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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) HAYATI Journal of Biosciences PHARMACON Jurnal Bios Logos Animal Production : Indonesian Journal of Animal Production Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE) Makara Journal of Science Heca Journal of Applied Sciences Malacca Pharmaceutics Health & Medical Sciences Journal of Educational Management and Learning Infolitika Journal of Data Science Grimsa Journal of Science Engineering and Technology Jurnal Inovasi Global Borneo Journal of Pharmacy
TRINA EKAWATI TALLEI
Faculty of Mathematics and Natural Sciences, Sam Ratulangi University Manado
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Education Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

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Evaluation of Antibacterial Properties from Endophytic Fungi of Chrysanthemum indicum (L.) Flowers against Escherichia coli and Staphylococcus aureus Ratte, Titah Amelia; Fatimawali, Fatimawali; Tallei, Trina Ekawati; Suoth, Elly Juliana; Antasionasti, Irma; Yamlean, Paulina
Grimsa Journal of Science Engineering and Technology Vol. 1 No. 2 (2023): December 2023
Publisher : Graha Primera Saintifika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61975/gjset.v1i2.15

Abstract

Uncovering the therapeutic potential of secondary metabolites produced by plants, animals, and microbes constitutes the foundation for the development of novel medications. The objective of this investigation is to discern the classes of secondary metabolites and assess the antibacterial properties of endophytic fungal extracts obtained from Chrysanthemum indicum L. flowers. Through the isolation process, five isolates designated as JEC1, JEC2, JEC3, JEC4, and JEC5 were identified. The cultivation of endophytic fungal isolates spanned a three-week period before undergoing extraction with ethyl acetate. The phytochemical tests revealed the presence of alkaloids, flavonoids, steroids, terpenoids, saponins, and tannins in the ethyl acetate extract. Antibacterial activity was determined using the agar well diffusion method, with ciprofloxacin serving as a positive control. Notably, all ethyl acetate extracts from endophytic fungi exhibited antibacterial activity. The most substantial inhibitory diameter against Staphylococcus aureus was recorded as 19.1±0.8 mm for the JEC3 endophytic fungi, while Escherichia coli exhibited an inhibitory diameter of 16±1.1 mm for the JEC2 endophytic fungi.
Student Perspectives on the Role of Artificial Intelligence in Education: A Survey-Based Analysis Idroes, Ghazi Mauer; Noviandy, Teuku Rizky; Maulana, Aga; Irvanizam, Irvanizam; Jalil, Zulkarnain; Lensoni, Lensoni; Lala, Andi; Abas, Abdul Hawil; Tallei, Trina Ekawati; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 1 No. 1 (2023): August 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i1.58

Abstract

Artificial intelligence (AI) has emerged as a powerful technology that has the potential to transform education. This study aims to comprehensively understand students' perspectives on using AI within educational settings to gain insights about the role of AI in education and investigate their perceptions regarding the advantages, challenges, and expectations associated with integrating AI into the learning process. We analyzed the student responses from a survey that targeted students from diverse academic backgrounds and educational levels. The results show that, in general, students have a positive perception of AI and believe AI is beneficial for education. However, they are still concerned about some of the drawbacks of using AI. Therefore, it is necessary to take steps to minimize the negative impact while continuing to take advantage of the advantages of AI in education.
Leveraging Artificial Intelligence to Predict Student Performance: A Comparative Machine Learning Approach Maulana, Aga; Idroes, Ghazi Mauer; Kemala, Pati; Maulydia, Nur Balqis; Sasmita, Novi Reandy; Tallei, Trina Ekawati; Sofyan, Hizir; Rusyana, Asep
Journal of Educational Management and Learning Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i2.132

