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METODE ARIMA BOX-JENKINS PADA DATA KUNJUNGAN WISATAWAN MANCANEGARA DI INDONESIA Ruliana, Ruliana; Aidid, Muhammad Kasim; Adiyanty, Tri Amelia
Prosiding Seminar Nasional Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika (VARIANSI) Vol 2 (2020)
Publisher : Program Studi Statistika, FMIPA, Universitas Negeri Makassar

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Tujuan dari penelitian ini adalah untuk mengetahui deskripsi jumlah kunjungan wisatawan mencanegara ke Indonesia mulai dari Januari 2015 - Februari 2020, untuk mengetahui model Time Series Box-Jenkins dan nilai ramalan jumlah kunjungan wisatawan mencanegara ke Indonesia pada tahun-tahun berikutnya. Sumber data yang digunakan diperoleh dari data publikasi kunjungan wisatawan asing di Indonesia dari Januari 2015 - Februari 2020. Data yang digunakan meliputi data jumlah kunjungan wisatawan mancanegara (wisman) setiap bulannya di 26 pintu masuk utama yang meliputi Bandara, Pelabuhan dan Stasiun. Data tersebutÂ di publikasi oleh kementrian Parawisata yang adobsi dari data Ditjen Imigrasi dan BPS. Variabel yang digunakan dalam penelitian ini adalah t (waktu kedatangan) dan Variabel Yt (jumlah kunjungan wisatawan). Hasil analisis deskriptif dari penelitian ini diperoleh rata-rata kunjungan wisatawan mancanegara ke Indonesia sejak Januari 2015 â€“ Februari 2020 adalah 1,129,341 kunjungan. Kunjungan paling rendah terjadi pada Januari 2015 sebanyak 785,973, sedangkan kunjungan paling tinggi sebanyak 1,547,231 kunjungan yang terjadi pada Juli 2018. Model time series Box-Jenkins yang didapatkan adalah model ARIMA (1, 1, 0) dengan persamaan model . Hasil peramalan dari model ini adalah tidak adanya kunjungan ke Indonesia mulai dari Maret 2020- hingga Februari 2022Â Kata Kunci: ARIMA, Box-jenkins, Kunjungan Wisatawan Mancanegara, Peramalan.

EKSPLORASI LITERASI STATISTIKA DESKRIPTIF MAHASISWA PROGRAM STUDI STATISTIKA UNIVERSITAS NEGERI MAKASSAR DALAM SUASANA PEMBELAJARAN DARING AKIBAT DARURAT COVID-19 Tiro, Muhammad Arif; Ruliana, Ruliana; Aswi, Aswi
Prosiding Seminar Nasional Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika (VARIANSI) Vol 2 (2020)
Publisher : Program Studi Statistika, FMIPA, Universitas Negeri Makassar

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Sejak Maret 2020, pembelajaran di Universitas Negeri Makassar dilaksanakan secara daring (online) akibat pandemi COVID-19. Situasi ini tentu mempengaruhi kegiatan pembelajaran di kampus. Penelitian eksploratif ini bertujuan untuk mengeksplorasi pencapaian peubah literasi statistika deskriptif bagi mahasiswa Program Studi Statistika Universitas Negeri Makassar di masa pandemi menurut lima kompetensi dasar literasi statistika. Peneliti mengadaptasi model pengembangan instrument 4-D menjadi model 4-P dalam pengembangkan instrumen penilaian literasi statistika deskriptif. Selanjutnya, instrumen penilaian yang diperoleh diterapkan untuk memetakan mutu literasi statistika deskriptif mahasiswa. Kelima kompetensi dasar literasi statistika tersebut adalah: (1) pemahaman konsep statistika deskriptif, (2) keterampilan menghitung nilai statistika deskriptif, (3) wawasan aplikasi statistika deskriptif, (4) kecermatan interpretasi nilai statistika deskriptif, dan (5) keterampilan visualisasi dan komunikasi informasi statistika deskriptif. Kompetensi yang mencapai tingkat capaian tertinggi adalah keterampilan visualisasi (60%) sedangkan kecermatan interpretasi merupakan capaian terendah (32%). Secara umum, capaian dalam hal literasi statistika deskriptif mahasiswa Program Studi Statistika UNM dalam suasana pembelajaran daring akibat darurat Covid-19 tergolong sedang. Materi statistika deskriptif yang diajarkan dan sistem penilaian di perguruan tinggi perlu menekankan pada lima kompetensi dasar yang telah dijelaskan.Â Kata Kunci: literasi statistika deskriptif, kompetensi literasi statistika deskriptif

