Improving Automatic Essay Scoring for Indonesian Language using Simpler Model and Richer Feature

Automatic essay scoring is a machine learning task where we create a model that can automatically assess student essay answers. Automated essay scoring will be instrumental when the answer assessment process is on a large scale so that manual correction by humans can cause several problems. In 2019, the Ukara dataset was released for automatic essay scoring in the Indonesian language. The best model that has been published using the dataset produces an F1-score of 0.821 using pre-trained fastText sentence embedding and the stacking model between the neural network and XGBoost. In this study, we propose to use a simpler classifier model using a single hidden layer neural network but using a richer feature, namely BERT sentence embedding. Pre-trained model BERT sentence embedding extracts more information from sentences but has a smaller file size than fastText pre-trained model. The best model we propose manages to get a higher F1-score than the previous models on the Ukara dataset, which is 0.829

Classroom Attendance Based on Smiling Face Patterns and Nearby Wifi with Deep Learning

Students' attendance in class is often mandatory in education and becomes a benchmark for assessing students. Sometimes there are still fraudulent practices by students to achieve minimum attendance. From the administrative perspective, a paper-based presence system is potentially wasteful and extends the administrative stage because it requires manual recapitulation. This study aims to design a class attendance application based on facial pattern recognition, smile, and closest Wi-Fi. The method used in this research is a deep learning approach with CNN based architecture, FaceNet, to recognize faces. In addition to facial images, the system will also validate the attendance with location and time data. Location data is obtained from matching SSID from the database, and time data is taken when the user sends attendance data through API. This attendance system consists of three applications: web, mobile, and services installed on a mini-computer, which are integrated to sending attendance data to the academic system automatically. As confirmation, students are required to smile selfies to strengthen the validity of their presence. The testing model's accuracy results are 92.6%, while for live testing accuracy the model obtained 66.7%.  Kehadiran mahasiswa dalam suatu pembelajaran di kelas seringkali menjadi syarat wajib dalam dunia pendidikan, dan menjadi tolak ukur dalam menilai mahasiswa. Terkadang masih dijumpai praktik curang oleh mahasiswa dalam presensi agar mencapai kehadiran minimal. Dari sisi administrasi, presensi berbasis kertas berpotensi pemborosan dan juga memperpanjang tahapan administrasi karena membutuhkan rekapitulasi manual. Penelitian ini bertujuan untuk merancang bangun aplikasi presensi kelas berbasis pengenalan pola wajah, senyum, dan Wi-Fi terdekat. Metode yang digunakan dalam penelitian ini adalah pendekatan Deep Learning dengan arsitektur CNN FaceNet untuk mengenali wajah. Selain gambar wajah, sistem juga akan memvalidasi  presensi dengan kesesuaian lokasi dan waktu. Data lokasi diperoleh dari pencocokan SSID dengan database, dan data waktu diambil saat mahasiswa mengirimkan data kehadiran melalui API. Sistem presensi ini terdiri dari tiga aplikasi yaitu web, mobile, dan service yang dipasang di komputer mini, yang saling terintegrasi untuk mengirimkan data presensi ke sistem akademik secara otomatis. Sebagai konfirmasi, siswa diwajibkan selfie tersenyum untuk memperkuat validitas kehadiran. Sistem terintegrasi ini masih dalam bentuk purwarupa. Hasil akurasi dari testing model sebesar 92,6% sedangkan untuk testing live akurasi sebesar 66,7%. Nilai testing live lebih kecil dan cukup jauh dari testing model dapat diartikan hasil training model terlalu overfitting

SEMANTIC RETRIEVAL FOR INDONESIAN QURAN AUTOCOMPLETION

Attending lectures is a common way to learn Islamic knowledge. The speaker talks in front of the forum, and participants take notes on the lecture material. Many participants listen to the lecture while taking notes either in books or on other digital devices to avoid forgetting the discussed topics. However, note-taking during the lecture can be challenging, with no complementing module from the speaker. Lecturers have different paces and varying ways of delivering. In addition, sometimes, participants cannot always focus during the lecture. Those factors can cause problems in the note-taking process: some details can be lost or even shift the meaning. For note-taking on sensitive topics, such as verses from the Quran, the note-taking process must be done carefully and avoid mistakes. In this study, we proposed an autocomplete system for the Indonesian translation of the Quran that will help the user in note-taking Islamic lectures. The user writes down words, the parts of the Quran verse that he hears, and the system will retrieve the most similar verse. With semantic retrieval, the user does not need to write down the exact words of the verses he heard. The system can also handle typographical-error that usually occur in note-taking. We use Fasttext and calculate the cosine distance between the query and verses for the retrieval process. We also performed several optimization steps to create a robust system for the production stage. The system is evaluated by comparing how close the returned verse is with the ground truth. The proposed method's result accuracy reached 79.41% for the top 5 retrieved verse and 85.29% for the top 10 retrieved verse. [JJCIT 2023; 9(2.000): 94-106

Personalized Al-Quran Memorization Testing System Using Group Decision Support System

Memorizing Al-Quran is one of the most important acts of worship for Muslims. After memorizing some parts of the Al-Qur’an, the hafizh or memorizer is recommended to repeat or muraja’ah their memorization to strengthen it. This process is usually done in pairs by listening to each other’s memorization or testing by asking questions about Al-Quran. This study proposes a system that can help memorizers test their memorization independently without a partner. The system will perform a memorization test to support the user’s process of memorizing the Al-Quran. The system records and analyzes user data and uses it to personalize memorization testing from time to time. The system was made using the Group Decision Support System (GDSS) approach with the help of several Al-Quran memorizers as decision-makers. The GDSS algorithm used combines Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Weighted Geometric Mean. The evaluation is conducted with the help of a human evaluator, and the system showed 72% suitability