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Suhendra, Rivansyah
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Journal : Infolitika Journal of Data Science

 1 
ANFIS-Based QSRR Modelling for Kovats Retention Index Prediction in Gas Chromatography Idroes, Rinaldi; Noviandy, Teuku Rizky; Maulana, Aga; Suhendra, Rivansyah; Sasmita, Novi Reandy; Muslem, Muslem; Idroes, Ghazi Mauer; Jannah, Raudhatul; Afidh, Razief Perucha Fauzie; Irvanizam, Irvanizam
Infolitika Journal of Data Science Vol. 1 No. 1 (2023): September 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i1.73

Abstract

This study aims to evaluate the implementation and effectiveness of the Adaptive Neuro-Fuzzy Inference System (ANFIS) based Quantitative Structure Retention Relationship (QSRR) to predict the Kovats retention index of compounds in gas chromatography. The model was trained using 340 essential oil compounds and their molecular descriptors. The evaluation of the ANFIS models revealed promising results, achieving an R2 of 0.974, an RMSE of 48.12, and an MAPE of 3.3% on the testing set. These findings highlight the ANFIS approach as remarkably accurate in its predictive capacity for determining the Kovats retention index in the context of gas chromatography. This study provides valuable perspectives on the efficiency of retention index prediction through ANFIS-based QSRR methods and the potential practicality in compound analysis and chromatographic optimization.
Cardiovascular Disease Prediction Using Gradient Boosting Classifier Suhendra, Rivansyah; Husdayanti, Noviana; Suryadi, Suryadi; Juliwardi, Ilham; Sanusi, Sanusi; Ridho, Abdurrahman; Ardiansyah, Muhammad; Murhaban, Murhaban; Ikhsan, Ikhsan
Infolitika Journal of Data Science Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i2.131

Abstract

Cardiovascular Disease (CVD), a prevalent global health concern involving heart and blood vessel disorders, prompts this research's focus on accurate prediction. This study explores the predictive capabilities of the Gradient Boosting Classifier (GBC) in cardiovascular disease across two datasets. Through meticulous data collection, preprocessing, and GBC classification, the study achieves a noteworthy accuracy of 97.63%, underscoring the GBC's effectiveness in accurate CVD detection. The robust performance of the GBC, evidenced by high accuracy, highlights its adaptability to diverse datasets and signifies its potential as a valuable tool for early identification of cardiovascular diseases. These findings provide valuable insights into the application of machine learning methodologies, particularly the GBC, in advancing the accuracy of CVD prediction, with implications for proactive healthcare interventions and improved patient outcomes.
Decision Tree versus k-NN: A Performance Comparison for Air Quality Classification in Indonesia Sasmita, Novi Reandy; Ramadeska, Siti; Kesuma, Zurnila Marli; Noviandy, Teuku Rizky; Maulana, Aga; Khairul, Mhd; Suhendra, Rivansyah
Infolitika Journal of Data Science Vol. 2 No. 1 (2024): May 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v2i1.179

Abstract

Air quality can affect human health, the environment, and the sustainability of ecosystems, so efforts are needed to monitor and control air quality. The Plume Air Quality Index (PAQI) is one of the indices to measure and determine the level of air quality. In measuring the accuracy of the air quality level, it is necessary to do the right classification. Some previous studies have conducted classification analysis using the decision tree and K-Nearest Neighbor (k-NN) methods, but only evaluated using accuracy values. Therefore, this study uses both methods to evaluate the results of air quality level classification not only with accuracy but also with precision, recall, and F1-score. Secondary data of pollutant concentration values and PAQI categories based on particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), and ozone (O3) derived from Plume Labs for 33 provincial capitals in Indonesia in the time period from July 1 to December 31, 2022, were used in this study. From the results of comparing the performance of the two methods, it is found that the decision tree has a greater performance value than the performance value of k-NN. The decision tree performance values for accuracy, precision, recall and F1-score are 90.67%, 90.61%, 90.67%, and 90.63%, respectively. So, it can be concluded that the decision tree performs better than k-NN in classifying PAQI categories with better overall evaluation metric values.
Backpropagation Neural Network-Based Prediction of Kovats Retention Index for Essential Oil Compounds Safhadi, Aulia Al-Jihad; Noviandy, Teuku Rizky; Irvanizam, Irvanizam; Suhendra, Rivansyah; Karma, Taufiq; Idroes, Rinaldi
Infolitika Journal of Data Science Vol. 2 No. 1 (2024): May 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v2i1.197

Abstract

The identification of chemical compounds in essential oils is crucial in industries such as pharmaceuticals, perfumery, and food. Kovats Retention Index (RI) values are essential for compound identification using gas chromatography-mass spectrometry (GC-MS). Traditional RI determination methods are time-consuming, labor-intensive, and susceptible to experimental variability. Recent advancements in data science suggest that artificial intelligence (AI) can enhance RI prediction accuracy and efficiency. However, the full potential of AI, particularly artificial neural networks (ANN), in predicting RI values remains underexplored. This study develops a backpropagation neural network (BPNN) model to predict the Kovats RI values of essential oil compounds using five molecular descriptors: ATSc1, VCH-7, SP-1, Kier1, and MLogP. We trained the BPNN on a dataset of 340 essential oil compounds and optimized it through hyperparameter tuning. We show that the optimized BPNN model, with an epoch count of 100, a learning rate of 0.1, a hidden layer size of 10 neurons, and the ReLU activation function, achieves an R² value of 0.934 and a Root Mean Squared Error (RMSE) of 76.98. These results indicate a high correlation between predicted and actual RI values and a low average prediction error. Our findings demonstrate that BPNNs can significantly improve the efficiency and accuracy of compound identification, reducing reliance on traditional experimental methods.
Advanced Anemia Classification Using Comprehensive Hematological Profiles and Explainable Machine Learning Approaches Noviandy, Teuku Rizky; Idroes, Ghifari Maulana; Suhendra, Rivansyah; Bakri, Tedy Kurniawan; Idroes, Rinaldi
Infolitika Journal of Data Science Vol. 2 No. 2 (2024): November 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v2i2.237

