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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Technology and Computer Science Knowledge Engineering and Data Science InComTech: Jurnal Telekomunikasi dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika JOISIE (Journal Of Information Systems And Informatics Engineering) JISKa (Jurnal Informatika Sunan Kalijaga) CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknologi Informatika dan Komputer Jurnal Teknik Informatika (JUTIF) Jurnal Restikom : Riset Teknik Informatika dan Komputer JINAV: Journal of Information and Visualization Jurnal Pendidikan dan Teknologi Indonesia Engineering, Mathematics and Computer Science Journal (EMACS) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Pengabdian Masyarakat Bhinneka Journal of Training and Community Service Adpertisi Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Journal of Artificial Intelligence and Digital Business Jurnal Penelitian Sistem Informasi Jurnal Indonesia : Manajemen Informatika dan Komunikasi The Journal of Enhanced Studies in Informatics and Computer Applications J-KOMA : Jurnal Ilmu Komputer dan Aplikasi International Journal of Computer Science and Information Technology Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Journal of World Future Medicine, Health and Nursing
Mochammad Anshori
Institut Teknologi, Sains, Dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Other

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Journal : journal of applied informatics and computing

 1 
Comparing Different KNN Parameters Based on Woman Risk Factors to Predict the Cervical Cancer Saletia, Maria Claudia; Anshori, Mochammad; Haris, M Syauqi
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10746

Abstract

Cervical cancer remains a major cause of mortality among women, particularly in low-resource regions where access to conventional screening is limited. Early detection through predictive modeling offers a low-cost and non-invasive alternative to clinical diagnostics. This study aims to evaluate the effectiveness of the k-Nearest Neighbors algorithm for predicting cervical cancer risk using behavioral and psychosocial attributes. The research utilized the publicly available Sobar cervical cancer behavioral dataset comprising 72 instances with 18 input features and a binary target label. Data preprocessing included removal of incomplete records, encoding of categorical variables, and normalization. The algorithm was tested across varying numbers of neighbors and distance metrics, with performance evaluated using 10-fold cross-validation and multiple classification metrics. The optimal configuration was achieved with three neighbors and the Manhattan distance metric, yielding an accuracy of 93.06%, sensitivity of 93.10%, specificity of 85.90%, precision of 93.10%, F1-score of 92.90%, and an area under the curve of 0.8952. This performance surpassed the reported baseline of a probabilistic classifier and demonstrated the algorithm’s capability to capture complex behavioral patterns associated with cervical cancer risk. These findings confirm the feasibility of applying optimized instance-based learning to behavioral data for early cancer risk assessment. The approach offers potential for integration into community health programs to support early detection and prevention strategies.
Outperforming DNN Using MLP in Water Quality Assessment for Aquaculture Anshori, Mochammad; Musthofa, Mufid
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11835

Abstract

Aquaculture production relies heavily on stable water quality conditions, requiring accurate and efficient assessment methods to support early environmental monitoring and sustainable management. Although deep neural network models have been widely applied to water quality classification, their high computational complexity often limits their applicability in real-time and resource-constrained aquaculture systems. This study aims to evaluate whether a systematically optimized Multilayer Perceptron can outperform a reported deep neural network benchmark in aquaculture water quality assessment while maintaining computational efficiency. The study adopts a structured methodology involving dataset characterization, extreme outlier removal, feature normalization, and stratified data partitioning. A single-hidden-layer Multilayer Perceptron is trained using a feedforward backpropagation learning process, with systematic exploration of hidden neuron configurations and training epochs to identify the optimal architecture. Model performance is evaluated using multiple classification metrics, including accuracy, precision, recall, F1-score, confusion matrix analysis, and receiver operating characteristic and precision–recall curves. Results indicate that the optimal Multilayer Perceptron configuration, consisting of 80 hidden neurons and 200 training epochs, achieves an accuracy of 96.62%, surpassing the deep neural network benchmark accuracy of 95.69%. The proposed model demonstrates strong class-level performance, clear separation between water quality categories, stable convergence behavior, and reduced computational overhead compared to deeper architectures. These findings highlight that increasing model depth does not necessarily improve predictive performance for heterogeneous aquaculture datasets. In conclusion, this study provides empirical evidence that a well-optimized shallow neural network can outperform deeper models in aquaculture water quality assessment. The results emphasize the importance of model parsimony and systematic hyperparameter optimization, offering a practical and efficient solution for real-time aquaculture water quality monitoring applications.
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.
Classification of the Effectiveness of Balur Therapy on Patients at the Malang Health Center Using the Decision Tree Algorithm Riski Puji Lestari; Mochammad Anshori; Wahyu Teja Kusuma
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.v10i3.12730

