Article Per Year (5 Year)
p-Index From 2021 - 2026
0.659
P-Index
This Author published in this journals
All Journal Jurnal Varian Prosiding Seminar Nasional Official Statistics Jurnal Informatika: Jurnal Pengembangan IT
Shafira, Hervira Nur
Unknown Affiliation
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Social Sciences Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Perbandingan Agglomerative Nesting dan K-Means untuk Klasterisasi Ketimpangan Gender berdasarkan Dimensi Kesehatan Reproduksi Raihannabil, Syfriza Davies; Ilyas, Hilmi Malika Atim; Shafira, Hervira Nur; Riani, May Alya; Hastin, Nadya Noor; Siregar, Tiara Khorijah Hamid
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.1977

Abstract

Gender inequality in Indonesia is ranked 4th out of 11 ASEAN countries with key problems such as high maternal mortality rates and teenage births. Indonesia ranks 3rd with the highest maternal mortality rate in Southeast Asia. Besides that, around 61% of provinces still have adolescent birth rates above the national average. This research uses clustering techniques to group provinces based on reproductive health dimensions to provide insight for policymakers. The two clustering methods used are Agglomerative Nesting (AGNES) and K-Means. The analysis found that the K-Means method was more effective in producing three clusters: 15 provinces in the medium category, 10 provinces in the high category, and 9 provinces in the low category. It is hoped that the results of this research can help the government make appropriate policies regarding improvements in the reproductive health dimensions to achieve gender equality in Indonesia, especially in provinces with high categories.
Prediction of CO2 Emissions Using ANN, ARIMAX, and Hybrid ARIMAX-ANN Models Syaharani, Afifah Dayan; Shafira, Hervira Nur; Irianto, Hikmal Mardian; Kartiasih, Fitri
Jurnal Varian Vol. 8 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i3.5045

Abstract

The escalation of carbon dioxide (CO2) emissions has emerged as a critical environmental concern, particularly in the context of Indonesia’s pursuit of sustainable development. This study aims to forecast CO2 emissions in Indonesia using annual time-series data spanning 1967–2023. Three methodological approaches are employed: an artificial neural network (ANN), an autoregressive model with exogenous variables (ARIMAX), and a hybrid ARIMAX-ANN model. The dataset comprises Gross Domestic Product obtained from the World Bank, along with per capita CO2 emissions, per capita natural gas consumption, and per capita hydropower consumption sourced from Our World in Data. The findings of this research demonstrate that the hybrid ARIMAX-ANN model provides the best forecasting performance, as evidenced by the lowest RMSE, MAPE, and MAE values among the other two models. These results suggest that the hybrid model is currently the most reliable for predicting CO2 emissions in the Indonesian context. The study enriches the expanding literature on emission forecasting by providing empirical evidence to support data-driven policymaking for climate change mitigation and sustainable energy development in Indonesia. 
Pemodelan Topik Komentar Terhadap Aplikasi Allstat BPS Tahun 2017-2025 Simanungkalit, Gabriella Elisabeth; Shafira, Hervira Nur; Nooraeni, Rani
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.9350

Abstract

Penelitian ini dilatarbelakangi oleh meningkatnya kebutuhan akan data statistik yang mudah diakses melalui aplikasi mobile, salah satunya adalah aplikasi AllStat BPS. Tujuan dari penelitian ini adalah untuk menganalisis sentimen dan mengidentifikasi topik utama dalam ulasan pengguna aplikasi AllStat BPS pada periode 2017–2025. Metode yang digunakan mencakup analisis sentimen berbasis lexicon dengan kamus InSet dan klasifikasi menggunakan algoritma Naive Bayes, Random Forest, dan Support Vector Machine (SVM). Pemodelan topik dilakukan dengan pendekatan Latent Dirichlet Allocation (LDA). Hasil penelitian menunjukkan bahwa model Random Forest memberikan performa klasifikasi terbaik dengan akurasi pada data latih sebesar 88,16% dan nilai kappa 0,8046. Selain itu, LDA berhasil mengidentifikasi delapan topik utama dari ulasan pengguna, dengan Topik 1 memiliki nilai koherensi tertinggi (0,1784) yang mengindikasikan kekuatan semantik antar kata dalam topik tersebut. Topik-topik ini kemudian dipetakan ke dalam kerangka kualitas perangkat lunak berdasarkan standar ISO/IEC 25010, dengan aspek Functional Suitability dan Performance Efficiency sebagai topik dominan. Kesimpulan dari penelitian ini adalah bahwa kombinasi metode Random Forest dan LDA efektif dalam mengklasifikasikan sentimen serta menggambarkan fokus isu dalam ulasan pengguna aplikasi AllStat BPS.
Co-Authors Fitri Kartiasih Hastin, Nadya Noor Ilyas, Hilmi Malika Atim Irianto, Hikmal Mardian Nooraeni, Rani Raihannabil, Syfriza Davies Riani, May Alya Simanungkalit, Gabriella Elisabeth Siregar, Tiara Khorijah Hamid Syaharani, Afifah Dayan