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All Journal Jurnal Teknologi Informasi dan Multimedia Jurnal SASAK : Desain Visual dan Komunikasi Jurnal Bumigora Information Technology (BITe) Journal of Information System and Artificial Intelligence
Muhammad Maulana
Universitas Bumigora, Mataram, Indonesia
Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Languange, Linguistic, Communication & Media

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Animasi 3 Dimensi Proses Mimikri Bunglon Muhammad Maulana sumantri; Nyoman Yoga Sumadewa
Jurnal SASAK : Desain Visual dan Komunikasi Vol 2 No 1 (2020): SASAK
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (524.4 KB) | DOI: 10.30812/sasak.v2i1.754

Abstract

The limitations possessed by the Elementary School no 26 Mataram in the learning process that they only use printed learning media and books without using tools and materials to see the color changes in the chameleon, so that many students and students canot see how is the process of mimicry in the chameleon. The method used in this development is a method of developing multimedia so that there are results (outputs) in the form of 3-dimensional animated videos to display chameleons who are doing mimicry when threatened and catching prey. It is hoped that with this video the process of mimicry occurring in the chameleon can help and facilitate students in understanding the lesson.
Optimization of Support Vector Machine Using SMOTE and Grid Search for Kidney Health Data Classification Muhammad Maulana; Zulkipli Zulkipli; Tanwir Tanwir; Dading Oktaviadi Resmiranta; Naufal Hanif; Raisul Azhar
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 2 (2026): May
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i2.993

Abstract

Kidney disease is a highly prevalent health problem that can seriously impact the quality of life of those affected. To improve diagnostic accuracy, machine learning methods are widely used to classify patient data. Class imbalance (imbalanced data) is one of the problems that often occurs in the classification process and can affect the performance of machine learning models, especially in detecting minority classes. This study aims to improve the performance of the Support Vector Machine (SVM) algorithm by applying the SMOTE (Synthetic Minority Over-sampling Tech-nique) and Grid Search methods in the data classification process. SMOTE is used to balance the class distribution by adding synthetic data to the minority class, while Grid Search is used to ob-tain optimal model parameters. The results show that the SVM model without handling data im-balance produces relatively low performance with an accuracy value of 51%, precision 17%, re-call 33%, and F1-score 23%. After applying the SMOTE method, the model performance increases significantly to 81% accuracy, 81% precision, 80% recall, and 81% F1-score. Furthermore, the ap-plication of Grid Search to the SVM + SMOTE model provides the best results with an accuracy of 84%, precision 82%, recall 81%, and F1-score 81% with an AUC value of 0,92. The findings of this study indicate that the combination of SMOTE and Grid Search is effective in improving the per-formance of the SVM algorithm in data classification. The novelty of this study demonstrates that data imbalance management and hyperparameter optimization play a crucial role in producing more accurate and optimal classification models.
Feature importance of using explanaible artificial intelligence (xai) and machine learning for diabetes disease classification Muhammad Maulana Ahmad; Neny Sulistianingsih; Khasnur Hidjah
Journal of Information System and Artificial Intelligence Vol. 6 No. 1 (2025): Vol. 6 No.1(2025): Journal of Information System and Artificial Intelligence Vo
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/jisai.v6i1.252

