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All Journal Jurnal Ekonomi Pembangunan JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Pendidikan Ekonomi (JUPE) Sistemasi: Jurnal Sistem Informasi Paradigma JIKO (Jurnal Informatika dan Komputer) Jurnal Komputasi Faktor Exacta Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Teknoinfo EIGEN MATHEMATICS JOURNAL Jurnal Saintika Unpam : Jurnal Sains dan Matematika Unpam Sains, Aplikasi, Komputasi dan Teknologi Informasi J-3P (Jurnal Pembangunan Pemberdayaan Pemerintahan) Jurnal Statistika dan Aplikasinya Business Economic, Communication, and Social Sciences Journal (BECOSS) Engineering, Mathematics and Computer Science Journal (EMACS) Jurnal Kependudukan Indonesia Indonesian Journal of Statistics and Its Applications

Nooraeni, Rani

Politeknik Statistika STIS

Author-ID : 2378281

Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Physics Public Health Social Sciences Other

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Analisis Sentimen Publik dari Twitter Tentang Kebijakan Penanganan Covid-19 di Indonesia dengan Naive Bayes Classification Ni Putu Gita Naraswati; Rani Nooraeni; Delvira Cindy Rosmilda; Dinda Desinta; Fadhilatul Khairi; Riska Damaiyanti
Sistemasi: Jurnal Sistem Informasi Vol 10, No 1 (2021): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

AbstrakBeberapa bulan terakhir, penanganan COVID-19 menjadi salah satu permasalahan kompleks yang dihadapi oleh hampir seluruh negara di dunia. Menilik dari hal tersebut, pemerintah membentuk kebijakan guna mencegah semakin meluasnya penyebaran virus diantaranya Pembatasan Sosial Berskala Besar (PSBB), wajib masker, dan jam malam. Kebijakan tersebut mendapat tanggapan yang beragam, tidak terkecuali di media sosial seperti twitter. Berdaarkan hal tersebut, penelitian ini bertujuan untuk menganalisis sentimen publik dari cuitan Twitter mengenai penanganan COVID-19 di Indonesia. Adapun metode yang digunakan Naïve Bayes Classification karena memiliki algoritma yang sederhana dengan akurasi yang tinggi. Hasil penelitian menunjukkan, masyarakat lebih banyak memberikan sentimen negatif terhadap kebijakan penanganan COVID-19 khususnya PSBB, wajib masker, dan jam malam. Pada sentimen positif, tiga kata dengan frekuensi kemunculan terbanyak yaitu demo, jakarta, dan kerja. Sedangkan pada sentimen negatif yaitu jakarta, demo, dan orang. Kemunculan kata “demo” dan “jakarta” pada kedua sentimen menunjukkan bahwa tweet masyarakat mengenai kebijakan penanganan COVID-19 tidak lepas dari peristiwa/kejadian saat pengumpulan data dilakukan. Selain itu, tingginya frekuensi kata “jakarta” pada sentimen negatif juga menunjukkan bahwa pelaksanaan kebijakan penanganan COVID-19 di Jakarta belum dilaksanakan secara optimal. Berdasarkan hasil evaluasi, diperoleh tingkat akurasi klasifikasi sebesar 87,34%, sensitivitas sebesar 93,43%, dan spesifisitas 71,76% yang berarti metode ini sudah cukup baik.Kata Kunci: COVID-19, naïve bayes classification, kebijakan, text mining, twitter AbstractIn recent months, handling COVID-19 has become one of the complex problems faced by almost all countries in the world. In view of this, the government formed policies to prevent the spread of the virus, including Large-Scale Social Restrictions (PSBB), mandatory masks, and curfews. This policy received various responses, including on social media such as Twitter. Based on this, this study aims to analyze public sentiment from Twitter tweets regarding the handling of COVID-19 in Indonesia. The method used is the Naïve Bayes Classification because it has a simple algorithm with high accuracy. The results showed that the public gave more negative sentiments towards the policy of handling COVID-19, especially PSBB, mandatory masks, and curfews. On the positive sentiment, the three words with the highest frequency were “demo”, “jakarta”, and “work”. Meanwhile, the negative sentiment is “jakarta”, “demo”, and “orang”. The appearance of the words "demo" and "jakarta" in both sentiments shows that the public's tweet regarding the policy for handling COVID-19 cannot be separated from the events / incidents when data collection was carried out. In addition, the high frequency of the word “jakarta” in negative sentiments also shows that the implementation of policies for handling COVID-19 in Jakarta has not been implemented optimally. Based on the evaluation results, the classification accuracy rate is 87.34%, the sensitivity is 93.43%, and the specificity is 71.76%, which means that this method is good enough.Keywords: COVID-19, naïve bayes classification, policy, text mining, twitter

