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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Sinergi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Literasi: Jurnal Pengabdian Masyarakat dan Inovasi Jurnal Pengabdian Masyarakat Bangsa Kohesi: Jurnal Sains dan Teknologi
Anasanti, Mila Desi
Program Studi S2 Ilmu Komputer, Universitas Nusa Mandiri, Jakarta; Department Of Information Studies, University College London, London; Bart And London Genome Center, Queen Mary University Of London, London
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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DESIGNING TRANSLATION TOOL: BETWEEN SIGN LANGUAGE TO SPOKEN TEXT ON KINECT TIME SERIES DATA USING DYNAMIC TIME WARPING Zico Pratama Putera; Mila Desi Anasanti; Bagus Priambodo
SINERGI Vol 22, No 2 (2018)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (815.326 KB) | DOI: 10.22441/sinergi.2018.2.004

Abstract

The gesture is one of the most natural and expressive methods for the hearing impaired. Most researchers, however, focus on either static gestures, postures or a small group of dynamic gestures due to the complexity of dynamic gestures. We propose the Kinect Translation Tool to recognize the user's gesture. As a result, the Kinect Translation Tool can be used for bilateral communication with the deaf community. Since real-time detection of a large number of dynamic gestures is taken into account, some efficient algorithms and models are required. The dynamic time warping algorithm is used here to detect and translate the gesture. Kinect Sign Language should translate sign language into written and spoken words. Conversely, people can reply directly with their spoken word, which is converted into literal text together with the animated 3D sign language gestures. The user study, which included several prototypes of the user interface, was carried out with the observation of ten participants who had to gesture and spell the phrases in American Sign Language (ASL). The speech recognition tests for simple phrases have therefore shown good results. The system also recognized the participant's gesture very well during the test. The study suggested that a natural user interface with Microsoft Kinect could be interpreted as a sign language translator for the hearing impaired.
Exploring feature selection techniques on Classification Algorithms for Predicting Type 2 Diabetes at Early Stage Mila Desi Anasanti; Khairunisa Hilyati; Annisa Novtariany
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i5.4419

Abstract

Predicting early Type 2 diabetes (T2D) is critical for improved care and better T2D outcomes. An accurate and efficient T2D prediction relies on unbiased relevant features. In this study, we searched for important features to predict T2D by integrating ML-based models for feature selection and classification from 520 individuals newly diagnosed with diabetes or who will develop it. We used standard machine learning classifications, such as logistic regression (LR), Gaussian naive Bayes (NB), decision tree (DT), random forest (RF), support vector machine (SVM) with linear basis function, and k-nearest neighbors (KNN). We set out to systematically explore the viability of main feature selection representing each different technique, such as a statistical filter method (F-score), an entropy-based filter method (mutual information), an ensemble-based filter method (random forest importance), and a stochastic optimization (simultaneous perturbation feature selection and ranking (SpFSR)). We used a stratified 10-fold cross-validation technique and assessed the performance of discrimination, calibration, and clinical utility. We attained the highest accuracy of 98% using RF with the full set of features (16 features), then used RF as a classifier wrapper to select the important features. We observed a combination of SpFSR and RF as the best model with a P-value above 0.05 (P-value = 0.26), statistically attaining the same accuracy as the full features. The study's findings support the efficiency and usefulness of the suggested method for choosing the most important features of diabetic data: polyuria, gender, polydipsia, age, itching, sudden weight loss, delayed healing, and alopecia.
Pelatihan Optimalisasi Website Untuk Meningkatkan Kinerja Profesional Melalui Perencanaan (Planning) Website Eni Heni Hermaliani; Rifki Sadikin; Muhammad Haris; Mila Desi Anasanti; Waesul Bismi; Musriatun Napiah
Literasi: Jurnal Pengabdian Masyarakat dan Inovasi Vol 2 No 2 (2022)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (238.852 KB) | DOI: 10.58466/literasi.v2i2.580

Abstract

Al Birru Indonesia Foundation is a partner in a community service program located in the Jakasampurn area, Bekasi. This foundation is engaged in a humanitarian social institution initiated by a group of youth with their ideas and positive thoughts to build the character of an independent society, especially young teenagers. As a non-profit organization, it is very necessary to have a website that can be used as a promotional medium to introduce its programs, company profile, and as a form of accountability for the funds that have been given by donors. The current problem is that the website that is already owned is not optimal in its management, for example, related to the available menus that are still lacking and not updated. Thus, through this community service activity, training on website optimization was carried out which discussed website planning. The results of this activity were the audience of the trainees gaining knowledge and knowledge about website planning including creating page layouts, determining website facilities, display designs, mock-ups, to content structure. The results of the questionnaire showed that 60 to 70% were satisfied with the modules given and the delivery of tutors, 70% of participants stated that they were very useful for PkM activities, and 50% would participate again in the implementation of the next PkM activity.
Improving Cardiovascular Disease Prediction by Integrating Imputation, Imbalance Resampling, and Feature Selection Techniques into Machine Learning Model Fadlan Hamid Alfebi; Mila Desi Anasanti
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 1 (2023): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.80214

