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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Aptisi Transactions on Technopreneurship (ATT) Infolitika Journal of Data Science
Muhamad Safiih Lola
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Developing a Regional Framework for Disaster Risk Reduction Based on Disaster-Related Data from Aceh, Indonesia Yolanda, Yolanda; Oktari, Rina Suryani; Munawar, Munawar; Lola, Muhamad Safiih; Sofyan, Hizir
Infolitika Journal of Data Science Vol. 3 No. 1 (2025): May 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v3i1.269

Abstract

Aceh Province is highly vulnerable to various hazards, necessitating effective disaster risk reduction strategies. This study aims to develop an instrument to evaluate disaster risk reduction efforts in Aceh Province and to assess progress toward global disaster resilience targets. The data includes secondary disaster-related records from 2005 to 2024 and primary data from the instrument validation process, demonstrating excellent validity results based on the Content Validity Ratio (CVR) and Content Validity Index (CVI). The findings highlight significant improvements in key areas, including reductions in disaster mortality, affected populations, economic losses, damage to critical infrastructure, and strengthened early warning systems. However, challenges persist in implementing local disaster risk reduction strategies and enhancing international cooperation. This study offers practical insights for policymakers and contributes to strengthening disaster resilience and advancing disaster risk management research in sub-national contexts.
COX PROPORTIONAL HAZARD REGRESSION SURVIVAL ANALYSIS FOR TYPE 2 DIABETES MELITUS Mahmudah, Umi; Surono, Sugiyarto; Prasetyo, Puguh Wahyu; Lola, Muhamad Safiih; Haryati, Annisa Eka
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (610.339 KB) | DOI: 10.30598/barekengvol16iss1pp251-260

Abstract

One of the most widely used methods of survival analysis is Cox proportional hazard regression. It is a semiparametric regression used to investigate the effects of a number of variables on the dependent variable based on survival time. Using the Cox proportional hazard regression method, this study aims to estimate the factors that influence the survival of patients with type 2 diabetes mellitus. The estimated parameter values, as well as the Cox Regression equation model, were also investigated. A total of 1293 diabetic patients with type 2 diabetes were studied, with data taken from medical records at PKU Muhammadiyah Hospital in Yogyakarta, Indonesia. These variables have regression coefficients of 1.36, 1.59, -0.63, 0.11, and 0.51, respectively. Furthermore, the results showed the hazard ratio for female patients was 1.16 times male patients. Patients on insulin treatment had a 4.92-fold higher risk of death than those on other therapy profiles. Patients with normal blood sugar levels (GDS 140 mg/dl) had a 1.12 times higher risk of death than those with other blood glucose levels. Type 2 diabetes mellitus is a challenge for many Indonesians, in addition to being a deadly condition that was initially difficult to diagnose. As a result, patient survival analysis is needed to reduce the patient's risk of death.
A TWO-STEP CLUSTER FOR CLASSIFYING PROVINCES IN INDONESIA BASED ON ENVIRONMENTAL QUALITY Mahmudah, Umi; Lola, Muhamad Safiih
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1685-1694

Abstract

The main objective of this study was to conduct a cluster analysis of the environmental health index in Indonesia for all the provinces. Clustering the environmental health index was important to reveal regional disparities, target and intervention policies, monitor progress over time, and allocate resources more effectively for improved environmental health outcomes. In this study, a sample of 34 units was utilized, encompassing all provinces in Indonesia. The environmental health index was clustered based on five indicators, namely Water Quality Index, Air Quality Index, Soil Quality Index, Marine Quality Index, and Land Cover Quality Index. This research used the two-stage clustering method, which was unique in combining both hierarchical and non-hierarchical clustering methods to produce a more accurate and reliable solution. Four clusters were determined to group provinces in Indonesia based on the environmental health index. The analysis found that the quality of clustering was in the fair but close to good category. The clustering results showed that 32% of the provinces were in cluster 4 and 26.5% of the provinces were in cluster 1. Then, 23.5% and 17.6% of the provinces were in clusters 2 and 3, respectively. In addition, two indicators were found to be the most predictive of the overall clustering solution, namely the Soil Quality Index and the Land Cover Quality Index. The results also implied that provinces in cluster 3 had the lowest environmental quality so they must improve it by looking at provinces in cluster 4, which was the group with the best environmental quality index.
Leveraging A Hybrid Machine Learning Model for Enhanced Cyberbullying Detection Syafariani, Fenny; Lola, Muhamad Safiih; Mutalib, Sharifah Sakinah Syed Abd; Nasir, Wan Nuraini Fahana Wan; Hamid, Abdul Aziz K. Abdul; Zainuddin, Nurul Hila
Aptisi Transactions On Technopreneurship (ATT) Vol 7 No 2 (2025): July
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v7i2.536

Abstract

Cyberbullying is a form of bullying that occurs through digital technology on various social media platforms. This issue has become critical, particularly when it involves racial statements that can threaten community harmony. Many researchers worldwide are working on solutions for automatic hate speech and cyberaggression detection using different machine learning models. This study aims to introduce a novel hybrid method for detecting cyberbullying, utilizing a combination of Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA), collectively referred to as SVM-LDA. The methodology involves integrating SVM and LDA techniques. The models efficiency was assessed using various metrics, offering a comparative analysis of the hybrid model against individual machine learning models. The results show that the proposed hybrid model achieved 96.1% accuracy and outperformed single machine learning models on the Twitter dataset. The hybrid model also demonstrated robustness in handling imbalanced classes for cyberbullying detection. The proposed SVM-LDA hybrid approach shows significant potential in effectively detecting cyberbullying, even in cases of class imbalance. This model offers a more robust solution compared to traditional single machine learning models in detecting cyberaggression.
Co-Authors Hamid, Abdul Aziz K. Abdul Haryati, Annisa Eka Hizir Sofyan Mutalib, Sharifah Sakinah Syed Abd Nasir, Wan Nuraini Fahana Wan Norizan Muhammed Oktari, Rina Suryani Puguh Wahyu Prasetyo, Puguh R Fenny Syafariani Sugiyarto Surono, Sugiyarto Teoh Kah Seng Umi Mahmudah Umi Mahmudah Yolanda Yolanda, Yolanda Zainuddin, Nurul Hila