Article Per Year (5 Year)
p-Index From 2021 - 2026
0.778
P-Index
This Author published in this journals
All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Building of Informatics, Technology and Science Walisongo Journal of Information Technology
Mahfudh, Adzhal Arwani
Unknown Affiliation
Computer Science & IT Engineering

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 7 Documents
Search

 1 
Klasifikasi Berita Hoax Dengan Menggunakan Metode Naive Bayes Mustofa, Hery; Mahfudh, Adzhal Arwani
Walisongo Journal of Information Technology Vol 1, No 1 (2019): Walisongo Journal of Information Technology
Publisher : Universitas Islam Negeri Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/wjit.2019.1.1.3915

Abstract

Hoaxes contain false news or non-sourced news. Today, hoaxes are very widely spread through internet media. The development of information technology that has so quickly triggered the spread of hoax information through the internet has become uncontrolled. So we need an intelligent system that can classify hoax news content that is spread through internet media. The hoax classification process can be done through the preprocessing stage then weighting the word and classification using naive bayes. Measurements were made using the 10-ford cross validation method. The results obtained from these measurements, it is known that the value of fold 6 has the highest accuracy, which is equal to 85.28% which is classified as relevant documents as much as 307 and irrelevant as much as 53 or an error rate of 14.72%. While the average value based on hoax news and true news value precision 0.896 and recall 0.853
Tingkat Ketergantungan (Usability) E-learning di Fakultas Saitek UIN Walisongo Semarang Mahfudh, Adzhal Arwani; Rizki, Favian Agung; Alfaza, Ahilla Salma
Walisongo Journal of Information Technology Vol 2, No 2 (2020): Walisongo Journal of Information Technology
Publisher : Universitas Islam Negeri Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/wjit.2020.2.2.7172

Abstract

Utilizing technology, especially the internet to support the teaching and learning process, is what happened in the 21st century. E-learning is one of the learning innovations in the network that allows users to access material or collect assignments online. The benefits of e-learning are so numerous and can help lectures, users can access anywhere without being limited by space and time. The method used is descriptive research, which in its implementation consists of data collection, analysis and interpretation of the meaning of the data obtained. . This study aims to determine the level of usability or usability that exists on the e-leraning website of UIN WALISONGO Saintek Faculty, whether the system has been made that meets the usability criteria or not, in terms of Learnability, Efficiency, Memorability, Errors, and Satisfaction. After conducting research, the authors obtain data that e-learning meets usability criteria seen from the data in the field that is the result of the survey. So, Walisongo e-learning can be used as a support for learning effectively and efficiently.
Klasifikasi Pemahaman Santri Dalam Pembelajaran Kitab Kuning Menggunakan Algoritma Naive Bayes Berbasis Forward Selection Mahfudh, Adzhal Arwani; Mustofa, Hery
Walisongo Journal of Information Technology Vol 1, No 2 (2019): Walisongo Journal of Information Technology
Publisher : Universitas Islam Negeri Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/wjit.2019.1.2.4529

Abstract

Kitab kuning merupakan kitab tradisional yang mengandung diraasah islamiyah yang diajarakan pada pondok pesantren, mulai dari struktur bahasa arab (ilmu nahwu dan shorof), ‘ulumul qur’an, hadits, aqidah, tasawuf/akhlaq, tafsir, fiqh sampai ilmu sosial dan kemasyarakatan (mu’amalah).  Disebut juga dengan kitab gundul karena tidak memiliki harakat (fathah, kasroh, dhammah, sukun) untuk bisa membaca dan memahami secara menyeluruh dibutuhkan waktu yang relatif lama. Penelitian ini bertujuan untuk mendapatkan model klasifikasi dari data pembelajaran kitab kuning di pondok pesantren. Metode yang digunakan dalam penelitian ini adalah forward selection sebagai praproses dalam mengurangi dimensi data, menghilangkan data yang tidak relevan dan naive bayes yang berguna untuk mengklasifikasi data. Hasil dari klasifikasi data pembelajaran kitab kuning menggunakan atribut yang telah diklasifikasi berdasarkan fitur-fiturnya dan dilakukan iterasi pada cross validation sehingga menghasilkan akurasi yang tepat. Berdasarkan hasil pengujian dengan dua metode, pengujian dengan algoritma Naive bayes saja menghasilkan akurasi 96,02%, untuk algoritma Naive bayes berbasis forward selection menghasilkan akurasi 97,38% . Terdapat peningkatan akurasi dengan penambahan fitur seleksi.
Comparative Study of SVM and Decision Tree Algorithms on the Effect of SMOTE Technique on LinkAja Application Faruq, Muhammad Kholfan; Umam, Khothibul; Mustofa, Mokhamad Iklil; Mahfudh, Adzhal Arwani
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.9806