PEMODELAN GENERALIZED POISSON REGRESSION (GPR) UNTUK MENGATASI PELANGGARAN EQUIDISPERSI PADA REGRESI POISSON KASUS CAMPAK DI KOTA SEMARANG TAHUN 2013 Ruliana, Ruliana; Hendikawati, Putriaji; Agoestanto, Arief
Unnes Journal of Mathematics Vol 5 No 1 (2016)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v5i1.13103

The measles the Semarang experience fluctuates every year, so that the City Health Agency (DKK) Semarang put special attention to reducing many cases measles.In the case of smallpox semarang 2013 was data discrete Poisson. Regression Poisson is nonlinear regression used to analyze data count variable response Poisson and meet the equidispersi. In practice often occurs in violation of discrete overdispersi analysis of data in regression poisson underdispersi and models or improper use.To anticipate such violation used Generalized Poisson Regression in modeling (GPR) data. In this research are variable response used in the case of smallpox Semarang 2013 and variable prediktor used is many medicines measles, community health centers, the poverty and overcrowding every subdistrict across Semarang town. The best model Generalized Poisson Regression (GPR) was gotten.

Pengaruh Makanan Kaya Gizi (KAYAZI) sebagai Variasi Diet Energi Protein Tinggi terhadap Daya Terima Makanan Pasien Covid-19 Anggraeny Putri, Nur; Ruliana, Ruliana; Yulianingrum, Chintya
Jurnal Klinik dan Riset Kesehatan Vol 1 No 2 (2022): Edisi Februari
Publisher : RSUD Dr. Saiful Anwar Province of East Java

LATAR BELAKANG: Diet Energi Protein Tinggi (EPT) diberikan sebagai dukungan gizi terapi infeksi, termasuk pada Covid-19. Daya terima diet (sisa makanan dan tingkat kepuasan) yang rendah dapat berdampak buruk bagi penyembuhan pasien, namun dapat diselesaikan dengan modifikasi menu. Variasi menu diet EPT berupa KAYAZI (Makanan Kaya Gizi) menjadi inovasi gizi, termasuk bagi pasien Covid-19. TUJUAN: Mengetahui pengaruh pemberian KAYAZI sebagai variasi diet EPT terhadap daya terima ditinjau dari sisa makanan dan kepuasan diet PDP di RSUD dr. Saiful Anwar Malang. METODE: Penelitian Quasy Experimental, metode purposive sampling. Didapatkan 33 sampel tiap kelompok (diet TKTP standar; diet KAYAZI) sesuai kriteria inklusi-eksklusi. Data sisa makanan dari pengamatan dan pengisian form visual Comstock. Data kepuasan diet didapat dari pengisian kuesioner skala likert metode hedonic scale test. Data diolah menggunakan software statistik SPSS ver.16; uji hipotesis nonparametrik Mann-Whitney test nilai p<0,05. HASIL: persentase sisa makanan diet KAYAZI lebih kecil (10,5%) dibanding diet TKTP (25,03%); rerata nilai tingkat kepuasan diet KAYAZI lebih tinggi di semua kategori penilaian dibanding diet TKTP. Dari uji Mann-Whitney, nilai p<0,05 pada variabel sisa makanan dan kepuasan diet (parameter: aroma, rasa, warna, bentuk, tempat makan, alat makan). Untuk kepuasan diet pada parameter tekstur dan cara penyajian makanan, didapatkan nilai p>0,05. KESIMPULAN: Pemberian KAYAZI berpengaruh terhadap daya terima ditinjau dari sisa makanan dan kepuasan diet; kategori citarasa makanan (aroma, rasa), tampilan makanan (bentuk, warna), penyajian makanan (tempat makanan, alat makan). Namun tidak berpengaruh dari segi kepuasan diet untuk parameter tekstur makanan (kategori citarasa makanan) dan cara penyajian diet (kategori penyajian makanan).

