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All Journal ILMU KELAUTAN: Indonesian Journal of Marine Sciences Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Jurnal Sains Matematika dan Statistika INTEGER: Journal of Information Technology Jurnal Matematika: MANTIK JURNAL ILMIAH MATEMATIKA DAN TERAPAN BAREKENG: Jurnal Ilmu Matematika dan Terapan JRST (Jurnal Riset Sains dan Teknologi) JTAM (Jurnal Teori dan Aplikasi Matematika) Mathvision : Jurnal Matematika Building of Informatics, Technology and Science Transformasi : Jurnal Pendidikan Matematika dan Matematika ESTIMASI: Journal of Statistics and Its Application Jurnal Statistika dan Aplikasinya Jurnal Matematika Integratif STATISTIKA Scientica: Jurnal Ilmiah Sains dan Teknologi Engagement: Jurnal Pengabdian Kepada Masyarakat International Journal of Computing Science and Applied Mathematics-IJCSAM
Putroue Keumala Intan
Universitas Islam Negeri Sunan Ampel Surabaya
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Other

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Pendampingan Guru Madrasah untuk Mewujudkan Kompetensi Pedagogik Guru Matematika yang Berdaya Melalui Penguasaan Soal High Order Thinking Skills (HOTS) Moh Hafiyusholeh; Ahmad Lubab; Ahmad Hanif Asyhar; Aris Fanani; Yuniar Farida; Dian C. Rini Novitasari; Nurissaidah Ulinnuha; Putroue Keumala Intan; Wika Dianita Utami; Zainullah Zuhri; Ahmad Zaenal Arifin; Dian Yuliati; Abdulloh Hamid
Engagement: Jurnal Pengabdian Kepada Masyarakat Vol 4 No 1 (2020): May 2020
Publisher : Asosiasi Dosen Pengembang Masyarajat (ADPEMAS) Forum Komunikasi Dosen Peneliti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/engagement.v4i1.97

Abstract

High Order Thinking Skills (HOTS) is the ability to connect, manipulate, and change the knowledge and experience that is owned critically and creatively in determining decisions to solve problems in new situations. To include HOTS questions in a learning process is an obstacle for Madrasah teachers, including teachers of PC. LP. Maarif NU Lamongan. This community service aimed at improving the pedagogical competence of mathematics teachers of PC. LP. Maarif NU Lamongan. Community-Based Research (CBR) was employed through workshop and training administered by the Mathematics Study Program of UIN Sunan Ampel Surabaya in designing and completing high order thinking questions followed by assistance. The results indicated that the ability of Madrasah teachers to solve HOTS questions as well as its implementation in classroom teaching and learning activities improved significantly.
Implementasi Metode Regresi Logistik Biner Pada Keakuratan Klasifikasi Jenis Musik K-Pop dan Western Pada Spotify Imron, M.; Putroue Keumala Intan
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.v11i2.36374

Abstract

Musik yang didengar memiliki keunikan sendiri yang dapat menjadi suatu karakteristik, dimana dapat dengan adanya karakteristik tersebut lagu dapat dikelompokkan berdasarkan jenis lagu yang sama. Bersarkan data dari spotify penyanyi dikelompokkan oleh variabel Dancebility, Energy, Acousticness, Tempo dan beberapa variabel lain. Memanfaatkan variabel tersebut dilakukan penelitian bertujuan untuk melihat keakuratan klasifikasi jenis musik dimana musik yang akan di label adalah (1 = K-Pop) dan (0 = Western). Klasifikasi dilakukan dengan menggunakan regresi logistik biner. Data penelitian displit dengan proporsi perbandingan 80:20 kemudian diolah menggunakan metode regresi logistik biner, model yang terbentuk dapat menghasilkan akurasi klasifikasi 79.04% untuk data training, dan 79.69% untuk data testing.
Penerapan Metode Principal Component Analysis (PCA) dan Long Short-Term Memory (LSTM) dalam Memprediksi Prediksi Curah Hujan Harian Musfiroh, Musfiroh; Novitasari, Dian Candra Rini; Intan, Putroue Keumala; Wisnawa, Gede Gangga
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3114

