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Pemodelan Faktor-Faktor yang Mempengaruhi Kasus Stunting di Sulawesi Selatan Menggunakan Geographically Weighted Regression Putri, Siti Choirotun Aisyah; Salsabila, Afifah; Suardi, Shafira; Mutmainnah, Mutmainnah; Aswi, Aswi
ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 2, Juli, 2024 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v5i2.30142

Abstract

One of the prevalent nutritional issues affecting toddlers worldwide is stunting. Several studies on stunting cases have been conducted in Indonesia. However, modeling using the Geographically Weighted Regression (GWR) method in South Sulawesi has not been carried out. This study aims to identify the variables that affect the incidence of stunting in each district in South Sulawesi based on spatial modeling using the GWR method. Data on the number of stunting cases, the pproportion of low-birth-weight infants, the percentage of under-five who are malnourished, the percentage of proper drinking water, and the percentage of poor people in South Sulawesi in 2020 were used. The results show that the GWR model has an  value of 86.64%, which is higher than that of the global regression model. The factors that influence the percentage of stunting based on the GWR modeling results are the percentage of under-five who are malnourished and the percentage of proper drinking water. The findings of this study are anticipated to help the government address the issue of stunting in South Sulawesi. Early prevention may then be implemented.
PEMODELAN BAYESIAN SPASIAL CONDITIONAL AUTOREGRESSIVE (CAR) PADA KASUS DEMAM BERDARAH DENGUE DI INDONESIA Aswi; Sukarna
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 10 No 1 (2022): VOLUME 10 NOMOR 1 TAHUN 2022
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v10i1.29113

Abstract

Demam Berdarah Dengue (DBD) merupakan salah satu penyakit menular yang masih merupakan masalah utama dalam kesehatan masyarakat di Indonesia. Total kasus DBD di Indonesia pada tahun 2020 masih cukup tinggi yaitu 108.303 kasus. Beberapa penelitian terkait pemodelan DBD telah menggunakan metode Bayesian spasial. Akan tetapi, penelitian tersebut masih fokus pada salah satu provinsi yang ada di Indonesia. Penelitian ini bertujuan untuk memodelkan risiko relatif (RR) kasus DBD tahun 2020 di Indonesia dengan 34 provinsi dan menghasilkan peta tematik RR. Data yang digunakan data kasus DBD serta data jumlah penduduk di Indonesia tahun 2020 yang diperoleh dari Publikasi Kementerian Kesehatan Republik Indonesia 2021. Model Bayesian spasial Conditional Autoregressive (CAR) Leroux digunakan dengan pemilihan model terbaik didasarkan pada Deviance Information Criteria (DIC), Watanabe Akaike Information Criteria (WAIC), dan Modifikasi Moran’s I (MMI) untuk residual. Hasil yang diperoleh menunjukkan bahwa model Bayesian spasial CAR Leroux dengan hyperprior IG(1; 0,1) merupakan model terbaik dalam pemodelan kasus DBD tahun 2020 di Indonesia. Sekitar 53% provinsi yang ada di Indonesia merupakan wilayah dengan RR tinggi, dimana Provinsi Bali memiliki nilai RR tertinggi (6,84), diikuti oleh Provinsi Nusa Tenggara Timur (RR=2,70), dan Daerah Istimewa Yogyakarta (RR=2,33). Sebaliknya, provinsi dengan RR terendah adalah Provinsi Maluku (RR=0,11), diikuti oleh Provinsi Papua (RR = 0,13), dan Provinsi Kalimantan Barat (RR =0,38).
PEMODELAN KASUS COVID-19 DI INDONESIA MENGGUNAKAN ANALISIS SPASIAL DENGAN PENDEKATAN BAYESIAN Aswi; Sukarna; Nurhilaliyah
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 10 No 2 (2022): VOLUME 10 NOMOR 2 TAHUN 2022
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v10i2.33221

