Article Per Year (5 Year)
p-Index From 2021 - 2026
2.56
P-Index
This Author published in this journals
All Journal Journal of Family Science Review of Indonesian Economic and Business Studies Jurnal Penelitian Kesejahteraan Sosial BISEI: Jurnal Bisnis dan Ekonomi Islam International Journal of Economics, Management, Business, and Social Science Jurnal Ketenagakerjaan Transekonomika : Akuntansi, Bisnis dan Keuangan Abimanyu: Journal of Community Engagement Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Ekonomi dan Pembangunan Indonesia Economic and Finance in Indonesia Jurnal Ekonomi Kesehatan Indonesia Indonesian Journal of Humanities and Social Sciences
Kautsar, Achmad
Department Of Economics, Faculty Of Economics And Business, Universitas Pertamina
Religion Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Public Health Social Sciences

Published : 15 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 15 Documents
Search

 1   2 
Kelompok Menengah yang Hilang: Pekerja Gig dan Ketimpangan Akses terhadap Perlindungan Sosial di Indonesia Kautsar, Achmad; Noviani, Annisa Dwi; Salsabila, Nasywa Nayifa; Putri, Latifa Azzahra
Jurnal Ketenagakerjaan Vol 20 No 3 (2025): Gig Workers
Publisher : Pusat Pengembangan Kebijakan Ketenagakerjaan Kementerian Ketenagakerjaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47198/jnaker.v20i3.625

Abstract

-
Model Ensemble untuk Prediksi Risiko Diabetes dengan Pertimbangan Efisiensi Biaya Qosimah, Rofiatul; Dhenabayu, Riska; Kautsar, Achmad; Safitri, Anita
JOM Vol 6 No 4 (2025): Indonesian Journal of Humanities and Social Sciences , December
Publisher : Universitas Islam Tribakti Lirboyo Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33367/ijhass.v6i4.8505

Abstract

This study addresses the growing global burden of diabetes by evaluating whether ensemble-based machine learning models can support reliable and cost-efficient early risk prediction. Moving beyond accuracy-centered evaluation, the study integrates cost-sensitive threshold optimization and probability calibration to enhance clinical relevance. Random Forest and XGBoost are evaluated using two datasets with contrasting population characteristics. Model performance is examined in terms of discriminative ability, calibration quality, and total misclassification cost. The results indicate that while XGBoost remains competitive on small-scale datasets, Random Forest provides more stable calibration and more consistent cost efficiency. These findings suggest that cost-sensitive and calibrated ensemble approaches have the potential to support more rational and economically efficient diabetes screening policies.  
The Effect of Innovation and Digital Capital Adoption on MSME Performance Mediated by Competitive Advantage Ramadhan, Mochammad Havid Rizqi; Kautsar, Achmad; Dewi, Renny Sari; Fazlurrahman, Hujjatullah
TRANSEKONOMIKA: AKUNTANSI, BISNIS DAN KEUANGAN Vol. 5 No. 6 (2025): November 2025
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/transekonomika.v5i6.1120

Abstract

Amid the accelerated advancement of digital technologies and the intensification of market rivalry, continuous enhancement of business performance among MSMEs has become imperative through innovation and digital transformation initiatives. Nevertheless, empirical investigations examining the contribution of digital capital adoption and innovation to MSME performance, particularly when competitive advantage functions as an intervening mechanism, remain relatively scarce, especially within regional MSME settings. This study is conducted to investigate the influence of innovation and digital capital adoption on MSME performance, with competitive advantage positioned as a mediating construct. A quantitative research design is employed, utilizing survey responses obtained from 90 MSME proprietors in Probolinggo Regency. The data that were gathered are processed and evaluated through PLS-SEM to assess both direct and indirect causal pathways among the examined variables. The results reveal that innovation is found to exert a significant positive effect on competitive advantage, which in turn is shown to significantly enhance MSME performance. On the other hand, there is no evidence that innovation directly affects the performance of MSMEs. However, adopting digital capital has been shown to have a big and positive effect on performance results. Moreover, the linkage between innovation and MSME performance is fully mediated by competitive advantage. These results suggest that innovation enhances MSME performance only when it is transformed into competitive advantage, whereas digital capital adoption directly contributes to performance improvement. Therefore, for MSME practitioners, strengthening innovation strategies oriented toward competitive differentiation and expanding the effective use of digital capital are essential to improve business performance.
Marriage and Economic Status as Predictors of Depressed Symptoms Kautsar, Achmad; Widiani, Dini; Tarani, Ni Putu Mia; Wulandari, Grace; Siregar, Adiatma Y. M.
Economics and Finance in Indonesia Vol. 70, No. 1
Publisher : UI Scholars Hub

