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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Information Technology and Computer Science Knowledge Engineering and Data Science JOURNAL OF APPLIED INFORMATICS AND COMPUTING TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Jurnal Ilmu Komputer dan Desain Komunikasi Visual Jurnal Mnemonic JATI (Jurnal Mahasiswa Teknik Informatika) CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Jurnal Sistem Komputer dan Informatika (JSON) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknologi Informatika dan Komputer Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Jurnal Restikom : Riset Teknik Informatika dan Komputer Nusantara Hasana Journal Jurnal Informatika Terpadu Jurnal Janitra Informatika dan Sistem Informasi International Journal Software Engineering and Computer Science (IJSECS) Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Pengabdian Masyarakat Bhinneka Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Jikom: Jurnal Informatika dan Komputer Jurnal Penelitian Sistem Informasi ROUTERS: Jurnal Sistem dan Teknologi Informasi The Journal of Enhanced Studies in Informatics and Computer Applications Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Journal of World Future Medicine, Health and Nursing
M. Syauqi Haris
Institut Teknologi, Sains, Dan Kesehatan RS Dr Soepraoen Kesdam V/Brw, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Other

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Enhancing Cyberbullying Sentiment Detection: A Comparative Study of IndoBERT and IndoBERTweet over SMOTE and Bernoulli Naive Bayes Approach Eka Mardiana Putri; Mochammad Anshori; M. Syauqi Haris
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12632

Abstract

Cyberbullying has become a critical issue in social media use because it can negatively impact users’ mental health and social interactions. The high volume of aggressive comments and hate speech on digital platforms highlights the need for an automatic detection system that can accurately and reliably identify cyberbullying content. This research compares the performance of Indonesian language transformer models, IndoBERT and IndoBERTweet, in detecting text-based cyberbullying. Before modeling, the dataset undergoes Exploratory Data Analysis to understand its characteristics, class distribution, comment length, and potential data imbalance. Next, text preprocessing and tokenization are performed before dividing the data using stratified holdout splitting to preserve class proportions in training and testing sets. Both models are then trained with the same hyperparameter settings to ensure an objective and fair performance comparison. Results show that IndoBERT achieved an accuracy of 0.8333, while IndoBERTweet performed better with an accuracy of 0.8409. The analysis of the confusion matrix and ROC curve confirms that IndoBERTweet is more effective at detecting cyberbullying across different classes. Compared to previous studies using the SMOTE method and Bernoulli Naïve Bayes algorithm, which achieved 84.00% accuracy, this study's findings are slightly higher at 84.09%. Notably, this was achieved without using synthetic oversampling techniques. This suggests that the approach employed in this research can deliver competitive performance even without data balancing with SMOTE. Overall, these findings indicate that a transformer-based approach, combined with a more representative dataset, can improve cyberbullying detection more efficiently and practically. Therefore, IndoBERTweet is a more suitable model for implementing a cyberbullying content moderation system in Indonesia.
Evaluasi Usability pada Aplikasi Identitas Kependudukan Digital (IKD) Menggunakan System Usability Testing Faridatus Sholikhah; Mochammad Anshori; M. Syauqi Haris
JURNAL PENELITIAN SISTEM INFORMASI (JPSI) Vol. 4 No. 3 (2026): Agustus : JURNAL PENELITIAN SISTEM INFORMASI
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jpsi.v4i3.4473

Abstract

This study aims to evaluate the usability of the Digital Population Identity (Identitas Kependudukan Digital/IKD) application using the System Usability Scale (SUS) method. A descriptive quantitative approach was employed, with the study conducted at the Department of Population and Civil Registration (Disdukcapil) of Malang City. Data were collected through a Google Forms-based SUS questionnaire distributed to 50 respondents selected using purposive sampling, consisting of users with prior experience using the IKD application. The research instrument comprised 10 SUS statements measured on a five-point Likert scale. The validity test indicated that all questionnaire items were valid with r-values exceeding 0.2732, while the reliability test produced a Cronbach's Alpha value of 0.834, indicating good internal consistency. The SUS evaluation yielded an average score of 77.42, categorized as Acceptable, achieving Grade B with a Good adjective rating. However, the Net Promoter Score (NPS) remained in the Passive category, indicating the need for improvements in data integration stability and facial registration procedures to enhance user satisfaction and encourage stronger user advocacy.
Analysis of Image Augmentation Effects on MobileNetV2 for Gastroesophageal Reflux Disease Endoscopy Image Classification Dinda Chesar Putri Ramadhani; Risqy Siwi Pradini; M. Syauqi Haris
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16510