Analisis Sentimen Data Twitter Mengenai Isu RUU KPK Dengan Metode Support Vector Machine (SVM) Rani Nooraeni; Heny Dwi Sariyanti; Aulia Fikri Fadhilah Iskandar; Siti Fatimatul Munawwaroh; Suciarti Pertiwi; Yulianus Ronaldias
Paradigma Vol 22, No 1 (2020): Periode Maret 2020
Publisher : LPPM Universitas Bina Sarana Informatika

Komisi Pemberantasan Korupsi (KPK) merupakan lembaga independen di Indonesia yang dibentuk pada tahun 2002 untuk mengatasi masalah korupsi di Indonesia. KPK bertanggungjawab kepada publik dan menyampaikan laporannya secara terbuka dan berkala kepada presiden, DPR, dan BPK. Skor CPI Indonesia dari tahun 2015-2018 masih cenderung stagnan yakni berturut-turut 36, 37, 37 dan 38. Sidang Paripurna DPR menyepakati dilakukannya revisi UU KPK menjadi RUU Inisiatif DPR. Polemik yang terjadi sebagai akibat dari ketidakterbukaan DPR atas keputusannya untuk melakukan revisi UU KPK menimbulkan berbagai respon dari masyarakat. Salah satu media yang dapat digunakan untuk melihat respons masyarakat terkait isu ini adalah Twitter. Untuk menganalisis respon masyarakat dengan menggunakan data Twitter, dapat dilakukan dengan analisis sentiment. Metode pengklasifikasian yang digunakan adalah Support Vector Machine (SVM) Radial Basis Function.

Analysis of Multidimensional Poverty Indicators in Indonesia with Association Rules Diana Agustin; Aulia Adita Rahma; Frengky Sele; Raihan Fitrika Azzahra; Rhevita Lula Eksanti; Zahrotul Firdaus; Rani Nooraeni
Jurnal Ekonomi Pembangunan Vol. 18 No. 2 (2020): JURNAL EKONOMI PEMBANGUNAN
Publisher : Pusat Pengkajian Ekonomi dan Kebijakan Publik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/jep.v18i2.14244

This study was conducted to find patterns of relationships between 14 multidimensional poverty indicators in Indonesia from 2015-2019. To provide a more specific description of the relationship pattern, association rules with the apriori algorithm is used as the analysis method. The preprocessing stage to transform data was carried out using fuzzy functions and data reduction with Multiple Correspondence Analysis (MCA) to support the association analysis process. The results obtained are 15 relationship patterns or rules between items from the multidimensional poverty indicator with a support value of 60%-80% and 100% confidence. This means that the relationship pattern is significantly formed from objects with a strong relationship between the items and can represent poverty records in the last five years. The relationship pattern consists of four combinations of things. Suppose there is a high category decrease in the percentage of poor people indicator, a low category decrease in the open unemployment indicator, a high category increase in the percentage of households indicator according to the source of lighting from electricity, and a low category increase in the percentage indicator of households according to the broadest wall, not bamboo / other. In that case, there is a reduction in multidimensional poverty in Indonesia.

