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All Journal HAYATI Journal of Biosciences Jurnal Teknologi Industri Pertanian Indonesian Journal of Biotechnology Journal of Agromedicine and Medical Sciences Journal of Tropical Biodiversity and Biotechnology Manajemen Pendidikan BERITA BIOLOGI JURNAL BIOLOGI INDONESIA Jurnal Teknologi Laboratorium Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Chimica et Natura Acta Indonesian Journal of Chemistry Jurnal Sinergitas PkM & CSR FaST : Jurnal Sains dan Teknologi Walisongo Journal of Chemistry Menara Perkebunan Jurnal Teknik Informatika (JUTIF) Bioinformatics and Biomedical Research Journal Cermin Dunia Kedokteran Indonesian Journal of Life Sciences Makara Journal of Science Makara Journal of Technology Biosaintifika: Journal of Biology & Biology Education Jurnal Bioteknologi & Biosains Indonesia (JBBI) Cermin Dunia Kedokteran Kesmas: Jurnal Kesehatan Masyarakat Nasional (National Public Health Journal) Jurnal Bioteknologi & Biosains Indonesia Berita Biologi
Arli Aditya Parikesit
Department Of Bioinformatics, School Of Life Sciences, Indonesia International Institute For Life Sciences (i3l), Jakarta, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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THE COMPARISON BETWEEN LOGISTIC REGRESSION AND CONVOLUTIONAL NEURAL NETWORK FOR MULTI-DRUG RESISTANT TUBERCULOSIS PREDICTION Widjaja, Albert; Wibowo, Satrio; Parikesit, Arli Aditya
Jurnal Bioteknologi & Biosains Indonesia (JBBI) Vol. 12 No. 1 (2025)
Publisher : BRIN - Badan Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jbbi.2025.9769

Abstract

Multi-drug resistant tuberculosis (MDR-TB) is caused by Mycobacterium tuberculosis strains that resist at least two first-line anti-TB drugs. This disease presents a major global health challenge, particularly affecting middle to lower income countries where affordable and rapid diagnostic tools are urgently needed. To address this, researchers are exploring the combination of whole genome sequencing and machine learning for drug resistance predictions. Using Mycobacterium tuberculosis genomic data from databases, both Logistic Regression (LR) and Convolutional Neural Network (CNN) models were trained to predict drug resistance. Performance evaluation revealed that CNN slightly outperformed LR in accuracy and specificity for Rifampicin and Pyrazinamide predictions, while LR showed better results for Isoniazid and Ethambutol. In terms of sensitivity, LR demonstrated superior performance for most drugs, except Ethambutol where CNN excelled. Though computational complexity assessment was incomplete due to hardware limitations, both models showed distinct advantages in predicting first-line anti-TB drug resistance.
Data Clustering for Sentiment Classification with Naïve Bayes and Support Vector Machine Yanuargi, Bayu; Ema Utami; Kusrini; Parikesit, Arli Aditya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.6139

Abstract

Visitor reviews play a crucial role in determining the success of a business, particularly those offering hospitality and services, such as hotels. The growth of internet technology has made it easier for guests to share their experiences, which can influence potential customers. Google Maps is one of the platforms used for giving and searching reviews This research uses data crawled from Google Maps Review using the playwright library. However, the large volume of reviews can make analysis and topic-based categorization—such as service quality, hotel location, and operational hours—challenging. To address this, DBSCAN is used to cluster reviews based on these topics. Clustering helps improve sentiment classification, making it more targeted and allowing a comparison of two machine learning algorithms: Naïve Bayes and Support Vector Machine (SVM). Naïve Bayes achieved higher accuracy (0.87) in the operational hours cluster, while SVM scored 0.78. However, SVM showed improved accuracy in the location (0.89) and service (0.88) clusters, with Naïve Bayes maintaining a stable 0.86 accuracy in both. Both models demonstrated an average training time of less than one second, excluding preprocessing.
Pemanfaatan bioinformatika dalam bidang pertanian dan kesehatan (The utilization of bioinformatics in the field of agriculture and health) Arli Aditya PARIKESIT; Dito ANUROGO; Riza A PUTRANTO
Menara Perkebunan Vol. 85 No. 2 (2017): 85 (2), 2017
Publisher : INDONESIAN OIL PALM RESEARCH INSTITUTE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22302/iribb.jur.mp.v85i2.237

