DEDIAVER Sebagai Aplikasi Alternatif Tes Denver II untuk Tes Deteksi Dini Perkembangan Anak

One of the focuses of research and innovation in Indonesia is to encourage the development of health resilience and independence, and one of the concerns is child development. According to the Indonesian Pediatrician Association (IDAI), about 5-10% of children are estimated to have developmental delays and around 1-3% under the age of 5 years experience general developmental delays. Early Detection of Child Development Based on the Denver II Test (DEDIAVER) is a decision support system (DSS) application that can be used to detect child development using the Denver II Test instruments. This research aims to evaluate the DEDIAVER application and assess the feasibility of the DEDIAVER application as an alternative application for the Denver II Test for early detection tests of child development. The test is carried out by matching the results of the Denver II Test, which is carried out manually, with the results of the DEDIAVER application. As a result, for the test for children aged 0-2 years, the total accuracy for each developmental sector and age is 90.67%, while the test for children aged 2-6 years has an accuracy of 36%. The accuracy of the test for ages 2-6 years is only 36% due to the difference in the application\u27s age calculation, which is 4-6 months older than the actual age. The age difference causes the questions for children over their age to be questioned

Model optimisation of class imbalanced learning using ensemble classifier on over-sampling data

Data imbalance is one of the problems in the application of machine learning and data mining. Often this data imbalance occurs in the most essential and needed case entities. Two approaches to overcome this problem are the data level approach and the algorithm approach. This study aims to get the best model using the pap smear dataset that combined data levels with an algorithmic approach to solve data imbalanced. The laboratory data mostly have few data and imbalance. Almost in every case, the minor entities are the most important and needed. Over-sampling as a data level approach used in this study is the synthetic minority oversampling technique-nominal (SMOTE-N) and adaptive synthetic-nominal (ADASYN-N) algorithms. The algorithm approach used in this study is the ensemble classifier using AdaBoost and bagging with the classification and regression tree (CART) as learner-based. The best model obtained from the experimental results in accuracy, precision, recall, and f-measure using ADASYN-N and AdaBoost-CART

DEDIAVER Sebagai Aplikasi Alternatif Tes Denver II untuk Tes Deteksi Dini Perkembangan Anak

One of the focuses of research and innovation in Indonesia is to encourage the development of health resilience and independence, and one of the concerns is child development. According to the Indonesian Pediatrician Association (IDAI), about 5-10% of children are estimated to have developmental delays and around 1-3% under the age of 5 years experience general developmental delays. Early Detection of Child Development Based on the Denver II Test (DEDIAVER) is a decision support system (DSS) application that can be used to detect child development using the Denver II Test instruments. This research aims to evaluate the DEDIAVER application and assess the feasibility of the DEDIAVER application as an alternative application for the Denver II Test for early detection tests of child development. The test is carried out by matching the results of the Denver II Test, which is carried out manually, with the results of the DEDIAVER application. As a result, for the test for children aged 0-2 years, the total accuracy for each developmental sector and age is 90.67%, while the test for children aged 2-6 years has an accuracy of 36%. The accuracy of the test for ages 2-6 years is only 36% due to the difference in the application's age calculation, which is 4-6 months older than the actual age. The age difference causes the questions for children over their age to be questioned.Salah satu fokus riset dan inovasi di Indonesia adalah mendorong pembangunan ketahanan dan kemandirian Kesehatan dan salah satu yang menjadi perhatian adalah perkembangan anak. Menurut Ikatan Dokter Anak Indonesia (IDAI) ada sekitar 5-10% anak diperkirakan mengalami keterlambatan perkembangan dan sekitar 1-3% di bawah usia 5 tahun mengalami keterlambatan perkembangan umum. Deteksi Dini Perkembangan Anak Berdasarkan Tes Denver II (DEDIAVER) merupakan sebuah aplikasi decision support system (DSS) yang dapat digunakan untuk deteksi dini perkembangan anak menggunakan instrumen Tes Denver II. Tujuan dari penelitian ini adalah melakukan pengujian lanjutan atau evaluasi aplikasi DEDIAVER. Hal ini dilakukan sebagai salah satu upaya untuk menilai kelayakan aplikasi DEDIAVER sebagai aplikasi alternatif Tes Denver II untuk tes deteksi dini perkembangan anak. Pengujian dilakukan dengan mencocokkan hasil Tes Denver II yang dilakukan secara manual dengan hasil dari aplikasi DEDIAVER. Hasilnya, untuk tes anak usia 0-2 tahun mendapatkan total akurasi untuk setiap sektor perkembangan dan usia sebesar 90,67% sedangkan untuk tes anak usia lebih dari 2-6 tahun mendapatkan total akurasi sebesar 36%. Akurasi dari tes untuk usia lebih dari 2-6 tahun hanya 36% diakibatkan karena perbedaan perhitungan usia dari aplikasi yang mencapai 4-6 bulan lebih tua dari usia sebenarnya yang mengakibatkan pertanyaan yang muncul di atas usia anak sehingga anak gagal melakukan tugas pada usianya

