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Prosiding Seminar Nasional Official Statistics
Published by Politeknik Statistika STIS
ISSN : 27721970     EISSN : 27721970     DOI : https://doi.org/10.34123/semnasoffstat.v2022i1
Core Subject : Economy, Humanities, Science, Social,
Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences
prosiding seminar ini bertujuan untuk menghasilkan berbagai pemikiran solutif, inovatif, dan adaptif terkait isu, strategi, dan metode yang memanfaatkan official statistics
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 729 Documents
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Perbedaan Kualitas Sekolah Unggulan di Kabupaten Banyuwangi Setelah Diberlakukannya Sistem Zonasi Studi Kasus di SMPN 1 Banyuwangi Marita Mutiara Sinsyi; Yaya Setiadi
Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (416.24 KB) | DOI: 10.34123/semnasoffstat.v2021i1.1015

Abstract

Setelah diberlakukannya sistem zonasi, sekolah unggulan tidak dapat lagi menyeleksi siswa yang berprestasi guna mempertahankan kualitasnya. Tujuan penelitian ini adalah untuk mengetahui perbedaan kualitas sekolah unggulan di Kabupaten Banyuwangi setelah diberlakukannya sistem zonasi dengan analisis Wilcoxon Signed Rank Test dan mengkonfirmasi dimensi serta indikator kualitas sekolah unggulan di Kabupaten Banyuwangi dengan CFA. Responden dalam penelitian ini adalah semua guru yang sudah mengajar sejak sebelum diberlakukannya sistem zonasi dan masih aktif mengajar hingga saat ini. Variabel yang digunakan yaitu sarana dan prasarana, guru, manajemen sekolah, dan proses pembelajaran. Hasil yang diperoleh yakni hanya proses pembelajaran saja yang berbeda antara sebelum dan setelah zonasi. Dimensi guru merupakan dimensi yang paling memiliki korelasi kuat dengan kualitas sekolah unggulan SMPN 1 Banyuwangi di Kabupaten Banyuwangi.
Pemodelan Analisis Rantai Markov untuk Mengestimasi Potensi Kasus Narkoba di Indonesia Bagaskoro Cahyo Laksono; Nucke Widowati Kusumo Projo
Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (290.756 KB) | DOI: 10.34123/semnasoffstat.v2021i1.1016

Abstract

Drug abuse not only threatens the survival and future of the abuser, but also the future of the nation and state. For this reason, up-to-date information is needed regarding the severity of drug abuse, including through recording the number of drug cases. This study aims to analyze the potential for drug cases in six provinces with the highest number of reported drug cases in Indonesia, namely North Sumatra, Jambi, Bali, Central Kalimantan, South Kalimantan and East Kalimantan. The methodology used in this research is descriptive analysis and Markov chain analysis. The results of the estimation of the number of drug cases in five years, from 2019 to 2023, show that East Kalimantan Province is the province with the most drug cases. Then followed by North Sumatra, Bali, South Kalimantan, Central Kalimantan, and Jambi.
Pendekatan Model Machine Learning dalam Pemeringkatan Status Sosial Ekonomi Rumah Tangga di Indonesia Nuri Taufiq; Siti Mariyah
Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (535.002 KB) | DOI: 10.34123/semnasoffstat.v2021i1.1018

Abstract

The method used for ranking the socioeconomic status of households in the Integrated Database is to predict the value of household expenditures using the Proxy Mean Testing (PMT) method. In general, this method is a predictive model using a regression technique. The choice of statistical model used is forward-stepwise. In practice it is assumed that the predictor variables used in PMT have a linear correlation with the expenditure variable. This study tries to apply a machine learning approach as an alternative prediction method other than the forward-stepwise model. The model is built using several machine learning algorithms such as Multivariate Adaptive Regression Splines (MARS), K-Nearest Neighbors, Decision Tree, and Bagging. The results show that the machine learning model produces an average inclusion error (IE) value that is lower than the average exclusion error (EE) value. Machine learning model works effectively in reducing IE but is not sensitive enough to reduce EE. The average value of IE machine learning model is 0.21 while the average value of IE PMT model is 0.29.
Analisis Ekspor Biji Pala Indonesia ke Tujuh Negara Uni Eropa Periode 2012-2019 Amalia Susanti; Lia Yuliana
Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (355.535 KB) | DOI: 10.34123/semnasoffstat.v2021i1.1019

