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All Journal JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA Majalah Farmaseutik Journal of Information Systems and Informatics Jurnal Penelitian Farmasi dan Herbal Jurnal INFOTEL
Azka Khoirunnisa
Telkom University, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Medicine & Pharmacology

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Klasifikasi Data Malaria Menggunakan Metode Support Vector Machine Nur Ghaniaviyanto Ramadhan; Azka Khoirunnisa
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3347

Abstract

Malaria is a life-threatening disease, caused by a parasite that is transmitted to humans through the bite of an infected female Anopheles mosquito. In 2019, there were an estimated 229 million cases of malaria worldwide and the death toll reached 409,000. The area most frequently affected by malaria, according to WHO, is the African region. Malaria can be detected beforehand by using the information inpatient data and applying machine learning techniques. This study aims to detect and classify severe malaria based on the history of examining patient data using the Support Vector Machine (SVM) method with a normalization technique using min-max on the dataset and a cross-validation technique with several experiments on the K value of the results. This study also compares the Support Vector Machine method with Naïve Bayes (NB) where the accuracy of the SVM model is superior to Nave Bayes with an average accuracy gap of 25%. The accuracy generated by the application of the proposed method is 92.3%.
Improving malaria prediction with ensemble learning and robust scaler: An integrated approach for enhanced accuracy Azka Khoirunnisa; Nur Ghaniaviyanto Ramadhan
JURNAL INFOTEL Vol 15 No 4 (2023): November 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i4.1056

Abstract

Mosquito bites are the primary transmission method for malaria, a prevalent and significant health concern worldwide. In the context of malaria incidence, Indonesia is the second most affected country after India. According to the Ministry of Health's report, Papua Province reported 216,380 malaria cases in 2019. Additionally, East Nusa Tenggara and West Papua said 12,909 and 7,029 points, respectively, reflecting the substantial national burden of this disease. Predicting malaria occurrence based on symptomatic presentation is a crucial preventive strategy. Machine learning models offer a promising approach to malaria prediction. This study focused on malaria detection by using patient data from Nigeria. This research proposes a detection system utilizing the Ensemble method, such as Decision Tree, Random Forest, and Bagging. This study also employing Robust Scaler for effective normalization and integrating K-fold cross-validation to enhance model robustness. Various experiments were conducted by systematically varying K values and the number of decision trees to ascertain the most effective hyperparameters yielding the highest accuracy. The findings indicate that the optimal accuracy 82% is achieved at a K value of 20, showing comparable accuracies across different decision tree quantities, underlining the robustness of the employed method. This research significantly advances malaria detection strategies, offering valuable insights into the effective deployment of machine learning in healthcare decision-making.
ANALISIS KETERKAITAN NETWORK PHAMACOLOGY SENYAWA METABOLIT SEKUNDER Abrus precatorius L. SECARA IN SILICO Khoirunnisa, Azka; Jamil, Ahmad Shobrun; Muchlisin, M. Artabah
Jurnal Penelitian Farmasi Dan Herbal Vol 6 No 2 (2024): Jurnal Penelitian Farmasi & Herbal
Publisher : Fakultas Farmasi Institut Kesehatan DELI HUSADA Deli Tua