Abstract

Cardiovascular disease (CVD) is the leading cause of death worldwide. Primary prevention is by early prediction of the disease onset. Using laboratory data from the National Health and Nutrition Examination Survey (NHANES) in 2017-2020 timeframe (N= 7.974), we tested the ability of machine learning (ML) algorithms to classify individuals at risk. The ML models were evaluated based on their classification performances after comparing four imputation, three imbalance resampling, and three feature selection techniques.Due to its popularity, we utilized decision tree (DT) as the baseline. Integration of multiple imputation by chained equation (MICE) and synthetic minority oversampling with Tomek link down-sampling (SMOTETomek) into the model improved the area under the curve-receiver operating characteristics (AUC-ROC) from 57% to 83%. Applying simultaneous perturbation feature selection and ranking (spFSR) reduced the feature predictors from 144 to 30 features and the computational time by 22%. The best techniques were applied to six ML models, resulting in Xtreme gradient boosting (XGBoost) achieving the highest accuracy of 93% and AUC-ROC of 89%.The accuracy of our ML model in predicting CVD outperforms those from previous studies. We also highlight the important causes of CVD, which might be investigated further for potential effects on electronic health records. 
Autism Spectrum Disorder (ASD) Identification Using Feature-Based Machine Learning Classification Model Anton Novianto; Mila Desi Anasanti
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 3 (2023): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.83585

Abstract

Autism Spectrum Disorder (ASD) is a developmental disorder that impairs the development of behaviors, communication, and learning abilities. Early detection of ASD helps patients to get beter training to communicate and interact with others. In this study, we identified ASD and non-ASD individuals using machine learning (ML) approaches. We used Gaussian naive Bayes (NB), k-nearest neighbors (KNN), random forest (RF), logistic regression (LR), Gaussian naive Bayes (NB), support vector machine (SVM) with linear basis function and decision tree (DT). We preprocessed the data using the imputation methods, namely linear regression, Mice forest, and Missforest. We selected the important features using the Simultaneous perturbation feature selection and ranking (SpFSR) technique from all 21 ASD features of three datasets combined (N=1,100 individuals) from University California Irvine (UCI) repository. We evaluated the performance of the method's discrimination, calibration, and clinical utility using a stratified 10-fold cross-validation method. We achieved the highest accuracy possible by using SVM with selected the most important 10 features. We observed the integration of imputation using linear regression, SpFSR and SVM as the most effective models, with an accuracy rate of 100% outperformed the previous studies in ASD prediciton
META ANALYSIS OF THE EFFECTIVENESS OF CURCUMIN IN COVID 19 PATIENTS Eko Priyono; Sukur Ma’mun; Mila Desi Anasanti
Kohesi: Jurnal Sains dan Teknologi Vol. 1 No. 6 (2023): Kohesi: Jurnal Sains dan Teknologi
Publisher : CV SWA Anugerah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.3785/kjst.v1i6.452

Abstract

Background: As SARS-CoV-2 virus (COVID-19) emerged and spread widely, chaos began to wreak havoc globally. Because the health emergency area persuaded the WHO to officially declare it a global pandemic, the COVID-19 epidemic had severe impacts. However, research on drugs is still minimal and as a result herbal medicines are emerging as the closest and fastest alternatives around.Objective: This study aims to evaluate the acceleration of recovery in patients with the COVID-19 pandemic with curcumin. Method: To conduct a literature search, various sources such as PubMed, Google Scholar, Science Direct, Cross Ref and Web of Science and utilize the PRISMA methodologyResults: Seventy RCTs (Randomized Controlled Trials) were included in the quantitative analysis study during the COVID-19 pandemic. Although there was significant variation between studies, they all had a low risk of bias. Analysis of forest plots before and after the intervention showed a non-significant effect (p-Val=0.0672) with a Standardized Mean Difference (SMD) of 0.57 (95% CI: 0.46; 0.67). This shows that curcumin did not significantly reduce the cure score among patients exposed to the COVID-19 pandemic. And heterogeneity is not significant (p-Val = 0.0005).Conclusions: This meta-analysis includes outcome-based evidence regarding curcumin treating patients among the COVID-19 population, as indicated by the lower ranking. Given the less promising results and potential to significantly enhance healing, curcumin therefore requires a broader perspective.
Assessing Performance Across Various Machine Learning Algorithms with Integrated Feature Selection for Fetal Heart Classification Amanda, Laura Rizka; Anasanti, Mila Desi
International Journal of Artificial Intelligence Research Vol 8, No 1 (2024): June 2024
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1110

