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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Transmisi: Jurnal Ilmiah Teknik Elektro Jurnal Masyarakat Informatika Jurnal Simetris JAIS (Journal of Applied Intelligent System) Journal of Information System Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING Dinamisia: Jurnal Pengabdian Kepada Masyarakat ILKOM Jurnal Ilmiah CARADDE: Jurnal Pengabdian Kepada Masyarakat IJISTECH (International Journal Of Information System & Technology) Building of Informatics, Technology and Science Infotekmesin International Journal of Engineering, Technology and Natural Sciences (IJETS) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Ilmiah Intech : Information Technology Journal of UMUS Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Teknik Informatika (JUTIF) IJISTECH JAMU : Jurnal Abdi Masyarakat UMUS Moneter : Jurnal Keuangan dan Perbankan Journal of Computing Theories and Applications Jurnal Informatika: Jurnal Pengembangan IT Advance Sustainable Science, Engineering and Technology (ASSET)
Alzami, Farrikh
Faculty Of Computer Science, Universitas Dian Nuswantoro
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Other

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Journal : journal of applied informatics and computing

 1 
Aspect-Based Sentiment Analysis for Enhanced Understanding of 'Kemenkeu' Tweets Sejati, Priska Trisna; Alzami, Farrikh; Marjuni, Aris; Indrayani, Heni; Puspitarini, Ika Dewi
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.v8i2.8558

Abstract

The perceptions and expressions shared by the public on social media play a crucial role in shaping the reputation of government institutions, such as the Ministry of Finance MOF (Kemenkeu) in Indonesia which also has faced increased scrutiny, particularly on Twitter. This study analyzes public sentiment towards the Indonesian Ministry of Finance (MoF) through Aspect-Based Sentiment Analysis (ABSA) on Twitter data. Using a dataset of 10,099 tweets from January to July 2024, this study combines IndoBERT for sentiment classification and Latent Dirichlet Allocation (LDA) for topic modeling. Here, LDA was tested across four scenarios that considered various combinations of stopwords removal and stemming techniques, resulting in coherence scores of 0.314256, 0.369636, 0.350285, and 0.541752. The most optimal results were achieved in the scenario of stopwords removal without stemming (with 0.314256 coherence score). The main results show: 1) Identification of four main topics related to MoF: Economy, Budget, Employees, and Tax; 2) The dominance of negative sentiment (6,837 tweets) compared to positive sentiment (198 tweets) across all topics; 3) The effectiveness of IndoBERT in handling the complexity of the Indonesian language, especially in interpreting context and language nuances; 4) The importance of proper preprocessing, with a scenario of removing stopwords without stemming resulting in the most relevant topics. This study provides valuable insights for MoF to understand public perception and identify areas that require special attention in public communication and policy.
Addressing Extreme Class Imbalance in Multilingual Complaint Classification Using XLM-RoBERTa Ariyanto, Muhammad; Alzami, Farrikh; Sani, Ramadhan Rakhmat; Gamayanto, Indra; Naufal, Muhammad; Winarno, Sri; Iswahyudi
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.v10i1.11606

Abstract

Government complaint management systems often suffer from extreme class imbalance, where a few public service categories accumulate most reports while many others remain under-represented. This research examines whether simple class weighting can improve fairness in multilingual transformer models for automatic routing of Indonesian citizen complaints on the LaporGub Central Java e-governance platform. The dataset comprises 53,877 Indonesian-language complaints spanning 18 service categories with an imbalance ratio of about 227:1 between the largest and smallest classes. After cleaning and deduplication, we stratify the data into training, validation, and test sets. We compare three approaches: (i) a linear support vector machine (SVM) with term frequency inverse document frequency (TF-IDF) unigram and bigram and class-balanced weights, (ii) a cross-lingual RoBERTa (XLM-RoBERTa-base) model without class weighting, and (iii) an XLM-RoBERTa-base model with a class-weighted cross-entropy loss. Fairness is operationalised as equal importance for categories and quantified primarily using the macro-averaged F1-score (Macro-F1), complemented by per-class F1, weighted F1, and accuracy. The unweighted XLM-RoBERTa model outperforms the SVM baseline in Macro-F1 (0.610 vs 0.561). The class-weighted variant attains similar Macro-F1 (0.608) while redistributing performance towards minority categories. Analysis shows that class weighting is most beneficial for categories with a few hundred to several thousand samples, whereas extremely rare categories with fewer than 200 complaints remain difficult for all models and require additional data-centric interventions. These findings demonstrate that multilingual transformer architectures combined with simple class weighting can provide a more balanced backbone for automated complaint routing in Indonesian e-government, particularly for low- and medium-frequency service categories.
Evaluation of Histogram-Based Image Enhancement Methods for Facial Images in Drowsy Driver Using No-Reference Metrics Naufal, Muhammad; Al Azies, Harun; Alzami, Farrikh; Brilianto, Rivaldo Mersis
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.v10i1.12055

