Abstract:Penelitian ini bertujuan untuk menganalisis pengaruh pertumbuhan ekonomi, populasi, kepadatan penduduk, investasi, dan kredit terhadap Indeks Kualitas Lingkungan Hidup (IKLH) kabupaten/kota di Provinsi Bali dengan pendekatan…
atan Environmental Kuznets Curve (EKC). Metode yang digunakan adalah analisis regresi data panel pada periode 2020–2024. Hasil penelitian menunjukkan bahwa secara simultan seluruh variabel berpengaruh signifikan terhadap IKLH. Secara parsial, populasi dan investasi berpengaruh positif dan signifikan terhadap IKLH, sedangkan kepadatan penduduk berpengaruh negatif dan signifikan. Sementara itu, pertumbuhan ekonomi dan kredit tidak berpengaruh signifikan terhadap IKLH. Temuan ini menunjukkan bahwa hubungan antara pertumbuhan ekonomi dan kualitas lingkungan tidak selalu linear, sehingga diperlukan kebijakan pembangunan yang berkelanjutan. Penelitian ini diharapkan dapat menjadi dasar dalam perumusan kebijakan ekonomi daerah yang berwawasan lingkungan.
Abstract:In the Industry 4.0 era, achieving sustainable business success requires organizations to harness unique, rare, and inimitable resources. These resources demand a long learning curve within the organization and are critical…
cal for sustaining competitive advantage. This study explores the Era 4.0 Organizational Sustainability Model, a hybrid framework that demonstrates the interrelation of key organizational elements, including core competencies, business outcomes, and strategic objectives essential for long-term operational sustainability. In a landscape of intense competition, survival and growth are imperative goals for organizations. Central to this endeavor is the management of human resources, particularly the Millennial workforce, known for its unique challenges in turning weaknesses into opportunities for development. This research highlights the critical role of tailored talent management strategies in addressing generational characteristics, fostering employee growth, and aligning workforce capabilities with organizational needs. By employing an innovative and holistic HR strategy, organizations can enhance their ability to compete sustainably while driving long-term profitability and resilience in the face of rapid technological and market changes.
Abstract:Penggunaan dompet digital yang terus meningkat menghasilkan banyak ulasan pengguna yang dapat dimanfaatkan untuk mengevaluasi kualitas layanan. Penelitian ini bertujuan meningkatkan akurasi klasifikasi sentimen pengguna…
dompet digital menggunakan metode Stacking Ensemble Machine Learning yang mengombinasikan Support Vector Machine (SVM), Multinomial Naïve Bayes (MNB), dan AdaBoost dengan Logistic Regression sebagai meta-learner. Data ulasan diproses melalui tahapan text preprocessing meliputi case folding, cleaning, tokenizing, stopword removal, stemming, dan pembobotan fitur menggunakan TF-IDF. Penyeimbangan data dilakukan dengan SMOTE, sedangkan evaluasi model menggunakan 5-Fold Cross-Validation. Hasil penelitian menunjukkan bahwa model Stacking Ensemble memperoleh akurasi rata-rata 80,55%, lebih tinggi dibandingkan algoritma dasar. Evaluasi menggunakan Confusion Matrix, Classification Report, dan ROC Curve juga menunjukkan peningkatan nilai precision, recall, F1-score, dan kemampuan diskriminasi model. Hasil ini menunjukkan bahwa pendekatan Stacking Ensemble Machine Learning efektif untuk meningkatkan akurasi klasifikasi sentimen pengguna dompet digital serta mendukung evaluasi kualitas layanan berbasis opini pengguna.
The rapid growth of digital wallet usage has generated a large volume of user reviews that can be utilized to evaluate service quality. This study aims to improve the accuracy of digital wallet user sentiment classification using a Stacking Ensemble Machine Learning approach that combines Support Vector Machine (SVM), Multinomial Naïve Bayes (MNB), and AdaBoost with Logistic Regression as the meta-learner. User reviews were processed through text preprocessing stages, including case folding, text cleaning, tokenization, stopword removal, stemming, and TF-IDF feature weighting. Synthetic Minority Over-sampling Technique (SMOTE) was employed to address class imbalance, while model performance was evaluated using 5-Fold Cross-Validation. The experimental results show that the proposed Stacking Ensemble model achieved an average accuracy of 80.55%, outperforming the individual base learners. Furthermore, evaluations based on the Confusion Matrix, Classification Report, and Receiver Operating Characteristic (ROC) Curve demonstrated improvements in precision, recall, F1-score, and the model's discriminative capability. These findings indicate that the proposed Stacking Ensemble Machine Learning approach is effective in improving the accuracy of digital wallet user sentiment classification and can serve as a reliable tool for supporting service quality evaluation based on user opinions.
