh Faktor Adaptasi Model UTAUT terhadap Intensi Adopsi Sistem Hijau pada Bank Indonesia

Bank Indonesia (BI) plays a strategic role in promoting a green financial system, yet faces internal challenges in adopting environmentally sustainable technologies, as reflected in its low “leading by example” score in the Green Central Banking Scorecard. This study applies an adapted UTAUT model, incorporating stakeholder engagement, to examine green Information Systems (IS) adoption at BI. PLS-SEM results show stakeholder engagement significantly influences adoption (β = 0.792, p < 0.001) and performance expectancy positively affects behavioral intention (β = 0.420, p = 0.014). In contrast, facilitating conditions negatively impact adoption (β = –0.374, p = 0.027), indicating limited resource support. Effort expectancy and social influence are not statistically significant. Stakeholder feedback suggests BI remains at the initial stage of green IT maturity (level 1: incipient), highlighting the need for stronger institutional and government support and clearer implementation strategies to advance its green digital transformation

Importance of Feature Selection for Multiple Disease Classification

The performance of machine learning in disease classification heavily depends on effective feature selection. This study explores feature selection methods—Boruta and Recursive Feature Elimination (RFE)—with ensemble models like Random Forest, Decision Tree, Gradient Boosting, LightGBM, and XGBoost using Electronic Health Records (EHR) data. Results show that combining Boruta with LightGBM achieves the highest accuracy of 99%. Feature selection enhances precision by focusing on relevant variables and removing unnecessary ones. Further analysis reveals that features such as Red Blood Cells, Insulin, Heart Rate, and Cholesterol significantly influence the classification of specific diseases. These findings highlight the importance of feature selection in multi-disease classification and medical data analysis, improving the efficiency of machine learning systems. Future research should develop more flexible feature selection methods and test models on diverse disease datasets

Sistem Pakan Cerdas Berbasis IoT Untuk Optimalisasi Peternakan Kambing Umbaran di Era Digital Farm

This research aims to create a smart feeding system based on the Internet of Things (IoT) to enhance the efficiency of feed delivery in goat farming. The system automatically regulates the feed dispenser according to a predetermined schedule, making it easier for farmers to manage feed. System testing demonstrates its effectiveness in reducing feed delivery time and minimizing waste. The system features an LCD screen that displays the dispenser status, providing real-time information to farmers. This technology also allows for remote monitoring, enabling farmers to manage feed more effectively. The implementation of this system is expected to improve productivity and animal welfare while promoting modernization in farming practices in Indonesia. This innovation is anticipated to offer a sustainable solution to challenges in feed management, providing long-term benefits for farmers and the livestock industry

Malicious JavaScript Detection using Obfuscation Analysis and String Reconstruction Techniques

Detecting malicious JavaScript remains a persistent challenge in cybersecurity, particularly as obfuscation techniques become more sophisticated. This study presents a dual-model detection framework that separates the analysis of obfuscation from malicious behavior to enhance precision. The first model detects obfuscated scripts using 20 features, including entropy, string ratios, and syntax. The second model classifies malicious code based on 92 features, incorporating outputs from the first model and semantically meaningful strings reconstructed using a novel technique called atomic search. Both models utilize the random forest algorithm and are trained on balanced datasets of labeled JavaScript samples. Experimental results demonstrate high performance, with the obfuscation model achieving 99.1% accuracy and the malicious detection model reaching 99.52%. The proposed approach provides a scalable and effective solution for detecting hidden threats in modern web environments by clearly addressing obfuscation and incorporating semantic reconstruction

Peningkatan Akurasi Rekomendasi Dokter pada Kondisi Data Sparsity Menggunakan Algoritma Content-Based Filtering

The growth of healthcare applications such as Halodoc, Alodokter, and Klikdokter has enabled easier access to doctor recommendations. However, generating relevant recommendations remains challenging. One key issue is data sparsity, where limited doctor attributes reduce the system’s accuracy. This study develops a doctor recommendation system using a Content-Based Filtering (CBF) approach based on five main attributes: specialization, rating, consultation fee, years of practice, and gender. Data imputation and attribute weighting techniques are applied to enhance accuracy. Results show that the proposed method reduces the Mean Absolute Error (MAE) from 0.142 to 0.102 and the Root Mean Squared Error (RMSE) from 0.205 to 0.150. These findings indicate that the implemented techniques improve the recommendation system under sparse data conditions