Abstract

The widespread adoption of digital wallets like LinkAja in Indonesia has led to a surge in user-generated reviews, which are valuable for assessing service quality. This study compares the classification performance of Support Vector Machine (SVM) and Decision Tree algorithms on user reviews from the LinkAja application. 7.000 reviews were gathered through web scraping and processed with standard text cleaning, tokenization, stopword removal, and stemming, resulting in 6,261 usable entries. These were divided into training and testing sets in a 70:30 ratio. The performance of each algorithm was evaluated both before and after the application of Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Prior to SMOTE, SVM recorded an accuracy of 77.97%, precision of 0.74, recall of 0.33, and F1 score of 0.45, while Decision Tree reached 72.01% accuracy, 0.50 precision, 0.62 recall, and 0.55 F1 score. After SMOTE, SVM accuracy slightly improved to 78.29%, with notable increases in recall (0.74) and F1 score (0.60); Decision Tree also saw an accuracy rise to 74.56% but experienced a slight decline in F1 score to 0.52. These findings demonstrate that SVM, particularly when used with SMOTE, offers better overall performance and class balance in classifying reviews with imbalanced sentiment distribution, making it more suitable than Decision Tree for this application.
Public Opinion on The MBG Program: Comparative Evaluation of InSet and VADER Lexicon Labeling Using SVM on Platform X Zakiyah, Na'ilah Puti; Umam, Khothibul; Mahfudh, Adzhal Arwani
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.9978

Abstract

This study aims to examine public opinion regarding the MBG program on platform X by utilizing the Support Vector Machine (SVM) algorithm using two sentiment labeling methods, namely InSet Lexicon and VADER Lexicon. The data was then divided into 70% for training and 30% for testing, and extracted using Term Frequency–Inverse Document Frequency (TF-IDF) to convert the text into numerical representations. The SVM model was trained on both labeled data sets to compare their performance based on evaluation metrics such as accuracy, precision, recall, and F1 score. The results show that labeling with VADER produces a more dominant number of neutral sentiments, while InSet Lexicon produces a more balanced distribution between positive, negative, and neutral sentiments. At the modeling stage, SVM with InSet labels achieved an accuracy of 80.10%, with precision of 0.81, recall of 0.80, and an F1 score of 0.79. Meanwhile, SVM with VADER labels achieved an accuracy of 93.83%, precision of 0.94, recall of 0.94, and an F1 score of 0.93. Although VADER showed higher accuracy values, InSet Lexicon is considered more efficient and relevant for sentiment analysis in Indonesia because it is capable of producing more balanced and contextual classifications.
Implementasi Arsitektur MobileNetV2 Berbasis Citra untuk Deteksi Penyakit Dropsy dan Popeye pada Ikan Cupang Musyaffa, Fadhilah Rafi; Mahfudh, Adzhal Arwani; Subowo, Moh Hadi
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9379

Abstract

The identification of diseases in betta fish based on visual symptoms remains a challenge, particularly for beginners who lack experience in recognizing disease characteristics. This study aims to implement an image-based MobileNetV2 architecture as a diagnostic support system to detect dropsy and popeye diseases in betta fish that have already exhibited visual symptoms. The dataset used in this study consists of 600 betta fish images divided into three classes: healthy, dropsy, and popeye, with 200 images in each class, collected from the internet. Data preprocessing was conducted through image ratio adjustment, normalization, and data augmentation to increase data variability. A transfer learning approach was applied by freezing most layers of the MobileNetV2 feature extractor and fine-tuning several of the final layers. Model evaluation was performed using 5-Fold Cross Validation to ensure experimental stability and reproducibility. The best model from each fold was then combined using an ensemble method based on average probability to improve prediction performance on the test dataset. Experimental results show that the average 5-Fold Cross Validation accuracy reached 74.71% with a standard deviation of ±4.57%, while the Macro-F1 score achieved ±74.43%. The ensemble approach produced a test accuracy of 85.56% with balanced classification performance across all classes. Grad-CAM visualizations indicate that the model is able to focus on image regions relevant to disease symptoms. These findings demonstrate that the MobileNetV2 architecture is effective as an image-based diagnostic support tool for betta fish diseases.
Analisis Sentimen Persepsi Publik Terhadap Program MBG Pada Komentar YouTube Menggunakan Naïve Bayes dan Resampling Najib, Lutfi; Mahfudh, Adzhal Arwani; Bakhri, Syaiful
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9400

Abstract

The Free Nutritious Meal Program (MBG), launched by the Indonesian government in 2025, has generated diverse public responses on social media, particularly on YouTube as an open digital discussion space. This study aims to analyze public perception of the MBG program through sentiment classification of YouTube comments using the Multinomial Naïve Bayes algorithm combined with Term Frequency–Inverse Document Frequency (TF-IDF) weighting. The dataset consists of 1,082 comments categorized into three sentiment classes: negative, neutral, and positive. The data distribution reveals significant class imbalance, with negative sentiment dominating at 70.61%. The baseline model achieved an accuracy of 70.67% with a macro F1-score of 27.60%, indicating bias toward the majority class. To address this imbalance, Random Oversampling (ROS) and Synthetic Minority Over-sampling Technique (SMOTE) were applied. Although overall accuracy decreased to approximately 51% after resampling, the macro F1-score improved to 36.24% (SMOTE) and 37.09% (ROS), indicating enhanced performance in detecting minority classes. In the context of public policy evaluation, improved sensitivity to minority sentiment is considered more representative than high but biased accuracy. These findings highlight the importance of handling class imbalance in social media–based sentiment analysis for public policy monitoring.
Co-Authors Alfaza, Ahilla Salma Faruq, Muhammad Kholfan Khothibul Umam Mokhamad Iklil Mustofa Mustofa, Hery Musyaffa, Fadhilah Rafi Najib, Lutfi Rizki, Favian Agung Subowo, Moh Hadi Syaiful Bakhri Zakiyah, Na'ilah Puti