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Model Periodic Autoregressive with Exogenous Variable dan Estimasi Parameternya dengan Metode Kuadrat Terkecil Dua Tahap Ningrum, Hanifah Listya; Saputro, Dewi Retno Sari
Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya 2018: Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Publisher : Universitas Muhammadiyah Surakarta

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Abstract

Model periodic autoregressive with exogenous variable (PARX) adalah model runtun waktu yang digunakan untuk mengetahui hubungan dinamis antara variabel endogen dengan variabel eksogen.Model PARX merupakan pengembangan dari model periodic autoregressive (PAR) dengan menambahkan variabel eksogen ke dalam modelnya.Variabel eksogen adalah variabel yang berpengaruh terhadap variabel lainnya, namun sebaliknya tidak dipengaruhi oleh variabel lainnyadalam satu model.Pada umumnya, metode estimasi parameter untuk model PARX adalah metode kuadrat terkecil (least square/LS) namun tidak dipertimbangkan parameter yang tidak signifikan, akibatnya estimator yang dihasilkan tidak akurat. Dengan demikian diperlukan pembatas linear untuk parameter tertentu, sehingga metode kuadrat terkecil dua tahap (two stage least square/2SLS)tepat untuk mengatasi masalah tersebut. Tujuanpenelitian untuk melakukan kajian ulang model PARX dan estimasi parameternya dengan metode kuadrat terkecil dua tahap. Perhitungan metode kuadrat terkecil dua tahap pada dasarnya sama dengan metode kuadrat terkecil (least square/LS) namun proses estimasinya melalui dua tahap LS. Hasil kajian menunjukkan diperoleh model PARX dan asumsinya serta estimasi parameter dengan metode kuadrat terkecil dua tahap.
Proporsionalitas Autokorelasi Spasial dengan Indeks Global (Indeks Moran) dan Indeks Lokal (Local Indicator of Spatial Association (LISA)) Saputro, Dewi Retno Sari; Widyaningsih, Purnami; Kurdi, Nughthoh Arfawi; Susanti, Ade
Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya 2018: Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Publisher : Universitas Muhammadiyah Surakarta

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Abstract

Autokorelasi spasial merupakan teknik dalam analisis spasial untuk mengukur kemiripan nilai atribut dalam suatu ruang (jarak, waktu dan area). Jika terdapat pola sistematik dalam nilai atribut tersebut,maka terdapat autokorelasi spasial.Adanya autokorelasi spasial mengindikasikan bahwanilai atribut pada area tertentu terkait oleh nilai atribut tersebut pada area lain yang letaknya salingberdekatan (bertetangga). Ketetanggaan tersebut diharapkan dapat mencerminkan derajatketergantungan area (spasial) yang tinggi apabila dibandingkan dengan area lain yang letaknyaterpisah jauh.Autokorelasi spasial diukur melalui dua indeks yaitu indeks global dan indeks lokal. Indeks Moran adalah indeks global tertua yang membandingkan nilai atribut area dengan nilaiatribut area lainnya. Sementara, Local Indicator of Spatial Association (LISA)adalah indeks lokal yang dipergunakan untuk mengevaluasi kecenderungan adanya pola secara lokal dengan menunjukkan beberapa bentuk dari hubungan spasial. Indeks Moran cenderung mengabaikan pola lokal hubungan spasial sehingga LISA memberikan hubungan spasial pada setiap wilayah pengamatan. Keduanya, baik indeks global maupun lokal mempunyai nilai yang proporsional yaitu indeks Moran proporsional dengan jumlah nilai LISA melalui matriks pembobotan spasial (W) dengan taxonomic levels. Dalam artikel ini dibuktikan proporsionalitas tersebut yakni nilai indeks Moran proporsional dengan jumlah nilai LISA.
Performance of Ridge Regression, Least Absolute Shrinkage and Selection Operator, and Elastic Net in Overcoming Multicollinearity Saputro, Dewi Retno Sari; Wahyu, Nugroho Lambang; Widyaningsih, Yekti
Journal of Multidisciplinary Applied Natural Science Vol. 5 No. 2 (2025): Journal of Multidisciplinary Applied Natural Science
Publisher : Pandawa Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47352/jmans.2774-3047.251