Pengaruh Jenis Stopwords terhadap Akurasi Model Multinomial Naïve Bayes dalam Proses Sentimen Analisis

Implementing machine learning in business has enabled producers and sellers to assess product quality by analyzing customer reviews through Sentiment Analysis (SA). This study investigates the impact of different stopword categories on the accuracy of the Multinomial Naïve Bayes (MNB) model for SA. This research considered ten stopword categories: general, conjunctions, slang, temporal terms, nouns, pronouns, interjections, adverbs, and single-letter words. A Friedman test conducted on commentary from three shoe products revealed that removing conjunction stopwords (MNB-conjunction) could potentially improve the predictive accuracy of the MNB model for SA by approximately 1%. A T-test further validated this result, showing that two out of three datasets provided evidence that MNB-conjunction outperformed the MNB model without removing stopwords

Ekstraksi Pengetahuan dari Ulasan Aplikasi CapCut Menggunakan Metode Aspect-Based Sentiment Analysis dan Klasifikasi

Indonesia is experiencing rapid technological development, especially in the use of the internet and editing platforms like CapCut. These platforms enable video editing on various devices; however, user satisfaction is not always guaranteed due to individual differences in experience. This research aims to identify user sentiment towards the CapCut application based on aspects, using an Aspect-Based Sentiment Analysis (ABSA) approach supported by Machine Learning algorithms for the aspect-based sentiment classification task. The algorithm used in the classification process is Support Vector Machine. The data used are reviews of the CapCut application from the Google Play Store, with a total of 22,668 data points. The results show that the Support Vector Machine (SVM) algorithm performs well in each aspect, with accuracy values of 0.88 for the feature aspect and 0.87 for the user experience aspect. The results of knowledge extraction are obtained in the form of XML, which contains user sentiment information on two main aspects: features and user experience

Implementasi Algoritma Apriori sebagai Association Rule Learning untuk Mengidentifikasi Pola Item Dataset Penjualan

Retail store competition is becoming more intense, so marketing and product arrangement are crucial for shopping efficiency, maintaining comfort, and increasing profits. This study analyzes consumer shopping habits for goods in each transaction through market basket analysis. The Apriori algorithm is a common technique for finding frequent item search techniques in building association rules, namely the relationships between item combinations in a dataset. The aim is to implement the Apriori algorithm as an association rule learning method to identify patterns within sales data. The Apriori association rule is compared to the frequent pattern growth algorithm, which finds the most frequently occurring patterns in a dataset. Based on the tests, the average lift ratio for the Apriori algorithm is 1.58, while for the frequent pattern growth algorithm, it is 1.28. This indicates that the Apriori algorithm performs better than the frequent pattern growth algorithm

Sistem Penjadwalan Karyawan dengan Algoritma Genetika

Employee scheduling is a complex problem in Human Resource Management (HRM) that significantly impacts operational efficiency. This study develops an employee scheduling system using a genetic algorithm. The employee schedules are constructed by considering scheduling rules and various components such as the number of days, shifts, employee quality, and scheduling requests. The genetic algorithm, proven effective in solving various optimization problems, is employed to generate optimal schedules through the processes of selection, crossover, and mutation. The results indicate that the genetic algorithm can effectively produce employee schedules, with fitness values indicating improved schedule quality as iterations increase. The findings of this study are anticipated to be useful in HRM, aiming to improve both employee efficiency and satisfaction

Implementasi Data Mining untuk Estimasi Produksi Cabai menggunakan Metode Exponential Smoothing

Cabai merupakan komoditas hortikultura yang banyak dibudidayakan dan berpengaruh pada fluktuasi ekonomi di Kabupaten Sleman. Dalam upaya menstabilkan fluktuasi harga dan pertumbuhan ekonomi di Kabupaten sleman, maka perlu dilakukan estimasi produksi cabai untuk periode ke depan. Estimasi produksi cabai yang dilakukan dalam penelitian ini menggunakan tiga jenis metode Exponential Smoothing dengan kombinasi parameter alpha, beta, dan gamma. Penelitian ini bertujuan untuk mengembangkan model estimasi produksi cabai dengan menggunakan Single, Double, dan Triple Exponential Smoothing. Hasil penelitian ini menunjukkan bahwa Triple Exponential Smoothing adalah metode yang paling tepat digunakan untuk mengestimasi produksi cabai di masa mendatang, dengan persentase tingkat error sebesar 6.5%