Abstract

Kasus terkonfirmasi positif Covid-19 dilaporkan pertama kali di Indonesia pada tanggal 2 Maret 2020. Hingga 30 September 2022, Indonesia memiliki 6.465.207 kasus. Berbagai penelitian mengenai pemodelan kasus Covid-19 telah dilakukan. Akan tetapi, penelitian menggunakan model Bayesian spasial Conditional Autoregressive (CAR) Leroux (BSCL) untuk kasus Covid-19 di 34 provinsi di Indonesia belum sepenuhnya dilakukan. Penelitian ini bertujuan untuk mendapatkan model BSCL terbaik dalam mengestimasi risiko relatif (RR) kasus Covid-19 di 34 provinsi di Indonesia dan menghasilkan peta tematik RR. Data agregat kasus positif Covid-19 (2 Maret 2020-30 September 2022) digunakan dalam penelitian ini. Selain itu, data jumlah penduduk di 34 provinsi di Indonesia tahun 2021 juga digunakan. Kriteria dalam memilih model terbaik adalah dengan melihat nilai residual dari Modifikasi Moran’s I (MMI) yang lebih dekat ke nol, nilai Watanabe Akaike Information Criteria yang terkecil serta nilai Deviance Information Criteria (DIC) terkecil. Hasil pengolahan data Covid-19 menunjukkan bahwa model BSCL dengan hyperprior IG (0,1;0,1). merupakan model terbaik dalam mengestimasi RR kasus Covid-19 di Indonesia. Sekitar 29,41% (10 provinsi) di Indonesia yang memiliki nilai RR kategori tinggi terjangkit Covid-19. Provinsi dengan RR tertinggi dan terendah masing-masing adalah Provinsi DKI Jakarta (RR=5,68) dan Provinsi Nusa Tenggara Barat (RR=0,28).
Analisis Dampak Covid-19 Terhadap Tingkat Inflasi di Indonesia Aswi; Zulhijrah; Isnaini, Mardatunnisa; Sri Sulastri
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 11 No 2 (2023): VOLUME 11 NO 2 TAHUN 2023
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v11i2.40304

Abstract

Penyebaran corona virus diseases 19 (Covid-19) telah meluas ke seluruh penjuru dunia dan membawa dampak terhadap pendidikan, pariwisata maupun ekonomi. Inflasi adalah salah satu dampak pandemi COVID-19 terhadap kondisi makro Indonesia. Penelitian tentang dampak Covid-19 terhadap tingkat inflasi di Indonesia telah dilakukan, tetapi hasilnya tidak konsisten. Analisis yang mengaitkan antara Covid-19 dan tingkat inflasi di Indonesia menggunakan model Autoregressive Integrated Moving Average with exogenous variables (ARIMAX) belum dilakukan. Studi ini bertujuan untuk mendapatkan model ARIMAX terbaik dan menentukan apakah terdapat hubungan antara kasus Covid-19 dengan tingkat inflasi di Indonesia. Data inflasi bulanan dan data rata-rata Covid-19 bulanan (Maret 2020-September 2022) di Indonesia digunakan pada studi ini. Data Inflasi dituliskan sebagai variabel Zt yakni variabel independen yang diperoleh dari Badan Pusat Statistik dan data Covid-19 sebagai peubah Xt yang merupakan variabel independen yang diperoleh dari situs resmi Kementerian Kesehatan Republik Indonesia. Berdasarkan hasil analisis data, disimpulkan bahwa model ARIMA terbaik adalah ARIMA (0, 1, [6]). Dari hasil estimasi model ARIMAX diperoleh bahwa dampak Covid-19 terhadap inflasi di Indonesia tidak berpengaruh secara signifikan.
Implementasi Backpropagation Neural Network Pada Status Preeklampsia Ibu Hamil Sapriani; Bobby Poerwanto; Aswi
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 11 No 2 (2023): VOLUME 11 NO 2 TAHUN 2023
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v11i2.40657

