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

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%

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

Pembentukan Vector Space Model Bahasa Indonesia Menggunakan Metode Word to Vector

Abstract. Extracting information from a large amount of structured data requires expensive computing. The Vector Space Model method works by mapping words in continuous vector space where semantically similar words are mapped in adjacent vector spaces. The Vector Space Model model assumes words that appear in the same context, having the same semantic meaning. In the implementation, there are two different approaches: counting methods (eg: Latent Semantic Analysis) and predictive methods (eg Neural Probabilistic Language Model). This study aims to apply Word2Vec method using the Continuous Bag of Words approach in Indonesian language. Research data was obtained by crawling on several online news portals. The expected result of the research is the Indonesian words vector mapping based on the data used.Keywords: vector space model, word to vector, Indonesian vector space model.Abstrak. Ekstraksi informasi dari sekumpulan data terstruktur dalam jumlah yang besar membutuhkan komputasi yang mahal. Metode Vector Space Model bekerja dengan cara memetakan kata-kata dalam ruang vektor kontinu dimana kata-kata yang serupa secara semantis dipetakan dalam ruang vektor yang berdekatan. Metode Vector Space Model mengasumsikan kata-kata yang muncul pada konteks yang sama, memiliki makna semantik yang sama. Dalam penerapannya ada dua pendekatan yang berbeda yaitu: metode yang berbasis hitungan (misal: Latent Semantic Analysis) dan metode prediktif (misalnya Neural Probabilistic Language Model). Penelitian ini bertujuan untuk menerapkan metode Word2Vec menggunakan pendekatan Continuous Bag Of Words model dalam Bahasa Indonesia. Data penelitian yang digunakan didapatkan dengan cara crawling pada berberapa portal berita online. Hasil penelitian yang diharapkan adalah pemetaan vektor kata Bahasa Indonesia berdasarkan data yang digunakan.Kata Kunci: vector space model, word to vector, vektor kata bahasa Indonesia

USULAN PERANCANGAN TATA LETAK RUMAH SAKIT

Permasalahan yang ada pada Rumah Sakit Palang Biru ialah adanya rencana pembangunan gedung baru dengan penambahan area dengan mempertahankan sebagian bangunan lama. Bangunan baru diharapkan dapat mengakomodasi keinginan pihak manajemen rumah sakit dan pengguna. Hal ini memerlukan pengaturan tata letak fasilitas rumah sakit secara keseluruhan. Metode yang digunakan untuk penyusunan tata letak ialah dengan menggunakan keterkaitan antar ruangan, pedoman bangunan rumah sakit serta input dari pihak rumah sakit. Tata letak awal dihasilkan menggunakan metode CORELAP, sedangkan program BLOCPLAN digunakan sebagai pembanding tata letak awal yang dihasilkan CORELAP. Hasil pengelompokan ruangan dari kedua metode diatas digunakan sebagai dasar penyusunan alternatif tata letak dengan mempertimbangkan pedoman bangunan rumah sakit serta batasan yang ada. Tata letak akhir dihasilkan dari pengembangan alternatif tata letak dengan memasukkan input perancangan dari pihak rumah sakit. Relayout rumah sakit menghasilkan pemisahan unit rawat jalan dan rawat inap, serta penempatan fasilitas penunjang yang berdekatan dengan unit rawat jalan. Relayout juga memisahkan jalan masuk dan keluar pasien kondisi biasa dan darurat, jalan keluar masuk antara pasien, personil dan logistik, serta menyediakan area parkir baru. Kapasitas total rumah sakit setelah relayout ialah sebanyak 140 tempat tidur untuk pasien rawat inap

Defining gamification: From lexical meaning and process viewpoint towards a gameful reality

In a state where games and their elements have been extensively used not only for pleasure but also for other purposes, gamification still has some pros and cons about its definition, which might influence people's decisions on their game-related strategies to improve their performance. This work tries to define gamification by using lexical meaning approach as the starting point and viewing it from a process viewpoint. Lexical meaning approach interprets gamification as a process or a product of the process. From this perspective, gamification can be viewed as a process that adds certain characteristics to an object that makes the object different from its previous condition and feasible to be formalized. Furthermore, the resulting definition is tested by comparing it to other existing gamification definitions and the understanding that constructs the definition is used as the foundation to explain the differences between gamification and serious games. This paper then defines gamification as a process that integrates game elements into gameless objects in order to have gameful characteristics. There will be a situation where gamification will produce a state of gameful reality: a situation in the real world where people can feel the significant presence of gamefulness in their daily life