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

Analisis Hubungan Frekuensi Misi Terhadap Debat Capres Melalui Tweet Capres-Cawapres Menggunakan Pembelajaran Tidak Terkontrol (Studi Kasus: Capres-Cawapres 2019)

Era digitalisasi yang semakin mengglobal mengakibatkan perubahan praktik komunikasi bagi masyarakat Indonesia, termasuk komunikasi yang terjadi saat kampanye calon presiden periode 2019-2024. Twitter menjadi salah satu media sosial yang saat ini ramai dimanfaatkan saat kampanye presiden periode 2019-2025 sehingga penting dilakukan analsis frekuensi misi calon presiden melalui media sosial Twitter. Tujuan dari penelitian ini adalah mencari hubungan frekuensi misi terhadap tweet calon presiden dan calon wakil presiden periode 2019-2024. Penelitian ini berfokus pada pengklasteran tweet menggunakan algoritma Improved K-Means untuk menganalisis tweet. Secara garis besar penelitian ini melewati 3 (tiga) tahap yaitu crawling, pre- processing, dan cluster analysis. Hasil crawling data didapatkan 1.564 tweet paslon 01 dan 1.393 tweet paslon 02, hasil pre-processing didapatkan 6.801 kata untuk paslon 01 dan 3.631 kata untuk paslon 02, dan hasil cluster analysis didapatkan sebanyak 79 tweet dari paslon 01 dan 92 tweet dari paslon 02 yang dihapus karena memiliki density rendah. Total tweet yang dipertahankan dan akan masuk tahap inisiasi centroid sebanyak 1.485 tweet dari paslon 01 dan 1.301 tweet dari paslon 02. Dapat disimpulkan bahwa terdapat hubungan antara frekuensi tweet misi calon presiden dengan tema debat pilihan presiden periode 2019-2024. Hal ini dibuktikan oleh Paslon 01 yang terbagi menjadi 16 kelas, sedangkan paslon 02 yang terbagi menjadi 14 kelas. Paslon 01 lebih dominan membicarakan tentang Misi ke- 4, sedangkan paslon 02 lebih dominan membicarakan tentang Misi ke-2. Hal ini menunjukkan adanya kesesuaian antara misi yang banyak dibicarakan di Twitter dengan tema debat calon presiden periode 2019-2024

Sensor Array System Based on Electronic Nose to Detect Borax in Meatballs with Artificial Neural Network

The categorization of odors utilizing gas sensor arrays with various meatball borax concentrations has been studied. The samples included meatballs with a borax content of 0.05%, 0.10%, 0.15%, 0.20%, and 0.25% (%mm) and meatballs without any borax. Six TGS gas sensors with a baseline of 10 seconds, a detecting period of 120 seconds, and a purging period of 250 seconds make up the gas sensor array used in this work. Artificial neural networks (ANNs) and principal component analysis (PCA), which are beneficial for feature extraction and classification, are used to handle the collected data based on machine learning approaches. Two models were produced by the data analysis: model 1, which only used the PCA approach, and model 2, which only used the ANN methodology. 90.33% is the total variance value of PC from model 1. In addition, the multilayer perceptron artificial neural network (ANN-MLP) technique for model 2 yielded accuracy values of 95%

Variational autoencoder analysis gas sensor array on the preservation process of contaminated mussel shells (Mytilus edulis)

Mussel shells is a macro zoobenthos that lives on soft substrates in the mud (infauna) and is classified as a bivalve. This research detects formalin in mussel shells utilizing an Electronic Nose comprised of gas sensor's array. The samples used were formalin mussel shells with several concentrations from 100 ppm to 500 ppm with the addition of 100 ppm. The research was conducted using six sensors with a sampling time of 120 s. The output voltage from each sensor is then clustered based on principal component analysis and classified using several techniques, which are support vector machine, decision tree and random forest. We demonstrate that all classifiers have an accuracy of 1. The phenomenon occurs because all feature representations can produce enough information to classify data. Principal component analysis achieves the best score in preserving the local structure. PCA can keep an average of 33% nearest data in the same neighbourhood. While variational autoencoder can keep 14% nearest data in the same neighbour, and autoencoder can keep 8% nearest data in the same area