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36656/jpfh.v6i2.1686

Abstract

Cancer remains a global health challenge, prompting extensive research efforts worldwide. Lung cancer, the second most diagnosed cancer, presents a particularly grim survival rate. In Indonesia, cancer incidence ranks significantly, with millions affected and hundreds of thousands succumbing to the disease annually. Traditional medicine persists as a preferred option among many, perceived as safer and more affordable. Abrus precatorius L., an ancient medicinal plant, holds promise in this regard, with a rich history of use and a diverse range of pharmacological activities, including anti-cancer properties. Employing in silico modeling and network pharmacology, this study explores the interaction between Abrus precatorius L. compounds and various proteins associated with cancer. Through bioinformatics tools and databases, 27 bioactive compounds are identified and their physicochemical properties assessed, ensuring adherence to pharmacological guidelines. The study predicts potential protein targets for Abrus precatorius L. compounds, revealing interactions with 453 proteins, including those implicated in cancer pathways. Further analysis using StringDB and DISEASES database establishes protein-protein interaction networks, highlighting key proteins like EGFR and TERT, pivotal in multiple cancer types. The study validates the compounds' adherence to Lipinski's Rule of Five, indicating their potential for pharmacological activity and oral absorption. False Discovery Rate (FDR) analysis confirms significant associations between Abrus precatorius L. compounds and various cancers, further underscoring their therapeutic potential. In conclusion, Abrus precatorius L. compounds, particularly targeting EGFR and TERT proteins, emerge as promising candidates for cancer treatment. Their diverse pharmacological activities and interactions with key cancer-related proteins pave the way for further exploration and development of these compounds as alternative medicinal agents. In vitro and in vivo studies are warranted to validate their efficacy, particularly in addressing the complexities of different cancer types. Ultimately, this research offers valuable insights into leveraging natural compounds for combating cancer, addressing a critical need in global healthcare.
Network Pharmacology Analysis of Secondary Metabolites of Ciplukan (Physalis angulata L.) Against Lung Cancer Khoirunnisa, Azka; Jamil, Ahmad Shobrun; Muchlisin, Muhammad Artabah
Majalah Farmaseutik Vol 20, No 2 (2024)
Publisher : Faculty of Pharmacy, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/farmaseutik.v20i2.96275

Abstract

Lung cancer is the most common and high-risk type of cancer. Ciplukan (Physalis angulata L.) has antibacterial, anti-inflammatory, and anticancer activities, as well as cytotoxic ability and inhibits cancer cell growth. Research that discusses the molecular cellular mechanism of P. angulata's potential as an anti-lung cancer has not been widely informed, especially on the network pharmacology aspect of this plant's active compounds. This study reveals the prediction of the molecular mechanism of active compounds of P. angulata as anti-lung cancer using several tools including: Compound database retrieval with Knapsack and PubChem. Absorption Distribution Metabolism and Excretion (ADME) screening with SwissADME. Target protein identification with Gene Card, SwissTargetPrediction and Venny Diagram. Network pharmacology construction with String-DB and Cytoscape. Network pharmacology analysis using Gene Ontology (GO), and Cellular Component and Molecular Function. Based on the results of the analysis of P. angulata protein potential based on Maximal Clique Centrality (MCC) on CytoHubba in Cytoscape application, it shows that the Protein-protein Interaction (PPI) network has 10 main targets, namely ERBB2, KRAS, TP53, PTEN, CDKN2A, NRAS, PIK3CA, BRAF, NF1, and EGFR which interact with each other to regulate cell growth, differentiation, and survival. The results of this study can be concluded that the secondary metabolite compounds of P. angulata have the potential to control and alternative for lung cancer therapy.
An Integrated Random Forest for Analyzing Public Sentiment on the “Makan Bergizi Gratis” Program Ramadhan, Nur Ghaniaviyanto; Khoirunnisa, Azka
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1184

Abstract

The “Makan Bergizi Gratis” (MBG) Program is a public policy aimed at improving the nutritional quality of the community, particularly vulnerable groups. However, the success of this program is heavily influenced by public sentiment and perception. This research analyzes public sentiment toward the MBG program thru the social media platform X using an ensemble-based machine learning approach. The proposed framework integrates the Random Forest algorithm and compares it with four other ensemble models: AdaBoost, XGBoost, Bagging, and Stacking. A total of 3,417 tweets were analyzed using the TF-IDF method, both with and without stemming. The Random Forest model showed the best performance with an accuracy of 91.15% and an ROC-AUC of 95.46% on the data without stemming, consistently outperforming the other models. Additionally, a visual analysis of word frequency provides a strong indication of public opinion. These findings demonstrate the effectiveness of Random Forest in managing unstructured sentiment data and provide valuable insights for policymakers to monitor public responses and improve program implementation with greater precision.
A Hybrid ROS-SVM Model for Detecting Target Multiple Drug Types Ramadhan, Nur Ghaniaviyanto; Khoirunnisa, Azka; Kurnianingsih, Kurnianingsih; Hashimoto, Takako
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1171

