Abstract:The dynamics of modern education, marked by rapid social, cultural, and moral changes, require Christian Religious Education (CRE) not only to preserve its faith-based foundations but also to respond critically and constructively…
ructively to contemporary challenges. The main issue addressed in this study is how CRE can integrate the steadfastness of theological values with social sensitivity without losing its Christian identity. This study aims to analyze the relevance and contribution of the philosophical streams of Essentialism and Reconstructionism in the development of contemporary Christian Religious Education. The research employs a qualitative approach through a literature review, using descriptive–interpretative analysis of relevant scientific, philosophical, and theological sources. The findings indicate that Essentialism makes a significant contribution to strengthening the foundations of faith, shaping Christian character, and internalizing core Christian values that are normative and sustainable. Conversely, Reconstructionism positions CRE as a means of social renewal that encourages learners to actively engage in justice, social responsibility, and societal transformation. The integration of these two philosophical approaches enables CRE to develop holistically by balancing spiritual, moral, and social dimensions within the educational process. The conclusion of this study affirms that Essentialism and Reconstructionism possess strategic relevance as a philosophical foundation for contextual and transformative Christian Religious Education. The novelty of this research lies in its integrative philosophical model that harmonizes the firmness of faith with an orientation toward social change, thereby offering a new conceptual framework for the development of Christian Religious Education that is adaptive to the challenges of modern education without losing its theological essence.
Abstract:Abstract: This study aims to classify the nutritional status of toddlers based on anthropometric data using the K-Nearest Neighbor (KNN) algorithm. Data were obtained from 20 Integrated Health Posts (Posyandu) in Rumbai…
Timur District, including Lembah Sari Village and Limbungan Village with a total of 1,000 toddler data. After cleaning and preprocessing, 782 data were obtained ready for use. The preprocessing stages include data cleaning and transformation, outlier removal, minority class handling, and data normalization. Next, data balancing was carried out using the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. The data was divided into 80% training data and 20% test data, then the K parameter was tested from 1 to 15 using 5-fold cross-validation. The results showed that the value of K = 1 provided the best performance with a macro recall of 0.8827 and an accuracy of 86.26%. These results indicate that the combination of the KNN algorithm with the SMOTE method and Min-Max normalization is effective in improving classification performance on imbalanced data and producing accurate and balanced predictions of toddler nutritional status between classes.
Keywords: k-nearest neighbor; toddler nutritional status; SMOTE; min-max scaling; classification; anthropometric data
Abstrak: Penelitian ini bertujuan untuk mengklasifikasikan status gizi balita berdasarkan data antropometri menggunakan algoritma K-Nearest Neighbor (KNN). Data diperoleh dari 20 Posyandu di Kecamatan Rumbai Timur, meliputi Kelurahan Lembah Sari dan Kelurahan Limbungan dengan total 1.000 data balita. Setelah melalui proses cleaning dan preprocessing, diperoleh 782 data yang siap digunakan. Tahapan pra-pemrosesan meliputi pembersihan dan transformasi data, penghapusan outlier, penanganan kelas minoritas, serta normalisasi data. Selanjutnya dilakukan penyeimbangan data menggunakan Synthetic Minority Oversampling Technique (SMOTE) untuk mengatasi ketidakseimbangan kelas. Data dibagi menjadi 80% data latih dan 20% data uji, kemudian dilakukan pengujian parameter K dari 1 hingga 15 menggunakan 5-fold cross-validation. Hasil penelitian menunjukkan bahwa nilai K = 1 memberikan performa terbaik dengan recall macro sebesar 0,8827 dan akurasi 86,26%. Hasil ini menunjukkan bahwa kombinasi algoritma KNN dengan metode SMOTE dan normalisasi Min-Max efektif dalam meningkatkan kinerja klasifikasi pada data tidak seimbang serta menghasilkan prediksi status gizi balita yang akurat dan seimbang antar kelas.