Abstract

This study addresses the classification of balur therapy effectiveness as a complementary treatment using a machine learning approach, aiming to develop an accurate, balanced, and transparent model to support clinical decision-making. The methodology employs the Decision Tree algorithm, data imbalance handling using Synthetic Minority Oversampling Technique, and model interpretation through Local Interpretable Model-Agnostic Explanations. The dataset consists of 520 medical records, reduced to 478 after preprocessing, including data cleaning, binning, and outlier removal. The results indicate that the model without data balancing achieved the highest specificity of 0.8276 at a 90:10 split ratio, while the application of Synthetic Minority Oversampling Technique improved sensitivity toward the minority class but reduced specificity. Key influential features include occupation, diagnosis, and therapy duration. The interpretability analysis demonstrates that the model can clearly explain feature contributions to predictions. This study concludes that integrating classification, data balancing, and explainable modeling enhances medical data analysis. The findings imply strong potential for developing objective and transparent clinical decision support systems.
Identifying Fear of Missing Out (FOMO) in Adolescents Using K-Nearest Neighbors: An Experimental Study of k-Values and Distance Metrics Ricco Wahyu Pamungkas; Mochammad Anshori; Wahyu Teja Kusuma
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.v10i3.12879

Abstract

Fear of Missing Out (FOMO) is a psychological phenomenon commonly experienced by teenagers due to the high intensity of social media use, and has the potential to cause emotional and social impacts if not identified early. The main problem in identifying FOMO is its internal nature and the difficulty in measuring it objectively using conventional methods. This research proposes a data mining-based classification approach using K-Nearest Neighbor (KNN) to identify the level of FOMO in adolescents. The dataset was obtained from 136 respondents through a questionnaire that included demographic data and the ON-FoMO scale. The research stages include data preprocessing (encoding and Min-Max normalization), data splitting using stratified holdout (80:20), and experiments varying K (3–19) and distance metrics (Euclidean, Manhattan, Chebyshev). The experimental results show that the combination of Euclidean distance with K=11 yields the best performance with an accuracy of 85.71%, ROC AUC of 0.786, Precision–Recall AUC of 0.826, and sensitivity of 100%. The experimental results indicate that the selection of the K parameter and the distance method significantly affect classification performance. Overall, this study concludes that the KNN algorithm with the optimal configuration is effective as an initial screening method for the level of FOMO in adolescents in an systematic and data-based manner.
Co-Authors ACHMAD JAELANI RUSDI Afifah Vera Ferencia Fitria Ningrum Ahmad Afif Supianto Ahsanun Naseh Khudori Ali Hasan, Hildan Adisqi Anis Ansyori Ardimansyah Ardimansyah Arif Wahyudi Busatto Marilia Choirunnisa, Rosyida Danang Bagus Baskoro Dio Wahyu Saputra Dita Kurnia Rachmasari Dyah Utami, Kartika Eka Mardiana Putri Faridatus Sholikhah Faridatus Sholikhah Fita Rusdian Ikawati Geraldo, Tostes Heri Wahyu Wibowo Herman Tolle Herry Prasetyo Wibowo Herry Wahyu Wibowo Irnanda Septian Ika Putri Javier Fajri Zachary Jimmy H Moedjahedy Khofida, Nisa M. Syauqi Haris Marilia, Busatto Mentari Putri Jati Mufid Musthofa Muhammad Hasbi Musthofa, Mufid Naufal Alif Vivaldi Nindynar Rikatsih Nindynar Rikatsih Ningrum, Afifah Vera Ferencia Fitria Novelia Mega Puspita Nugroho Teguh Yuono Prasetiyo, Bagus Dadang Putri, Irnanda Septian Ika Putrinugroho, Ivana Aikozora R. A. Rengganis Ularan Ricco Wahyu Pamungkas Riski Puji Lestari Risqy Siwi Pradini Risqy Siwi Pradini Risqy Siwi Pradini Saletia, Maria Claudia Samuel PD Anantadjaya Saputra, Dio Wahyu Susi Indriyani Tostes Geraldo vita mei fudnia watiningsih vita mei fudnia watiningsih Wahyu Teja Kusuma Wahyu Teja Kusuma Wahyu Tejakusuma, Wahyu Wayan Firdaus Mahmudy Wibowo, Heri Wahyu Yahya, Furqon Nurbaril Yuono, Nugroho Teguh