Abstract

Bioinformatics can be used to manage the data storage resulted from in silico molecular biology experiments. Off-network (offline) applications require large computing resources, in which researchers in the bioinformatics field of agriculture and health sectors do not necessarily possess. This review paper addressed examples of affordable and applicable in silico analytical cases in both mentioned sectors. Genome sequence analysis and in silico drug design using (1) a computational method, pharmacokinetic parameter prediction, (2) Computer Aided Design and Drafting (CADD) technology, (3) potential protein action prediction, (4) OMICs application in stem cell biology, and (5) lncRNAs based database computing internet sites is one of examples. In agriculture, bioinformatics-based research has been used in (1) the development of molecular markers; (2) the design of primer for differential gene expression analysis; (3) the development of genetic maps; and (4) gene expression analysis. Further application of bioinformatics also targets the design of applicative products for pest control and the protection of plant varieties in the farm. Through this example, novice researchers in the bioinformatics field of agriculture and health sectors can conduct sophisticated research using standard computer tools, internet networks, and sufficient knowledge about bioinformatics. On the other hand, multidisciplinary collaboration between these scientists can be carried out through social networking. The synergy can be directed to improve computing capabilities and data analysis via procurement of computing resources and use of public information clusters. [Key words: genome sequences, in silico drug design, online, bioinformatics, health, agriculture.] AbstrakBioinformatika dapat digunakan dalam manajemen informasi di bidang penyimpanan data in silico dari eksperimen biologi molekuler. Aplikasi luar jaringan (luring) memerlukan sumber daya komputasi yang besar, yang belum tentu dimiliki oleh para peneliti dalam bidang bioinformatika kesehatan dan pertanian. Kajian ilmiah ini membahas contoh kasus analisis in silico yang terjangkau dan aplikatif dalam bidang kesehatan dan pertanian. Contoh kasus tersebut adalah analisis sekuen genom dan desain obat in silico, menggunakan pendekatan metode komputasional, prediksi parameter farmakokinetik, teknologi Computer Aided Design and Drafting (CADD), prediksi potensial aksi protein, aplikasi OMICs pada biologi sel punca, hingga komputasi basis data lncRNAs berbasis situs internet. Pada bidang pertanian, penelitian berbasis bioinformatika telah digunakan dalam (1) pengembangan penanda molekuler; (2) desain primer untuk analisis ekspresi gen diferensial; (3) pengembangan peta genetik; dan (4) analisis ekspresi gen. Pemanfaatan bio-informatika dalam ilmu terapan dibidang pertanian juga menyasar desain produk aplikatif untuk pengendalian hama dan perlindungan varietas tanaman. Melalui contoh tersebut, peneliti pemula dibidang bioinformatika kesehatan dan pertanian dapat melakukan penelitian canggih hanya dengan alat komputer standar, jaringan internet, dan pengetahuan mencukupi tentang bioinformatika. Disisi lain, sinergi dan kolaborasi antar peneliti multi-displiner dapat dilakukan melalui penggunaan jejaring sosial. Sinergi tersebut dapat diarahkan untuk meningkatkan kemampuan komputasi dan analisis data melalui pengadaan sumber daya komputasi dan penggunaan klaster informatika publik.[Kata kunci: sekuen genom, desain obat in silico, daring, bioinformatika, kesehatan, pertanian]
Computational Study of Photosynthetic Pigments: Toward Synthetic Photosynthesis Engineering Wicaksono, Adhityo; Prakoso, Muhammad Ja'far; Ridarto, Afif Maulana Yusuf; Parikesit, Arli Aditya
Indonesian Journal of Chemistry Vol 25, No 4 (2025)
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijc.105059