Kata kunci: k-nearest neighbor; status gizi balita; SMOTE; min-max scaling; klasifikasi; data antropometri
Abstract:Abstract: Mental health issues, particularly depression among young adult university students, are often detected late due to stigma and reluctance to seek medical consultation. The objective of this study is to develop…
an early screening model employing machine learning techniques, specifically the random forest algorithm, on a dataset of 268 students (aged 17-29 years; consisting of 98 males and 170 females) within a multicultural educational setting. The principal challenges associated with this dataset are class imbalance and the potential for data leakage from clinical scores. This study implements a rigorous feature selection approach that involves the elimination of depression score features and the utilization of the Synthetic Minority Over-sampling Technique (SMOTE) to balance the training data distribution. Furthermore, a Threshold Tuning strategy is employed to prioritize detection sensitivity (Recall). The findings indicate that reducing the decision threshold to an optimal value of 0.25 led to a substantial enhancement in the recall value, increasing it from 36% (baseline) to 77%. A feature importance analysis was conducted, the results of which indicated that Total Social Connectedness (ToSC) is the most dominant predictor. In summary, the present study corroborates the notion that optimizing sensitivity through threshold tuning is of paramount importance for medical screening. Furthermore, social isolation factors emerge as more significant indicators of depression risk than demographic attributes.
Keywords: data mining; depression; imbalanced data; random forest; smote; threshold tuning
Abstrak: Masalah kesehatan mental, khususnya depresi di kalangan mahasiswa dewasa muda, sering terdeteksi terlambat akibat stigma dan enggan mencari konsultasi medis. Tujuan studi ini adalah mengembangkan model skrining dini menggunakan teknik machine learning, khususnya algoritma random forest, pada dataset 268 mahasiswa (usia 17-29 tahun; terdiri dari 98 laki-laki dan 170 perempuan) dalam lingkungan pendidikan multikultural. Tantangan utama yang terkait dengan dataset ini adalah ketidakseimbangan kelas dan potensi kebocoran data dari skor klinis. Studi ini menerapkan pendekatan seleksi fitur yang ketat, yang melibatkan eliminasi fitur skor depresi dan penggunaan Teknik Over-sampling Minoritas Sintetis (SMOTE) untuk menyeimbangkan distribusi data pelatihan. Selain itu, strategi Penyesuaian Ambang Batas diterapkan untuk memprioritaskan sensitivitas deteksi (Recall). Hasil penelitian menunjukkan bahwa mengurangi ambang batas keputusan ke nilai optimal 0,25 menyebabkan peningkatan signifikan dalam nilai recall, dari 36% (dasar) menjadi 77%. Analisis pentingnya fitur dilakukan, hasilnya menunjukkan bahwa Total Social Connectedness (ToSC) adalah prediktor yang paling dominan. Secara ringkas, studi ini membenarkan bahwa mengoptimalkan sensitivitas melalui penyesuaian ambang batas sangat penting untuk skrining medis. Selain itu, faktor isolasi sosial muncul sebagai indikator risiko depresi yang lebih signifikan daripada atribut demografis.
Kata kunci: penambangan data; depresi; data tidak seimbang; hutan acak; smote; penyesuaian ambang batas
Abstract:Justice collaborators, or "crown witnesses," have become essential in modern criminal justice systems, particularly in dismantling organized crime and uncovering complex murder cases. This study explores the legal protections…
tions afforded to justice collaborators in Indonesia through a doctrinal analysis of the Supreme Court Decision No. 1704 K/PID.SUS/2022, commonly known as the Richard Eliezer verdict. The objective is to critically examine the adequacy and application of legal safeguards provided to individuals who cooperate with law enforcement while implicated in serious crimes. Employing normative legal research methods and a statutory and case approach, the paper reveals discrepancies in the implementation of protections for justice collaborators. While the Indonesian Witness and Victim Protection Agency (LPSK) offers procedural protections, this analysis identifies significant gaps in enforcement, judicial interpretation, and institutional coordination. The findings underscore a need for stronger legislative frameworks and consistent judicial standards to uphold the rights and safety of justice collaborators. The implications extend to criminal law reform and the balancing of retributive justice with restorative mechanisms. This study contributes to the legal discourse on human rights protections in criminal procedure, particularly concerning vulnerable individuals assisting the justice system under duress or threat.