Abstract

Preeklampsia adalah penyakit yang diderita ibu hamil yang ditandai adanya kenaikan tekanan darah. Preeklampsia dapat membahayakan ibu dan juga janin karena dapat menyebabkan komplikasi, hingga kondisi terburuk yaitu kematian. Tujuan dari penelitian ini adalah untuk mengetahui hasil klasifikasi sekaligus prediksi berdasarkan lima variabel yang diduga mempengaruhi status preeklampsia ibu hamil. dengan menggunakan metode Backpropagation Neural Network (BNN). Data rekam medis ibu hamil di RSIA Sitti Khadijah 1 Makassar dan RS TK II Pelamonia Makassar yang berjumlah 167 data digunakan pada penelitian ini. Terdapat lima variabel yang diduga mempengaruhi status preeklampsia ibu hamil yaitu usia ibu saat kehamilan, paritas, riwayat penyakit, indeks massa tubuh, dan status pekerjaan ibu. Hasil studi menghasilkan bahwa skenario pembagian data terbaik yaitu data training 80% dan data testing 20% dengan hasil akurasi sebesar 67,65%, sensitifitas 69,23%, spesifikasi 66,67%, presisi 56,25%, Score 62,07%, nilai hidden layer 7 dan learning rate 0,001.
Modeling Factors Influencing Covid-19 Cases in South Sulawesi Using Bayesian Conditional Autoregressive Localised Yassar, La Ode Salman; Shanty, Meyrna Vidya; Mahadtir, Muhamad; Aswi, Aswi; Annas, Suwardi
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 13, No 1 (2024): Maret
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat131606362024

Abstract

South Sulawesi Province is listed as the province with the highest number of Covid-19 cases in the Sulawes island. Research on Covid-19 modeling has been carried out by many researchers, but until now, there has been no research using the Bayesian spatial Conditional Autoregressive Localized model which involves a combination of factors such as distance to the provincial capital, population density, and the number of elderly people in each district in South Sulawesi Province. The aim of this research is to get the best Bayesian Conditional Autoregressive Localized model. The best model is based on four criteria, namely: Deviance Information Criteria, Watanabe Akaike Information Criteria, residuals from Modified Moran's I, and the number of areas included in a group. It was found that model with G=3 by including population density covariates was the best model. A significant factor influencing the increase in Covid-19 cases is the population density factor which has a positive effect. This shows that the more densely populated an area is, the greater the chance of being infected with Covid-19. Makassar has the highest relative risk value for Covid-19 followed by Toraja district and Pare-Pare City. Meanwhile, Bone district has the lowest relative risk value for Covid-19, followed by Wajo district and Enrekang district.
Statistical Modeling and Factors Influencing School Dropout in Indonesia: A Review Shanty, Meyrna Vidya; Mahadtir, Muhamad; Awaluddin, Awaluddin; Natalia, Derliani; Ramadani, Reski Aulia; Aswi, Aswi
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 13, No 1 (2024): Maret
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat131608032024

Abstract

The education enrollment rate is crucial for Indonesia to improve its human resources and sustain its economic development. In reality, the dropout student rate is still relatively high. Previous research has highlighted several factors and models related to the dropout student rate in Indonesia. The purpose of the study is to identify the most popular statistical modeling and factors influencing school dropout in Indonesia. We searched in February 2023 using ScienceDirect, ProQuest, and Google Scholar. The search was restricted to refereed journal articles published in English from January 2013 to December 2022. This study underwent four stages: identification, screening, eligibility, and inclusion. The study finds that the most popular statistical modeling is the Logistic Regression Model, and the most significant factor increasing the school dropout rate in Indonesia is family and economic factors. The findings suggest that children who were not attending school came from families with lower levels of education. The well-being of these families was directly linked to their children's educational status. The primary reasons for young students dropping out of elementary and junior schools include an inability to pay school fees and a desire to work on farms to support their parents.
Pelatihan Penulisan Artikel Ilmiah dan Manajemen Referensi bagi Dosen Institut Ilmu Kesehatan Pelamonia Poewanto, Bobby; Aswi, Aswi; Fahmuddin, Muhammad; Sukarna, Sukarna
ADMA : Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol 3 No 2 (2023): ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/adma.v3i2.2516

