Hendry Hendry

I-Kuan Tao emphasizes spiritual cultivation through the practice of the Three Treasures, which is believed to influence physical, mental, and spiritual well-being through changes in brain wave activity. The purpose of this study was to investigate the effects of Tao-receiving of I-Kuan Tao and the practice of the Three Treasures on brain wave patterns. To achieve this, 32 volunteers were recruited, and their brain waves were measured using an electroencephalograph developed by Neurosky Company. Brain wave data were collected under various conditions. Initially, brain waves were measured for 3 minutes in the natural state with 32 samples. After Tao-receiving, brain waves were measured again in the natural state for 3 minutes with 15 samples. Subsequently, brain waves were measured for another 3 minutes during the practice of the Three Treasures, with 32 samples collected. After one month of practicing the Three Treasures, participants were invited back for a second round of testing. In the natural state, brain waves were measured for 3 minutes with 4 samples. Following the Three Treasures practice, brain waves were measured again for 3 minutes with 4 samples. The collected data were analyzed using descriptive statistics and paired-sample t-tests. The results showed a very significant difference (p < 0.001) in attention improvement and fatigue reduction. The δ wave showed a very significant difference (p < 0.01), while the relaxation index (Meditation) and pressure index (Pressure) demonstrated a significant difference (p < 0.05). In other words, after participating in the Tao-receiving of I-Kuan Tao, there was an immediate improvement in attention, meditation, δ and θ waves, along with a reduction in fatigue and pressure, contributing positively to the coordination of body and mind. Furthermore, after one month of practicing the Three Treasures, significant changes were observed in brain wave patterns in the natural state, including reduced fatigue and increased intensity of the δ wave. This indicates that sustained practice of the Three Treasures enhances the production of δ waves, facilitating deeper sleep and effectively reducing fatigue.

Seiring lonjakan data diera digitalisasi, efisiensi komputasi menjadi aspek krusial dalam penerapan analisis sentimen Bahasa Indonesia. Hal ini didasari oleh kendala pada model bahasa standar yang cenderung berat dan lambat, sehingga sulit diterapkan pada skenario pemrosesan data skala besar secara realtime atau pada perangkat dengan spesifikasi terbatas. Penelitian ini bertujuan mengembangkan metode analisis sentimen teks Bahasa Indonesia dengan menyederhanakan arsitektur deep learning IndoBERT menjadi DistilBERT yang lebih ringan secara komputasi. Dataset Indo4B digunakan melalui tahapan preprocessing, pelabelan, dan fine-tuning dengan model DistilBERT. Selanjutnya, optimalisasi hasil klasifikasi model DistilBERT fine-tuned dilakukan menggunakan algoritma Trapezoidal Fuzzy Membership dan Self Organizing Maps (SOM) berdasarkan nilai confidence dan distribusi representasi node, agar performanya dapat menyamai IndoBERT. Hasil evaluasi menunjukkan peningkatan akurasi dari 79.85% menjadi 80.42%, serta peningkatan pada Precision, Recall, dan F1-Score. Hal ini membuktikan bahwa kombinasi DistilBERT dengan Fuzzy dan SOM menghasilkan klasifikasi sentimen multilevel yang lebih efisien dan akurat untuk teks Bahasa Indonesia

EEG (electroencephalography) is a tool to determine human brain waves and the function of the human brain. A complete EEG device consists of 33 channels and is expensive for independent research. This study aimed to determine which EEG features play an important role in attention and meditation. Data were obtained from 83 respondents with 8 channels and 5 waves in each channel. This study examines three feature selection methods. Using Random Forest, selecting the Fp1_beta, Fp1_gamma, Fp2_beta, Fp2_gamma, Fz_beta, Fz_gamma, C4_beta, C4_gamma, O1_beta, O1_gamma, O2_alpha, O2_beta, O2_gamma features led to an accuracy of 98.2%. Selecting the Fp1_beta, Fp1_gamma, Fp2_beta, Fp2_gamma, Fz_delta, Fz_alpha, Fz_beta, Fz_gamma, C4_beta, O1_beta, O1_gamma, O2_alpha, O2_beta, O2_gamma features with a tree-based model led to an accuracy of 98.0%. Finally, with recursive feature elimination, the Fp1_beta, Fp2_beta, Fz_beta, O2_beta, and O2_gamma features led to an accuracy of 96.7%.

This study is intended to evaluate and contrast the performance of varying combinations of embedding algorithms and weighting systems in measuring perception-based text similarity using the Cosine Similarity approach. Within a structured experiment design, a hybrid model referred to as HyEWCos (Hybrid Embedding and Weighting for Cosine Similarity) was built incorporating conventional embedding models (Word2Vec, FastText), transformer-based models (BERT, GPT), and statistical and linguistic word weighting schemes (TFIDF, BM25, POS-weighting, and N-weighting). The test results indicate that Word2Vec merged with the CBOW architecture and TFIDF weighting always returned the most reliable performance, with lowest error values (RMSE and MAE of 0.9868) and the highest rating correlation with expert judgment (Pearson’s, 0.524; Spearman’s, 0.543). These results show that contextually conditioned distributional representation approaches perform better in maintaining the semantic subtlety of short and subjective texts than transformer models that are not fine-tuned. This work is unique in terms of its evaluation framework because it integrates embedding and weighting approaches that have hitherto been examined mostly in separation. The main contribution of the study is the development of an experimental framework that serves as a foundation for building more stable and accurate text-based assessment systems. The research also proves the need for making decisions on representation methods based on the data type and domain and opens a door for continuing research in adaptive hybrid models and how their potential can be achieved through combining the best of various approaches.

