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All Journal Jurnal Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Rekayasa Sistem Informasi dan Teknologi Jurnal Kecerdasan Buatan dan Teknologi Informasi
Zulpahmi, M.
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management

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Detecting Fake Reviews Using BERT and Sublinear_TF Methods on Hotel Reviews in the Lombok Tourism Area Hadi, Zulpan; Zulpahmi, M.; ., Zaenudin; Asrory, Akmaludin
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8721

Abstract

The number of visitors to Lombok, one of the famous tourist destinations in Indonesia, increased from 400,595 in 2020 to 1,376,295 in 2022. Although the government supports the hotel industry, fake reviews are a significant problem that can damage hotel reputations and mislead tourists. This study uses BERT and Sublinear_TF feature extraction techniques to analyze fake reviews from three main areas: Gili Trawangan, Senggigi, and Kuta. BERT detects fake reviews by understanding the context of words, while Sublinear_TF emphasizes more informative words by reducing the weight of irrelevant common words. The results showed that the more extensive and diverse dataset from Gili Trawangan had the best classification results. The combination of BERT and Random Forest achieved the highest accuracy of 0.84. Overall, BERT excels in Gili Trawangan with an accuracy of 0.79 for SVM and 0.84 for Random Forest. In contrast, smaller and more homogeneous datasets such as Senggigi and Kuta have lower accuracy. In addition, Sublinear_TF performed well on Gili Trawangan with an accuracy of 0.82 using SVM and 0.83 using Random Forest; however, its performance declined in Senggigi and Kuta. BERT and Sublinear_TF techniques are more effective on large and diverse datasets such as Gili Trawangan. Sublinear_TF is better for varied data but less effective on more homogeneous datasets, while BERT with Random Forest showed the highest accuracy due to its ability to capture broader language context. This suggests that the size and variety of the dataset highly influence the success of fake review classification techniques.
SemetonBug: A Machine Learning Model for Automatic Bug Detection in Python Code Based on Syntactic Analysis Imran, Bahtiar; Riadi, Selamet; Suryadi, Emi; Zulpahmi, M.; Zaeniah, Zaeniah; Wahyudi, Erfan
Jurnal Informatika Vol 12, No 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i2.25340

Abstract

Bug detection in Python programming is a crucial aspect of software development. This study develops an automated bug detection system using feature extraction based on Abstract Syntax Tree (AST) and a Random Forest Classifier model. The dataset consists of 100 manually classified bugged files and 100 non-bugged files. The model is trained using structural code features such as the number of functions, classes, variables, conditions, and exception handling. Evaluation results indicate an accuracy of 86.67%, with balanced precision and recall across both classes. Confusion matrix analysis identifies the presence of false positives and false negatives, albeit in relatively low numbers. The accuracy curve suggests a potential overfitting issue, as training accuracy is higher than testing accuracy. This study demonstrates that the combination of AST-based feature extraction and Random Forest can be an effective approach for automated bug detection, with potential improvements through model optimization and a larger dataset.
ANALISIS KEAMANAN JARINGAN WIRELESS PADA SMK ISLAM MANBA’UL ULUM MENGGUNAKAN PENDEKATAN PENETRATION TESTING Sari, Ika Mayang; Samsumar, Lalu Delsi; Zulpahmi, M.
Jurnal Rekayasa Sistem Informasi dan Teknologi Vol. 3 No. 2 (2025): November
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70248/jrsit.v3i2.3068

Abstract

Perkembangan teknologi informasi mendorong pemanfaatan jaringan nirkabel dalam kegiatan pendidikan. Penelitian ini bertujuan menganalisis keamanan jaringan wireless di SMK Islam Manba’ul Ulum yang menggunakan protokol WPA2. Metode yang digunakan adalah penetration testing berbasis Kali Linux melalui simulasi serangan Handshake Capture dan WPA2 Cracking untuk menguji autentikasi, Deauthentication Attack untuk menguji kestabilan koneksi, serta Man-in-the-Middle (MiTM) Attack untuk menguji kerahasiaan data, dengan bantuan tools Aircrack-ng, Aireplay-ng, Airodump-ng, Bettercap, dan Wireshark. Hasil penelitian menunjukkan seluruh simulasi serangan berhasil dilakukan, meliputi peretasan kata sandi Wi-Fi, pemutusan koneksi pengguna, dan penyadapan data komunikasi, sehingga membuktikan jaringan masih rentan terhadap ancaman keamanan.
COLONOSCOPIC POLYP SEGMENTATION USING SEGFORMER-B0 WITH A DICE-BCE HYBRID LOSS Yani, Ahmad; Sudirman, San; Zulpahmi, M.; Suryadi, Emi; Imran, Bahtiar
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.476

Abstract

Colorectal cancer is one of the leading causes of cancer-related deaths worldwide, with most cases originating from early lesions such as colon polyps. Early detection through colonoscopy is essential to reduce mortality rates; however, accurate polyp identification remains challenging due to variations in shape, size, texture, and illumination conditions. This study aims to implement and evaluate the SegFormer-B0 architecture combined with a Dice-BCE hybrid loss function for polyp segmentation in colonoscopy images. The study utilized the public Kvasir-SEG dataset consisting of 1,000 colonoscopy images with pixel-level annotations. The dataset was divided into 80% training data and 20% validation data. Image preprocessing included resizing to 256×256 pixels and normalization using ImageNet statistics. The model was trained for 25 epochs using the AdamW optimizer with a learning rate of 1×10⁻⁴. Performance evaluation was conducted using Dice Coefficient, Intersection over Union (IoU), Sensitivity, and Specificity metrics. The experimental results demonstrated that the proposed model achieved a Dice Coefficient of 89.92%, Mean IoU of 81.90%, Sensitivity of 89.12%, and Specificity of 98.51%. The training process also showed stable convergence, supported by a training loss of 7.53% and validation loss of 23.30%. The findings indicate that the integration of SegFormer-B0 with the Dice-BCE hybrid loss effectively improves segmentation accuracy and stability while addressing class imbalance issues in colonoscopy images. Therefore, the proposed approach has strong potential to support computer-aided diagnosis systems for colorectal cancer screening.
Co-Authors ahmad yani Asrory, Akmaludin Erfan Wahyudi Imran, Bahtiar Lalu Delsi Samsumar, M.Eng. Sari, Ika Mayang Selamet Riadi Sudirman, San Suryadi, Emi Zaeniah Zaeniah Zaenudin ., Zaenudin Zulpan Hadi