Abstract

This study explores the application of artificial intelligence (AI) and machine learning (ML) in predicting high school student performance during the transition to university. Recognizing the pivotal role of academic readiness, the study emphasizes the need for tailored interventions to enhance student success. Leveraging a dataset from Portuguese high schools, the research employs a comparative analysis of six ML algorithms—linear regression, decision tree, support vector regression, k-nearest neighbors, random forest, and XGBoost—to identify the most effective predictors. The dataset encompasses diverse attributes, including demographic details, social factors, and school-related features, providing a comprehensive view of student profiles. The predictive models are evaluated using R-squared, Root Mean Square Error, and Mean Absolute Error metrics. Results indicate that the Random Forest algorithm outperforms others, displaying high accuracy in predicting student performance. Visualization and residual analysis further reveal the model's strengths and potential areas for improvement, particularly for students with lower grades. The implications of this research extend to educational management systems, where the integration of ML models could enable real-time monitoring and proactive interventions. Despite promising outcomes, the study acknowledges limitations, suggesting the need for more diverse datasets and advanced ML techniques in future research. Ultimately, this work contributes to the evolving field of educational AI, offering practical insights for educators and institutions seeking to enhance student success through predictive analytics.
Machine Learning Approach for Diabetes Detection Using Fine-Tuned XGBoost Algorithm Maulana, Aga; Faisal, Farassa Rani; Noviandy, Teuku Rizky; Rizkia, Tatsa; Idroes, Ghazi Mauer; Tallei, Trina Ekawati; El-Shazly, Mohamed; Idroes, Rinaldi
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.v1i1.72

Abstract

Diabetes is a chronic condition characterized by elevated blood glucose levels which leads to organ dysfunction and an increased risk of premature death. The global prevalence of diabetes has been rising, necessitating an accurate and timely diagnosis to achieve the most effective management. Recent advancements in the field of machine learning have opened new possibilities for improving diabetes detection and management. In this study, we propose a fine-tuned XGBoost model for diabetes detection. We use the Pima Indian Diabetes dataset and employ a random search for hyperparameter tuning. The fine-tuned XGBoost model is compared with six other popular machine learning models and achieves the highest performance in accuracy, precision, sensitivity, and F1-score. This study demonstrates the potential of the fine-tuned XGBoost model as a robust and efficient tool for diabetes detection. The insights of this study advance medical diagnostics for efficient and personalized management of diabetes.
Ensemble Machine Learning Approach for Quantitative Structure Activity Relationship Based Drug Discovery: A Review Noviandy, Teuku Rizky; Maulana, Aga; Idroes, Ghazi Mauer; Emran, Talha Bin; Tallei, Trina Ekawati; Helwani, Zuchra; Idroes, Rinaldi
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.v1i1.91

Abstract

This comprehensive review explores the pivotal role of ensemble machine learning techniques in Quantitative Structure-Activity Relationship (QSAR) modeling for drug discovery. It emphasizes the significance of accurate QSAR models in streamlining candidate compound selection and highlights how ensemble methods, including AdaBoost, Gradient Boosting, Random Forest, Extra Trees, XGBoost, LightGBM, and CatBoost, effectively address challenges such as overfitting and noisy data. The review presents recent applications of ensemble learning in both classification and regression tasks within QSAR, showcasing the exceptional predictive accuracy of these techniques across diverse datasets and target properties. It also discusses the key challenges and considerations in ensemble QSAR modeling, including data quality, model selection, computational resources, and overfitting. The review outlines future directions in ensemble QSAR modeling, including the integration of multi-modal data, explainability, handling imbalanced data, automation, and personalized medicine applications while emphasizing the need for ethical and regulatory guidelines in this evolving field.
Successful Primer Picking and Pooling for the Design of Multiplex PCR Primers Specific to Pork, Beef, Chicken, and Rat DNA Kusumawaty, Diah; Faridah, Nurul; Fibriani, Azzania; Priyandoko, Didik; Dzikrina, Hanina; Puspitasari, Diah; Tallei, Trina Ekawati; Aryani, Any
HAYATI Journal of Biosciences Vol. 31 No. 4 (2024): July 2024
Publisher : Bogor Agricultural University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.4308/hjb.31.4.678-686

Abstract

DNA markers and Multiplex-PCR have emerged as methods for species detection in processed meat products. The primary objective of this study is to design multiplex primer sequences for pork, rat, beef, and chicken, generating distinguishable amplicons through agarose gel electrophoresis for halal detection in processed meat products. Primer design involved utilizing mitochondrial genomic data and the NCBI-Primer BLAST site to obtain specific pork and beef primer sequences. In silico simulations, including single and multiplex-PCR, were conducted using Primer Pooler. In vitro validation encompassed Single-PCR and Multiplex-PCR annealing temperature optimization, using samples of chicken, beef, pork, and rat as well as processed meat products like meatballs, sausages, and nuggets. In vitro validation demonstrated that the halal marker gene's multiplex primer efficiently amplified the target sequence, specifically at the optimal annealing temperature of 58°C. Amplicons from beef (1,217 bp), pork (860 bp), rat (622 bp), and chicken (272 bp) primers could be distinguished on a 1.5% agarose gel. The study's results can aid in cost-effective and rapid halal testing and authentication of processed meat products, offering advantages over PCR with a single primer.
Ficus minahassae (Teijsm. & de Vriese) Miq.: A Fig Full of Health Benefits from North Sulawesi, Indonesia: A Mini Review Abas, Abdul Hawil; Tallei, Trina Ekawati; Idroes, Rinaldi; Fatimawali, Fatimawali
Malacca Pharmaceutics Vol. 1 No. 1 (2023): June 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v1i1.24