Abstract

Low-light facial images suffer significant quality degradation, leading to performance degradation in surveillance and face recognition systems, where conventional enhancement methods often produce over-enhancement or unnatural noise artifacts. This study compares three histogram equalization methods, namely HE, AHE, and CLAHE, for low-light facial image enhancement, with evaluation using no-reference quality assessment metrics, including NIQE, LOE, and Entropy, as well as visual analysis and histogram distribution. The results showed that AHE produced the lowest NIQE (4.96 ± 1.38) and the highest entropy (7.86 ± 0.11) but had significant noise artifacts, HE produced an overly even distribution with NIQE of 6.34 ± 1.41, while CLAHE showed the most balanced performance with the lowest LOE (0.07 ± 0.02) and the best visual quality when using the optimal clip limit in the range of 1.2-2.0, providing an optimal trade-off between contrast enhancement, naturalness preservation, and artifact minimization with computational efficiency below 1 ms.
Comparing Decision Tree and Optimized LightGBM for Attrition Prediction Dhea Maharani; Farrikh Alzami; MY. Teguh Sulistyono; Aris Nurhindarto; Dewi Agustini Santoso; Muslih Muslih; Henry Bastian
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.v10i3.12678

Abstract

Employee turnover poses a considerable challenge for organizations, impacting productivity and raising recruitment expenses. This research seeks to evaluate the effectiveness of Decision Tree and Light Gradient Boosting Machine (LightGBM) models in forecasting employee attrition. The study utilizes a quantitative experimental design, leveraging a secondary dataset sourced from Mendeley. Before model development, data preprocessing was performed, and model evaluation was carried out using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Each algorithm was assessed under three different configurations baseline, regularization, and hyperparameter tuning through GridSearchCV. The experimental findings indicate that the Decision Tree model is prone to overfitting and has limited capabilities in detecting attrition classes, even though optimization raises the ROC-AUC score to 0.80. In comparison, LightGBM demonstrates more reliable and consistent performance. The Tuned LightGBM model achieved the highest performance on the test dataset, with an Accuracy of 0.81, a Precision of 0.82, a Recall of 0.71, F1-Score of 0.76, and an ROC-AUC of 0.85. An analysis of feature importance reveals that job satisfaction, work-life balance, emotional commitment, work experience, and allowances are the key factors influencing attrition prediction. These results indicate that LightGBM not only performs exceptionally well, but it is also able to offer insights into the critical factors that are important for data-driven retention strategies.
An Integrated Topic–Sentiment Analysis of User Reviews in Vidio Application Using BERTopic and IndoRoBERTa Nalendra Whisnu Pinilih; Ika Novita Dewi; Farrikh Alzami
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.v10i3.12952

Abstract

User reviews on digital platforms provide valuable insights into user experience; however, the large volume and unstructured nature of such data make systematic analysis challenging. In the case of the Vidio application, user feedback frequently reflects concerns related to advertisements, subscription systems, and technical performance. Nevertheless, existing researches often apply sentiment analysis and topic modeling separately, limiting the ability to understand how specific discussion themes are associated with user sentiment. To address this limitation, this research proposes an integrated topic–sentiment analysis approach for analyzing user reviews of the Vidio application collected from the Google Play Store. After filtering and quality control, 8,854 reviews were retained for further analysis using BERTopic for topic modeling and IndoRoBERTa for sentiment classification. The topic modeling process was optimized through parameter tuning, resulting in an improvement of the coherence score from 0.4076 to 0.6878, indicating better semantic consistency among the identified topics. Meanwhile, the sentiment classification model achieved an accuracy of 72%, although its performance was affected by class imbalance, particularly in identifying neutral sentiment. The analysis identified seven primary topics, where advertising-related issues emerged as the dominant topic and were strongly associated with negative sentiment, followed by concerns regarding subscription mechanisms and login accessibility. In contrast, content-related topics, particularly sports broadcasts, were consistently associated with positive sentiment. Furthermore, statistical evaluation confirmed a significant relationship between topic categories and sentiment distribution. Overall, the findings demonstrate that integrating topic modeling and sentiment analysis provides a more comprehensive understanding of user opinions and can support improvements in application quality and user experience.
Improving YOLO12 Performance Using Efficient Channel Attention For Ship Object Detection Richard Christoper Subianto; Muhammad Naufal; Farrikh Alzami
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.v10i3.13067