Abstract:This study investigates the tensile uplift capacity of screw piles in clay soil as an alternative anchoring system for slope stabilization in cohesive ground conditions. The research aims to address the limited availability…
ity of empirical field data on screw-pile behavior under repeated loading and to evaluate the agreement between theoretical predictions and actual in situ responses. The methodology employs an experimental approach through in situ testing with screw-pile diameters of 10 cm, 15 cm, and 20 cm, and embedment depths ranging from 0.6 m to 1.0 m. The tests were conducted under both static and repeated tensile loading using a hydraulic jack system, accompanied by vertical deformation measurements to establish load–displacement curves. Theoretical capacity was calculated using a limit equilibrium approach for comparison with experimental results. The findings reveal a nonlinear load–displacement response, characterized by initial stiffness followed by progressive deformation into the post-yield stage. At a maximum load of 2.858 tons, deformation increased with diameter, from 5.10 cm (10 cm) to 8.49 cm (20 cm). Under repeated loading, failure occurred at a lower load of approximately 1.6 tons with a maximum deformation of 1.648 cm, indicating potential capacity degradation due to cyclic loading. The comparison between theoretical and field results shows significant deviations, with analytical predictions generally underestimating the in situ capacity. This highlights the limitations of simplified models that do not fully account for shaft adhesion, installation disturbance, soil heterogeneity, pore-water pressure effects, and cyclic degradation. Overall, this study contributes valuable field pull-out test data for screw piles in clay under repeated loading, emphasizing the need for design calibration based on full-scale testing for slope stabilization applications. The results also suggest opportunities for further research involving long-term monitoring and advanced modeling of cyclic degradation.
Abstract:Abstract: Brain cancer is a serious medical condition that requires intensive and meticulous care. One of the critical steps in identifying brain cancer is the accurate measurement of tumor volume. Magnetic Resonance Imaging…
ging (MRI) is one of the most important diagnostic tools used in the medical field for brain visualization. In this discussion, we will explain how the Active Contour method can be used to calculate brain tumor volume in MRI images and how 3D visualization can assist doctors in making better diagnoses and treatment decisions. The Active Contour method, also known as the “Snake,†is an image processing technique used to identify the contours or edges of objects in images. This method works by defining an initial curve around the desired object and then iteratively shifting the curve to match the actual contour of the object in the image. In this study, the Active Contour method will be applied to brain MRI images to identify tumor edges. This research represents an important step in improving the care of brain cancer patients, enabling more accurate diagnoses and more effective treatments
Keywords: active contour; brain cancer diagnosis; 3D visualization; tumor volume
Abstrak: Kanker otak merupakan kondisi medis yang bersifat serius dimana memerlukan perawatan yang intensif dan teliti. Salah satu langkah penting dalam mengidentifikasi kanker otak adalah dengan mengukur volume tumor secara akurat. Citra Magnetic Resonance Imaging (MRI) adalah salah satu alat diagnostik yang paling penting dalam bidang medis yang digunakan untuk visualisasi otak. Dalam pembahasan ini, akan dijelaskan bagaimana metode Active Contour dapat digunakan untuk menghitung volume tumor otak pada citra MRI dan bagaimana visualisasi 3D dapat membantu dokter dalam diagnosis dan perawatan yang lebih baik. Metode Active Contour, juga dikenal sebagai “Snake,†yaitu teknik pengolahan citra yang digunakan untuk mengidentifikasi kontur atau tepi objek dalam citra. Metode ini bekerja dengan mendefinisikan suatu kurva awal di sekitar objek yang diinginkan dan kemudian menggeser kurva tersebut secara iteratif untuk menyesuaikan dengan kontur objek yang sesungguhnya dalam citra. Dalam penelitian ini, metode Active Contour akan diterapkan pada citra MRI otak untuk mengidentifikasi tepi tumor. Penelitian ini merupakan langkah penting dalam meningkatkan perawatan pasien yang terkena kanker otak dan memungkinkan diagnosis yang lebih tepat dan perawatan yang lebih efektif.