Abstract

Uni Eropa merupakan pasar kedua terbesar bagi ekspor biji pala Indonesia selama periode 2012-2019 dengan rata-rata total nilai ekspor sebesar 44,46 juta US$. Meski memiliki prospek yang baik, kegiatan ekspor biji pala Indonesia ke Uni Eropa sempat mengalami kendala yang berakibat pada menurunnya volume ekspor. Oleh karena itu, penelitian ini bertujuan untuk mengetahui daya saing serta faktor apa saja yang diduga berpengaruh terhadap volume ekspor biji pala Indonesia periode 2012-2019. Metode yang digunakan adalah analisis deskriptif menggunakan RCA dan EPD serta analisis inferensia menggunakan analisis regresi data panel. Hasil penelitian menunjukkan bahwa biji pala Indonesia telah memiliki keunggulan komparatif dengan posisi pasar falling star dan retreat. Kemudian, regulasi HC, laju produksi pala Indonesia, dan indeks daya saing (RCA) berpengaruh positif signifikan terhadap volume ekspor biji pala Indonesia. Sedangkan harga riil ekspor berpengaruh negatif signifikan.
Penerapan Bayesian Network dalam Memodelkan Kondisi Ekonomi Hijau Indonesia di Era Pandemi Berdasarkan Big Data Salwa Rizqina Putri; Thosan Girisona Suganda; Setia Pramana
Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (681.519 KB) | DOI: 10.34123/semnasoffstat.v2021i1.1023

Abstract

To support Indonesia's green economic growth, further analysis is needed regarding economic activity during the pandemic and its relationship to environmental conditions. This study aims to apply the Bayesian Network approach in modeling Indonesia's green economy conditions during the pandemic based on variables that are allegedly influential, such as economic activity, air quality, population mobility levels, and positive cases of COVID-19 obtained through big data. The Bayesian Network model that was constructed manually with the Maximum Spanning Tree algorithm was chosen as the best model with an average 5-cross validation accuracy in predicting four classes of GRDP is 0.83. The best model chosen shows that Indonesia's economic conditions in the pandemic era are directly influenced by the intensity of night light (NTL) which shows economic activity, air quality (AQI), and positive cases of COVID-19. Analysis of parameter learning shows that the economic growth of the Indonesian provinces still tends not to be in line with the maintenance of air quality so that efforts to achieve a green economy condition still have to be improved.
Kajian Pemanfaatan Data Google Maps untuk Pemenuhan Variabel Jumlah dan Jarak Infrastruktur PODES Masyitah Ayuning Setyo; Waris Marsisno
Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (347.391 KB) | DOI: 10.34123/semnasoffstat.v2021i1.1024

Abstract

Statistik Potensi Desa (PODES) merupakan produk Official Statistics yang pada umumnya dihasilkan dari kegiatan pemutakhiran menjelang dilaksanakannya suatu Sensus oleh Badan Pusat Statistik. Variabel yang relatif banyak ditanyakan pada kuesioner PODES adalah jumlah dan jarak infrastruktur yang terdapat di suatu desa. Variabel-variabel ini digunakan untuk penyusunan berbagai indeks, sehingga di-update setiap tahunnya di luar tahun pendataan PODES. Di sisi lain, ketersediaan Big Data memiliki potensi untuk memudahkan pemutakhiran data PODES. Salah satu sumber dari Big Data yang memiliki potensi untuk dimanfaatkan dalam pemutakhiran PODES adalah Google Maps. Penelitian ini dilakukan untuk mengetahui pola dan keakuratan data yang dihasilkan oleh Google Maps. Pengumpulan data infrastruktur dilakukan dengan pembangunan web-scraper dengan Bahasa Python untuk studi kasus pada wilayah Kota Yogyakarta. Dari penelitian ini ditemukan bahwa proses pengumpulan dan pre-processing data membutuhkan waktu dan proses yang lama dan secara umum memiliki tingkat akurasi data yang masih rendah untuk mengestimasi jumlah infrastruktur per desa. Sedangkan untuk akurasi dari titik koordinat Google Maps sudah relatif baik, namun variabel jarak yang diinformasikan oleh Google Maps masih memerlukan penelitian lanjutan ke lapangan. Selain itu, ditemukan bahwa data Google Maps belum dapat mengidentifikasi secara langsung infrastruktur puskesmas dan pasar sesuai kebutuhan dalam PODES. Berdasarkan temuan dari penelitian ini, disimpulkan bahwa Google Maps belum dapat dimanfatkaan untuk pemenuhan variabel jumlah dan jarak infrastruktur pada PODES.
Pengaruh Infrastruktur Ekonomi dan Sosial terhadap Pertumbuhan Ekonomi Indonesia, 2015-2019 Divia Angelina; Krismanti Tri Wahyuni
Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (430.254 KB) | DOI: 10.34123/semnasoffstat.v2021i1.1025

