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All Journal SMARTek MEKTEK Foristek Jurnal MEKANIKAL JURNAL SISTEM INFORMASI BISNIS Jurnal Teknologi dan Manajemen Informatika Bulletin of Electrical Engineering and Informatics Telematika : Jurnal Informatika dan Teknologi Informasi MEKTRIK CESS (Journal of Computer Engineering, System and Science) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research SemanTIK : Teknik Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF APPLIED INFORMATICS AND COMPUTING ILKOM Jurnal Ilmiah Jurnal Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) CCIT (Creative Communication and Innovative Technology) Journal EDUMATIC: Jurnal Pendidikan Informatika ScientiCO : Computer Science and Informatics Journal Sains, Aplikasi, Komputasi dan Teknologi Informasi TIN: TERAPAN INFORMATIKA NUSANTARA Indonesia Berdampak: Jurnal Pengabdian Kepada Masyarakat
Deny Wiria Nugraha
Universitas Tadulako
Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Analysis of the Use of MTCNN and Landmark Technology to Improve the Accuracy of Facial Recognition on Official Documents Chandra, Ferri Rama; Ngemba, Hajra Rasmita; Hamid, Odai Amer; Lapatta, Nouval Trezandy; Hendra, Syaiful; Nugraha, Deny Wiria; Syahrullah, Syahrullah
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8814

Abstract

A face recognition system consists of two stages: face detection and face recognition. Detection of features such as eyes and mouth is important in facial image processing, especially for official documents such as identity cards. To ensure identification accuracy, this research applies facial landmark extraction technology and MTCNN (Multi-Task Cascaded Convolutional Neural Network). The purpose of this research is to evaluate the accuracy of MTCNN in detecting facial features at the Department of Population and Civil Registration (dukcapil) Palu City, using facial landmarks and waterfall methods as an application development methodology. The evaluation results show that MTCNN has high face recognition accuracy and good positioning ability regardless of what GPU in use as long have right CPU and System Operation. In comparison, the Viola-Jones algorithm is effective for high-speed applications, while SSD offers balanced performance with GPU device requirements for optimal performance. While MTCNN proved to be effective, challenges still exist, such as false positives and false negatives, especially in poor lighting conditions and extreme poses. Image and camera quality, including resolution and facial expression, also affects detection accuracy. These findings suggest that the application of MTCNN can improve face recognition accuracy for official documents, although it requires addressing existing challenges. With this technology, it is expected that errors in facial recognition can be minimized, resulting in more reliable data that meets the standards for issuing identity documents. This research contributes to the development of a more accurate and efficient face recognition system for personal identification applications.
Implementation of ResNet-50-Based Convolutional Neural Network For Mobile Skin Cancer Classification Asriani, Asriani; Lapatta, Nouval Trezandy; Nugraha, Deny Wiria; Amriana, Amriana; Wirdayanti, Wirdayanti
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9696

Abstract

The skin is one of the most important parts of the human body, serving vital functions such as protecting internal organs from injury, shielding against direct bacterial exposure, regulating body temperature, and more. However, the skin is also susceptible to diseases, one of which is skin cancer. Skin cancer can be extremely dangerous if not treated promptly, as it can lead to death. Therefore, early detection is crucial. This study proposes a technology-based solution by classifying skin cancer using a convolutional neural network (CNN) with a ResNet50 architecture implemented into a mobile application via a REST API using Flask. The HAM10000 dataset, consisting of 10,015 skin lesion images across seven classes, was used for model training. Various testing scenarios were conducted to determine the optimal parameter combination. The best results were achieved with an accuracy of 83.84%, precision and recall of 83%, and an F1-score of 83%, using a training data configuration of 70%, dropout of 0.4, and a batch size of 64. The model implemented in this Android application can perform early detection of skin cancer quickly, practically, and easily accessible to the general public, though healthcare professionals must still supervise it. However, although this model can assist users in making early predictions, the prediction results from this model are only a tool for early detection and do not replace clinical diagnosis by professional medical personnel.2) Figure 8 shows the display for taking pictures through the gallery or camera. Users can choose the image they want to upload from the gallery or the camera to be analysed and predicted by the model.
Performance Comparison of Multilayer Perceptron (MLP) and Random Forest for Early Detection of Cardiovascular Disease Setiawan, Dita Widayanti; Lapatta, Nouval Trezandy; Amriana, Amriana; Nugraha, Deny Wiria; Lamasitudju, Chairunnisa Ar.
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.v9i6.10826