Abstract

Ship object detection in aerial imagery remains a critical challenge due to complex marine backgrounds, varying object scales, and occlusion, which often lead to unstable model performance. This research proposes integrating the Efficient Channel Attention (ECA) module into the YOLO12-L architecture to enhance feature selectivity and prediction robustness. The model was trained for 500 epochs on the Ship Detection from Aerial Images dataset, comprising 621 images and 1,951 annotated ship instances, and performance was evaluated across five distinct random seeds to ensure statistical reliability. Quantitative results demonstrate that the proposed YOLO12-L + ECA model achieved a median Average Precision (mAP@50) of 71.32% and a Precision of 92.5%, outperforming the baseline YOLO12-L model. To evaluate statistical validity, a Paired Bootstrap Median Test with 100 resamples confirmed a statistically significant improvement in median performance (Δ = +1.01%, p = 0.02). Furthermore, the standard deviation of mAP@50 decreased from 1.1% in the baseline to 0.3% in the ECA model, representing a 72.7% reduction in performance variance. Computational efficiency analysis revealed that the ECA module introduced negligible overhead, adding merely 5 parameters (totaling 26,389,880) and keeping FLOPs constant at 89.4, while maintaining a high inference speed of 10.7 FPS (a marginal 2.5% reduction). These findings confirm that ECA effectively suppresses background noise, stabilizes detection outputs, and provides statistically significant improvements without compromising architectural efficiency. The proposed architecture offers a lightweight and reliable solution for automated maritime monitoring systems, particularly in challenging visual environments.
Performance Analysis of YOLO26 in Pothole Detection on an Indonesian Road Dataset Mohammad Alwi Nanda Saputra; Farrikh Alzami; Christy Atika Sari
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13585