PREDIKSI KASUS COVID-19 MELALUI ANALISIS DATA GOOGLE TREND DI INDONESIA: PENDEKATAN METODE LONG SHORT TERM MEMORY (LSTM) Lisa Widyarsi; Ivana Yoselin Purba Siboro; Peterson Hamonangan Immanuel Sihotang; Satria Dirgantara; Yakobus Natanael Tarigan; Yuniar Putri Awaliyah Risky; Rani Nooraeni
JURNAL SAINTIKA UNPAM Vol 3, No 2 (2021)
Publisher : Program Studi Matematika FMIPA Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jsmu.v3i2.7786

Salah satu faktor yang diperlukan untuk menekan angka kasus COVID-19 adalah tingginya perhatian atau atensi masyarakat. Hal tersebut terlihat dari intensitas pencarian informasi publik mengenai COVID-19 di platform online bernama Google Trend. Makalah ini bertujuan untuk mendeskripsikan kondisi wabah COVID-19 di masyarakat dengan menggunakan data Google Trend dan memprediksi kasus COVID-19 baik dengan metode nowcasting maupun forecasting dengan menggabungkan data atensi publik dari Google Trend dengan data resmi pertumbuhan COVID-19 di Indonesia. Data yang digunakan berupa data time series harian dari tanggal 1 April hingga 30 September 2020. Metode Regresi Linear Berganda juga digunakan untuk membandingkan hasil prediksi dengan LSTM. Hasil regresi time series menghasilkan RMSE 1060,80. Selain metode analisis time series, prediksi penambahan kasus COVID-19 juga dilakukan menggunakan metode LSTM dengan empat skenario, di mana skenario pertama menghasilkan RMSE 526,59, skenario kedua menghasilkan RMSE 528,81, skenario ketiga menghasilkan RMSE 528,81. RMSE 483,25 dan skenario terakhir menghasilkan RMSE 482,21. Prediksi menggunakan metode LSTM dengan scnario keempat menghasilkan RMSE, sehingga metode LSTM merupakan metode keempat dengan prediksi yang cukup baik.

Analysis of User Sentiment of Twitter to Draft KUHP Nawang Indah Cahyaningrum; Danty Welmin Yoshida Fatima; Wisnu Adi Kusuma; Sekar Ayu Ramadhani; Muhammad Rizqi Destanto; Rani Nooraeni
Jurnal Matematika, Statistika dan Komputasi Vol. 16 No. 3 (2020): JMSK, MAY, 2020
Publisher : Department of Mathematics, Hasanuddin University

Twitter is one of social media where its user can share many responses for a phenomenon through a tweet. This research used 5000 tweets from Twitter users in Bahasa Indonesia with keyword “RUU KUHP(Draft Law of KUHP)” from 16th of September until 22nd of September 2019. That tweets were processed using Rstudio software with sentiment analysis that is one of Text Mining methods. This research aims to classify Twitter users’ responses to RUU KUHP to be negative sentiment, poisitive negative, and neutral. Also, this research also aims to know about topics’ frequencies that were related to RUU KUHP through visualization with bar plot and also wordcloud. This research also aims to know words that are associated with the most frequent words. Form this research, can be known that Twitter users’ responses to RUU KUHP tend to have neutral sentiment that means they did not take side between agreeing or disagreeing. From this research, also can be known about 10 most frequent words, there are kpk, tunda, dpr, pasal, kesal, jokowi, presiden, masuk, ya, and sahkan. Beside that, can be known the other words that are associated with them and also their probability.

