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All Journal Techno.Com: Jurnal Teknologi Informasi CALYPTRA : Jurnal Ilmiah Mahasiswa Universitas Surabaya Journal of ICT Research and Applications Journal of Information Systems Engineering and Business Intelligence JOIV : International Journal on Informatics Visualization Indonesian Journal of Information System Teknika ABDIMASTEK
Joko Siswantoro
Departement Of Informatics Engineering, Faculty Of Engineering, Universitas Surabaya, Jalan Raya Kali Rungkut, Surabaya, 60293
Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Journal : Teknika

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Analisis Sentimen Multi-Kelas Untuk Film Berbasis Teks Ulasan Menggunakan Model Regresi Logistik Anasthasya Averina; Helen Hadi; Joko Siswantoro
Teknika Vol 11 No 2 (2022): Juli 2022
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v11i2.461

Abstract

Pengutaraan pendapat atau pengutaraan pemikiran secara sukarela terhadap suatu film pada situs pengulas film merupakan hal yang sering dilakukan oleh pengguna. Beberapa pengguna kadang-kadang memberikan ulasan yang ambigu terhadap sebuah film, yaitu dengan memberikan komentar yang buruk tetapi memberikan rating yang baik atau sebaliknya. Hal ini dapat berpengaruh pada citra film tersebut. Maka dari itu, diperlukan sistem yang dapat memprediksi rating agar sesuai dengan komentar yang diberikan atau sistem pembenaran rating. Penelitian ini bertujuan untuk memprediksi rating suatu film berdasarkan ulasan yang diberikan oleh pengguna menggunakan model Regresi Logistik. Dataset yang digunakan pada penelitian ini adalah data ulasan 10 film yang berbeda dari Mendeley Data. Tahap pra-pemrosesan dilakukan dengan penghapusan kata umum, tanda baca, pengurangan dimensi, dan pengekstrakan ciri dari teks ulasan menggunakan library scikit-learn. Dengan 80% data sebagai training dan sisanya digunakan untuk testing, hasil perhitungan akurasi prediksi 10 kelas rating yang didapatkan dari feature extraction CountVectorize adalah 36% dan TfidfVectorizer sebesar 32%. Sedangkan hasil dari perhitungan akurasi prediksi 2 class sentiment, didapatkan hasil tertinggi sebesar 83% oleh feature extraction CountVectorizer dan feature extraction TfidfVectorizer sebesar 76%.
Facial Expression Recognition to Detect Student Engagement in Online Lectures Joko Siswantoro; Januar Rahmadiarto; Mohammad Farid Naufal
Teknika Vol 13 No 2 (2024): Juli 2024
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v13i2.853

Abstract

In synchronous online lectures, the lecturers often provide the lecture material directly through video conference technology. On the other hand, there are many students who do not pay attention to the lecturers when they are participating in online lectures. As a consequence, in this research, an application was developed to assist lecturers in gathering data regarding the degree to which students who participate in online lectures pay attention to the presented information. The application employed a convolutional neural network (CNN) model to recognize each student's facial expressions and place them into one of two classes: either engaged or disengaged. The captured student facial image was preprocessed to facilitate the classification process. The preprocessing stage consisted of image conversion to gray scale, face detection using the Haar-Cascade Classifier model, and a median filter to reduce noise. In the process of designing a CNN model, three different hyperparameter tuning scenarios were implemented. These tuning scenarios aimed to obtain the best possible CNN model by determining which CNN model hyperparameters were the most optimal. The results of the experiments indicate that the CNN model from the second scenario has the highest level of accuracy in terms of recognizing facial expressions, coming in at 86%. The results of this research have been tested to measure the level of student participation in online lectures. The trial results show that the proposed application can help lecturers evaluate student engagement during online lectures.
Co-Authors Anasthasya Averina Anton Satria Prabuwono Anton Satria Prabuwono Arwoko, Heru Asmawti, Endah Azizi Abdullah Azizi Abdullah Bahari Indrus Edwin Indarto Helen Hadi Januar Rahmadiarto Jessica Millenia Mohammad Farid Naufal Mohammad Farid Naufal Mohammad Farid Naufal, Mohammad Farid Monica Widiasri, Monica Muhammad Alifian Fajar Pratama Muhammad Ghifari Kusuma Wicaksono Soebroto, Juan Timothy Sofia Ariyani Vincentius Riandaru Prasetyo