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All Journal REKAYASA Jurnal Informatika dan Teknik Elektro Terapan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 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) Infotek : Jurnal Informatika dan Teknologi 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 UPGRADE: Jurnal Pendidikan Teknologi Informasi JOKI: Journal of Computing and Informatics SINAR: Sinergi Pengabdian dan Inovasi untuk Masyarakat ABDIMASTEK JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Abu Tholib
Universitas Nurul Jadid
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture 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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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.
K-Nearest Neighbor Performance Optimization for Multiclass Imbalance of Intrusion Detection Data Using SMOTE and Distance Variation-Based Parameter Tuning Hairani Hairani; Christopher Michael Lauw; Sri Farida Utami; Afrig Aminuddin; Abu Tholib
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7489

Abstract

The increasing use of computer networks and internet-based services has made cybersecurity threats more complex. Intrusion Detection Systems (IDS) play a crucial role in identifying network attacks; however, conventional signature- or rule-based approaches are limited in handling novel attacks and dynamically changing attack patterns. Therefore, machine learning approaches are applied to enhance the adaptive capabilities of IDS. Nevertheless, the use of machine learning in IDS still faces a major challenge: data imbalance, where normal traffic significantly outweighs attack traffic. This condition biases models toward the majority class, leading to suboptimal detection of minority attacks. Based on this issue, this study aims to improve the performance of the K-Nearest Neighbor (KNN) method in network attack detection by applying the Synthetic Minority Over-sampling Technique (SMOTE) and parameter tuning. The study employs KNN with parameter tuning and SMOTE to address multiclass data imbalance in network attack detection. Parameter tuning is conducted to determine the optimal value of k and distance functions, including Euclidean, Manhattan, and Cosine Similarity. The results show that KNN with k = 3 and Manhattan distance on SMOTE-balanced data achieves the highest accuracy of 96.51%, outperforming Euclidean and Cosine Similarity distances. These findings conclude that applying SMOTE and appropriately selecting k and distance metrics significantly improve KNN performance in network attack detection and increase overall detection accuracy.
Realistic 3D Object Visualization in Early Childhood Educational Games Using Ray Tracing Algorithms Moh. Ainol Yaqin 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.
Enhancing Hotel Recommendation Using Multi-Criteria Neural CollaborativeFiltering Abu Tholib; Fathorazi Nur Fajri; Ilham Saifudin; Hairani; Juvinal Ximenes Guterres
Upgrade : Jurnal Pendidikan Teknologi Informasi Vol 4 No 1 (2026): Agustus 2026 In-Press
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/upgrade.v4i1.6253

Abstract

The increasing volume of hotel information on online travel platforms made hotel selection more difficult for users because decision making had to consider multiple aspects simultaneously, including value, accessibility, service, room quality, cleanliness, and sleep quality. Conventional recommendation methods often depended on overall ratings and therefore were not sufficiently capable of representing the multidimensional nature of hotel preferences. This study proposed an improved multi-criteria neural collaborative filtering (MCNCF) model for hotel recommendation using the Bali Hotel Review dataset. The proposed model integrated user identity, hotel identity, and six structured hotel evaluation criteria to learn user preferences in a more detailed and preference-sensitive manner. The experimental design was also strengthened through a more reliable preprocessing and evaluation pipeline, including data splitting before scaling, training-based imputation for missing values, and user ranking evaluation. The model was implemented using embedding-based neural interaction learning to capture nonlinear relationships between users, hotels, and multi-criteria features. The results showed that the proposed approach achieved stable and competitive performance across testing splits of 10%, 20%, 30%, and 40%. On the original rating scale, the model produced the best Root Mean Square Error of 0.416400 and the lowest Mean Absolute Error of 0.351719. In addition, the ranking performance remained consistently high, with Normalized Discounted Cumulative Gain values ranging from 0.976540 to 0.996243. These findings demonstrated that the proposed approach provided an effective and robust solution for hotel recommendation by leveraging structured multi-criteria preference information within a neural recommendation framework.
Perbandingan Penggunaan Arsitektur Graph GCN Dan LightGCN Pada Sistem Rekomendasi Hotel Berbasis Rating Pengguna Dengan Dataset Terbatas Rhodil Fauzi; Abu Tholib; Ahmad Hudawi AS
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 2 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i2.35058