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

Abstract

Mental disorders can result in more significant economic losses than chronic physical illnesses. Mental disorders can be influenced by both economic and non-economic factors. This study investigates the influence of marital status and economic status interventions on depression symptoms with Propensity Score Matching (PSM). The data of this analysis was obtained from the Indonesian Family Life Survey (IFLS V). The findings indicated that individuals who were married experienced a 6% reduction in depressive symptoms, while those in the middle to high economic status category experienced a 3% decrease. Enhancing well-being and promoting effective communication play crucial roles in mitigating depressive symptoms.
Evaluasi Augmentasi Generatif dan Oversampling pada Berbagai Arsitektur Model untuk Deteksi Penipuan Kartu Kredit : Evaluation of Generative Augmentation and Oversampling Across Model Architectures for Credit Card Fraud Detection Ramadhan, Gilang Bagus; Dhenabayu, Riska; Kautsar, Achmad; Fitro, Achmad; Ariyani, Nurul Fajrin
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2821

Abstract

Deteksi penipuan kartu kredit pada data dengan ketidakseimbangan kelas ekstrem merupakan tantangan karena model klasifikasi cenderung bias terhadap kelas mayoritas sehingga menurunkan kemampuan mendeteksi transaksi penipuan. Metode oversampling tradisional seperti Synthetic Minority Over-sampling Technique (SMOTE) banyak digunakan, tetapi sering belum mampu merepresentasikan kompleksitas distribusi data tabular. Sebaliknya, pendekatan generatif seperti Conditional Tabular Generative Adversarial Network (CTGAN) berpotensi menghasilkan data sintetis yang lebih realistis, meskipun evaluasinya pada berbagai model klasifikasi masih terbatas. Penelitian ini bertujuan membandingkan efektivitas empat metode augmentasi data, yaitu SMOTE, Borderline-SMOTE, SMOTE-ENN, dan CTGAN, pada model Extreme Gradient Boosting (XGBoost), TabTransformer, dan Deep Autoencoder. Evaluasi dilakukan menggunakan metrik yang sensitif terhadap kelas minoritas, meliputi Receiver Operating Characteristic–Area Under the Curve (ROC-AUC), Area Under the Precision–Recall Curve (AUPRC), serta metrik pendukung lainnya. Hasil menunjukkan bahwa kombinasi XGBoost dan Borderline-SMOTE memberikan keseimbangan precision–recall terbaik dengan F1-score 0,9269 dan AUPRC 0,8306, sedangkan TabTransformer dengan CTGAN mencapai kemampuan diskriminasi tertinggi dengan ROC-AUC 0,9846 dan precision 0,9795. Temuan ini menunjukkan bahwa efektivitas augmentasi bergantung pada kesesuaian karakteristik data sintetis dengan mekanisme pembelajaran model serta memberikan implikasi praktis bagi pengembangan sistem deteksi penipuan yang andal pada data tidak seimbang
Co-Authors Adiatma YM Siregar Amiroh, Amri Umu Annisa Fitri, Annisa Azmil Chusnaini Badrus Sholeh Citra Kumala Dewie Tri Wijayati Dinda Ayu Maharani Donny Hardiawan Estro Dariatno Sihaloho Fazlurrahman, Hujjatullah Fitro, Achmad Indriastuty, Nina Insan Lutfiana Alfarizy Jacobis, Riedel Paulus Kumala, Citra Kusumaningrum, Amalia Larosa, Nina Ardya Putri Mukholidah, Ainul Munthe, MaySarah Qonita Naflah, Jihan Duhita Nihaya, Ina Uswatun Noviani, Annisa Dwi Nugrahanti, Ratri Buda Nurul Fajrin Ariyani Oktavia, Windy Dwi Putri, Latifa Azzahra Qosimah, Rofiatul R.A. Sista Paramita Rahman, Muhammad Fajar Wahyudi Ramadhan, Gilang Bagus Ramadhan, Mochammad Havid Rizqi Ratih Amelia Renny Sari Dewi Riska Dhenabayu Safitri, Anita Salsabila, Anggun Nur Salsabila, Nasywa Nayifa Septiavin, Qori'atul Sibarani, Rachel Arielle Siregar, Adiatma Y. M. Siti Aminah Tarani, Ni Putu Mia Trias Madanika Kusumaningrum Widiani, Dini Wulandari, Grace Yunianto, Tias Andarini