Abstract

Diabetes is one of the most significant global health problems in the modern era. This disease not only has a serious impact on the quality of life of sufferers, but also poses a great economic and social burden, both for individuals and the health service system as a whole. Therefore, early detection and effective treatment are very important in an effort to reduce the prevalence and negative impact of this disease. Therefore, the purpose of this study is to design a machine learning classification model that is able to identify feature importance with the help of the Explainable Artificial Intelligence (XAI) method in the case of diabetes. This model is expected to provide a clear interpretation of the most relevant features or symptoms, making it easier to detect whether a person has diabetes or not based on the symptoms that have been selected more optimally. The results of this study in the treatment or prediction of diabetes show that the results of the selection of LIME model features are higher than the accuracy of the SHAP model, where the highest is the LIME model which is processed using classification using the XGBoost algorithm with an accuracy of 98.47%, in addition to the LIME model using the Decisien Tree and Random Forest algorithms producing an accuracy of 91.97% and 91.49%, respectively. then the SHAP model using the XGBoost algorithm produced an accuracy of 0.9094%, the Decisien Tree algorithm produced an accuracy of 0.8059% and the Random Forest produced an accuracy of 88.46%, with the amount of data used as many as 70000 data, with 80% training data and 20% test data. The findings of this study are that the LIME feature selection combined with the XGBoost classification method has the best accuracy rate of 98.47% compared to the SHAP feature selection which is the same in combination with XGBoost with an accuracy of 90.94%. These findings also show that the selection of LIME features combined with the XGBoost algorithm is able to improve the interpretability of the model as well as maintain or even improve the accuracy of the predictions. This approach allows for the identification of the most relevant features more efficiently, thus supporting more informed decision-making in the data analysis process
Peningkatan Kinerja Klasifikasi Scabies Sapi MenggunakanEdited Nearest Neighbours (ENN) pada Model Random Forestdan XGBoost M. Khaerul Ihsan; Muhammad Maulana; Tanwir Tanwir; Abi Mas’ud; Naufal Hanif; Dading Oktaviadi Resmiranta
Jurnal Bumigora Information Technology (BITe) Vol. 7 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v7i2.6055

Abstract

Background: Scabies disease in cattle causes significant economic losses for farmers due to declines in the animals’physical condition and productivity.Objective: This study aims to evaluate the effectiveness of the Edited Nearest Neighbours (ENN) method in improvingclassification performance for scabies in cattle.Methods: This research employs machine learning methods, including Random Forest and XGBoost. A dataset of 600clinical symptom samples was converted to numerical data and cleaned of noise using the ENN technique.Result: Applying ENN significantly improved the accuracy of both the Random Forest and XGBoost models, increasing itfrom around 0.60 to 0.91. In addition, both models achieved a perfect recall of 1.00, indicating maximum capability todetect positive cases.Conclusion: This study concludes that noise reduction using ENN can produce a more accurate and reliable diagnosticsystem. This method is highly recommended to optimize the performance of classification algorithms on animal clinicaldata with high levels of inconsistency. 
Implementation Anti-Fraud Security on Smart Campus Attendance System Using Face Recognition Liveness Detection and Anti-Fake GPS Geofencing Azral Satrani; Tanwir Tanwir; Dading Oktaviadi Resminanta; Muhammad Maulana; Kartarina Augustin; Gede Yogi Pratama
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 3 (2026): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i3.1057

Abstract

In the digital era, the fingerprint-based attendance system used by University of Bumigora where considered ineffective and inefficient due to hygiene issues, physical vulnerability, the risk of for-gery, and slow processing speed during peak hours. In addition, as the digital attendance system develops, new challenges arise in the form of biometric data manipulation through presentation attacks (photos/videos/masks) and location manipulation using fake GPS applications. This dual vulnerability is a critical issue that can damage data integrity and the governance of educational institutions. To address these issues, this study proposes the design and development of a Smart Campus attendance system that integrates two anti-fraud security mechanisms, namely Face Recognition liveness  detection and Anti-Fake GPS Geofencing. Liveness Detection technology functions to detect real-life signs to distinguish real faces from fake representations, while An-ti-Fake GPS validates the signal consistency and physicality of users within a predetermined campus geographic radius (geofence). This system was developed using Agile methods to ensure adaptive and structured development. From a technological perspective, this system integrates the Vue JS framework in the user interface (UI) due to its robust performance, and the Laravel frame-work in the backend due to its advantages in Model-View-Controller (MVC) architecture, security, and high scalability. Through this approach, the study aims to evaluate the effectiveness of a lay-ered anti-fraud mechanism in preventing fraud and measure improvements in administrative effi-ciency compared to conventional fingerprint systems.
Co-Authors Abi Mas’ud Azhar, Raisul Azral Satrani Dading Oktaviadi Resmiranta Gede Yogi Pratama Kartarina Augustin Khasnur Hidjah M. Khaerul Ihsan Naufal Hanif Neny Sulistianingsih Nyoman Yoga Sumadewa Tanwir Tanwir Tanwir Tanwir Tanwir Tanwir Zulkipli Zulkipli