Abstract

Multicollinearity is a violation of assumptions in multiple linear regression analysis that can occur if there is a high correlation between the independent variables. Likewise, the variants of multiple linear regression models such as the Geographically Weighted Regression model (GWR). Multicollinearity causes parameter estimation using the Quadratic Method (QM) unstable and produces a large variance. On the other hand, what is expected in the estimation parameters is an estimate with a minimum variance, even though it is biased. Thus, one way to overcome multicollinearity can be to use biased estimators, such as Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (LASSO), and Elastic Net (EN). In RR, the Least Square Method (LSM) coefficient is reduced to zero but it can’t select the independent variable. However, the parameter model obtained from the Ridge Regression is biased, and the variance of the resulting regression coefficients is relatively tiny. In addition, the RR is increasingly difficult to understand if a huge number of independent variables are used. Meanwhile, LASSO is a computational method that uses quadratic programming and can act out the RR principles and perform variable selection. The LASSO method became known after discovering the Least-Angle Regression (LARS) algorithm. The LASSO method can reduce the LSM coefficient to zero to perform variable selection. LASSO also has a weakness, so EN is used. In this article, the performance of the three methods is compared from the mathematical aspect. The performance of each is written as follows, RR is helpful for clustering effects, where collinear features can be selected together; LASSO is proper for feature selection when the dataset has features with poor predictive power and EN combines LASSO and RR, which has the potential to lead to simple and predictive models.
SUSCEPTIBLE VACCINATED INFECTED RECOVERED MODEL WITH THE EXCLUSIVE BREASTFEEDING AND ITS APPLICATION TO PNEUMONIA DATA IN INDONESIA Widyaningsih, Purnami; Musta'in, Ghufron; Saputro, Dewi Retno Sari
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp999-1008

Abstract

The spread of infectious diseases can occur directly or indirectly. Pneumonia is an infectious respiratory tract disease. Indonesia is among the top 10 countries in the world concerning deaths caused by pneumonia. The spread of infectious diseases can be prevented through vaccination and exclusive breastfeeding, which play a role in providing body immunity. This study aims to formulate an SVIR model with exclusive breastfeeding, apply it to pneumonia in Indonesia, and determine its spread pattern and interpretation regarding the target of free pneumonia by 2030. The methods used were literature and applied studies. Through literature studies, the characteristics of infectious diseases were identified, assumptions and parameters of the model were added, and relationships between variables were determined. The applied method was to estimate the parameters and initial values of the model based on annual data on pneumonia disease in Indonesia. The formulated model is a system of first-order nonlinear differential equations. The model is applied to pneumonia based on annual data from 2013 to 2022 in Indonesia, and its solution is determined using the fourth-order Runge-Kutta method. Based on the model solution and 2021-2022 data, a MAPE value of 15% is obtained, indicating that the model is sufficiently accurate in explaining the spread of pneumonia in Indonesia. The spread pattern of pneumonia in Indonesia from 2013 to 2030 indicates a downward. However, as of 2030, there are still 67,261 individuals infected, indicating that the target of pneumonia-free Indonesia has not been achieved. Simulation shows that with exclusive breastfeeding rate value = 0.438852 and Hib vaccination rate = 0.25 it is estimated that the target of free pneumonia in Indonesia in 2030 will be achieved. The free target can also be achieved by increasing the exclusive breastfeeding rate to 73.9% and the Hib vaccination rate to 0.22.
SENTIMENT ANALYSIS OF REVIEWS ON X APPS ON GOOGLE PLAY STORE USING SUPPORT VECTOR MACHINE AND N-GRAM FEATURE SELECTION Kusumo, Fahri Aimar; Saputro, Dewi Retno Sari; Widyaningsih, Purnami
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1037-1046