Implementation of the Support Vector Regression (SVR) Method in Inflation Prediction in Makassar City Ruliana, Ruliana; Rais, Zulkifli; Marni, Marni; Ahmar, Ansari Saleh
ARRUS Journal of Mathematics and Applied Science Vol. 4 No. 1 (2024)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience2608

Inflation is an important economic indicator, the growth rate is always kept low and stable. One step to deal with the possibility of a high inflation rate is to know the picture of the inflation rate in the future by making predictions. Prediction is a method used to determine a value or need in the next period. Support Vector Regression (SVR) is a development of the Support Vector Machine (SVM) method which is used for regression cases which can handle non-linear data cases. The problem that often occurs when using the SVR method is determining optimal model parameters. One way to determine the best parameters for the SVR method is to use Grid Search Optimization. The stages of the SVR method include data normalization, dividing training data and testing data, using the Radial Basis Function kernel, selecting the best parameters using Grid Search Optimization, and making predictions using the best model obtained with parameters γ = 10, ∁ = 100, and ε. = 0.1 with k = 5. The prediction results obtained were then evaluated by looking at the RMSE value, the RMSE value obtained was 0.029, which means the model's ability to follow the data pattern well and the prediction results made were very good.

Pelatihan Software R Bagi Dosen Universitas Al-Syariah Mandar Annas, Suwardi; Nusrang, Muhammad; Irwan, Irwan; Rais, Zulkifli; Ruliana, Ruliana
Panrannuangku Jurnal Pengabdian Masyarakat Vol. 3 No. 1 (2023)
Publisher : Lembaga Penelitian dan Pengembangan Teknologi dan Rekayasa, Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/panrannuangku1810

This Community Partnership Program (PKM) partner is a lecturer at FKIP Al-Syariah Mandar University. The problems are: (1) R as an open source software that is free of the license is not widely known in scope, (2) Lack of skills in statistical data analysis mainly related to regression analysis, path analysis, and SEM using R. The methods used are lectures, demonstrations, discussions, questions and answers, and co-partners. The results achieved are (1) partners have R software knowledge related to downloading and installing the software, (2) partners have superior knowledge possessed by the R package, (3) partners have knowledge and ability to analyze data related to path analysis and SEM analysis with using R.

Analysis of Application of Accounting Systems and Procedures In Village Fund Management (Case Study of Sukaramai Village, Kec.Sei Balai of Batu Bara Regency) Ruliana, Ruliana; Nurlaila, Nurlaila; Harahap, Muhammad Ikhsan
Journal of Management, Economic, and Accounting Vol. 2 No. 2 (2023): Juli-Desember
Publisher : Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmea.v2i2.185

This research was conducted with the aim of knowing how to apply accounting systems and procedures that are implemented in the Sukaramai Village office. This research is a qualitative research using a descriptive approach. Data collection techniques used are observation, interviews and documentation. The results of the study show that the accounting systems and procedures for financial management in Sukaramai Village are in accordance with Permendagri Number 20 of 2018. It's just that in the accounting system, Village officials have insufficient human resources, so they experience difficulties managing the accounting system. Then in the accounting procedures at the Sukaramai Village Office there were recording errors in reporting the realization of the 2020 budget. Even though the Village had attended training held both from the District and from the Regent the Village apparatus still experienced difficulties in terms of the accounting system. Even so, the Sukaramai Village officials are still trying and trying to work optimally.