Abstract

Since the last three years North Luwu has experienced frequent hydrological disasters in the form of floods and landslides. The disaster had a negative impact on the availability of clean water, failed to plant and even tended to reduce the quality of the harvest. Cocoa is one of the leading commodities of North Luwu Regency whose productivity has decreased due to the impact of climate change so that it will affect the sustainability of the local population's income. Therefore, the purpose of this research is to anticipate rainfall that will occur to prevent or reduce the risk of failure and loss. Principal Component Analysis (PCA) Method is used as feature extraction to find out the most influential variables and the Long Short-Term Memory (LSTM) method is used as a prediction method. Future rainfall is predicted using meteorological variables such as pressure, evaporation, maximum temperature, average humidity, and sunshine duration from 1 January 2017 to 30 September 2022. Based on the PCA results, 4 variables are obtained that have the most influence on rainfall, namely: variable evaporation, maximum temperature, average humidity, and length of sunlight. These variables are used as input to predict rainfall using LSTM. In this study using trial parameters, namely the number of hidden, batch size, and learn rate drop period. The best prediction results were obtained for MAPE of 0.0018 with the number of hidden, batch size and learn rate drop periods of 100, 32, and 50 respectively. The prediction results show very heavy rainfall occurring on August 28, 2021 of 101.9734 mm, 21 September 2021 of 108.6528 mm, and 5 April 2022 of 116.5510 mm. In this study PCA was able to increase accuracy in considering all parameters and choosing the most effective.
PERAMALAN HARGA EMAS DENGAN METODE BACKPROPAGATION NEURAL NETWORK ( STUDY KASUS : PT. ANEKA TAMBANG TBK ) Sufriyah, Lailiyatus; Fanani, Aris; Hamid, Abdulloh; Ulinnuha, Nurissaidah; Intan, Putroue Keumala
Scientica: Jurnal Ilmiah Sains dan Teknologi Vol. 2 No. 12 (2024): Scientica: Jurnal Ilmiah Sains dan Teknologi
Publisher : Komunitas Menulis dan Meneliti (Kolibi)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.572349/scientica.v2i12.3479

Abstract

Investasi emas merupakan investasi yang cukup mudah dan banyak digemari oleh berbagai kalangan. Dengan melakukan investasi emas, maka kekayaan yang dimiliki akan terjaga. Hal ini dikarenakan investasi emas hampir tidak dipengaruhi dengan adanya inflasi. Sebelum memutuskan untuk berinvestasi, seorang investor harus memiliki pengetahuan mengenai keuntungan dan risiko yang akan terjadi. Pada penelitian ini penulis akan meramalkan harga emas PT Aneka Tambang Tbk di masa yang akan datang menggunakan metode backpropagation neural network. Dari hasil pengujian data harga emas didapatkan jaringan paling optimal yaitu 12-30-1, dengan varian 2,82, mean 9,441, standar deviasi 1,67, dan nilai error MSE sebesar 0,037517. Dari nilai eror tersebut didapatkan harga emas pada bulan Juli 2022 sebesar 921891. Dan metode Backpropagation Neural Network terbukti dapat digunakan untuk menyelesaikan peramalan harga emas PT. Aneka Tambang Tbk.
Identifikasi Faktor-Faktor Pengaruh Indeks Gini Ratio Menggunakan Regresi Logistik Ordinal Nurfadila, Monika Refiana; Intan, Putroue Keumala
JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 20 No. 1 (2023)
Publisher : Program Studi Matematika, Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/2540766X.2023.v20.i1.16258

Abstract

Ketimpangan pendapatan menjadi permasalaan yang masih dialami oleh negara Indonesia. Perlu dilakukan suatu upaya untuk menangani ketimpangan pendapatan dengan menurunkan nilai indeks gini ratio. Hal tersebut dapat dilakukan dengan meminimalisir faktor penyebab tingginya nilai indeks gini ratio. Penelitian ini bertujuan untuk mengidentifikasi faktor penyebab tingginya indeks gini ratio serta mengetahui besarnya pengaruh faktor indeks gini ratio menggunakan metode regresi logistik ordinal. Penelitiian ini menggunakan variabel dependen berupa indeks gini ratio sedangkan untuk variabel independennya yaitu IPM, pengangguran terbuka, upah minimum provinsi, PDRB, jumlah penduduk dan presentase penduduk miskin. Berdasarkan hasil analisis didapatkan bahwa faktor-faktor yang berpengaruh positif dan signifikan terhadap indeks gini ratio yaitu IPM, jumlah penduduk serta presentase penduduk miskin. Model regresi logistik ordinal yang didapatkan mampu menjelaskan pengaruh terhadap indeks gini ratio sebesar 61,7%.Kata kunci : Ketimpangan Pendapatan, Indeks Gini Ratio, Regresi Logistik Ordinal
ANALYSIS OF DIABETES MELITES DISEASE USING BINARY LOGISTIC REGRESSION Anistya, Mery; Putroue Keumala Intan; Ahmad Hanif Asyhar; Wika Dianita Utami
Jurnal Statistika dan Aplikasinya Vol. 9 No. 1 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09102