Abstract

In Indonesia, one of the benchmarks for lecturer performance can be noticed from their publications. Therefore, it is important for lecturers to continue improving their knowledge and skills in writing a scientific article such as reference management skills. Prior to the service activities, an analysis of problems related to writing a scientific article was carried out. The service activity began with a pre-test to measure the extent of the participants' understanding in writing a scientific article and reference management. Furthermore, two trainings namely scientific article writing training and reference management training were presented by the speakers. After the training, a discussion session was held between the participants and team. The activity ends with a post-test to measure the extent to which the participants' knowledge and skills have increased in scientific article writing and reference management. Reflection on the implementation of this program is carried out by team leader and team to review all the benefits and weakness of this program and its implementation. Partners in this service are Lecturers of Institut Ilmu Kesehatan Pelamonia with 46 participants. The results achieved in this service are: before the presentation of the material, 75% of the participants still lacked in understanding regarding the writing a scientific article in Reputable International Journals. After the presentation of the material, 93.5% of the participants had understood the writing a scientific article in Reputable International Journals.
Pemberdayaan Masyarakat Sekolah melalui Pelatihan Literasi Data dan Infografis dalam Menciptakan Generasi Melek Data Aswi, Aswi; Poewanto, Bobby; Fakhri, M. Miftach
ADMA : Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol 4 No 2 (2024): ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/adma.v4i2.3351

Abstract

This activity aims to improve data literacy competencies so that participants are able to process data and turn it into information, as well as being able to create infographics from the processed data. There were 19 training participants in this activity consisting of 15 students and 4 teachers who taught crafts at Sekolah Menengah Atas Negeri 8 Gowa. The method of this activity consists of 3 main activities, namely the first is online data collection training which aims to find out tools that can help in data collection and how to arrange question items on the instrument. The second main activity is training in simple data processing using various kinds of diagrams available in Microsoft Excel as well as material on descriptive statistics which includes measures of central symptoms and measures of data spread. Finally, the participants were given infographic design training using Canva with the aim of simplifying the results of data processing that was previously carried out into infographics that were more interesting to look at. The results obtained were that the majority of participants felt that there had been great progress from this activity and that this activity was also in accordance with the current competency requirements.
Pemberdayaan Masyarakat Sekolah Melalui Pelatihan Pemanfaatan Data Tracer Study sebagai Upaya Mempercepat Keterserapan Alumni Poerwanto, Bobby; Aswi, Aswi; Rahmat, Abdul
ADMA : Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol 5 No 2 (2025): ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/adma.v5i2.4339

