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All Journal Media Statistika Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Journal of Natural Science and Integration Pendas : Jurnah Ilmiah Pendidikan Dasar JURNAL EKSAKTA PENDIDIKAN (JEP) Eksakta : Berkala Ilmiah Bidang MIPA Imajiner: Jurnal Matematika dan Pendidikan Matematika Pelita Eksakta Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Leibniz: Jurnal Matematika Journal of Mathematics UNP Entalpi Pendidikan Kimia UNP Journal of Statistics and Data Science Jurnal Pendidikan Sains Indonesia (Indonesian Journal of Science Education) Journal of The Indonesian Society of Integrated Chemistry
Yenni Kurniawati
Jurusan Matematika, FMIPA Universitas Negeri Padang
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Other

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Implementation of an Artificial Neural Network Based on the Backpropagation Algorithm in Forecasting the Closing Price of the Jakarta Composite Index (IHSG) Muhammad Fadhil Aditya Aditya; Zilrahmi; Yenni Kurniawati; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 2 No. 1 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss1/137

Abstract

Investing is highly common in Indonesia. Continuous investment activities carried out by the community will increase economic activity and employment opportunities, increase national income, and increase the level of prosperity of the community. In carrying out share buying and selling transactions, there is a means for companies to obtain funds from official financiers or investors, which is called the capital market. One of the indices issued by the IDX is the Jakarta Composite Index (IHSG). Statistics can be used to help investors, the government, or related institutions to predict the value of the IHSG. One method that can be used to predict data is an Artificial Neural Network (ANN). Backpropagation method is a multi-layer ANN method that works in a supervised learning. The idea of the Backpropagation algorithm is that the input of the neural network is evaluated against the desired output results. The purpose of this research is to give forecasting values with high accuracy to describe the movement of IHSG close price values using the ANN method based on the Backpropagation algorithm. The research showed that the BP (4,6,1) model produced an RMSE value of 28,24024 and a MAPE value of 0.00342%. Based on the results of this research, an Artificial Neural Network model based on the Backpropagation Algorithm can be applied to predict the IHSG Closing Price value.
Sentiment Analysis of DANA Application Reviews on Google Play Store Using Naïve Bayes Classifier Algorithm Based on Information Gain Cindy Caterine Yolanda; Syafriandi Syafriandi; Yenni Kurniawati; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 2 No. 1 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss1/147

Abstract

DANA is a digital payment platform that provides various features to make it easier for users to make payments, transfers, and balance replenishment online. DANA application users provide a variety of reviews that include both constructive and critical opinions, which can be valuable input for DANA application developers. The purpose of this research is to evaluate the results of sentiment classification of DANA application user reviews on the Google Play Store service using the Naïve Bayes Classifier method and Information Gain feature selection. In addition, this study aims to assess the effect of applying IG feature selection on the performance of the resulting model. In this study, reviews are divided into two categories, namely positive and negative based on lexicon-based labeling. Furthermore, data weighting, feature selection, and data division are carried out with a proportion of 80% train data and 20% test data before model building. There are two models, namely a model without feature selection (NBC model) and a model with feature selection (NBC-IG model). The evaluation results showed that the NBC model with 1106 features performed well, with 82.91% accuracy, 83.96% precision, and 90.23% recall. Meanwhile, the NBC-IG model with 536 features showed higher performance, with 85.09% accuracy, 85.79% precision, and 92.09% recall. The application of IG feature selection with the IG value limit parameter > 0.01 in the NBC model successfully reduced the number of features by 570, and improved model performance with an increase in accuracy by 2.18%, precision by 1.83%, and recall by 1.86%.
Artificial Neural Network Model for Estimating the Poor Population in Indonesia as an Effort to Alleviate Poverty Febi Febiola Putri; Atus Amadi Putra; Yenni Kurniawati; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 2 No. 2 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss2/154

Abstract

Forecasting the poverty rate in Indonesia is one of the activities that is considered to be able to help various parties, such as being able to help the government in planning more effective and efficient poverty alleviation programs. In this study, forecasting the poverty rate in Indonesia was carried out using the backpropagation artificial neural network method. The purpose of this research is to model and predict the poverty rate using the backpropagation artificial neural network model, and to determine the accuracy of the forecasting results produced by this method. This research is an applied researc. The data used is annual data on proverty in Indonesia from 2917-2021. The data is then divided into two parts, namely training data and test data. The results show that the best artificial network model is BP (7,7,2) with 7 neurons in the input layer, 7 neurons in the hidden layer, and 2 neurons in the output layer. The accuracy of this model is good with a MAPE value of 0.07633%. The forecasting results in the next period show that the highest number of poor people is East Java province with a value of 3604.1698 thousand people in the first semester (March) of 2022 and has increased in the second semester period (September) of 2022 with a value of 3698.822 thousand people
Perbandingan Algoritma C4.5 dan C5.0 Dalam Klasifikasi Status Gizi Balita Stunting dhea afrila harelvi; Admi Salma; Yenni Kurniawati; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 2 No. 2 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss2/172