Accurately predicting stock market trends remains a significant challenge for investors due to its dynamic nature. This study explores the performance of Gradient Boosting models, including XGBoost, XGBoost Random Forest, CatBoost, and Gradient Boosting Scikit-Learn, in predicting stock market trends such as sideways movement, uptrends, downtrends, and volatility. Using four datasets from the Indonesia Stock Exchange, the research integrates technical, fundamental, and sentiment data, encompassing 37 features. Modeling and testing are conducted using Orange tools and Python, with performance evaluated through metrics such as Mean Absolute Percentage Error (MAPE), R-squared (R²), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Results indicate that XGBoost and XGBoost Random Forest consistently outperform other models in predicting stock price movements. These findings highlight the potential of Gradient Boosting models in providing accurate and reliable predictions, offering valuable insights for investors, financial analysts, and researchers to enhance investment strategies and adapt to market fluctuations effectively.

Travel pattern variations pose challenges in building a prediction model that accurately captures seasonal patterns or precision of BRT passenger numbers. An approach that integrates sophisticated prediction algorithms with high accuracy is needed to address the Transjakarta BRT passenger number prediction model problem. The proposed prediction model with the best accuracy is sought using deep learning on 8 models. The prediction model is used for short-term and long-term predictions, as well as looking for correlations in the prediction results of 13 Transjakarta corridors. The Python programming language with the Deep Learning Tensor Flow framework is run by Google Colaboratory used in the prediction simulation environment. The combination of BiLSTM-CNN was found to have the best accuracy of the evaluation value (SMAPE = 15.9387, MAPE = 0.598, and MSLE = 0.0425), although it has the longest time (134 seconds). Fluctuations in short-term predictions of passenger numbers evenly occur simultaneously across all corridors. Fluctuations in long-term predictions evenly occur simultaneously across all corridors, except in February. There is no negative correlation in the 13 prediction results and there are 8 corridors that have a close positive correlation. The prediction results can be used by transportation operators and the government to optimize resource planning and transportation policies to support sustainable community and economic mobility.

Penciptaan budaya kolaboratif terhadap inovasi dalam suatu orgnisasi menjadi sangat penting saat ini. Budaya kolaboratif terhadap inovasi bukan hanya tentang bekerja sama secara fisik, tetapi juga tentang menciptakan lingkungan yang mendukung komunikasi terbuka, penghargaan terhadap ide baru, dan penerimaan terhadap risiko. Organisasi yang menganut budaya ini dapat menciptakan nilai tambah yang signifikan dan berkembang di lingkungan yang terus berubah. Penelitian ini bertujuan melakukan analisis konten terhadap sejumlah artikel ilmiah bereputasi berbasis Grounded Theory dengan menggunakan Text Mining yang melibatkan penggunaan teknik pengkodean untuk mengklasifikasikan informasi dan mengidentifikasi kategori atau kode tertentu yang mewakili elemen-elemen tertentu dalam teks. Hasil analisis berupa suatu model jaringan konsep yang menghubungkan elemen-elemen yang mempengaruhi budaya kolaboratif terhadap inovasi, seperti Openness, Diversity, Shared Goals, Trust, Teamwork, Support, dan Use of Technology. Model ini digunakan organisasi untuk menciptakan budaya kolaboratif terhadap inovasi di lingkungannya atau dapat dipakai penelitian selanjutnya untuk menguji modelnya menggunakan uji statistik.

Penelitian ini membahas implementasi sistem deteksi kegagalan pada transformator menggunakan analisis gas terlarut Dissolved Gas Analysis (DGA) dengan menerapkan metode Logistic Regression. Tujuan penelitian ini adalah membuat sistem yang dapat meningkatkan keandalan transformator, mengurangi risiko gangguan listrik, dan memberikan kontribusi pada praktik industri berupa penerapan metode yang efisien dalam pemeliharaan transformator daya. Penggunaan data DGA dari transformator yang mengalami berbagai tingkat kerusakan sebagai sampel pelatihan dan pengujian menunjukkan bahwa Logistic Regression memberikan performa yang sangat baik dengan tingkat akurasi, presisi, F1-Score, dan recall berturut-turut sebesar 97%, 97%, 97%, dan 97%. Hasil ini mencerminkan kemampuan metode ini dalam mengklasifikasi hasil positif dan negatif. Sementara itu key gas memiliki akurasi, presisi, dan F1-Score yang rendah (42%, 17%, dan 24%), Recall 42% yang sedikit lebih tinggi menunjukkan kemampuan metode ini untuk mendeteksi sejumlah besar positif aktual, tetapi dengan tingkat kesalahan yang lebih tinggi. Penemuan ini berpotensi meningkatkan keandalan sistem tenaga listrik melalui pemeliharaan dini dan deteksi tepat waktu terhadap potensi masalah pada transformator.