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Classification of Final Project Titles Using Bidirectional Long Short Term Memory at the Faculty of Engineering Nurul Jadid University Warda, Faridatul; Fajri, Fathorazi Nur; Tholib, Abu
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1723

Abstract

Every year, the Faculty of Engineering at Nurul Jadid University forms a committee to manage the process of students' final projects from the title selection stage to the final examination process until graduation. The process of selecting the final project title is still done manually, namely by checking the titles one by one, which takes a long time and allows errors because there is a lot of data to check, so human errors can also occur. Therefore, this research proposes to use the Bidirectional Long Short Term Memory (BiLSTM) method to classify the final project title based on its grade category. Several experiments were conducted to generate the most appropriate labels. The first experiment produced 4 labels and the second experiment produced 2 labels. From the results of several experiments, it was concluded that the second experiment had the best accuracy results with the 'good enough' and 'good' classes. The oversampling technique was then applied to overcome overlapping data, and the turning process was then performed on several parameters that could re-optimize the previous accuracy result of 75.24% to 91.15%. With a configuration of 10 random state parameters, using 64 batch sizes and 50 epochs. In addition, model adjustments were made to the hidden layer by adding a dropout layer and relu activation.
Realistic 3D Object Visualization in Early Childhood Educational Games Using Ray Tracing Algorithms Yaqin, Moh. Ainol Yaqin; Abu Tholib; Juvinal Ximenes Guterres
JOKI: Jurnal Komputasi dan Informatika Vol 2 No 1 (2025): June 2025
Publisher : Laskar Karya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Computer graphics plays a crucial role in creating engaging and interactive learning experiences for young children. This study implements ray tracing algorithms to enhance 3D object visualization in educational games designed for early childhood. The objective is to improve visual realism, thereby increasing children's interest and engagement in learning through interactive gameplay. The game was developed using C++ and OpenGL, incorporating ray tracing techniques to simulate light behavior accurately and produce realistic shading and reflections. The research followed a systematic development process, including literature review, game design, algorithm implementation, and user evaluation. The evaluation, involving early learners, showed a significant increase in attention span, comprehension, and enthusiasm among children exposed to ray-traced 3D environments, compared to traditional visualization techniques. These findings suggest that realistic 3D visualization through ray tracing can be a valuable asset in educational media, supporting cognitive development and learning motivation in early childhood education.
Comparison of C4.5 and Naive Bayes for Predicting Student Graduation Using Machine Learning Algorithms Tholib, Abu; Fadli Hidayat, M Noer; yono, Supri; Wulanningrum, Resty; Daniati, Erna
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 2 No. 2 (2023): September 2023
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v2i2.3364

Abstract

Student graduation is a very important element for universities because it relates to college accreditation assessment. One of them is at the Faculty of Engineering Nurul Jadid University, which has problems completing the study period within a predetermined time. So that it can be detrimental because accreditation is less than optimal, and the number of active students makes it less ideal in teaching and learning activities. This study aimed to compare the level of accuracy using the C4.5 algorithm and Naïve Bayes method in predicting graduation on time. The C4.5 and Naïve Bayes algorithms are one of the methods in the algorithm for classifying. Tests were carried out using the C4.5 and Naïve Bayes algorithms using Google Colab with Python programming language, then validated using 10-fold cross-validation. The results of this study indicate that the Naïve Bayes method has a higher accuracy value with an accuracy rate of 96.12%, while the C4.5 algorithm method is 93.82%.
Thesis Topic Modeling Study: Latent Dirichlet Allocation (LDA) and Machine Learning Approach Hairani, Hairani; Janhasmadja, Mengas; Tholib, Abu; Ximenes Guterres, Juvinal; Ariyanto, Yuri
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 3 No. 2 (2024): September 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i2.4375

Abstract

The thesis reports housed in the campus repository have yet to be analyzed to reveal valuable knowledge patterns. Analyzing trends in thesis research topics can facilitate the selection of research topics, aid in mapping research areas, and identify underexplored topics.Therefore, this research aims to model and classify thesis topics using Latent Dirichlet Allocation (LDA) and the Naïve Bayes and Support Vector Machine (SVM) methods. This study employs the LDA method for thesis topic modeling, while SVM and Naïve Bayes are used for classifying these topics. The research results show that LDA successfully modeled five of the most popular thesis topics, namely two related to computer networks, two on software engineering, and one on multimedia. For thesis topic classification, the SVM method demonstrated higher accuracy than Naïve Bayes, reaching 92.80% after the data was balanced using Synthetic Minority Oversampling Technique (SMOTE). The implication of this study is that the topic modeling approach using LDA is able to identify dominant thesis topics. In addition, the SVM classification results obtained better accuracy than Naïve Bayes in the thesis topic classification task.
Co-Authors Agusmawati, Nanda Kurnia Ahmad Baidowi Eko Fitra Firmanda Ahmad Baidowi Eko Fitra Firmanda Ahmad Halimi Ahmad Hudawi As Ahmad Hudawi AS ahmad taufiqul imam Alfan Maulan Andi, Moh syaiful Basit, Illiyah Ibnul Cahyuni Novia Deddy Junaedi Deniyanto Muchlizin Wahidillah Devita Alif Barmansyah Eka Wahyu Ramadhan Eko Fitra Firmandani, Ahmad Muzakki Eliyanto, Andik Elfandiyono Erna Daniati Fadli Hidayat, M Noer Fadli Hidayat, M. Noer Fathorazi Nur Fajri Fauziah, Gustin Fitwatul Khoiriyah Furqon, Ainul Gulpi Qorik O tagalu .P Guterres, Juvinal Ximenes Hairani Hairani Halimi, Ahmad Hidayat, M. Noer Hudawi AS, Ahmad Ihsan, Gilang Hafidzul Inayatul Maula Itqan, Moh Syadidul Janhasmadja, Mengas Juvinal Ximenes Guterres Juvinal Ximenes Guterres Khoiriyah, Fitwatul Linda Uswatun Hasanah Marzuki, Muhammad Ismail Maula, Inayatul Maulidiansyah, Maulidiansyah Misbahul Munir Moh Ali Ishaq Moh Lailul Ilham Moh Syadidul Itqan Muafi Muafi Muafi Muh Nurul Imam Musfiroh Musfiroh, Musfiroh Nanda Kurnia Agusmawati Qurrotu Aini, Qurrotu Rahman, M Fadhilur Ratri Enggar Pawening Resty Wulanningrum Rian Hidayat Rianto, M. Erfan Rizal Sulton Salman, Moh Setiyo Adi Nugroho Sholeha, Selfia Hafidatus Sholehah, Baitus Shudiq, Wali Ja'far Sihabillah, Ahmad Soleh, Paisal Sukron, Moh Supri yono Supri Yono, Supri Supriadi, Ahmad Syafiih, M Syaroni, Wahab Syaroni, Wahab Taufiqur Rahman, Taufiqur Tsabbit Albannani, Nur Wahyu Virda Virdausih Putri Wahab Syaroni Wahab Syaroni Wali Ja’far Shudiq Warda, Faridatul Wiwin Yuliana Ximenes Guterres, Juvinal Yaqin, Moh. Ainol Yaqin, Moh. Ainol Yaqin Yayat Hidayat Yoga Yuniadi Yuliana, Wiwin Yuri Ariyanto Zain, Ahmad Naufal Waliyus Zainal Arifin Zainal Arifin