Abstract

Stunting is one of the health conditions that reflect aspects of nutrition and child growth, allowing us to observe the nutritional status of toddlers. The aim of this study is to determine the classification results of the C4.5 and C5.0 algorithms in cases of stunted toddler nutritional status and to compare the results between the C4.5 and C5.0 algorithms in classifying stunted toddler nutritional status using k-fold cross-validation. The data in this study are secondary data. Which is collected from Puskesmas IV Pesisir Selatan Regency. The research variables are divided into two, namely the response variable Y, which is Toddler Nutritional Status, and predictor variables X including Age, Toddler Gender, Toddler Weight, and Toddler Height. The result of the study obtain the algorithm C5.0 produse accuracy value of the C5.0 algorithm is higher than that of the C4.5 algorithm. The C5.0 algorithm provides an average accuracy result of 83% while the C4.5 algorithm provides an accuracy result of 79%. Thus, it can be concluded that the C5.0 algorithm is better at classifying stunted toddler nutritional status.
ANALISIS KEMISKINAN DI INDONESIA MENGGUNAKAN LOCAL INDICATOR OF SPATIAL ASSOCIATION DAN SPATIAL ERROR MODEL Khairani, Putri Rahmatun; Kurniawati, Yenni; Amalita, Nonong; Mukhti, Tessy Octavia
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 6 No. 1 (2025): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v6i1.966

Abstract

Poverty in Indonesia remains a significant socio-economic challenge with notable regional disparities. The eastern provinces, particularly Papua, Maluku, and East Nusa Tenggara, experience persistently high poverty rates, suggesting a strong spatial influence. This study examines the spatial distribution of poverty using the Local Indicators of Spatial Association and the Spatial Error Model with 2024 data from the Indonesian Central Statistics Agency (BPS) for 38 provinces. The analysis employs a K-Nearest Neighbors weighting matrix (k = 10) for spatial dependencies. The LISA results identify High-High poverty clusters in Papua, Maluku, and East Nusa Tenggara. In contrast, Low-Low clusters are concentrated in Java and Bali, indicating a strong spatial pattern (Moran’s I = 0.4448). SEM findings reveal that the Gini index (β = 29.97) and population density (β = 0.016) significantly influence poverty, whereas inflation and total population do not. The model explains 76.1% of poverty variance (R² = 0.760966), highlighting its superiority over traditional regression models. These findings underscore the need for spatially adaptive policies to address poverty effectively. Policymakers should prioritize equitable economic development, regional investment, and infrastructure improvements, particularly in high-poverty clusters. Integrating spatial econometric models with KNN provides deeper insights into interregional disparities, supporting more precise and inclusive development strategies
Peramalan Curah Hujan Sebagai Upaya Mitigasi Bencana Menggunakan Seasonal Autoregressive Integrated Moving Average Fayyadh Ghaly; Amelia Susrifalah; Yenni Kurniawati
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 13 No 1 (2025): VOLUME 13 NO 1 TAHUN 2025
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v13i1.55289

Abstract

Rainfall prediction is important in disaster mitigation to reduce impacts such as drought, flood, and landslide. Rainfall data that has a seasonal pattern requires an appropriate forecasting method, one of which is SARIMA. This study predicts rainfall at the Deli Serdang Climatology Station, North Sumatra, based on monthly observation data for 2018–2023, showing a seasonal pattern with a 12-month cycle. The best model obtained is SARIMA (0,0,1) (0,0,1)12 with a MAPE of 19.5%, indicating a prediction accuracy of 80.5%. The forecasting results indicate a decrease in rainfall in the first semester of 2024, which is in the medium rainfall category. These findings can support disaster risk mitigation strategies and natural resource management planning related to climate change. The SARIMA model also has the potential to be applied in further climatology studies.
Pemodelan Geographically Weighted Regression pada Kasus Pneumonia di Indonesia Oktaviani, Bernadita; Amalita, Nonong; Kurniawati, Yenni; Martha, Zamahsary
Leibniz: Jurnal Matematika Vol. 5 No. 02 (2025): Leibniz: Jurnal Matematika
Publisher : Program Studi Matematika - Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas San Pedro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59632/leibniz.v5i02.564