Abstract

Plants have been widely utilized as traditional medicine for an extended period of time. Numerous traditional remedies have demonstrated inherent anti-disease properties. Among the countries that extensively rely on traditional medicine, Indonesia stands out. Within the region of North Sulawesi, Indonesia, Ficus minahassae, an indigenous plant possessing several health benefits, is utilized by the local community as a traditional medicinal resource. This plant is employed for the treatment of various ailments such as rheumatism, physical discomfort, stimulation of lactation in breastfeeding women, bruises, relapse, fever, fatigue, migraines, bodily pain, headaches, convulsions, colds, coughs, influenza, and fractures. Typically, the leaves, roots, and stems of F. minahassae are boiled and consumed. Additionally, this plant has been reported to possess antibacterial and antioxidant properties. However, scientific investigations exploring the health advantages of F. minahassae are significantly limited in comparison to other traditional medicines. Consequently, it is highly recommended to conduct further research on the health benefits associated with this plant.
In Vitro Antioxidant Activity of Chrysanthemum indicum Flowers Extract and Its Fraction Dolongtelide, Jeclin Inebel; Fatimawali, Fatimawali; Tallei, Trina Ekawati; Suoth, Elly Juliana; Simbala, Herny Emma Inonta; Antasionasti, Irma; Kalalo, Marko Jeremia
Malacca Pharmaceutics Vol. 1 No. 2 (2023): October 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v1i2.26

Abstract

Chrysanthemum indicum L., commonly known as Chrysanthemum flower, is an herbaceous plant that has a long-established history of medicinal usage. There has been extensive research about C. indicium L, especially about its antioxidant activities, but not much has been done on its fraction. This study aimed to explore the efficacy of the ethanol extract and its fraction derived from Chrysanthemum flowers in scavenging free radicals. The antioxidant potential of the ethanol extract, as well as its aqueous and n-hexane fractions, was evaluated using the 2,2-diphenyl-1-picrilhidrazine (DPPH) method in vitro. The degree of antioxidant activity was quantified by determining the IC50 value, which corresponds to the concentration of the extract or fractions required to inhibit 50% of DPPH free radicals. The results obtained from this investigation provide strong evidence that the ethanolic extract, as well as its aqueous and n-hexane fractions, exhibited significant antioxidant activity. The measured IC50 values for the ethanolic extract, aqueous fraction, and n-hexane fraction were 1.350 µg/mL, 1.109 µg/mL, and 7.588 µg/mL, respectively.
A Comprehensive Network Pharmacology Study on the Diabetes-Fighting Capabilities of Yacon Leaf Extract Wawo, Arsianita Ester; Simbala, Herny Emma Inonta; Fatimawali, Fatimawali; Tallei, Trina Ekawati
Malacca Pharmaceutics Vol. 2 No. 2 (2024): September 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v2i2.161

Abstract

Indonesia ranks fourth in the world for the number of diabetes mellitus (DM) sufferers. DM is a group of metabolic disorders characterized by hyperglycemia due to insulin abnormalities. This research employs Network Pharmacology analysis to examine the target proteins and pharmacological network profiles predicted to be influenced by compounds in the leaves of Smallanthus sonchifolius (yacon) for their anti-diabetic effects. Gas chromatography-mass spectrometry (GC-MS) identified 41 secondary metabolite compounds in yacon leaves, seven of which have a Pa value > 0.5. Compound C28 has the highest Pa value as an insulin promoter, at 0.662. A total of 129 target proteins were found for the secondary metabolite compounds in yacon leaves, and 5,112 target proteins were identified for Type 2 Diabetes Mellitus (T2DM). The intersection analysis between yacon leaves and T2DM revealed 32 common proteins. Network analysis highlighted 10 top proteins: ESR1, PPAR-α, HMGCR, CYP19A1, PPARD, PTP1N, GRIN2B, FYN, AR, and SHBG. Among these, PPAR-α shows great potential and promising prospects as a target for further exploration. Considering several parameters, it can be concluded that PPAR-α is a promising protein and a potential target for new drug candidates for T2DM.
QSAR Modeling for Predicting Beta-Secretase 1 Inhibitory Activity in Alzheimer's Disease with Support Vector Regression Noviandy, Teuku Rizky; Idroes, Ghifari Maulana; Tallei, Trina Ekawati; Handayani, Dian; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 2 No. 2 (2024): September 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v2i2.226