Abstract

Road damage is one of the infrastructure problems that can compromise safety, comfort, and the smooth flow of traffic. The road inspection process, which is still carried out manually, requires a relatively large amount of time, labor, and cost, making a more efficient method necessary. Advances in computer vision and deep learning technologies enable the automatic detection of road damage through an object detection approach. This study aims to analyze the performance of the YOLO26l model as a baseline model in detecting four categories of road damage such as potholes, alligator cracking, lateral cracking, and longitudinal cracking using the Road Damage Indonesia Dataset. The dataset was divided into 70% training data, 15% validation data, and 15% testing data. The training process was conducted using the pre-trained weights from yolo26l.pt via the Ultralytics framework without any architectural modifications or the application of image enhancement methods. Performance evaluation was conducted using the Precision, Recall, mAP@0.50 (mAP@0.50), and mAP@0.50:0.95 (mAP@0.50:0.95) metrics. The results of the study show that the YOLO26l model achieved a Precision of 0.7028, a Recall of 0.6492, a mAP@0.50 of 0.6890, and a mAP@0.50:0.95 of 0.3391. Analysis using a confusion matrix, precision–recall curve, and visualization of the detection results showed that the model was able to identify all four categories of road damage well, although there were still some objects that went undetected under poor lighting conditions, due to small object sizes, or complex road surface textures. Based on these results, it can be concluded that YOLO26l performs well as a baseline model for road damage detection on the Indonesian road dataset
Co-Authors Abdul Syukur Abu Salam Aditya Rahman Adriani, Mira Riezky Ahmad Akrom Ahmad Khotibul Umam, Ahmad Khotibul Ahmad Zainul Fanani Ahmad Zaniul Fanani Akrom, Ahmad Al-Azies, Harun Alpiana, Vika Alvin Steven Arifin, Zaenal Aris Marjuni Aris Nurhindarto Aris Nurhindarto ARIYANTO, MUHAMMAD Ashari, Ayu Ashraf Alomoush Asih Rohmani, Asih Atha Rohmatullah, Fawwaz Azzami, Salman Yuris Adila Brilianto, Rivaldo Mersis Budi, Setyo Candra Irawan Candra Irawan Caturkusuma, Resha Meiranadi Chaerul Umam Chaerul Umam Chaerul Umam Chaerul Umam Choirinnisa, Dina Christy Atika Sari Dewi Agustini Santoso Dewi Agustini Santoso Dewi Agustini Santoso Dewi Pergiwati Dhea Maharani Diana Aqmala Dwi Puji Prabowo Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Enrico Irawan Erika Devi Udayanti Esa Wahyu Andriansyah Fahmi Amiq Farah Syadza Mufidah Fikri Diva Sambasri Fikri Firdaus Tananto Fikri Firdaus Tananto Filmada Ocky Saputra Filmada Ocky Saputra Firman Wahyudi Firman Wahyudi Firman Wahyudi, Firman Fitri Susanti Ghina Anggun Go, Agnestia Agustine Djoenaidi Guruh Fajar Shidik Hadi, Heru Pramono Hartono, Andhika Rhaifahrizal Harun Al Azies Harun Al Azies Hasan Aminda Syafrudin Heni Indrayani Henry Bastian Herfiani, Kheisya Talitha Ifan Rizqa Ika Novita Dewi Ika Novita Dewi Indra Gamayanto Indra Gamayanto Indrayani, Heni Iswahyudi ISWAHYUDI ISWAHYUDI Jumanto Karin, Tan Regina Khariroh, Shofiyatul Khoirunnisa, Emila Krisnawati, Dyah Ika Kukuh Biyantama Kukuh Biyantama Kurniawan Aji Saputra Kurniawan, Defri Kusmiyati Kusmiyati Kusmiyati Kusmiyati Kusmiyati*, Kusmiyati Kusumawati, Yupie L. Budi Handoko Lalang Erawan Lesmarna, Salsabila Putri Mahmud Mahmud Mahmud Mahmud Marjuni, Aris Maulana, Isa Iant Megantara, Rama Aria Mila Sartika Mila Sartika, Mila Mira Nabila Mira Nabila Moch Arief Soeleman Moh Hadi Subowo Moh Yusuf, Moh Moh. Yusuf Mohammad Alwi Nanda Saputra Mohammad Arif Muhammad Naufal Muhammad Naufal Muhammad Noufal Baihaqi Muhammad Ridho Abdillah Muhammad Riza Noor Saputra Muhammad Rizal Nurcahyo Muslich Muslich, Muslich Muslih Muslih Muslih Muslih MY. Teguh Sulistyono MY. Teguh Sulistyono Nabila, Mira Nalendra Whisnu Pinilih Novita Kurnia Ningrum Nuanza Purinsyira Nugraini, Siti Hadiati Nurhindarto, Aris Nurhindarto, Aris Nurwijayanti Pergiwati, Dewi Pratidina Kusuma Dewi Pulung Nurtantio Andono Pulung Nurtantyo Andono Puri Sulistiyawati Puri Sulistiyawati Puri Sulistiyawati Purwanto Purwanto Purwanto Purwanto Puspitarini, Ika Dewi R. Daniel Hartanto Rama Aria Megantara Rama Aria Megantara Ramadhan Rakhmat Sani Riadi, Muhammad Fatah Abiyyu Ricardus Anggi Pramunendar Richard Christoper Subianto Rifqi Mulya Kiswanto Rini Anggraeni Risky Yuniar Rahmadieni Ritzkal, Ritzkal Rivaldo Mersis Brilianto Rofiani, Rofiani Rohman, M. Hilma Minanur Ruri Suko Basuki Sambasri, Fikri Diva Saputra, Filmada Ocky Saputra, Resha Mahardhika Saputri, Pungky Nabella Sasono Wibowo Sejati, Priska Trisna Sendi Novianto Sendi Novianto Sigit Muryanto Sigit Muryanto, Sigit Sinaga, Daurat Soeleman, Arief Soeleman, M Arief Sofiani, Hilda Ayu Sri Handayani Sri Winarno Sri Winarno Steven, Alvin Subowo, Moh Hadi Sukamto, Titien Suhartini Sulistiyono, MY Teguh Sulistyono, Teguh Sulistyowati, Tinuk Sutriawan Tamamy, Aries Jehan Thifaal, Nisrina Salwa Viry Puspaning Ramadhan Wellia Shinta Sari Wibowo, Isro' Rizky Widodo Widyatmoko Karis Winarsih, Nurul Anisa Sri Yuniar Rahmadieni, Risky Yunita Ayu Pratiwi Yusianto Rindra Yuventius Tyas Catur Pramudi Zaenal Arifin Zahro, Azzula Cerliana Zulfiningrumi, Rahmawati