Abstract

Chlorophyll is a widely known photosynthetic pigment in plants, algae, and cyanobacteria, along with bacteriochlorophyll in some photosynthetic bacteria. The pigments consist of tetrapyrrole structures that carry a single magnesium atom at the center. They play important parts in the light-harvesting process in photosynthesis. This study aimed to characterize and compare the electronic profiles of chlorophyll and bacteriochlorophyll pigments by using in silico computational approaches, such as density functional theory (DFT), electronic transfer property analysis, and protein-pigment interaction studies via molecular docking. The results showed that chlorophylls a, b, and c have the highest energy gaps at the ground state DFT. For bacteriochlorophylls, bacteriochlorophylls g and b have the highest energy gaps. The time-dependent DFT and the follow-up calculations, including extinction coefficient, tunneling rate, and coherence time, indicated bacteriochlorophyll g as a highly promising and efficient pigment. Additionally, chlorophyll c and bacteriochlorophylls c and d showed the strongest binding affinities with the chlorophyll-binding protein of plant photosystem II. This study provides a comprehensive and replicable computational pipeline for pigment profiling, advancing future synthetic photosynthesis designs through combined quantum and synthetic biology insights.
Risk Factors Associated with Long COVID Among Hospitalized Adults in Several Hospitals in Palembang City, Indonesia Hutapea, Hotma Martogi Lorensi; Sudaryo, Mondastri Korib; Parikesit, Arli Aditya; Miko Wahyono, Tri Yunis; Salim, Nelda Aprilia
Kesmas Vol. 20, No. 2
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Long COVID is characterized by one or more symptoms experienced by individuals prior to a COVID-19 infection that last for ≥2 months, and its risk factors remain unclear. This study aimed to identify risk factors associated with long COVID among patients admitted between June 1, 2020, and October 31, 2023, at three referral COVID-19 hospitals in Palembang City, Indonesia. This cohort study included adults who were admitted for ≥5 days. The participant’s medical records were reviewed for admission and discharge dates, sociodemographic and clinical characteristics, and vaccination and therapy status. A standardized and validated instrument was used to assess fatigue during admission, and a structured questionnaire was used to evaluate long COVID. Cox regression was employed to determine factors associated with long COVID. Among 256 patients, long COVID was identified in 39.1%. Fatigue during admission, chronic kidney disease, thrombocytosis, and positive RT-PCR test at hospital discharge increased the risk of long COVID, whereas being fully vaccinated decreased its risk. This study identifies five risk factors for long COVID and determines that fatigue during admission is the strongest.
Improving Model Capability for Sentiment Trend Analysis in Hotel Visitor Reviews with Bi-LSTM Multistage Approach Yanuargi, Bayu; Utami, Ema; Kusrini, Kusrini; Parikesit, Arli Aditya
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5185

Abstract

This study focuses to improve the sentiment analysis of hotel reviews using Multistage mechanism of two-stage approach based on the Bidirectional Long Short-Term Memory (Bi-LSTM) architecture with 53,000 data from 28 hotels in Yogyakarta that captured from google maps review for hotel in Yogyakarta. Hotel customer reviews often contain mixed sentiment expressions, making it crucial to filter out only sentences with a single dominant sentiment to avoid ambiguity. In the first stage, the model detects sentiment at the token level and counts the number of sentiment expressions in each sentence. Only sentences with a single polarity are passed to the final classification stage. In the second stage, the overall sentiment is classified as positive, negative, or neutral using pooled contextual representations. Experimental results from 30 iterations demonstrate consistently high performance, with precision, recall, and F1-scores above 0.95, and overall accuracy exceeding 96%. The confusion matrix analysis shows strong model performance, although some challenges remain in distinguishing between positive and neutral sentiment. Additionally, sentiment trend analysis of hotel reviews from properties such as Lafayette Boutique Hotel and The Westlake Resort Jogja reveals dynamic shifts in guest perception over time. This multistage mechanism approach proves effectiveness of improving sentiment classification accuracy by avoid the bias on sentiment and also in providing valuable temporal insights for monitoring customer satisfaction.
Development of a Multi-Epitope Peptide Vaccine Against Monkeypox Virus: Immunoinformatics Analysis for South East Asian HLA Alleles Chandra, Nelson; Herdiansyah, Mochammad Aqilah; Kharisma, Viol Dhea; Ansori, Arif Nur Muhammad; Parikesit, Arli Aditya
Makara Journal of Science Vol. 29, No. 1
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The monkeypox virus (MPXV), a DNA virus causing zoonotic disease, poses major global public health challenges, with mortality rates between 3%–6%. Although smallpox vaccines provide partial cross-protection, there is a critical need for a dedicated, effective monkeypox (mpox) vaccine. This study aimed to design a multi-epitope peptide-based vaccine specifically adapted to the HLA allele profiles common in Southeast Asian populations, where MPXV cases are rising. Using immunoinformatics, we screened for and detected B and T cell epitopes from the MPXV cell surface antigen and IFN-alpha/beta receptor proteins. The vaccine design was validated through a rigorous evaluation of its antigenicity, immunogenicity, allergenicity, and toxicity to ensure both safety and efficacy. Key epitopes were mapped to HLA alleles including HLA-A*11:01, HLA-A*24:02, and HLA-B*15:02, which are highly prevalent in Southeast Asia populations. Molecular docking analyses demonstrated stable interactions between the vaccine construct and TLR3/TLR4 immune receptors, suggesting a robust immune response activation. Additionally, molecular dynamics simulations confirmed the structural stability of the vaccine-receptor complex. This immunoinformatics-driven multi-epitope vaccine design offers a promising candidate for combating MPXV, with high projected coverage and immuno-genic potential for Southeast Asian populations. Validation in laboratory and clinical settings is recommended to con-firm these findings.
Computational Design of siRNA Targeting Homo sapiens HER2 Splice Variant mRNA: A Potential Strategy for Breast Cancer Intervention Parikesit, Arli Aditya; Ansori, Arif Nur Muhammad; Kharisma, Viol Dhea
Biosaintifika: Journal of Biology & Biology Education Vol. 16 No. 3 (2024): December 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/biosaintifika.v16i3.3685