Abstract

Kegiatan ini bertujuan untuk meningkatkan kompetensi literasi data untuk memanfaatkan data tracer study sehingga dapat dijadikan dasar penyusunan program kerja sehingga dapat mempercepat penyerapan alumni di dunia industri, selain itu kegiatan ini juga bertujuan untuk membantu alumni dan mahasiswa tingkat akhir untuk mempersiapkan diri menghadapi tes wawancara kerja. Peserta pelatihan pada kegiatan ini berjumlah 20 orang guru dan tenaga kependidikan, serta 20 orang mahasiswa tingkat akhir dan alumni. Materi yang diberikan merupakan standar pelaksanaan dan pemanfaatan data tracer study, pengolahan data dan persiapan wawancara kerja. Hasil diukur dari respon peserta berupa kesesuaian materi dengan kebutuhan peserta, tingkat kesulitan materi yang diterima, sistematika narasumber dalam menjelaskan, dan tingkat penguasaan materi oleh narasumber. Hasilnya, mayoritas memberikan respon sangat jelas. Dari kegiatan ini, pihak sekolah sudah mengetahui standar pelaksanaan tracer study, sudah mampu mengolah data tracer study menjadi informasi untuk membuat program kerja, dan sudah mampu membuat laporan pelaksanaan tracer study untuk kebutuhan akreditasi.
Co-Authors A. Nurul Amalia AA Sudharmawan, AA Abdul Rahman Abdul Rahmat Abidin, Muh. Zulkifli Abidin, Muhammad Rais Ahmar, Ansari Saleh Ambo Upe Andi Feriansyah Andi Feriansyah Andi Gagah Palarung Taufik Andi Gagah Palarungi Taufik Andi Gagah Palarungi Taufik Andi Ilham Azhar Mangkona Andi Muhammad Ridho Yusuf Sainon Andin P Andi Shahifah Muthahharah Annas, Suwardi Annas, Suwardi Annas, Suwardi Annas, Suwarni Arismunandar Arismunandar Asrirawan Assagaf, Said Fachry Awaluddin Awi Dassa Awi Dassa, Awi Awi, Awi Bakri, Nurul Aulya Besse Sulfiani Bobby Poerwanto Bobby Poerwanto Bobby Poerwanto BQ Nurkhalishah K. Bustan, Muhammad Nadjib Cramb, Susanna Derliani Natalia Dewi Aprilia Wardani Syam Diana Eka Pratiwi Ernawati Ernawati Fahmuddin, Muhammad Fahmuddin, Muhammad Fajar Arwadi Folorunso, Serifat Adedamola Haekal, Muh. Fahri Halimah Husain Hammado, Nurussyariah Hisyam Ihsan Idul Fitri Abdullah Ikhwana, Nur Irwan Irwan Irwan, Irwan Ishma Azizah S Isnaini, Mardatunnisa Isnaini, Wulan Maulia Kasmir Kasmir, Kasmir La Ode Salman Yassar Lalu Ramzy Rahmanda M Nadjib Bustan M. Miftach Fakhri Mahadtir, Muhamad Mangkona, Andi Ilham Azhar Mar'ah, Zakiyah Mardatunnisa Isnaini Maria Dominggo Wea Mauliyana, Andi Meyrna Vidya Shanty Muhammad Abdy Muhammad Abdy Muhammad Abdy Muhammad Ammar Naufal Muhammad Arif Tiro Muhammad Arif Tiro Muhammad Arif Tiro Muhammad Arif Tiro Muhammad Arif Tiro Muhammad Arif Tiro, Muhammad Arif Muhammad Fahmuddin Muhammad Fahmuddin Muhammad Fahmuddin Sudding Muhammad Kasim Aidid Muhammad Kasim Aidid Muhammad Mahadtir Muttaqin, Imam Akbar Natalia, Derliani Nini Harnikayani Hasa Novianti, Andi Rima Nunung Marlika Nur Aziza S Nur Salsabila Wero Nurhikmawati, Nurhikmawati Nurhilaliyah Nurhilaliyah Nurhilaliyah Nurhilaliyah Nurhilaliyah Nurhilaliyah, Nurhilaliyah Nurkaila Kaito Nurlaila Kaito Nurlia Nurlia Nurul Aulya Bakri Nurul Fadilah Syahrul Nurul Ilmi Nusrang, Muhammad Oktaviana Oktaviana Oktaviana Oktaviana Palarungi, Andi Gagah Panessai Sir Poerwanto, Bobby Poerwanto, Bobby Poewanto, Bobby Putri Ananda, Elma Yulia Putri, Siti Choiratun Aisyah Putri, Siti Choirotun Aisyah Rahma, Ina Rahman, Abdul Rahmat Hidayat Rahmat Hidayat Rahmat Hidayat Rahmat Hidayat Rahmat Hidayat Rahmawati Rahmawati Rahmawati Ramadani, Reski Aulia Reski Aulia Ramadani Rezki Amalia Idrus Rika Saniarti Riska Saputri Risma Mastory Ruliana Ruliana Ruliana Ruliana Ruliana Ruliana Ruliana, Ruliana S, Muhammad Fahmuddin Sahlan Sidjara Saleh, Andi Rahmat Salsabila, Afifah Sapriani Shanty, Meyrna Vidya Siti Choirotun Aisyah Putri Siti Choirotun Aisyah Putri Sitti Aminah Sri Ayu Astuti Sri Rahayu Stevani Stevani Suardi, Shafira Suci Amaliah Sudarmin Sudarmin Sudarmin Sudarmin Sudarmin Sudarmin Sudarmin Sudarmin Sukarna Sukarna Sukarna Sukarna Sukarna Sukarna Sukarna Sukarna Sukarna Sukarna Sukarna, Sukarna Sulistiawaty Sulistiawaty, Sulistiawaty Sumarni Sumarni Supriadi Yusuf Susanna Cramb Suwardi Anas Suwardi Annas Suwardi Annas Syafruddin Side Syamsiar, Syamsiar Taufik, Andi Gagah Palarungi Vivianti Vivianti Vivianti Wahidah Sanusi Wahyudin Wahyudin Wanda Yudi Yassar, La Ode Salman Yudi, Wanda Yunus, Sitti Rahma Zakiah, Andi Sitti Zakiyah Mar'ah Zulhijrah Zulhijrah Zulhijrah Zulhijrah Zulhijrah Zulkifli Rais