Penerapan Metode Random Forest dalam Pengklasifikasian Penerima Kartu BPJS Kesehatan Penerima Bantuan Iuran (PBI) di Kabupaten Karangasem, Provinsi Bali 2017 Qonita Raihananda; I Wayan Edy Darma Putra; Monica Seftaviani Sijabat; Sifa Rofatunnisa; Ibnu Maruf; Hermarwan Hermarwan; Rani Nooraeni
Jurnal Matematika, Statistika dan Komputasi Vol. 17 No. 2 (2021): JANUARY 2021
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/jmsk.v17i2.11710

BPJS Kesehatan is a social security facility provided by the government to all people who are registered as members. BPJS Kesehatan membership is divided into two, namely BPJS for Contribution Assistance Recipients (BPJS PBI) and BPJS Non-Contribution Assistance Recipients (BPJS Non-PBI). In 2019, Bali Province is targeted to achieve Universal Health Coverage of 95 percent so that the Bali Provincial Government has budgeted funds worth IDR 945 billion to finance JKN - KBS services which are integrated with JKN - KIS. Karangasem is one of the four districts in Bali Province that received the most percentage of financing, which is 51 percent of the total budget needed when compared to other areas. This study aims to classify the BPJS-PBI recipient community based on education variables, employment indicators, age, and per capita expenditure in Karangasem Regency in 2017. The classification method used in this study is the random forest method. The results showed that the per capita expenditure variable had the largest contribution in classifying the status of PBI participants. The model that is formed produces an accuracy of 0.8017. This means that the model can predict 80.17 percent testing data correctly.

DETERMINAN PENGANGGURAN LULUSAN PERGURUAN TINGGI DI INDONESIA TAHUN 2018 Vina Astriani; Rani Nooraeni
Jurnal Pendidikan Ekonomi (JUPE) Vol 8 No 1 (2020)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jupe.v8n1.p31-37

Abstrak Pengangguran merupakan salah satu permasalahan kompleks yang dihadapi oleh setiap negara berkembang di dunia, termasuk Indonesia. Berdasarkan data resmi Badan Pusat Statistik (BPS), jumlah pengangguran di Indonesia sejak tahun 2015 sampai dengan tahun 2018 tidak pernah bernilai lebih sedikit dari 7 juta orang, di mana jumlah Tingkat Pengangguran Terbuka (TPT) lulusan perguruan tinggi masih termasuk tinggi. Penelitian ini bertujuan untuk mengetahui faktor-faktor yang memengaruhi pengangguran lulusan perguruan tinggi di Indonesia tahun 2018 dengan menggunakan analisis regresi logistik biner. Adapun determinan pengangguran lulusan perguruan tinggi adalah umur, status kawin dan status KRT berpengaruh signifikan dan negatif terhadap pengangguran lulusan perguruan tinggi. Sedangkan, jenis kelamin dan sektor pekerjaan berpengaruh signifikan dan positif terhadap pengangguran lulusan perguruan tinggi. Kata Kunci: Pengangguran, Lulusan perguruan tinggi, Regresi Logistik Biner. Abstract Unemployment is one of the complex problems faced by every developing country in the world, including Indonesia. Based on official data from Statistics Indonesia (BPS), the number of unemployed people in Indonesia from 2015 to 2018 was never worth less than 7 million people, where the number of open unemployment rate (TPT) of college graduates is still high. This study aims to determine the factors that influence unemployment of tertiary education graduates in Indonesia in 2018 by using binary logistic regression analysis. The determinants of unemployment for college graduates are age, marital status and KRT status have a significant and negative effect on unemployment of college graduates. Meanwhile, gender and employment sector have a significant and positive effect on unemployment of college graduates. Keywords: Unemployment, College Graduates, Binary Logit Regression.

ANALISIS DETERMINAN BALITA PENDEK DAN SANGAT PENDEK DI INDONESIA 2015-2018 DENGAN REGRESI DATA PANEL Astrid C. A. Pangaribuan; Kuncoro Dwi Dhanutama; Miko Oktavio Wijaya; Putri Tareka Navasha; Rani Nooraeni
Jurnal Pendidikan Ekonomi (JUPE) Vol 8 No 2 (2020)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jupe.v8n2.p56-61