Abstract

Hotel recommendation systems often fail to recommend new hotels due to extreme data sparsity problems (item cold-start) and are vulnerable to the computational over-smoothing phenomenon. This study aims to comprehensively evaluate and compare the ranking architectures of Graph Convolutional Network (GCN) and LightGCN. The method used is a computational experiment using an Ablation Study approach to dissect the effect of propagation depth (1-Layer vs. Multi-Layer) and the injection of external features. The evaluation was conducted on a small-sized dataset from Mendeley Data containing review histories for 16 hotel entities. The main results show that the 1-Layer LightGCN with features is the most superior model for active users (warm-start), achieving an NDCG@10 score of 0.8702. However, in the extreme new hotel scenario (0-shot cold-start), this shallow architecture failed, and the best solution was actually won by the pure Multi-Layer model without features (No-Feature), which achieved a Hit Ratio (HR@10) of 71.43%. In conclusion, there is no single perfect model for all conditions; system implementation is recommended to adopt a dual-framework that integrates the speed of 1-Layer LightGCN and the propagation robustness of the Multi-Layer model.
Pelatihan Karya Tulis Ilmiah Jurnal Bereputasi Internasional pada Mahasiswa Doktoral dan Pra-Doktoral Ilham Saifudin; Abu Tholib; Samsurizal Samsurizal; Muhammad A’an Auliq
ABDIMASTEK Vol. 5 No. 1 (2026): Juli
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/abdimastek.v5i1.6140

Abstract

Publikasi pada jurnal bereputasi internasional merupakan salah satu indikator penting dalam pengembangan kapasitas akademik mahasiswa doktoral dan pra-doktoral. Namun, masih banyak mahasiswa yang mengalami kesulitan dalam menentukan baseline paper, merumuskan novelty penelitian, serta menyusun artikel ilmiah sesuai standar jurnal internasional. Kegiatan Pengabdian kepada Masyarakat ini bertujuan untuk meningkatkan kompetensi peserta dalam penulisan karya tulis ilmiah dan strategi publikasi pada jurnal bereputasi internasional. Kegiatan dilaksanakan secara daring melalui Zoom Meeting dalam dua kali pertemuan. Metode yang digunakan meliputi penyampaian materi, diskusi interaktif, praktik identifikasi research gap dan novelty, serta pendampingan penyusunan artikel ilmiah. Pertemuan pertama diikuti oleh 6 peserta, sedangkan pertemuan kedua dihadiri oleh 8 peserta. Hasil kegiatan menunjukkan bahwa peserta memperoleh pemahaman yang lebih baik mengenai teknik menentukan baseline paper, menemukan novelty, menyusun struktur artikel ilmiah, memilih jurnal yang sesuai, serta memahami proses submission dan peer review pada penerbit internasional, seperti Elsevier, Wiley, IEEE, Taylor & Francis, ACM, dan Springer Nature. Program ini diharapkan menjadi langkah awal dalam meningkatkan kualitas publikasi ilmiah mahasiswa doktoral dan pra-doktoral serta mendukung peningkatan produktivitas riset dan reputasi akademik perguruan tinggi.
Identifikasi Faktor Literasi Digital Siswa Pasca Pelatihan dengan Algoritma Random Forest Abu Tholib; Melany Putri Dianita; Alfiani Nur Sakinah; Khaerun Nisak; 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 Afrig Aminuddin 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 Alfiani Nur Sakinah Andi, Moh syaiful Basit, Illiyah Ibnul Cahyuni Novia Christopher Michael Lauw 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 Juvinal Ximenes Guterres Khaerun Nisak Khoiriyah, Fitwatul Linda Uswatun Hasanah Marzuki, Muhammad Ismail Maula, Inayatul Maulidiansyah, Maulidiansyah Melany Putri Dianita Misbahul Munir Moh Ali Ishaq Moh Lailul Ilham Moh Syadidul Itqan Moh. Ainol Yaqin Yaqin Muafi Muafi Muafi Muh Nurul Imam Muhammad A’an Auliq Musfiroh Musfiroh, Musfiroh Mustofa, Nazzel Maulana Nanda Kurnia Agusmawati Qurrotu Aini, Qurrotu Rahman, M Fadhilur Ratri Enggar Pawening Resty Wulanningrum Rhodil Fauzi Rian Hidayat Rianto, M. Erfan Rizal Sulton Saifudin, Ilham Salman, Moh Samsurizal Samsurizal Setiyo Adi Nugroho Sholehah, Baitus Shudiq, Wali Ja'far Sihabillah, Ahmad Siska Siska Soleh, Paisal Sri Farida Utami Sukron, Moh Supri yono Supri Yono, Supri Supriadi, Ahmad Syafiih, M Syaroni, Wahab Syaroni, Wahab 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 Yayat Hidayat Yoga Yuniadi Yuliana, Wiwin Yuri Ariyanto Zain, Ahmad Naufal Waliyus Zainal Arifin Zainal Arifin