Abstract

Gastroesophageal Reflux Disease (GERD) is a common digestive tract disorder, and endoscopic image-based diagnosis still faces challenges due to variations in lighting, image capture angles, and limited data that can cause deep learning models to overfit and reduce generalization capabilities. While previous studies frequently prioritize overall accuracy, this study distinguishes itself by focusing on increasing recall as a critical indicator of clinical sensitivity for positive case detection. This study aims to analyze the effect of applying image augmentation on the performance of GERD endoscopic image classification using a lightweight MobileNetV2 architecture, offering a practical solution for resource-constrained clinical settings. The research methods include data collection, class mapping, 80:20 data split, and standardized preprocessing stages. Two training scenarios were compared: without augmentation and with strategic on-the-fly augmentation applied through a transfer learning approach. Evaluation was carried out using accuracy, precision, recall, F1-score, and ROC-AUC. The results showed that augmentation improved model performance, with accuracy increasing from 76.72% to 78.06%, recall increasing from 79.33% to 85.27%, F1-score increasing from 77.86% to 80.04%, and AUC increasing from 0.8317 to 0.8481. These findings indicate that the augmentation strategy effectively improves the generalization of the lightweight model, significantly reducing missed diagnoses to support early detection of GERD. In addition, the results demonstrate that applying augmentation techniques can help the model learn more robust visual features from limited medical image datasets. This approach also contributes to reducing overfitting and improving model reliability in practical clinical image analysis scenarios.
Prediction Model for Diagnosing Heart Disease Using Classification Algorithm Risqy Siwi Pradini; Mochammad Anshori; M. Syauqi Haris; Busatto Marilia; Tostes Geraldo
Journal of World Future Medicine, Health and Nursing Vol. 1 No. 2 (2023)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/health.v1i2.347

Abstract

Heart disease often causes death if not treated quickly and appropriately. Early diagnosis can prevent more serious complications and treat heart disease patients best. The existence of a disease prediction model can help health workers to diagnose diseases more quickly and accurately. The heart disease prediction model using a classification algorithm is a system built using machine learning techniques. The classification algorithm chosen is NN, Naive Bayes, Random Forest, and SVM because it is the best algorithm for predicting heart disease. This study makes a comparison of the four algorithms using a dataset of 918 instances with 11 features. The result is that the Random Forest algorithm produces the highest accuracy, with 86.8%, and has the best ability to distinguish classes based on the ROC curve.
Co-Authors Abda Abda ACHMAD JAELANI RUSDI Ahsanun N. Khudori Ahsanun Naseh Khudori Anwar, David Saiful Arif Wahyudi Bagus Prasetyo Abdi , Benben Basmalah Abirestu Maulisuriandhy Busatto Marilia Carrina A. Tesa Dinda Chesar Putri Ramadhani Eka Mardiana Putri Faridatus Sholikhah Fatwa Ramdani, Fatwa Faurika Faurika, F Faurika, Faurika Ferdy Riza Firahmi Rizky Fita Rusdian Ikawati Geraldo, Tostes Herry Prasetyo Wibowo Hevlie Winda Nazry S Khofida, Nisa Magdalena Amelia Rizky Marilia, Busatto Mawardi, Ade Bagus Mika Debora Br Barus Mochammad Anshori Mourryn Galuh Erawati Nindynar Rikatsih Nugroho Teguh Yuono Putriana, Rena Risqy Siwi Pradini Risqy Siwi Pradini Risqy Siwi Pradini Saletia, Maria Claudia Tostes Geraldo Tri Astoto Kurniawan, Tri Astoto Wahyu Tejakusuma, Wahyu Wicaksana, Gigih Wisam Syahputra, Kelvin Yaniar Ramdani, Dandi Zuli Agustina Gultom