Abstract

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by cognitive decline, with the accumulation of β-amyloid (Aβ) plaques playing a key role in its progression. Beta-Secretase 1 (BACE1) is a crucial enzyme in Aβ production, making it a prime therapeutic target for AD treatment. However, designing effective BACE1 inhibitors has been challenging due to poor selectivity and limited blood-brain barrier permeability. To address these challenges, we employed a machine learning approach using Support Vector Regression (SVR) in a Quantitative Structure-Activity Relationship (QSAR) model to predict the inhibitory activity of potential BACE1 inhibitors. Our model, trained on a dataset of 7,298 compounds from the ChEMBL database, accurately predicted pIC50 values using molecular descriptors, achieving an R² of 0.690 on the testing set. The model's performance demonstrates its utility in prioritizing drug candidates, potentially accelerating drug discovery. This study highlights the effectiveness of computational approaches in optimizing drug discovery and suggests that further refinement could enhance the model’s predictive power for AD therapeutics.
Co-Authors Abas, Abdul Hawil Adikila, Gregorius Giani Angelina Stevany Regina Masengi Antasionasti, Irma Any Aryani Arifin, Mulyani Asep Rusyana Azzania Fibriani Balansa, Endrile Golmen Barasarathi , Jayanthi BEIVY JONATHAN KOLONDAM Celik, Ismail Daniel Febrian Sengkey Dantje Tarore Diah - Kusumawaty Diah Puspitasari Dian Handayani Diana Setya Ningsih, Diana Didik Priyandoko Dolongtelide, Jeclin Inebel Dzikrina, Hanina El-Shazly, Mohamed Elly Suoth Emran, Talha Bin Erwin Wantasen Estevam, Ethiene Castellucci Faisal, Farassa Rani Fatimawali . Florencia N. Sompie Ghazi Mauer Idroes Halimatushadyah, Ernie Hariyanto, Yuanita Amalia Herny E.I. Simbala Hizir Sofyan Idroes, Ghifari Maulana Illah Sailah Irvanizam, Irvanizam Jein Rinny Leke, Jein Rinny Kalalo, Marko Jeremia Kemala, Pati Kepel, Billy Johnson Khairan Khairan Laksono Trisnantoro Lala, Andi Lydia E. N. Tendean, Lydia E. N. Mamahit, Juliet Merry Eva Martha Marie Kaseke Masengi, Kyoko Itsuko Etsuko Gabriela Maulana, Aga Maulydia, Nur Balqis Mirda, Erisna Moh. Yani Mohd Fauzi, Fazlin Monoarfa, Alexander James Muliadi Ramli Musdalifah, Annisa Nabila, Fiki Farah Niode, Nurdjannah Jane Nurul Faridah, Nurul Patwekar, Mohsina Paulina yamlean Pendong, Christa Hana Angle Purukan, Christy Purwanto, Diana Shintawati Rahman, Sunarti Abd Ratte, Titah Amelia Rinaldi Idroes Rizkia, Tatsa Roni Koneri Runtunuwu, Stefanus Vicky Bernhard Elisa Salaki, Christina Leta Salaswati, Salaswati Sambul, Alwin Melkie Sari, Nadia Warda Sekar Sasmita, Novi Reandy Siampa, Jainer Pasca Sri Sudewi, Sri Takawaian, Agrita Feisilia Tamala, Yulianida Tania, Adinda Dwi Tendean, Lydia Estelina Naomi Teuku Rizky Noviandy Tumilaar, Sefren Geiner Turalaki, Grace Lendawati Amelia Unsratdianto Sompie, Sherwin Reinaldo Utami, Wulandari Putri Wawo, Arsianita Ester Wijaya, Puspita Wungouw, Herlina Ineke Surjane Zuchra Helwani, Zuchra Zulkarnain Jalil