Abstract

This research focuses on an innovative approach utilizing in silico methods to design small interfering RNA (siRNA) targeting the HER2 splice variant mRNA in Homo sapiens. HER2 is known to be overexpressed in certain types of breast cancer, contributing to tumor progression and poor prognosis. By designing siRNA molecules that can specifically bind to and degrade HER2 mRNA, this study aims to reduce HER2 protein levels, thereby hindering the growth and spread of breast cancer cells. The in-silico design process involves identifying optimal siRNA sequences that maximize target specificity and minimize off-target effects, which is crucial for potential therapeutic applications. This approach represents a promising step towards personalized medicine in the treatment of breast cancer, offering a targeted strategy to combat this variant associated with aggressive disease. The methodology comprises the RNA computational tools used for the design, the selection criteria for siRNA candidates, and the potential implications of this research in a clinical setting. The resulting outcomes are 2D and 3D siRNA designs that could potentially silence HER2 mRNA through an in-silico approach. The leads were generated using a de novo modeling approach, with no existing template available in GenBank. Moreover, it is concluded that computational tools can generate sufficiently stable 2D and 3D RNA models that could be advanced for further molecular simulation studies. The benefit of this outcome is that it facilitates better preparation for wet laboratory experiments in siRNA assays, with future implementation in vivo and clinical trial settings.
THE COMPARISON BETWEEN LOGISTIC REGRESSION AND CONVOLUTIONAL NEURAL NETWORK FOR MULTI-DRUG RESISTANT TUBERCULOSIS PREDICTION Widjaja, Albert; Wibowo, Satrio; Parikesit, Arli Aditya
Jurnal Bioteknologi & Biosains Indonesia (JBBI) Vol. 12 No. 1 (2025)
Publisher : BRIN - Badan Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jbbi.2025.9769

Abstract

Multi-drug resistant tuberculosis (MDR-TB) is caused by Mycobacterium tuberculosis strains that resist at least two first-line anti-TB drugs. This disease presents a major global health challenge, particularly affecting middle to lower income countries where affordable and rapid diagnostic tools are urgently needed. To address this, researchers are exploring the combination of whole genome sequencing and machine learning for drug resistance predictions. Using Mycobacterium tuberculosis genomic data from databases, both Logistic Regression (LR) and Convolutional Neural Network (CNN) models were trained to predict drug resistance. Performance evaluation revealed that CNN slightly outperformed LR in accuracy and specificity for Rifampicin and Pyrazinamide predictions, while LR showed better results for Isoniazid and Ethambutol. In terms of sensitivity, LR demonstrated superior performance for most drugs, except Ethambutol where CNN excelled. Though computational complexity assessment was incomplete due to hardware limitations, both models showed distinct advantages in predicting first-line anti-TB drug resistance.
ANALISIS GEN KOMPARATIF KARSINOMA SEL SKUAMOSA PARU-PARU ANTARA INDIVIDU MEROKOK DAN TIDAK MEROKOK [Comparative Gene Analysis of Squamous Cell Lung Carcinoma Between Smoking and Non-smoking Individuals] Shemuel, Josia; Angelique, Priscilla; Josephine, Evalina; Ryan Fugaha, Daniel; Gabriela, Vania; Alhussain, Shaheer; Parikesit, Arli Aditya
Berita Biologi Vol 22 No 3 (2023): Berita Biologi
Publisher : BRIN Publishing (Penerbit BRIN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/beritabiologi.2023.908

