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All Journal Unisda Journal of Mathematics and Computer Science (UJMC) Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) International Journal of Research in Community Services Jurnal Sains dan Teknologi Journal of Sustainable Community Development Shof Journal of Social Impact Joong-Ki Jurnal Ekonomi Manajemen dan Sekretari
Melda Juliza
Universitas Jenderal Soedirman
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Evaluasi Metode Inisialisasi pada Model Pemulusan Eksponensial melalui Data IPM Banyumas Raya Melda Juliza; Novita Eka Chandra; Felinda Arumningtyas; Puce Angreni
UJMC (Unisda Journal of Mathematics and Computer Science) Vol. 12 No. 1 (2026): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department, Faculty of Sciences and Technology Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v12i1.13519

Abstract

The forecasting accuracy of exponential smoothing models is significantly influenced by the determination of initial values (initialization). This study aims to evaluate the performance of initialization methods for Brown’s Double Exponential Smoothing model using Human Development Index (HDI) data from the Banyumas Raya region for the period 2010-2025. The research stages included identifying data patterns, constructing models using both simple initialization and optimal initialization with numerical optimization, performing the Ljung-Box test for residual diagnostics, and comparing model accuracy. Evaluation results indicate that the Brown model using the optimal initialization method effectively captures trend patterns. The application of optimal initialization consistently improved model accuracy across all regencies. The highest error improvement was observed in Banyumas Regency (28.65%), followed by Cilacap (28.29%), Banjarnegara (24.77%), and Purbalingga (22.89%). Based on these results, the optimal initialization model was used to project HDI values for the next three periods, revealing a sustained upward trend. In conclusion, determining initial values is a crucial component that alongside smoothing parameter optimization must be seriously considered when developing forecasting models.
Analisis Hubungan Indeks Ketahanan Pangan dan Presentase Kemiskinan di Jawa Tengah dengan Korelasi Kendall Tau Novita Eka Chandra; Melda Juliza; Abdul Aziz; Ari Wardayani
UJMC (Unisda Journal of Mathematics and Computer Science) Vol. 12 No. 1 (2026): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department, Faculty of Sciences and Technology Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v12i1.13623

Abstract

Food security and poverty are two important economic indicators that are interrelated and are a primary focus of the Sustainable Development Goals (SDGs). This study aims to analyze the strength and direction of the relationship between the Food Security Index (IKP) and the percentage of district/city poverty in Central Java Province from 2022 to 2025. The data used do not meet the assumption of a normal distribution and have several identical values (ties), so the analysis method used is the Kendall Tau correlation. The results show a significant negative relationship between the IKP and the percentage of poverty in Central Java. These results indicate that the IKP at the district/city level plays a role in reducing the percentage of poverty.
MODEL EXTENDED COX UNTUK MENGATASI NON-PROPORTIONAL HAZARD PADA DATA STUDI KANKER PARU-PARU Felinda Arumningtyas; Melda Juliza; Novita Eka Chandra; Amelia Wulandari
Jurnal Ilmiah Matematika dan Pendidikan Matematika Vol 17 No 2 (2025): Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP)
Publisher : Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jmp.2025.17.2.18061

Abstract

Lung cancer is a major cause of cancer-related deaths in Indonesia, making it essential to identify factors influencing patient survival. This study aims to analyze lung cancer patient survival using the Extended Cox Model as an alternative when the Proportional Hazard (PH) assumption is not met. Secondary data from 137 lung cancer patients were analyzed using variables such as type of treatment, treatment history, cancer cell type, Karnofsky score, age, and time of diagnosis. The results showed that only the Karnofsky score was significant in Cox-PH, but the assumption test showed that the cell type and Karnofsky score variables violated PH. Therefore, the analysis was continued with the Extended Cox Model. The final results showed that cell type and Karnofsky score had a significant effect on survival. The Hazard Ratio showed that a certain cell type reduced the risk of death by 22.7%, and an increase of one unit in the Karnofsky score reduced the risk of death by 3.1%. Cancer cell type and Karnofsky score are important factors in the survival of lung cancer patients, and the Extended Cox model has been proven to provide more reliable estimates than Cox-PH when the PH assumption is not being followed.
Co-Authors Abdul Aziz Adika Setia Brata Agung Prabowo Agung Prabowo Alfian Anhar Amelia Wulandari Ari Wardayani, Ari Arumingtyas, Felinda Felinda Arumningtyas M Tantry Abeng Evan Nugroho Muhammad Hafidh Nashrullah Novita Eka Chandra Puce Angreni Sherly Steffiyani Askarilia Suci Rahmawati Windy Lestari