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All Journal REKAYASA Jurnal Informatika dan Teknik Elektro Terapan Jurnal Sisfokom (Sistem Informasi dan Komputer) SENTIA 2016 Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika Jurnal Kreativitas PKM Jurnal Informatika Kaputama (JIK) Scientific Journal of Informatics Journal of Computer System and Informatics (JoSYC) Dinamika Informatika: Jurnal Ilmiah Teknologi Informasi SKANIKA: Sistem Komputer dan Teknik Informatika Journal of Electrical Engineering and Computer (JEECOM) International Journal of Computer and Information System (IJCIS) Indexia Kontribusi: Jurnal Penelitian dan Pengabdian Kepada Masyarakat Academic Journal of Computer Science Research (AJCSR) TRILOGI: Jurnal Ilmu Teknologi, Kesehatan, dan Humaniora Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Insand Comtech : Information Science and Computer Technology Journal JSITIK: Jurnal Sistem Informasi dan Teknologi Informasi Komputer JUSTIFY : Jurnal Sistem Informasi Ibrahimy COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Nusantara Journal of Computers and its Applications Transaction on Informatics and Data Science JOKI: Journal of Computing and Informatics SINAR: Sinergi Pengabdian dan Inovasi untuk Masyarakat JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Abu Tholib
Universitas Nurul Jadid
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Transportation Other

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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 : CV. Laskar Karya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65678/joki.v2i1.153

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.
Penerapan Algoritma Apriori Untuk Menentukan Pola Pembelian Konsumen Mustofa, Nazzel Maulana; Alfarisi, Ahmad Muharram; Tholib, Abu
JSITIK: Jurnal Sistem Informasi dan Teknologi Informasi Komputer Vol. 4 No. 1 (2025): Desember 2025
Publisher : Cipta Media Harmoni

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53624/jsitik.v4i1.710

Abstract

Latar Belakang: Di era digital, bisnis ritel menghadapi tantangan dalam memahami perilaku konsumen dan menyusun strategi pemasaran yang efektif. Market Basket Analysis (MBA) menjadi pendekatan populer untuk menganalisis pola pembelian konsumen guna mempertahankan daya saing. Tujuan: Menemukan pola pembelian pelanggan dan mengidentifikasi aturan asosiasi antar produk yang dapat dimanfaatkan dalam strategi pemasaran seperti penempatan produk, bundling, dan personalisasi.Metode: Algoritma Apriori pada dataset transaksi ritel dari Kaggle yang berisi lebih dari 90.000 entri. Data dianalisis setelah melalui tahap pra-pemrosesan dan transformasi dengan teknik one-hot encoding. Algoritma dijalankan dengan parameter minimum support 0,005 dan confidence 0,5. Hasil: Hasil menunjukkan bahwa produk “12V U1 L&G 6” memiliki nilai support tertinggi sebesar 2,92%. Pasangan produk “1.5V IND AAA ALK BULK” dan “1.5V IND AA ALK BULK” menunjukkan asosiasi kuat dengan confidence 68,9% dan lift 58,46%. Kesimpulan: Penelitian ini berhasil mengidentifikasi pola pembelian konsumen dan menghasilkan aturan asosiasi yang signifikan sebagai dasar strategi pemasaran berbasis data. Penelitian selanjutnya disarankan mengeksplorasi algoritma lain seperti FP-Growth atau Eclat untuk membandingkan efisiensi dan akurasi.
Identifikasi Faktor Literasi Digital Siswa Pasca Pelatihan dengan Algoritma Random Forest Tholib, Abu; Dianita, Melany Putri; Sakinah, Alfiani Nur; Nisak, Khaerun; Siska, Siska
TRILOGI: Jurnal Ilmu Teknologi, Kesehatan, dan Humaniora Vol 6, No 4 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/trilogi.v6i4.13247

Abstract

Digital literacy is a foundational competence for junior high school students as learning increasingly relies on digital platforms; however, empirical evidence identifying which measurable factors most strongly drive post-training improvement remains limited. This study aims to determine key predictors of digital literacy gains after structured training and to develop a predictive model that classifies improvement into three levels (low, moderate, high). Data were collected from 200 junior high school students who participated in a structured program in digital marketing and graphic design, comprising pre-test and post-test scores, participation indicators, learning motivation, and frequency of digital tool use. After data cleaning, transformation, and feature encoding, a Random Forest classifier was trained to model improvement categories. Model performance was assessed using an 80:20 train–test split and stratified five-fold cross-validation, reporting accuracy, precision, recall, F1-score, and confusion matrix analysis. The model achieved 78% accuracy and exhibited its strongest and most stable performance in the high-improvement category, while minority categories showed reduced sensitivity, suggesting the influence of class imbalance.
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 Alfarisi, Ahmad Muharram Andi, Moh syaiful Basit, Illiyah Ibnul Cahyuni Novia Deddy Junaedi Deniyanto Muchlizin Wahidillah Devita Alif Barmansyah Dianita, Melany Putri 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 Mustofa, Nazzel Maulana Nanda Kurnia Agusmawati Nisak, Khaerun Qurrotu Aini, Qurrotu Rahman, M Fadhilur Ratri Enggar Pawening Resty Wulanningrum Rian Hidayat Rianto, M. Erfan Rizal Sulton Sakinah, Alfiani Nur Salman, Moh Setiyo Adi Nugroho Sholeha, Selfia Hafidatus Sholehah, Baitus Shudiq, Wali Ja'far Sihabillah, Ahmad Siska Siska 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