Pengaruh Terapi Aktivitas Kelompok Stimulasi Persepsi:Halusinasi Dengan Kemampuan Pasien Mengontrol Halusinasi Sesi I Dan Ii Di Ruang Bangau Rumah Sakit Ernaldi Bahar Palembang

One therapeutic modality in nursing practice is the soul of Therapeutic Activity Group (TAK), where the Therapeutic Activity Group is a creative technique to facilitate a person's experience and to improve the social response and pride. This group therapy activity is common among patients with mental disorders one hallucinations. In which hallucinations are often influence the actions of someone who used to endanger themselves and others. The purpose of this study was to determine the patient's ability to control hallucinations before and after TAK Session I and II in Bangau Space Ernaldi Bahar Hospital Palembang in 2015. The research method used is quantitative method. Design used was quasi experiment with one group pre-test and post-test. How to sampling using purposive sampling with a total sample of 30 respondents using observation instrument interview. Analysis of studies using non-parametric test with Wilcoxon test. From the research, the patient's ability to control hallucinations before and after the first session TAK result p value 0.013 <α (0.05) while the second session is obtained p value 0,004 <α (0.05). So it can be concluded that there is a significant effect on TAK sessions I and II with a patient's ability to control hallucinations in Bangau Space Ernaldi Bahar Hospital Palembang 2015

Sentiment Analysis Models for Mapping Public Engagement on Twitter Data

Unstructured data in the form of text, which is widely distributed on the internet, often has valuable information. Due to its unstructured form, an effort is needed to extract that information. Twitter is a microblogging social media platform used by many people to express their opinions or thoughts. Sentiment analysis is a way to map a sentence whether the value is positive or not. Sentiment analysis is a series of processes used to classify text documents into two classes, namely positive sentiment class and negative sentiment class. The dataset is obtained from sentiment 140 as training data to build the sentiment analysis model. To test the model, the data used by the crawler algorithm were extracted using the Twitter API. This study focuses on determining public sentiment based on their writing on Twitter. The classification model used in the study is multiclass naive Bayes. The TF-IDF method was also used to weigh the selected feature. The experimental results show that the resulting model has an accuracy of 74.16% with an average precision of 74%, a recall of 74%, and an f-measure of 74%

Exploring Customer Relationship Management: Trends, Challenges, and Innovations

This study presents a comprehensive exploration of recent advancements in Customer Relationship Management (CRM), acknowledging its pivotal role in fostering crucial connections both within the industry and with customers at large. The study delves deeply into CRM, aiming to enhance overall customer satisfaction. The primary focus of this study centers around critical facets of CRM, encompassing strategies for managing customer relationships, applications of information technology, analysis of customer data, and approaches for customer retention. Employing a literature review methodology, this research rigorously examines the most recent journals germane to the field of CRM. A total of 46 articles sourced from reputable journal databases constitute the foundation of this investigation. The findings of this study yield an enriched comprehension of contemporary developments concerning challenges, factors driving success, relevant domains, and implementation goals within the realm of Customer Relationship Management

A Trustworthy Automated Short-Answer Scoring System Using a New Dataset and Hybrid Transfer Learning Method

To measure the quality of student learning, teachers must conduct evaluations. One of the most efficient modes of evaluation is the short answer question. However, there can be inconsistencies in teacher-performed manual evaluations due to an excessive number of students, time demands, fatigue, etc. Consequently, teachers require a trustworthy system capable of autonomously and accurately evaluating student answers. Using hybrid transfer learning and student answer dataset, we aim to create a reliable automated short answer scoring system called Hybrid Transfer Learning for Automated Short Answer Scoring (HTL-ASAS). HTL-ASAS combines multiple tokenizers from a pretrained model with the bidirectional encoder representations from transformers. Based on our evaluation of the training model, we determined that HTL-ASAS has a higher evaluation accuracy than models used in previous studies. The accuracy of HTL-ASAS for datasets containing responses to questions pertaining to introductory information technology courses reaches 99.6%. With an accuracy close to one hundred percent, the developed model can undoubtedly serve as the foundation for a trustworthy ASAS system

A Trustworthy Automated Short-Answer Scoring System Using a New Dataset and Hybrid Transfer Learning Method.

To measure the quality of student learning, teachers must conduct evaluations. One of the most efficient modes of evaluation is the short answer question. However, there can be inconsistencies in teacher-performed manual evaluations due to an excessive number of students, time demands, fatigue, etc. Consequently, teachers require a trustworthy system capable of autonomously and accurately evaluating student answers. Using hybrid transfer learning and student answer dataset, we aim to create a reliable automated short answer scoring system called Hybrid Transfer Learning for Automated Short Answer Scoring (HTL-ASAS). HTL-ASAS combines multiple tokenizers from a pretrained model with the bidirectional encoder representations from transformers. Based on our evaluation of the training model, we determined that HTL-ASAS has a higher evaluation accuracy than models used in previous studies. The accuracy of HTL-ASAS for datasets containing responses to questions pertaining to introductory information technology courses reaches 99.6%. With an accuracy close to one hundred percent, the developed model can undoubtedly serve as the foundation for a trustworthy ASAS system