Abstract

Misleading in determining the decision to use the target drug will be fatal, even to death. This study examines five pharmacological targets designated as types A, B, C, X, and Y. Early detection of misleading drug targeting will reduce the risk of death. This study aims to develop hybrid random oversampling techniques (ROS) and support vector machine (SVM) methods. The use of the oversampling technique in this study aims to balance classes in the dataset; due to the data collection in each class, there is a relatively large gap. This study applies five schemes to see which combination of models produces the highest accuracy. This study also uses five types of SVM kernels, linear, polynomial, gaussian, RBF, and sigmoid, combined with the ROS oversampling technique. Our proposed model combines the ROS oversampling technique with a linear SVM kernel. We evaluated the proposed model and resulted in an accuracy of 97% and compared it with several experiments, including the ROS technique with a sigmoid kernel which only resulted in 50% accuracy. It can be seen from the results obtained that the linear kernel is very adaptive to data types in the form of numeric and nominal compared to other kernels. The method proposed in this study can be applied to other medical problems. Future research can be carried out using a combination of other sampling techniques with deep learning-based methods on this issue.
Implementation of CRNN Method for Lung Cancer Detection based on Microarray Data Khoirunnisa, Azka; Adiwijaya, -; Adytia, Didit
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1339

Abstract

Lung Cancer is one of the cancer types with the most significant mortality rate, mainly because of the disease's slow detection. Therefore, the early identification of this disease is crucial. However, the primary issue of microarray is the curse of dimensionality. This problem is related to the characteristic of microarray data, which has a small sample size yet many attributes. Moreover, this problem could lower the accuracy of cancer detection systems. Various machines and deep learning techniques have been researched to solve this problem. This paper implemented a deep learning method named Convolutional Recurrent Neural Network (CRNN) to build the Lung Cancer detection system. Convolutional neural networks (CNN) are used to extract features, and recurrent neural networks (RNN) are used to summarize the derived features. CNN and RNN methods are combined in CRNN to derive the advantages of each of the methods. Several previous research uses CRNN to build a Lung Cancer detection system using medical image biomarkers (MRI or CT scan). Thus, the researchers concluded that CRNN achieved higher accuracy than CNN and RNN independently. Moreover, CRNN was implemented in this research by using a microarray-based Lung Cancer dataset. Furthermore, different drop-out values are compared to determine the best drop-out value for the system. Thus, the result shows that CRNN gave a higher accuracy than CNN and RNN. The CRNN method achieved the highest accuracy of 91%, while the CNN and RNN methods achieved 83% and 71% accuracy, respectively.
An Evaluation of SMOTE Effectiveness in Handling Class Imbalance in Public Opinion Data on the MBG Program Ramadhan, Nur Ghaniaviyanto; Khoirunnisa, Azka
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1495

Abstract

The “Makan Bergizi Gratis” (MBG) Program is one of the strategic policies of the Government of Indonesia that reaps various opinions from the public, especially through social media. This study aims to classify public sentiment towards the MBG program with an ensemble learning-based machine learning approach, as well as evaluate the effectiveness of the SMOTE algorithm in dealing with class imbalance in opinion data. The dataset was collected from platform X (formerly Twitter) for the January–April 2025 period, totaling 4,374 tweets with label distributions: 1,783 positive, 1,634 negative, and 957 neutral. The preprocessing process includes data cleansing, normalization, stemming, and vectorization with TF-IDF. Five ensemble algorithms were used, namely Random Forest, AdaBoost, Bagging, Stacking, and Voting, tested in two scenarios: with and without the implementation of SMOTE. The results of the experiments showed that Random Forest provided the best and most consistent performance, with the F1-score increasing from 72.03% to 72.66% after the implementation of SMOTE. However, not all models benefit from SMOTE, such as Voting which experienced a drop in F1-score. These findings suggest that SMOTE is effective in increasing the sensitivity of the model to minority classes, but its success depends on the characteristics of the algorithm used. This study suggests the selective selection of balancing methods as well as the development of a more adaptive approach to handle unstructured opinion data.
Co-Authors Adiwijaya, - Ahmad Shobrun Jamil Didit Adytia Hashimoto, Takako Kurnianingsih Kurnianingsih, Kurnianingsih M. Artabah Muchlisin Muchlisin, M. Artabah Nur Ghaniaviyanto Ramadhan