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All Journal Economics and Finance in Indonesia Akbis: Media Riset Akuntansi dan Bisnis Jurnal Bioteknologi & Biosains Indonesia (JBBI) Jurnal Bioteknologi & Biosains Indonesia
Albert Widjaja
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Economics, Econometrics & Finance Environmental Science Health Professions

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Dampak Kegiatan Perusahaan Multinasional terhadap Keadaan Sosial dan Politik di Indonesia Widjaja, Albert
Economics and Finance in Indonesia Volume 27, Number 4, 1979
Publisher : Institute for Economic and Social Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (28.873 KB) | DOI: 10.47291/efi.v27i4.469

Abstract

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Apakah Karakter Eksekutif, Kepemilikan Institusional dan Kualitas Audit Mempengaruhi Penghindaran Pajak ? Widjaja, Albert; Hananto, Hari; Girindratama, Muhammad Wisnu
Akbis: Media Riset Akuntansi dan Bisnis JURNAL AKBIS VOLUME 8 NOMOR 1 TAHUN 2024
Publisher : Universitas Teuku Umar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35308/akbis.v8i1.9084

Abstract

This research aims to assess whether executive characteristics, institutional ownership, and audit quality influence tax avoidance in manufacturing industry sector companies listed on the IDX for the 2018-2022 period. The research method used is quantitative. The variable used is the dependent variable consisting of tax avoidance. Meanwhile, the independent variables include executive character, institutional ownership, and audit quality. Control variables consist of firm size and leverage. The population of this research is all companies listed on the BEI in 2018-2022. Sampling is based on specific criteria using a non-probability sampling method with a purposive sampling technique. The sample criteria are completeness of the 2018-2022 financial reports, completeness of research variable data, and inclusion in the audited financial reports of the big four KAP companies. This research data analysis uses the SPSS application with multiple linear regression techniques. The results of the findings are that executive character has a positive effect on tax avoidance in manufacturing companies listed on the IDX. Institutional ownership has a negative effect on tax avoidance in manufacturing companies listed on the IDX. Audit quality has a negative effect on tax avoidance in manufacturing companies listed on the IDX.
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.
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.
Co-Authors Arli Aditya Parikesit Girindratama, Muhammad Wisnu Hari Hananto Satrio Wibowo