Balita pendek dan sangat pendek (kerdil) adalah kondisi dimana balita memiliki panjang atau tinggi badan yang kurang dibandingkan dengan umur. Penelitian ini bertujuan untuk menganalisis faktor-faktor yang mempengaruhi persentase balita kerdil di Indonesia pada tahun 2015–2018. Penelitian ini menggunakan data sekunder berupa data panel yang bersumber dari website Badan Pusat Statistik dan publikasi Kementerian Kesehatan Republik Indonesia. Variabel bebas dalam penelitian ini adalah angka partisipasi sekolah, rata-rata pengeluaran per kapita rumah tangga untuk makanan, tingkat pengangguran terbuka, dan persentase balita gizi buruk dan kurang. Metode analisis yang digunakan adalah regresi data panel dengan Fixed Effect Model (FEM). Setelah dilakukan estimasi model terpilih, didapatkan hasil bahwa rata-rata pengeluaran per kapita rumah tangga untuk makanan dan persentase balita gizi buruk kurang berpengaruh signifikan. Sementara itu, berdasarkan hasil Individual Effect atau Cross-Section Fixed Effect, persentase balita kerdil tertinggi berada di Provinsi Sulawesi Barat sedangkan yang terendah berada di Provinsi Kepulauan Riau. Kata kunci: Pengeluaran perkapita, partisipasi sekolah, tingkat pengangguran terbuka, balita gizi buruk Abstract Toddler short and very short (dwarf) is a condition where toddlers have a length or height less than age. This study aims to analyze the factors that influence the percentage of stunted toddlers in Indonesia in 2015-2018. This study uses secondary data in the form of panel data sourced from the website of the Central Statistics Agency and the publication of the Ministry of Health of the Republic of Indonesia. The independent variables in this study are school participation rates, the average per capita household expenditure for food, open unemployment rates, and the percentage of malnourished and under-aged children. The analytical method used is panel data regression with the Fixed Effect Model (FEM). After estimating the selected model, the results show that the average per capita expenditure of households for food and the percentage of malnourished children under five is not significantly influential. Meanwhile, based on the results of the Individual Effect or Cross-Section Fixed Effect, the highest percentage of dwarf children was in West Sulawesi Province while the lowest was in Riau Islands Province. Keywords: Per capita expenditure, school participation, open unemployment rate, malnutrition toddlers

Determinant Analysis of Open Unemployment Level in Banten Province, 2018 Using Panel Data Regression Isdhani Nurrahmah; Roy Pratama Wijaya; Syifa Rahmawati Hakim; Yusuf Yahya; Rani Nooraeni
Business Economic, Communication, and Social Sciences (BECOSS) Journal Vol. 2 No. 2 (2020): BECOSS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/becossjournal.v2i2.6332

This study aims to analyse the relationship of crude school participation rate, the number of poor people, and the GDRP growth rate to the open unemployment rate in Banten. The analytical method used in this study is panel data regression with fixed effect model estimation which is processed using statistical software EViews 10. The data that are used in this study are secondary data from Badan Pusat Statistik (BPS), by taking annual data for each district in Banten province from 2011 until 2018. The results of this study indicate that the GDRP growth rate significantly affects the open unemployment rate, but the crude school participation rate and the number of poor people do not significantly affect the open unemployment rate in Banten.

Analisis Determinan Berat Badan Lahir Rendah (BBLR) Di Provinsi Nusa Tenggara Timur Tahun 2017 Elina Mayasari; Geraldi Putra Prasetya Balebu; Latifah Hasanah; Rizka Wulandari; Rani Nooraeni
Business Economic, Communication, and Social Sciences (BECOSS) Journal Vol. 2 No. 2 (2020): BECOSS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/becossjournal.v2i2.6413

Health is one of the essential needs for human beings, and even became a major issue that indicates achievement of a country or a region. Health can also be viewed from the condition of the infants, which can be measure from Infant Mortality Rate (IMR). This indicator shows a high rate especially because of low birthweight. The cases of low birthweight is one of the highest case that occurred in developing countries, including Indonesia. Nusa Tenggara Timur (NTT) province in Indonesia, is one of the most common places where this case is most likely to happened. The percentage of the low birthweight case is higher than the average case in Indonesia. Therefore, this research paper aim to investigate variables which are responsible for causing low birthweight case in such a high number in NTT on 2017. The method used for analysis is logistic regression. The result indicate that mother’s education level is significantly affecting low birthweight cases in NTT.