Abstract:The Gondoriyo 1-phase network has a network length of 3.2 km supplied by a 1-phase network that is charged in phase S on BSB 1. The extent of the service area makes the load on phase S jump high. The measurement results…
obtained by the load current during peak load reached a total of 62A. While the load current of the repeater at the time of peak load is phase R of 32A, phase S of 98A, and phase T of 44A. With such conditions, it is necessary to expand the medium voltage distribution network in the Gondoriyo area with the aim of balancing the load on the BSB 1 extension. To facilitate load sharing in the expansion of the Gondoriyo area, the ETAP powerstaion 7.5.0 simulation program is used. which consists of 1 phase simulation before expansion and 3 phase simulation after expansion. From the results of this engineering shrinkage simulation, the total engineering shrinkage value in the 1-phase network before the expansion was 6480 kW, after the network expansion, the total engineering shrinkage changed to 2510.4 kW. With the simulation that has been made, it is expected to produce a load sharing plan that will make it easier for PT PLN (Persero) Rayon Boja to share the load on the network after the expansion.
Abstract:Load balancing is one of the things that must be considered by reducing the value of voltage drops or power losses that occur in distribution channels. It is necessary to know the characteristics of the load in an area or…
r area with a balanced supply so that each phase is burdened evenly. Calculating the voltage drop on the distribution network conductors, the voltage drop on the KBN09 Slawi feeder was calculated. What will be calculated here is from the line to the transformer furthest from the GI, namely from the three-phase 20 kV main line, single-phase branching. The results of power calculations from GI Kebasen Tegal show differences between R, S and T phases with three 20 kV phases in pre-balance and post-balance conditions. As written, the value for R wire is 82661.52 W, for S 52613 W, while for the T phase the value is 60127.06 W. Meanwhile, the calculation of power losses from GI Kebasen Tegal shows differences between the R, S and T phases with 1 phase voltage in the conditions before balance and after balanced. On the 3-phase line for the R wire, the power loss decreases from 34,198 W to 17,0425 W, for S it increases from 11,540 W to 19,077 W, while for the T phase the value remains 15,274 W.
Abstract:The demand for electrical energy continues to rise with the progression of time. This growth must be matched by a reliable and cost-effective supply of electricity, requiring power systems that are both dependable and economical.…
onomical. Since the amount of electricity consumed by users cannot be precisely predicted, balancing generation with consumption necessitates accurate electrical load forecasting. This study focuses on load forecasting using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. The forecast developed targets daily peak loads, which fall under short-term load forecasting. The data used for this forecasting consists of historical daily peak loads from January 1, 2017, to June 9, 2022. The forecasting process involves parameters such as radius, squash factor, accept ratio, reject ratio, and epoch. The forecast accuracy is evaluated using the Mean Absolute Percentage Error (MAPE) metric. The results are then compared with PLN’s load forecasting, which employs the load coefficient method. The ANFIS-based forecasting achieved a MAPE of 1.879%, using networks Jaringan_24 and Jaringan_25. This MAPE value is slightly lower than PLN’s load forecasting MAPE of 1.917%, indicating better accuracy by the ANFIS method.
Abstract:This study explores the impact of asset structure and sales growth on capital structure, emphasizing the moderating influence of profitability. As firms navigate the complexities of financing decisions, understanding how…
these variables interact is crucial for optimizing capital structure. The findings reveal that asset structure and sales growth significantly affect capital structure, with profitability playing a critical role in moderating these relationships. Firms with substantial tangible assets are better positioned to leverage debt financing, while those demonstrating strong sales growth are viewed favorably by investors and creditors. However, the extent to which sales growth influences capital structure is contingent upon profitability; high profitability enables firms to capitalize on growth opportunities, whereas low profitability may inhibit their capacity to leverage growth potential. Empirical research supports these conclusions, indicating that asset structure, sales growth, and profitability significantly shape capital structure decisions across various industries. Ultimately, this study provides valuable insights for financial managers, highlighting the importance of balancing growth aspirations with profitability to achieve effective capital structure management. This, in turn, can lead to sustained competitive advantage, a state where a firm outperforms its competitors over a prolonged period in a dynamic economic environment.