Abstract

This study aims to identify risk factors that affect the incidence of diabetes mellitus and evaluate the accuracy of the prediction model using binary logistic regression. The research method used secondary data from 140 patients at UPT Puskesmas Teja, Pamekasan, consisting of 60 diabetes negative patients and 80 diabetes positive patients. The variables analyzed included age, gender, heredity, smoking habit, body mass index (BMI), blood glucose level, cholesterol, and blood pressure. The results showed that the variables of gender and glucose levels had a significant influence on the incidence of diabetes, with significance values of 0.022 and 0.001, respectively. The gender variable has an Odds Ratio (OR) value of 0.135, indicating that female patients tend to have a lower risk of developing diabetes than men. Meanwhile, glucose levels showed a positive association with the incidence of diabetes, with each unit increase in glucose levels increasing the risk of diabetes by 1.016 times. The binary logistic regression model developed has an accuracy of 87.1% based on the Area Under Curve (AUC) value, which falls into the category of strong classification ability. This study provides important implications in supporting the development of more effective diabetes prevention and management strategies through an in-depth understanding of risk factors, so that it can be used as a basis for decision-making in public health services.
COMPARISON OF FORECASTING VIOLENCE CASES NUMBER AGAINST WOMEN AND CHILDREN USING DOUBLE EXPONENTIAL SMOOTHING (DES) AND AUTO REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) METHODS Fadilah, Siti Nur; Intan, Putroue Keumala; Utami, Wika Dianita
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 2 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (439.186 KB) | DOI: 10.30598/barekengvol16iss2pp443-450

Abstract

Violence is something that is being widely discussed. It is due to the increasing number of victims of violence in a scope where victims should feel safe. Therefore, the researchers took this case intending to predict the number of violence cases against women and children in Jakarta so that the government can anticipate the spike in cases and evaluate the policies that will be issued in this case. The data used was from the Office for the Empowerment of Child Protection and Population Control (DPPAPP) of DKI Jakarta Province from January 2018 to October 2021 to predict the number of cases in 2022. Based on the analysis results, it is known that the number of cases of violence against women and children has decreased throughout 2022. In addition, the accuracy of the model using the Double Exponential Smoothing (DES) method is 44.91%, and the Auto-Regressive Integrated Moving Average (ARIMA) is 39.03%.
MODELING CRIME IN EAST JAVA USING SPATIAL DURBIN MODEL REGRESSION Farida, Yuniar; Farmita, Mayandah; Intan, Putroue Keumala; Khaulasari, Hani; Wibowo, Achmad Teguh
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1497-1508

Abstract

The high crime rate will create unrest and losses for the community. One of the provinces with high crime rates is East Java. This study aims to analyze the factors that influence criminality in East Java to ensure appropriate crime prevention and control measures can be taken. The factors that potentially influence crime in East Java studied include population density, the number of poor people, unemployment, Human Development Index (HDI), Gross Regional Domestic Product (GRDP), and per Capita Expenditure, which are associated with geographical conditions in each region (regency/city) collected from BPS East Java in 2022. Meanwhile, the number of crimes is collected from the East Java Regional Police. This research uses a statistical method, namely the Spatial Durbin Model (SDM), which is a particular form of the Spatial Autoregressive Model (SAR) method with Queen Contiguity weighting by analyzing geographically (spatial processes). Based on the results of the analysis, it was found that the influential factors were unemployment, HDI, GRDP, and per Capita Expenditure, and the R-square result was obtained at 85.18%. This shows a relationship between spatial accessibility and crime, where unemployment, HDI, GRDP, and per Capita Expenditure in an area can affect regional vulnerability to crime
Analysis of Factors Influencing Traffic Accidents in Sidoarjo Regency Using the Geographically Weighted Regression Method Aprilianti, Inggrit Delima; Ulinnuha, Nurissaidah; Intan, Putroue Keumala
Statistika Vol. 25 No. 2 (2025): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v25i2.7772

