Article Per Year (5 Year)
p-Index From 2020 - 2025
0.888
P-Index
This Author published in this journals
All Journal JOIV : International Journal on Informatics Visualization Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Innovative: Journal Of Social Science Research
Putera, Muhammad Luthfi Setiarno
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Comparative Implementation of Legal Systems between Indonesia and Yemen Aulia, Ajeng Hijiratul; Latif, Abdul; Aditya, Muhammad; Oktaviany, Nadilla; Hidayati, Sri; Putera, Muhammad Luthfi Setiarno
Innovative: Journal Of Social Science Research Vol. 4 No. 6 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i6.16653

Abstract

The legal system is a framework of rules governing human behavior in society to ensure justice, order, and welfare. This study examines the legal systems of Indonesia and Yemen, highlighting their differences and similarities and their impact on society and political structures. Indonesia adopts a mixed legal system combining legislation, customary law, and Islamic principles, influenced by cultural pluralism and Dutch colonial heritage. In contrast, Yemen's legal system is predominantly based on Sharia law, with formal courts emphasizing Islamic principles as their primary foundation.Using a normative legal research method with a literature review approach, the study draws data from books, scholarly journals, prior research, and relevant articles. Findings reveal Indonesia's pluralistic approach to law reflects its diverse cultural, religious, and ethnic landscape, while Yemen's system is rooted in Islamic law, reflecting a more uniform legal framework. Despite structural differences, both countries acknowledge the central role of Islamic law in their legal systems. This research contributes to a deeper understanding of the legal dynamics in both nations, offering insights for future legal development and enriching the global discourse on legal pluralism.
Machine learning survival analysis on couple time-to-divorce data Putera, Muhammad Luthfi Setiarno; Setiarno, Setiarno
Desimal: Jurnal Matematika Vol. 5 No. 3 (2022): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v5i3.13742

Abstract

Marriage life does not always last harmoniously and occasionally can lead to divorce. The trend for the last three years since 2019 shows that divorce cases in Palangka Raya occur with a fluctuating trend that has recently been increasing. This research used a machine learning method called Survival Support Vector Machine on the divorce dataset in Palangka Raya. This research developed a feature selection technique using backward elimination to determine the factors influencing the couple’s decision to have their divorce registered in the religious court. The backward elimination method yielded the variables contributing to divorce: the number of children, the defendant's occupation, the plaintiff's age at marriage, the cause of divorce, and the defendant's education. Based on the comparison of the survival model performance between the Cox proportional hazard and the Survival Support Vector Machine, it was found that the latter was better since it had a higher concordance index and hazard ratio, which were 61.24 and 0.54, respectively. Thus, 61.24% of divorce cases were classified precisely by SUR-SVM in terms of the time sequence of events. Moreover, the hazard ratio of 0.54 indicated that the divorce rate of couples with censored status was 0.54 times than that of couples with failed/endpoint status.
PERAMALAN TRANSAKSI PEMBAYARAN NON-TUNAI MENGGUNAKAN ARIMAX-ANN DENGAN KONFIGURASI KALENDER Putera, Muhammad Luthfi Setiarno
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 14 No 1 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (918.678 KB) | DOI: 10.30598/barekengvol14iss1pp135-146

Abstract

Huge internet usage boosts transactions using non-cash payment. In Indonesia, lots of activities and transactions are influenced by calendar movement, particularly that of the Islamic calendar. This work is to obtain the model and to forecast total non-cash payment with calendar configuration as an exogenous variable. The methods being compared are ARIMA, ARIMAX, and hybrid ARIMAX-ANN. The best model to forecast a total of non-cash payment is ARIMAX-ANN due to the least RMSE, Rp 20,9 trillion. The specification of the best model is ARIMAX (2,1,1) combined with ANN whose input is selected through stepwise regression. Besides satisfying residual assumption, ARIMAX-ANN is quite well in capturing the dynamics and trend of non-cash payment, particularly that in Ied-Fitr month and end of the year.
Predictive Performance of Machine Learning on Low-Birth-Weight Classification: A Study from Asia Developing Countries Putera, Muhammad Luthfi Setiarno; Adawiyah, Rabiatul; Ahmidi, Ahmidi
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3384

Abstract

This study is aimed to evaluate the predictive performance of several machine learning models in classifying low birth weight (LBW) infants. This classification is necessary, as low birth weight is linked to many health hazards for newborns. This study conducted machine learning to examine socio-economic variables, maternal health, and additional pertinent aspects that influence low birth weight (LBW) in developing countries, such as India, Indonesia, Jordan, and the Philippines. The independent variables were type of residence, number of household members, mother's education level, mother's occupation, father's occupation, welfare status, number of births for the last 5 years, mother's age at first birth, mother's smoking status, birth order, infant's alive status, number of antenatal care visit, and type of antenatal care. The total eligible sample included 12,393 respondents of Indonesia, 21,681 of India, 6,365 of Jordan, and 5,704 of the Philippines. The findings demonstrate that several machine learning models, including Support Vector Machines (SVM), Random Forest, and Decision Trees, exhibit differing degrees of accuracy in predicting low birth weight (LBW) across India, Indonesia, Jordan, and the Philippines. For example, SVMs exhibited superior performance, although Naive Bayes attained elevated sensitivity. The results indicate that customized strategies reflecting regional attributes are necessary for enhancing prediction precision in LBW classification. This underscores the need of accounting for local socio-demographic variables when using machine learning models in healthcare study.
Co-Authors Abdul Latif Ahmidi, Ahmidi Aulia, Ajeng Hijiratul Muhammad Aditya, Muhammad Oktaviany, Nadilla Rabiatul Adawiyah Setiarno, Setiarno Sri Hidayati