Abstract

Anemia is a common health issue with serious clinical effects, making timely and accurate diagnosis essential to prevent complications. This study explores the use of machine learning (ML) methods to classify anemia and its subtypes using detailed hematological data. Six ML models were tested: Gradient Boosting, Random Forest, Naive Bayes, Logistic Regression, Support Vector Machine, and K-Nearest Neighbors. The dataset was preprocessed using feature standardization and the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Gradient Boosting delivered the highest accuracy, sensitivity, and F1-score, establishing itself as the top-performing model. SHapley Additive exPlanations (SHAP) analysis was applied to enhance model interpretability, identifying key predictive features. This study highlights the potential of explainable ML to develop efficient, accurate, and scalable tools for anemia diagnosis, fostering improved healthcare outcomes globally.
Assessing LightGBM Performance in Automated Leukemia Cell Classification Qaisa, Rara Syifa; Maghfirah, Hayatun; Suryadi, Suryadi; Husdayanti, Noviana; Suhendra, Rivansyah
Infolitika Journal of Data Science Vol. 4 No. 1 (2026): May 2026 (In Press)
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v4i1.351

Abstract

Leukemia is a type of blood cancer that requires fast and accurate diagnosis for effective treatment. Manual identification of leukemia blood cell subtypes is often challenging, time-consuming, and prone to observer variability, making automated image-based classification essential. This study evaluates the performance of the Light Gradient-Boosting Machine (LightGBM) as a computationally efficient and interpretable alternative to deep learning models for classifying leukemia subtypes. The dataset includes 3,000 microscopic images representing five classes: acute lymphocytic, acute myelogenous, chronic lymphocytic, chronic myelogenous, and healthy blood cells. Images were preprocessed using bilinear interpolation to balance quality and efficiency, and 90 statistical features were extracted across 13 distinct color spaces. The model was trained on an 80% subset and validated on a 20% hold-out set after hyperparameter optimization. LightGBM achieved robust performance with an accuracy of 93.3%, precision of 99.1%, recall of 94.9%, and an F-measure of 96.8%. Feature importance analysis revealed that texture variance in the YIQ color space (STD_YIQ_I) was the most critical predictor, highlighting the biological relevance of chromatin texture in classification. These results indicate that LightGBM is an effective, lightweight, and reliable approach for leukemia subtype classification, holding strong potential for implementation in resource-constrained automated diagnostic systems.
Co-Authors Abdurrahman Ridho Andini, Mirna Ria Arisna, Puput Asep Rusyana Asra, Saiful Ayu, Ratih Sari Bahri, Ridzky Aulia BAKRI, TEDY KURNIAWAN Dharma, Dian Budi Earlia, Nanda Erkata Yandri Fachruddin Fachruddin Fuqara, Fantashir Awwal Ghazi Mauer Idroes Hasibuan, M. Barru Airil Fizra Helda, Nurul Hizir Sofyan Husdayanti, Noviana Idroes, Ghazi M. Idroes, Ghifari M. Idroes, Ghifari Maulana Iffaty, Athiya Ikhsan Ikhsan Ilham Juliwardi Imelda, Eva Irvanizam, Irvanizam Isfanda, Isfanda Juliwardi, Ilham Kairupan, Tara S. Khairul, Mhd Lala, Andi M. Yogi Riyantama Isjoni Maghfirah, Hayatun Maria Paristiowati Maulana, Aga Maulydia, Nur Balqis Mikyal Bulqiah, Mikyal Muhammad Adam, Muhammad Muhammad Ardiansyah Muhammad Subianto Muhammad Walid Muhammad Yanis Muhammad Yusuf Murhaban Murhaban Muslem Muslem, Muslem Muzammil, Abdul Mu’amar Abdan Nica Astrianda Niode, Nurdjannah Jane Nizamuddin Nizamuddin Noviandy, Teuku R. Nurdjannah J. Niode Patwekar, Mohsina Prakoeswa, Cita RS. putri, miya amalia Qaisa, Rara Syifa Rafiqhi, Adis Aufa Ramadeska, Siti Raudhatul Jannah Raziah, Isyatur Razief Perucha Fauzie Afidh Ridho, Abdurrahman Rinaldi Idroes Rizki Agam Syahputra Safhadi, Aulia Al-Jihad Salsabila, Adinda Samuki, Khairul Sanusi , Sanusi Sanusi Sanusi Sasmita, Novi Reandy Satrio, Justinus Siti Rahmah Sufal Diansyah Suryadi Suryadi Suryadi Suryadi Syahputra , Dedek Syahril, Alfis Taufiq Karma Teuku Rizky Noviandy Uswatun Hasanah Zulham Zulham Zurnila Marli Kesuma