Abstract

Squamous cell lung carcinoma (SCC) is a form of non-small cell lung cancer that commonly arises in the primary airway. The development of SCC is closely linked to changes in squamous cells that line the airways, primarily caused by exposure to tobacco smoke. To gain insights into SCC, bioinformatics techniques have been employed to detect biomarkers and analyze gene expression patterns, utilizing data from the Cancer Genome Atlas (TCGA) database, which was preprocessed for analysis. By employing DESeq2, a differential gene expression analysis method, identified genes showed significant variations in expression between smoking and non-smoking groups among the 11,530 genes examined. Notably, five genes, namely CT45A1, GCGR, TPTE, ABCC2, and PI16, were found to play a significant role in tumor development and were susceptible to under- or over-expression due to smoking. The majority of these genes were found to be underexpressed rather than overexpressed. These identified genes hold potential as biomarkers for tumor development and exhibit a strong correlation between smoking history and the development of SCC. However, a limitation encountered during this analysis was the unavailability of data from normal non-tumor patients, which could have facilitated a more comprehensive analysis of differential gene expression. Furthermore, this research gives a deeper implementation regarding the molecular mechanisms and genomics underlying SCC development, identifies differentially expressed genes associated with SCC and smoking history, and highlights potential biomarkers that warrant further investigation.
Co-Authors Adi Sofyan Ansori, Muhammad Albert Widjaja Aldino Hafidzhah, Muhammad Alhussain, Shaheer Alyaa Farrah Dibha Angelique, Priscilla Arif Nur Muhammad Ansori Bernard, Stefanus Bhat, Nausheen Burkov, Pavel Chandra, Nelson David Agustriawan Dedy Sugiono Deidre Valeska, Margareta Derkho, Marina Dian, Farida Aryani Didik Huswo Utomo Dito Anurogo Dito ANUROGO Dito Anurogo Dito Anurogo Dito Anurogo Dito Anurogo Dito Anurogo, Dito Ema Utami Ezra Bernandus Wijaya Fugaha, Daniel Ryan Gabriela, Vania Gabriele Mustika Kresnia Gabriella Patricia Adisurja Hafidzhah, Muhammad Aldino Heerlie, Devita Mayanda Herdiansyah, Mochammad Aqilah Hutapea, Hotma Martogi Lorensi Imron Imron Jakhmola, Vikash Jeremias Ivan Josephine, Evalina Junaida Astina Karimah, Nihayatul Karimah, Nihayatul Kharisma, Viol Dhea KUSRINI Kusrini, Kusrini Maksim Rebezov Margareta Deidre Valeska Margaretha, Febrina Maria Kiseleva Maulani Wijaya, Renadya Miko Wahyono, Tri Yunis Muhammad Aldino Hafidzhah Muhammad Aldino Handzhah Muhammad Hermawan Widyananda Murtadlo, Ahmad Affan Ali Nadezhda Kenijz Natalia Satya, Putri Gabriella Angel Nelda Aprilia Salim Nihayatul Karimah Patricia Adisurja, Gabriella Patricia, Gabriella Posa, Gabrielle Ann Villar Prakoso, Muhammad Ja'far Pratama, Rico Alexander Putri Gabriella Angel Natalia Satya Rahadian Zainul Ramanto, Kevin Nathanael Ratnasari, Nanda Risqia Pradana Renadya Maulani Wijaya Ridarto, Afif Maulana Yusuf Riza A PUTRANTO Rizky Nurdiansyah Rizky, Wahyu Choirur Ryan Fugaha, Daniel Ryan Wijaya Ryan Wijaya, Ryan Satrio Wibowo Scherbakov, Pavel Sepiashvili, Ekaterina Shemuel, Josia Sofy Permana Sri Wahyuningsih Stefanus Bernard Sudaryo, Mondastri Korib Sugiono, Dedy Svetlana Artyukhova Tambunan, Usman Sumo Friend Teguh Hari Sucipto, Teguh Hari Theo A Tochary Tochary, Theo A. Usman Sumo Friend Tambunan Utomo, Didik Huswo Utomo, Didik Huswo Vikash Jakhmola Viol Dhea Kharisma Wicaksono, Adhityo Wijaya, Renadya Maulani Yanuargi, Bayu Yulia Matrosova