Spatio Temporal Modeling in Analyzing Temperature Humidity Index Using Google Earth Engine in South Sulawesi: Impact Analysis and Sustainable Mitigation Efforts Susiyanti, Susiyanti; Nasrul, Nasrul; Maddatuang, Maddatuang; Ruliana, Ruliana; Maru, Rosmini
Jambura Geoscience Review Vol 7, No 2 (2025): Jambura Geoscience Review (JGEOSREV)
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jgeosrev.v7i2.30644

Climate change is one of the biggest challenges and threats to humanity today whose impact has very high dynamics. The high population density, population number, and population activity cause physical changes that affect the microclimate. Microclimate change can reduce people's thermal comfort/temperature humidity index (THI). The impact felt by the community is in the form of discomfort in outdoor activities, health risks such as heat stress, impact on agriculture and natural resource conservation, and animal health on farms to impact emergency response in disaster situations. Moreover, the summer of 2023, a record-breaking summer in 2023, continues the long-term warming trend, indicating the need for a study on the state of THI. This study aims to analyze THI trends and map the distribution of THI in South Sulawesi Province in 2013-2023. The method used in this study is a quantitative-descriptive method through a remote sensing approach using Google Earth Engine (GEE). The Temperature Humidity Index (THI) of South Sulawesi Province in the 2013-2023 time bracket with a cold category with a THI value of 21 °C has an area of 994384.62 hectares, the comfortable category with a THI value of 21°C – 24°C has an area of 1190393.32 hectares, the fairly comfortable category with a THI value of 24 °C – 26°C has an area of 1810662.30 hectares and the uncomfortable category with a THI value of 26°C has an area of 532780.13 hectares.

Intervention Analysis In Time Series Data For Forecasting Bbri Stock Prices Mangkona, Andi Ilham Azhar; Aswi, Aswi; Ruliana, Ruliana
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 14, No 1 (2025): Maret
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat141670452025

Intervention model analysis is a statistical technique used to assess the impact of an intervention event, caused by internal or external factors, on a time series dataset. The primary goal of this analysis is to quantify the magnitude and duration of the effects on the time series. Intervention models are typically divided into two types: the step function and the pulse function. The step function represents an intervention event with a long-term influence, while the pulse function captures the effects of an intervention within a specific time span. This study examines the stock price data of BBRI from March 2017 to June 2020, with the intervention point identified as the onset of COVID-19 in Indonesia, specifically during the first week of March (t = 155). ARIMA modeling was applied to pre-intervention data to determine the order of intervention (b, s, r). The analysis concluded that the best-fitting model was ARIMA (2, 1, 0), with the intervention order characterized by a step function where b = 0, s = 2, and r = 0. The accuracy of the forecasting results was evaluated using the Mean Absolute Percentage Error (MAPE), which yielded a value of 8.48%.

Intervention Analysis In Time Series Data For Forecasting Bbri Stock Prices Mangkona, Andi Ilham Azhar; Aswi, Aswi; Ruliana, Ruliana
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 14, No 1 (2025): Maret
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat141670452025

Intervention model analysis is a statistical technique used to assess the impact of an intervention event, caused by internal or external factors, on a time series dataset. The primary goal of this analysis is to quantify the magnitude and duration of the effects on the time series. Intervention models are typically divided into two types: the step function and the pulse function. The step function represents an intervention event with a long-term influence, while the pulse function captures the effects of an intervention within a specific time span. This study examines the stock price data of BBRI from March 2017 to June 2020, with the intervention point identified as the onset of COVID-19 in Indonesia, specifically during the first week of March (t = 155). ARIMA modeling was applied to pre-intervention data to determine the order of intervention (b, s, r). The analysis concluded that the best-fitting model was ARIMA (2, 1, 0), with the intervention order characterized by a step function where b = 0, s = 2, and r = 0. The accuracy of the forecasting results was evaluated using the Mean Absolute Percentage Error (MAPE), which yielded a value of 8.48%.

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