Abstract

Pneumonia adalah penyakit infeksi pernafasan yang menjadi salah satu penyumbang terbesar kasus kematian pada balita dan termasuk dalam  salah satu masalah kesehatan secara global. Kematian balita akibat pneumonia di Indonesia mengalami peningkatan dari 459 kasus pada tahun 2022 menjadi 522 kasus pada  tahun 2023 yang menunjukkan bahwa pneumonia masih menjadi masalah serius bagi kesehatan balita. Geographically Weighted Regression (GWR) adalah metode yang digunakan dalam penelitian ini. Data penelitian ini diperoleh dari publikasi yang diterbitkan oleh Kemenkes RI, yaitu Profil Kesehatan Indonesia 2023. Tujuan penelitian ini untuk mengevaluasi penerapan model GWR dalam memodelkan data spasial dan untuk mengidentifikasi faktor-faktor yang berpengaruh terhadap jumlah kasus pneumonia balita di Indonesia. Hasil analisis menunjukkan bahwa model GWR memberikan hasil yang lebih baik dalam memodelkan jumlah kasus pneumonia pada balita dibandingkan model regresi linier berganda dengan nilai AIC sebesar 15,66953 dan  sebesar 94,66%. Faktor-faktor yang berpengaruh signifikan terhadap jumlah kasus pneumonia pada balita di Indonesia tahun 2023 adalah persentase balita yang mendapat vitamin A, persentase bayi mendapat ASI eksklusif sampai 6 bulan, jumlah puskesmas, persentase bayi yang mendapat imunisasi dasar lengkap, persentase rumah tangga yang memiliki akses terhadap sanitasi layak, persentase penduduk miskin, persentase kejadian gizi buruk pada balita usia 0-59 bulan, dan jumlah bayi berat badan lahir rendah (BBLR).
Pengelompokan Kabupaten/Kota Maluku dan Nusa Tenggara Barat Berdasarkan Faktor Kemiskinan Menggunakan Self Organizing Maps Aulia, Yuke; Sulistiowati, Dwi; Kurniawati, Yenni; Salma, Admi
Leibniz: Jurnal Matematika Vol. 5 No. 02 (2025): Leibniz: Jurnal Matematika
Publisher : Program Studi Matematika - Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas San Pedro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59632/leibniz.v5i02.607

Abstract

Provinsi Maluku dan Nusa Tenggara Barat masih menghadapi tantangan serius dalam upaya pengentasan kemiskinan. Kedua provinsi ini tidak hanya mengalami peningkatan persentase penduduk miskin, tetapi juga termasuk sebagai wilayah dengan persentase penduduk miskin tertinggi di Indonesia. Persentase penduduk miskin di Provinsi Maluku pada tahun 2023 mencapai 16,42%, naik sebesar 0,45%. Sementara itu, persentase penduduk miskin di Provinsi Nusa Tenggara Barat mencapai 13,85%, naik sebesar 0,17%. Angka-angka ini masih jauh dari target pemerintah yang menetapkan 6%-7% untuk persentase kemiskinan nasional. Penelitian ini bertujuan untuk mengelompokkan kabupaten/kota di Provinsi Maluku dan Nusa Tenggara Barat berdasarkan faktor yang memengaruhi kemiskinan serta mengidentifikasi karakteristik hasil klaster yang terbentuk. Penelitian ini menggunakan metode Self Organizing Maps (SOM). Data penelitian ini bersumber dari publikasi Badan Pusat Statistik (BPS), yaitu Maluku dalam Angka 2024 dan Nusa Tenggara Barat dalam Angka 2024. Hasil analisis menunjukkan terbentuknya 3 klaster wilayah yang divalidasi menggunakan pendekatan validasi internal (Connectivity, Dunn, dan Silhouette). Klaster 1 terdiri dari 2 kota ditandai oleh keunggulan dalam indikator pendidikan, kesehatan, dan ekonomi. Klaster 2 terdiri dari 15 kabupaten/kota yang dicirikan dengan potensi tenaga kerja yang tinggi, namun mengahadapi tantangan jumlah penduduk yang besar. Sementara itu, klaster 3 terdiri dari 4 kabupaten memiliki keterbatasan dalam berbagai aspek, termasuk pendidikan, kesehatan, ekonomi, dan infrastruktur.
Penerapan Vector Error Correction Model dalam Menganalisis Dampak Faktor Makroekonomi terhadap Inflasi di Indonesia Anjelisni, Nining; Amalita, Nonong; Kurniawati, Yenni; Martha, Zamahsary
Leibniz: Jurnal Matematika Vol. 5 No. 02 (2025): Leibniz: Jurnal Matematika
Publisher : Program Studi Matematika - Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas San Pedro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59632/leibniz.v5i02.654