Kata kunci: active contour; diagnosis tumor otak; visualisasi 3D; volume tumor
Abstract:Abstract: Children's nutritional issues are an important concern for parents to pay attention to growth and development, especially health and well-being. According to the results of the Ministry of Health's Indonesian Nutrition…
utrition Status Survey (SSGI), there are 4 nutritional problems for children in Indonesia, namely stunting, wasting, underweight and everweight. In this research, how to predict signs of symptoms of a decline in a child's nutritional status using a machine learning algorithm, a prediction model was designed using logistic regression in Python IDE to predict whether a child is indicated by a decline in nutrition or not. Dataset from Bengkayang Community Health Center data consisting of 657 pediatric patient data. The dataset is divided into 7 features (independent variables) and 1 predictor (dependent variable). Test results show perfect performance with precision, recall, F1-score, accuracy values of 100%. Then the visualization results on the ROC (Receiver Operating Characteristic) curve to depict the TP (True Positive) value on the Y axis against the FP (false Positive) value on the become overfit. It is recommended that in preparing the training dataset, measure the training data and reduce the features, after carrying out feature selection to increase the accuracy of the model.
Keywords: child nutritional status; growth and development logistic regression; machine learning
Abstract: Masalah Gizi anak menjadi perhatian penting bagi orangtua untuk memperhatikan tumbuh kembang, terutama kesehatan dan kejahteraan. Menurut hasil survei status Gizi Indonesia (SSGI) Kemenkes memperlihatkan 4 permasalahan gizi anak di Indonesia yaitu stunting, wasting, underweight, dan everweight. Dalam penelitian ini, bagaimana memprediksi tanda gejala penurunan status gizi anak menggunakan algoritma machine learning dirancang model prediksi menggunakan logistic regression pada Python IDE dengan memprediksi anak terindikasi penurunan gizi atau tidak. Dataset dari data Puskesmas Bengkayang yang terdiri 657 data pasien anak. Dataset dibagi menjadi 7 feature (variabel independen) dan 1 predictor (variabel dependen). Hasil Pengujian memperlihatkan kinerja yang sempurna dengan nilai presisi, recall, F1-score, akurasi, sebesar 100%. Kemudian hasil Visualisasi pada kurva ROC (Receiver Operating Characteristic) untuk menggambarkan nilai TP (True Positif) di sumbu Y terhadap nilai FP (false Positif) di sumbu X juga menunjukkan nilai yang sangat tinggi dan sudah mendekati angka 1 ini pertanda bahwa model ini menjadi overfit. Sebaiknya dalam persiapan training dataset diukur dengan data training dan mengurangi feature, setelah melakukan feature Selection untuk meningkatkan akurasi model.
Keywords: logistic regression; machine learning; status gizi anak; tumbuh kembang
Abstract:Abstract: DNS plays a vital role in the operation of services on the internet. Almost all services on the internet are under DNS control, such as email, FTP, web apps, etc. So, it is not surprising that various malicious…
activities involve DNS services such as financial fraud, phishing, malware, and malicious activity, etc. Fortunately, in DNS there is a record with the name time to live which can be used to detect a query or the address accessed from the user is a normal query or an abnormal query. Therefore, the purpose of this study is to determine the correlation value between time to live and abnormal queries on passive DNS data using the Binary Logistic Regression model. The results showed that the Binary Logistic Regression method could model the correlation between TTL, elapsed, and bytes which have an optimal model F1 Score of 0.9997 and also have a condition close to the ideal state by using the Precision-Recall Curve (PRC) graph plot.
Keywords: Binary Logistic Regression; DNS Passive; Precision-Recall Curve (PRC); Query Abnormal
Abstrak: DNS memegang peranan yang vital di dalam berjalanya service di internet. Hampir seluruh layanan di internet berada di bawah kendali DNS seperti email, ftp, app web dll. Jadi, tidak mengherankan bahwa berbagai kegiatan jahat melibatkan layanan DNS seperti financial fraud, phising, malware dan aktivitas malicious dll. Untungnya, di dalam DNS tersimpan sebuah record dengan nama time to live yang dapat digunakan untuk mendeteksi sebuah query atau alamat yang diakses dari user tersebut bersifat query normal atau query tidak normal. Oleh karena itu, tujuan penelitian ini adalah untuk mengetahui nilai korelasi antara time to live dengan query tidak normal pada data passive DNS dengan menggunakan model Binary Logistic Regression. Hasil penelitian menunjukkan bahwa metode Binary Logistic Regression dapat memodelkan korelasi antara TTL, elapsed dan bytes yang memiliki model optimal F1 Score sebesar 0.9997 dan juga memiliki kondisi hampir mendekati keadaan ideal dengan menggunakan plot grafik Precision Recall Curve (PRC).
Kata kunci: Binary Logistic Regression; DNS Passive; Precision-Recall Curve (PRC); Query Abnormal