Abstract

Compared to the previous period, in 2014, the infrastructure budget in 2015 increased by 62,7%. For five years, this figure continued to grow until 2019 increased by 62%. The increase in the infrastructure budget in President Jokowi's administration is to accelerate economic growth in Indonesia. However, until the end of the first period of his reign, a third of Indonesia's territory still had economic growth below 5%. In addition, almost 60% of Indonesia's economic growth comes from Java. This study aims to analyze the effect of economic and social infrastructure, namely road, electricity, water, health, and ICT infrastructure on economic growth in Indonesia during the 2015-2019. The analysis using panel data regression with the Fixed Effect Model SUR estimation method. The results showed that the five infrastructure variables had a positive and significant impact on economic growth in Indonesia in 2015-2019.
Pengaruh Sektor Industri, Sektor Pertanian, Dan Sumber Daya Manusia Terhadap Ketimpangan Pembangunan Di Jawa Barat Tahun 2015-2019 Ria Nurul Azizah; Atik Mar’atis Suhartini
Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (310.431 KB) | DOI: 10.34123/semnasoffstat.v2021i1.1026

Abstract

West Java is province having high contribution on Indonesian economy. However, Inequality in West Java are third highest in Indonesia and hasn’t reached the government target. Williamson Index of West Java always revolve around 0,64 every year, meaning high category nequality. West Java is the largest of center industrial areas in Indonesia, but industrial development aren’t evenly distributed and concentrated in the industrial areas. it can excacerbate development inequality between region. This study aims to analyze the development inequality, regional classification, and the influence of industry, agruculture, and human resources on development inequality in West Java. The methods include Klassen Typology analysis and panel data regression. The results shows large development inequality between region in West Java, changing classification in five region, and number of industri, industrial GRDP, and agriculture GRDP have positive effect on Bonet Index, number of industrial labor, RLS, and AHH have negative effect on Bonet Index.
Faktor - Faktor Yang Mempengaruhi Tingkat Kebahagiaan Masyarakat Yogyakarta Tahun 2017 Khairunissa Balqis Zhahira; Efri Diah Utami
Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (338.995 KB) | DOI: 10.34123/semnasoffstat.v2021i1.1029

Abstract

Yogyakarta Province is ranked as the 8th happiest province in Indonesia, which is 72.93. Although Yogyakarta has a fairly high Happiness Index rating, Yogyakarta Province has several problems from an economic perspective. First, the percentage of poor people in Yogyakarta in March 2017 was 13.02 percent and in September 2017 it was 12.36 percent, this percentage is above the national average percentage of poor people. Second, the Gini ratio of Yogyakarta is 0.432, this causes the Gini ratio of Yogyakarta to be the highest in all provinces of Indonesia, and the third problem, in 2017 the Provincial Minimum Wage (UMP) Yogyakarta is the smallest UMP in all of Indonesia, which is Rp. 1,337,645. Therefore, this study aims to determine the general description and variables that affect the level of happiness of the Yogyakarta population in 2017. The method used is Ordinal Logistics Regression. The results showed that the level of happiness of the people of Yogyakarta was influenced by the variables of age, income, savings ownership, and did not experience health problems.
Mobile Positioning Data: Prediktor Produk Domestik Regional Bruto (PDRB) Pada Masa Pandemi Amanda Pratama Putra; Heny Wulandari
Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (358.198 KB) | DOI: 10.34123/semnasoffstat.v2021i1.1033

Abstract

The Coronavirus Disease 2019 (COVID-19) pandemic has devastated economic activity in many countries, including Indonesia. Big data as an alternative data source to measure economic activity, especially in a pandemic situation, can be a valuable mine of information. In the present research, a new approach for measuring economic activities based on mobile positioning data (MPD) has been developed. The sample dataset of mobile network subscribers’ activity was aggregated at the area municipality level by the daily interval, where the activity itself is defined as the number of mobile transactions and detected locations, and the number of unique users. This paper shows that mobile positioning data (MPD) can be a proxy to estimate economic activity in Indonesia, especially during pandemic conditions.

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