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All Journal JURNAL ILMIAH MATEMATIKA DAN TERAPAN Buletin Sistem Informasi dan Teknologi Islam Priviet Social Sciences Journal
Safitriani, Nur Rezky
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Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences

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Temperature Data Prediction in South Sulawesi Province Using Seasonal-Generalized Space Time Autoregressive (S-GSTAR) Model Rizal, Muhammad Edy; Fathan, Morina A.; Safitriani, Nur Rezky; Yahya, Muhammad Zarkawi; Asfar
JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 21 No. 2 (2024)
Publisher : Program Studi Matematika, Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/2540766X.2024.v21.i2.17516

Abstract

Indonesia's distinct tropical climate is influenced by its geographic location near the equator and its complex topography, resulting in pronounced seasonal temperature patterns. This study examines the application of the Seasonal Generalized Space-Time Autoregressive (SGSTAR) model to forecast the average air temperature in four regions of South Sulawesi Province: North Luwu, Tana Toraja, Maros, and Makassar. The dataset comprises monthly average temperatures from January 2019 to October 2024, sourced from BMKG's online database. The analysis includes stationarity testing using the Augmented Dickey-Fuller (ADF) test, seasonal pattern identification with autocorrelation function (ACF), and formal seasonal tests such as QS, QS-R, and KW-R. Spatial weight matrices were constructed based on Euclidean distances between regions. The best model was selected based on Mean Square Error (MSE), Root Mean Square Error (RMSE), Akaike Information Criterion (AIC), and adjusted R² criteria. The findings reveal that the seasonal GSTAR model with AR orders (p=3), (ps=4), and (s=12) is the optimal model. Evaluation indicates that the model achieves high accuracy, with forecast errors (MSE and RMSE) below 1°C. This model effectively captures seasonal and spatio-temporal patterns in climate data. The study is expected to serve as a foundation for further development of seasonal GSTAR models for other climate datasets, supporting improved environmental planning and resource management.
Analysis of macroeconomic factors affecting poverty levels in Indonesia using a dummy regression model approach Safitriani, Nur Rezky; Ambarwati, Valina; Jannah, Miftahull; Safitri, Nabila
Priviet Social Sciences Journal Vol. 5 No. 8 (2025): August 2025
Publisher : Privietlab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55942/pssj.v5i8.630

Abstract

Poverty in Indonesia remains a complex macroeconomic issue, influenced by various social, economic, and regional disparities. This study employed a dummy variable regression model to analyze the factors affecting poverty more comprehensively, allowing for the identification of categorical geographic effects. This study examines the influence of the Gender Empowerment Index, Expected Years of Schooling, Gini Ratio, Open Unemployment Rate, and Formal Employment on the percentage of the poor population in Indonesia, while considering regional classifications in Western, Central, and Eastern Indonesia. The results show that the Gender Empowerment Index and proportion of Formal Employment have a significant negative effect on poverty, while the Gini Ratio has a significant positive effect. Additionally, the Western and Central regions exhibit significantly lower poverty rates than the eastern region. The dummy regression model explains 83,64% of the variation in poverty across provinces, making it a relevant basis for formulating region-specific and macro-economically informed poverty alleviation policies.
Aplikasi Sistem Monitoring Produksi dengan Diagram Kontrol Fuzzy Multivariat Berbasis Alpha-cut dan Transformasi Median Safitriani, Nur Rezky; Widyaningrum, Erlyne Nadhilah; Putri, Rizka Amalia; Khoirunnisa, Husna Afanyn; Fathan, Morina A.
Buletin Sistem Informasi dan Teknologi Islam (BUSITI) Vol 6, No 3 (2025)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v6i3.2874

Abstract

Pengendalian kualitas produksi yang adaptif menjadi kebutuhan mendesak dalam menghadapi data multivariat dengan ketidakpastian, disertai tuntutan untuk meningkatkan kualitas produk. Hal ini dapat diatasi menggunakan teori himpunan fuzzy melalui alat Statistical Process Control berupa diagram kontrol. Penelitian ini mengembangkan aplikasi sistem monitoring produksi menggunakan diagram kontrol multivariat fuzzy T2 Hotelling berbasis alpha-cut dan transformasi median. Aplikasinya dilakukan pada industri material bangunan di UD Tiga Beton sebagai penghasil batako press. Monitoring dilakukan pada dua karakteristik kualitas yang saling berkorelasi, yaitu kondisi fisik dan bidang permukaan, yang direpresentasikan dalam bentuk linguistik. Data pengamatan dikonversi ke dalam bilangan fuzzy menggunakan Triangular Fuzzy Number dan proses defuzzifikasi melalui transformasi median serta tambahan alpha-cut sebesar 0,6 agar dapat monitoring pergeseran mean yang kecil. Hasil penerapannya menunjukkan bahwa empat pengamatan terdeteksi berada di luar batas sehingga mengindikasikan proses produksi berada dalam keadaan out of control. Dengan demikian, aplikasi sistem ini terbukti mampu mendeteksi penyimpangan proses secara lebih akurat dan praktis. Diagram kontrol fuzzy multivariat berbasis alpha-cut dan transformasi median menjadi alternatif yang adaptif dalam pengendalian kualitas pada berbagai produksi.
Co-Authors Ambarwati, Valina Asfar Erlyne Nadhilah Widyaningrum Fathan, Morina A. Jannah, Miftahull Khoirunnisa, Husna Afanyn Muhammad Edy Rizal Putri, Rizka Amalia SAFITRI, NABILA Yahya, Muhammad Zarkawi