Abstract

Penelitian ini bertujuan menganalisis dampak faktor makroekonomi terhadap inflasi di Indonesia pada periode Januari 2020–Maret 2025 dengan menggunakan pendekatan matematis melalui metode Vector Error Correction Model (VECM). Data diperoleh dari situs resmi Badan Pusat Statistik (BPS) dan Bank Indonesia (BI), yang meliputi variabel inflasi, jumlah uang beredar, BI Rate, kurs, ekspor, dan impor. Hasil analisis menunjukkan terdapat empat hubungan kointegrasi signifikan, dengan pengaruh positif dari jumlah uang beredar, kurs, dan ekspor terhadap inflasi, serta pengaruh negatif dari BI Rate dan impor. Dalam jangka pendek, ekspor (lag 1) secara statistik signifikan memengaruhi inflasi, sedangkan variabel lainnya belum signifikan. Model VECM yang dibangun terbukti stabil dan valid melalui berbagai uji kelayakan, serta menunjukkan akurasi tinggi dalam peramalan dengan nilai MAPE sebesar 9,23%. Prediksi inflasi untuk enam bulan ke depan memperlihatkan tren kenaikan bertahap, sehingga diperlukan penguatan ekspor dan pengendalian kebijakan moneter untuk menjaga stabilitas harga. Kontribusi utama penelitian ini adalah penerapan model matematis VECM sebagai alat analisis kuantitatif yang komprehensif dalam studi dinamika inflasi.
Application of Area Sampling Frame for Digitizing Household Data in Talawi Mudiak to Support Sustainable Development Goals Syafriandi, Syafriandi; Fitria, Dina; Amalita, Nonong; Kurniawati, Yenni; Permana, Dony; Fitri, Fadhilah; Martha, Zamahsary; Mukhti, Tessy Octavia
Pelita Eksakta Vol 8 No 2 (2025): Pelita Eksakta, Vol. 8, No. 2
Publisher : Fakultas MIPA Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/pelitaeksakta/vol8-iss2/293

Abstract

Desa Talawi Mudiak menghadapi tantangan dalam pengelolaan data kependudukan. Meskipun mereka telah menyusun RPJMD 2022-2027 yang mengacu pada SDG's, pendataan yang dilakukan masih terbatas pada aspek kependudukan dan demografi. Padahal, pemutkhiran data harus mencakup 17 pilar SDg's agar dapat digunakan sebagai dasar dalam perencanaan pembangunan desa. Selain itu, keterbatasan akses internet dan kurangnya pemanfaatan teknologi informasi juga menjadi kendala pengembangan sistem informasi desa yang lebih komprehensif. Program Studi S1 Statistika hadir dalam menjembatani pencapaian beberapa pilar itu melalui pemutakhiran data hingga dilitalisasinya. Kegiatan diawali dengan pengumpulan data awal, perhitungan kerangka sampling, pelaksanaan survei, dan pemrosesan data pasca survei hingga diperoleh suatu kesimpulan yang dapat digunakan untuk pembangunan desa. Kegiatan melibatkan banyak pihak, mulai dari dosen program studi, perangkat desa, mahasiswa, dan masyarakat. Hasil yang diperoleh berupa data yang mutakhir dan sebuah buku berisikan kondisi Desa Talawi Mudiak tahun 2025.
Co-Authors Afifa Lufti Insani AL Rezki Ivansyah Amelia Susrifalah Amril M Anang Kurnia Andre Marvero Anggi Adrian Danis Anjelisni, Nining Annisa Rizki Amalia Arnellis Arnellis Atus Amadi Putra Aulia, Yuke Aurumnisva Faturrahmi Cindy Caterine Yolanda Deska Warita Dewi Murni dhea afrila harelvi Dina Fitria Dina Fitria Dina Fitria, Dina Disti Harlin Dodi Vionanda Dony Permana Dwi Sulistiowati, Dwi Elfiani Sarian Bur Elfin Innaka Hamidah Elza Vinora Fadhilah Fitri Fahmi Amri Faruku Aliyu Fashihullisan Fayyadh Ghaly Fayza Annisa Febrianti Febi Febiola Putri Fitri, Fadhilah Fitriah, Dewi Lailatul Hadiyanti Riskha Handayani Wulandari Hary Merdeka Helma Helma Helma Helma Ihsan Dermawan Imran, Musthafa Irwan Irwan Iswanda Iswanda Khairani, Putri Rahmatun Khairisa Putri, Nadya Kusman Sadik Lutfian Almash M Fathoni Arnas Manja Danova Putri Meira Parma Dewi Minora Longgom Nasution Muhammad Fadhil Aditya Aditya Muhammad Fadhil Irsyad Muhammad Safuan NA Mentacem Nofriadi, Berliana Nonong Amalita Nurfatmasari, Nova Oktaviani, Bernadita Permana, Dony Prida Nova Sari rahmad revi fadillah Rama Al Azmi Zega Rizieq, Muhammad Faizal Salma, Admi Siskha Maulana Basrul Syafriandi Syafriandi Syafriandi Syafriandi Tessy Octavia Mukhti Tessy Octavia Mukhti Wimmi Sartika Windi Dwi Saputra Yuni Fatisa Yunistika Ilanda Yusbarina, Yusbarina Zamahsary Martha Zilrahmi, Zilrahmi