Abstract

The global concern over declining perinatal death rates, particularly in low- and middle-income nations, underscores the importance of adopting Cardiotocography (CTG) as a vital fetal monitoring method. Recent strides in machine learning (ML) present promising opportunities to enhance the accuracy of assessing fetal health, providing a viable alternative to traditional approaches. This study aims to evaluate various ML methodologies and feature selection techniques for predicting fetal health using CTG data. The primary objective is to improve ML algorithms' accuracy, precision, recall, and F1 score while selecting the most critical features. The dataset includes 2,126 expectant mothers in the third trimester, with 35 variables related to fetal heart rate (FHR) and uterine contractions (UC). Preprocessing involves feature scaling, data balancing, and outlier elimination. Additionally, a 10-fold stratified cross-validation approach is employed to ensure robust evaluation and generalizability of the model's performance. Six ML algorithms—Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), Logistic Regression (LR), and K-Nearest Neighbors (KNN)—are employed, optimized through grid search cross-validation. The RF algorithm outperforms with an impressive 99% accuracy, closely followed by DT at 98.7%. Optimizing 15 features from the original 35 using Simultaneous Perturbation Feature Selection and Ranking (spFSR) yields a remarkable accuracy of 99%, mirroring the full feature set. This underscores the vital role of selected features in improving predictive power and overall model performance. The study emphasizes the efficacy of tree-based classification algorithms, especially RF, in predicting fetal health and highlights the impact of preprocessing on model performance. These findings suggest avenues for future research, including exploring alternative feature engineering methods and assessing algorithm performance in diverse scenarios.
Pelatihan Artificial Intelligence Dalam Membuat Power Point Pada Remaja Masjid Baitul Halim Hasanah, Riyan Latifahul; Kuntoro, Antonius Yadi; Saelan, M. Rangga Ramadhan; Anasanti, Mila Desi
Jurnal Pengabdian Masyarakat Bangsa Vol. 2 No. 8 (2024): Oktober
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v2i8.1475

Abstract

Pengembangan ilmu pengetahuan dan teknologi (IPTEK) memberikan peran dalam meningkatkan kesejahteraan dan perekonomian masyarakat. Salah satu bidang yang dapat merasakan kehadiran teknologi yaitu bidang pendidikan organisasi kemasyarakatan, termasuk pada organisasi Remaja Masjid Baitul Halim Jakarta Selatan. Untuk mengelola data organisasi diperlukan kemampuan administrasi yang baik, sehingga data bisa tertata dengan baik dan keberlanjutan bagi kegiatan organisasi. Dalam rangka menunaikan salah satu Tri Dharma Perguruan Tinggi, maka Universitas Nusa Mandiri melaksanakan Pengabdian Masyarakat berupa Pelatihan Artificial Intelligence Dalam Membuat Power Point untuk memudahkan proses pemaparan program kerja dan juga sebagai media promosi dan publikasi organisasi. Metode pengabdian masyarakat terdiri dari 4 tahapan, yaitu persiapan, pelaksanaan, evaluasi dan pelaporan. Adapun peserta kegiatan ini adalah para anggota Remaja Masjid Baitul Halim yang mengikuti pelatihan komputer secara offline. Dengan pelatihan ini, peserta merasakan manfaat kehadiran teknologi dimana dapat membantu kegiatan sosial, pendidikan dan keagamaan bagi organisasi. Pemanfaatan AI dalam pembuatan Power Point dapat digunakan untuk membuat presentasi yang menarik dalam slide yang bisa dimodifikasi sesuai kebutuhan.
Classifying Heart Disease through Fusion of Multi-Source Datasets: Integration of Feature Selection and Explainable Machine Learning Techniques Aprianto, Kasiful; Anasanti, Mila Desi
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 3 (2025): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.92395

Abstract

This study delves into heart disease classification through integrated feature selection and machine learning methodologies, utilizing three datasets comprising 4,728 participants and 11 features, with 4.27% missing data. Employing machine learning, we used XGBoost to achieve 0.95 accuracy for one feature, while Random Forest (RF) demonstrated accuracies of 0.92 and 0.99 for the remaining two features. Comparing 11 classification models, RF and XGBoost classified heart disease with 0.97 and 0.99 accuracy, respectively, using all available features. Applying Feature Elimination with Simultaneous Perturbation Feature Selection and Ranking (SpFSR) revealed that RF attained 0.99 accuracy by selecting only four features (cholesterol level, age, resting electrocardiographic measurements, and maximum heart rate), while XGBoost dropped to 0.91. Constructing an RF model with four features enhanced interpretability without compromising accuracy. Explainable Machine Learning (XAI) techniques, including Permutation Importance and SHAP Summary Plot analyses, gauged feature impact on heart disease prediction. The resting electrocardiographic measurements feature held the highest value (0.40 ± 0.01), followed by maximum heart rate (0.32 ± 0.01), cholesterol level (0.28 ± 0.01), and age (0.26 ± 0.005). These results underscore the significance of each feature in diagnosing heart disease via machine learning.
Co-Authors Amanda, Laura Rizka Annisa Novtariany Anton Novianto Antonius Yadi Kuntoro Aprianto, Kasiful Bagus Priambodo Bagus Priambodo Eko Priyono Eni Heni Hermaliani Fadlan Hamid Alfebi Hasanah, Riyan Latifahul Khairunisa Hilyati M. Rangga Ramadhan Saelan Muhammad Haris Musriatun Napiah Rifki Sadikin Sukur Ma’mun Waesul Bismi Zico Pratama Putra