Co-Authors Adinda Hermambang Aditya Firman Baktiar Afifatul Ilma Widyatami Aisyah Nur Fahira Amelia Syahadati Amirah Balqis Safiruddin Ana Pangestika Anindia Wahyu Inayah Annisa Putri Ramadhanty Apriliansyah Mahmud Arul Fathurrahman Arya Damar Prakasa Arya Wahyu Nugroho Astrid C. A. Pangaribuan Astry Julyana Eliawati Aulia Adita Rahma Aulia Fatin Afifah Aulia Fikri Fadhilah Iskandar Ayufi Reyza Zakaria Cesaria Dewi Choirul Ummah Danty Welmin Yoshida Fatima Delvira Cindy Rosmilda Dewi Retno Oscarini Diana Agustin Dinda Desinta Diva Arum Mustika Dwi Cahyo Firmansyah Elina Mayasari Elvera Wahyu Triana Emban Permata Siam Ersa Budi Sutanto Eunike Sola Gratia Evita Dyah Wardhani Fadhilatul Khairi Fajar Hari Dwiono Fathin Nadillah Fathul Sanusi Frengky Sele Galang Madya Putra Galuh Sri Natungga Dewi Susilo Putri Garinca Firgiana Santoso Geraldi Putra Prasetya Balebu Ghita Nurfalah Ghytsa Alif Jabir Gona Asri Wijayanti Helen Fricylya Br Tobing Heny Dwi Sariyanti Hermarwan Hermarwan Herpanindra Fadhilah I Wayan Edy Darma Putra Ian Tryaldi Halim Ibnu Maruf Indonesian Journal of Statistics and Its Applications IJSA Ineke Kristin Dwi Astuti Isdhani Nurrahmah Ivana Yoselin Purba Siboro Krisna Dwi Agung Kuncoro Dwi Dhanutama Lady Deborah Latifah Hasanah Lisa Widyarsi Machsus Machsus Margareth Dwiyanti Simatupang Marita Mutiara Sinsyi Megananda Ghowo Rizky Meilani Thereza Saragih Mikha Aprilio Miko Oktavio Wijaya Monica Seftaviani Sijabat Muhamad Zidan Nuralifian Muhammad Rizqi Destanto Mula Warman Mustika Putri Nada Nabila Rosyad Nadhifan Humam Fitrial Nawang Indah Cahyaningrum Ni Luh Putu Yayang Septia Ningsih Ni Putu Gita Naraswati Novert Cyril Lengkong Nurfitri Aulia Nurul Hanifah Septiani Ouditiana Safitri Peterson Hamonangan Immanuel Sihotang Pramudya Kusuma Putri Tareka Navasha Qonita Raihananda Raihan Fitrika Azzahra Rhevita Lula Eksanti Ria Dotul Ilmia Riska Damaiyanti Rizka Wulandari Roy Pratama Wijaya Salsa Vira Satria Dirgantara Satria Kurnia Areka Sekar Ayu Ramadhani Sifa Rofatunnisa Siti Andhasah Siti Andhasah Siti Fatimatul Munawwaroh Sri Rahayu Yogyana Sinurat Suciarti Pertiwi Syifa Rahmawati Hakim Viana Mei Reistiani Vina Astriani Wilda Maria Ulfa Windri Wucika Bemi Wisnu Adi Kusuma Yakobus Natanael Tarigan Yolanda Rizkie Aprilia Yongki Ramanda Putra Yulianus Ronaldias Yuniar Putri Awaliyah Risky Yusuf Yahya Zahrotul Firdaus

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