Abstract

Abstract. Traffic accidents are incidents that may result in trauma, injury, disability, or even death. One of the regencies in East Java Province experiencing an annual increase in traffic accident cases is Sidoarjo Regency. Geographically Weighted Regression (GWR) is a statistical approach that analyses the relationship between independent and dependent variables, taking into account spatial variation in each region. This study applies the GWR method to identify significant factors influencing the number of traffic accidents and to classify sub-regions within Sidoarjo Regency based on those factors. This study uses variables such as accident count, population density, vehicle types, gender ratio, and geographic coordinates to capture spatial differences across Sidoarjo's districts. The results indicate that the adaptive tricube kernel in GWR is the most suitable model, achieving a coefficient of determination (R²) of 99.96%. This performance indicates that the GWR model yields a slightly better fit than the multiple linear regression model, which obtained an R² of 99.86%. The types of vehicles, specifically trucks, cars, and motorcycles, are identified as significant variables in almost all districts. In Sidoarjo Regency, the districts are classified into two clusters based on the independent variables that significantly influence traffic accidents: Cluster 1, the density–vehicle accident cluster, and Cluster 2, the vehicle-only accident cluster. This classification provides a foundation for more targeted government interventions to reduce regional traffic accidents. Policy recommendations include controlling population density and improving road infrastructure in the first cluster, while focusing on vehicle safety, monitoring goods transportation, and implementing road safety campaigns in the second cluster.
Pengandalian Efek Moving Holiday dengan RegARIMA dalam Proses Peramalan Nilai Tukar Rupiah Terhadap US Dollar Tussholikhah, Anissa Nurul Farida; Ulinnuha, Nurissaidah; Utami, Wika Dianita; Intan, Putroue Keumala
Jurnal Matematika Integratif Vol 20, No 1: April 2024
Publisher : Department of Matematics, Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jmi.v20.n1.54416.63-80

Abstract

Naik turunnya nilai tukar rupiah merupakan salah satu elemen yang mempengaruhi keadaan ekonomi atau tingkat inflasi suatu negara. Fluktuasi nilai tukar mata uang juga dapat dipengaruhi oleh beberapa hari besar nasional, seperti Hari Raya Idul Fitri, yang memiliki periode yang tidak dapat diprediksi setiap tahunnya. Sehingga perlu dilakukan penelitian ini untuk mengetahui prediksi nilai tukar mata uang dengan mempertimbangkan efek moving holiday dan hasil akan dibandingkan dengan metode prediksi tanpa mempertimbangkan efek moving holiday. Dari banyaknya proses prediksi yang dapat dilakukan, penelitian ini menggunakan metode RegARIMA yang merupakan salah satu perkembangan dari ARIMA dengan pengendalian efek moving holiday. Perbandingan hasil diperoleh dari evaluasi ARIMA dengan RegARIMA, untuk mengetahui sebaik apa model menangani efek moving holiday. Berdasarkan nilai MAPE yang diperoleh, model RegARIMA lebih unggul dari ARIMA. MAPE dari RegARIMA bernilai lebih kecil, yakni sebesar 1.82% dibandingkan ARIMA yang memperoleh MAPE sebesar 2.43%. Sehingga model RegARIMA berhasil dalam menangani efek moving holiday dalam proses prediksi.
Co-Authors Abdulloh Hamid Abdulloh Hamid Achmad Teguh Wibowo Afifah Nur Aini Ahmad Hanif Asyhar Ahmad Naufal DS Ahmad Zaenal Arifin Anisa Nur Azizah Anistya, Mery Aprilianti, Inggrit Delima Aris Fanani Damayanti, Adelia Diah Restu Ningsih Dian C. Rini Novitasari Dian C.R. Novitasari Dian Yuliati Dian Yuliati Dian Yuliati Fadilah, Siti Nur Fajar Setiawan Fanani, Aris Farmita, Mayandah Ghaluh Indah Permata Sari Hani Khaulasari Kurniawan, Mohammad Lail Lubab, Ahmad M. Imron Margaretha, Adellia Putri Moh. Hafiyusholeh Muhammad Fahrur Rozi Muhammad Iqbal Widiaputra Musfiroh Musfiroh, Musfiroh Noviati Maharani Sunariadi Nurfadila, Monika Refiana Nurissaidah Ulinnuha Rozi, Muhammad Fahrur Saputra, Yahya Vigo Tri Sari, Dian Candra Rini Novita Shofinatul Wahdah Nur Aulia Sufriyah, Lailiyatus Tussholikhah, Anissa Nurul Farida USWATUN KHASANAH Utami, Wika Dianita Widiaputra, Muhammad Iqbal Wika Dianita Utami Wika Dianita Utami Wika Dianita Utami Wika Dianita Utami Wisnawa, Gede Gangga Yuliati, Dian Yuni Hariningsih Yuniar Farida Yuyun Monita Zainullah Zuhri Zufatul Aizzah