Abstract

Cardiovascular disease is a disorder of the heart and blood vessels that can lead to heart attacks, strokes, and heart failure, so early detection is essential. This study compares Multilayer Perceptron (MLP) and Random Forest for risk classification in a Kaggle dataset containing 70,000 samples with balanced targets. Pre-processing included age conversion, outlier cleaning, standardization, and feature selection based on feature importance. Both models were optimized using RandomizedSearchCV and evaluated using accuracy, precision, recall, F1-score, AUC-ROC, confusion matrix, and k-fold cross-validation. The results show that the accuracy of MLP is 73.90% and Random Forest is 74.23% with an AUC of 0.80 for both. Random Forest is more stable across all folds and performs better on the negative class, while MLP is slightly more sensitive to the positive class. Independent t-test and Mann-Whitney U tests show p>0.05, indicating that the difference in performance is not significant. The most influential features were diastolic blood pressure, age, cholesterol, and systolic blood pressure. The non-clinical Streamlit prototype demonstrated the model's potential for education and initial decision support.
Implementation of Collaborative Filtering in the Salted Fish Recommendation Process Rizky, Moh Taufiq; Rinianty, Rinianty; Nugraha, Deny Wiria; Amriana, Amriana; Lapatta, Nouval Trezandy
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.v9i6.11576

Abstract

The development of e-commerce in the current era has been so rapid that buying and selling transactions are carried out online through various media, including websites and applications. With so many products available in the application, users often feel confused when choosing the product they want to buy, so it takes a long time to choose a product to avoid regret after purchasing it. In this study, a web-based recommendation system was created for the process of recommending salted fish with the aim of making it easier for customers to choose the type of salted fish. The Collaborative Filtering method was used, employing Pearson Correlation as a tool to calculate the similarity value between users, then using Weighted Sum to calculate the prediction value. Collaborative Filtering often experiences the cold start problem, where the system has difficulty providing recommendations to users who do not yet have a transaction history. Therefore, the author proposes a popularity-based strategy as a measure to overcome this problem. Based on testing, the author obtained results of MAE = 0.63 and RMSE = 0.81 based on train-test split results with a data distribution of 80:20, 80% of the dataset for training and 20% of the dataset for testing with an accuracy of 70-80%, indicating that this system works well. This system has been tested using the Blackbox method.
Co-Authors A.Y. Erwin Dodu A.Y. Erwin Dodu A.Y. Erwin Dodu Abdul Mahatir Najar Agustinus Kali Ahmad Ilham, Amil Albrecht Yordanus Erwin Dodu Amil Ahmad Ilham Aminuyati Amriana Amriana Amriana Amriana Andani Achmad Andi Hendra Andipa Batara Putra Angraeni, Dwi Shinta Ardiyansyah, Rizka Arief Pratomo Arief, Ardiaty Asminar Asminar Asri Arif Asriani Asriani, Asriani Asrul Sani Ayu Hernita Ayyub, Mohammad Azhar Baso Mukhlis Candriasih, Ni Kadek Chairunnisa Ar. Lamasitudju Chandra, Ferri Rama Dessy Santi Dharmakirti, Dharmakirti Djohari, Riyandi Dwitama Dodu, A. Y. Erwin Dodu, A.Y Erwin Dwi Shinta Angreni Dwi Wijaya, Kadek Agus Dwimanhendra, Muhammad Rifaldi Dwiwijaya, Kadek Agus Erwin Dodu, Albrecht Yordanus Fajriyah, Nurul Fanny Astria, Fanny Hajra Rasmita Ngemba Hamid, Odai Amer Hasanuddin Hasanuddin Imat Rahmat Hidayat Isminarti, Isminarti Jeprianto Rurungan, Jeprianto K. Julianto, K. Kalatiku, Protus P Krisna Rendi Awalludin Luh Putu Ratna Sundari Maharani, Wulan Mery Subito Mohamad Ilyas Abas Mohamad Irfan, Mohamad Nouval Trezandy Lapatta Novilia Chandra Paloloang, Muhammad Fairus B. Priska, Salsa Dilah Protus Pieter Kalatiku Putra, Subkhan Dinda Rahma Tanti Rahmah Laila Raivandy, I Made Randhy Rieska Setiawaty Rinianty, Rinianty Rizka Ardiansyah Rizky, Moh Taufiq Ryfial Azhar, Ryfial Septiana, Stevi Septiano Anggun Pratama Setiawan, Dita Widayanti Sri Khaerawati Nur Stevi Septiana Syahrullah Syahrullah Syaiful Hendra Thia Wydia Astuti Wawagalang, A. Nolly Sandra Wirdayanti Wisanti, Widya Yuli Asmi Rahman Yuri Yudhaswana Joefrie Yuri Yudhaswana Joefrie Yusuf Anshori Zulkifli Zulkifli