Abstract

Sentiment analysis is an application of text mining that is used to find out opinions from a set of textual data about a particular event or topic. The main function of sentiment analysis is to extract information and find the meaning and opinions of a given user. Sentiment analysis requires classification algorithms, such as Support Vector Machine (SVM). SVM is a frequently used algorithm for text data classification because it can handle high-dimensional data. The concept of SVM is to determine the best hyperplane that serves as a separator of two classes in the input space. Text data with a large number of features causes data imbalance and affects the classification process so it is necessary to do feature selection. Feature selection is a technique used to reduce irrelevant attributes in the dataset. N-gram feature selection is a statistics-based approach to classifying text. N-grams are able to classify unknown text with the highest certainty. The characteristics of N-grams in sentiment analysis are that they function well despite textual errors, run efficiently, require simple storage, and fast processing time. This research aims to perform sentiment analysis on application reviews on the Google Play Store with SVM and unigram, bigram, and trigram feature selection. The methodology of this research includes conducting theoretical studies, web scraping, text preprocessing, labeling sentiments with VADER, weighting with TF-IDF, dividing data into training data (80%) and testing data (20%), training and evaluating models, classifying testing data, and interpreting results. Based on the research results, 3151 testing data were classified. SVM classification and unigram feature selection have the highest accuracy value of 90% and AUC of 0.93 (excellent). SVM classification and bigram feature selection have an accuracy value of 78% with an AUC value of 0.81 (good). SVM classification and trigram feature selection had the lowest accuracy value of 68% with an AUC value of 0.66 (poor).
Penerapan Stochastic Gradient Descent Support Vector Regression pada Data Laju Pertumbuhan Produk Domestik Bruto di Indonesia Arrazaq, Khamid Muhammad; Saputro, Dewi Retno Sari; Setiyowati, Ririn
Equiva Journal Vol 1 No 2 (2023)
Publisher : Jurusan Matematika dan Teknologi Informasi

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Abstract

Economic growth is defined as an increase in a country's income as measured using gross domestic product (GDP) data. The GDP growth rate tends to experience an upward trend even though fluctuates in each time period. This fluctuation will affect the decision of investors in investing or withdrawing capital. In this study, a regression model is applied to GDP growth rate data to help investors understand the pattern of GDP growth in the future. One of the regression models that can be used is support vector regression with stochastic gradient descent optimization algorithm (SGD-SVR). The results show that the SGD-SVR model is able to be applied to GDP growth rate data in Indonesia. In the training process, the MSE resulted was 0.2805 with a total of 360 iterations. Meanwhile, the testing process resulted in an MSE of 0.0325.
Penerapan Model Pembelajaran Kooperatif Tipe Jigsaw dan NHT Ditinjau dari Kecerdasan Interpersonal Siswa pada Pokok Bahasan Bangun Ruang Sisi Datar Kurniawati, Kiki Riska Ayu; Budiyono; Saputro, Dewi Retno Sari
Mathematics Education Journal Vol. 11 No. 1 (2017): Jurnal Pendidikan Matematika
Publisher : Universitas Sriwijaya in collaboration with Indonesian Mathematical Society (IndoMS)

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Abstract

The aims ofthis research were to find out the different effect of each categories of learning model, students interpersonal intelligence and their interaction towards students mathematics learning achievement on the subject of plane geometry.The research was quasi experimental with 3×3 factorial design. The hypothesis test used unbalanced two ways analysis of variance at the significance level of 0,05. Based on hypothesis test, it can be concluded that: (1) the cooperative learning model of Jigsaw type gives a better mathematics achievement than cooperative learning model of NHT type and direct learning model, and the cooperative learning model of NHT type gives a better mathematics achievement than direct learning model; (2) students with the high interpersonal intelligence had the same achievement as students with the medium interpersonal intelligence, students with the high interpersonal intelligence had better achievement than students with the low interpersonal intelligence, and the students with the medium interpersonal intelligence had the same achievement as students with the low interpersonal intelligence; (3) on the cooperative learning model of Jigsaw type, NHT type and direct learning model, students with the high interpersonal intelligence had the same achievement as students with the medium interpersonal intelligence, students with the high interpersonal intelligence had better achievement than students with the low interpersonal intelligence and the students with the medium interpersonal intelligence had the same achievement as students with the low interpersonal intelligence; and (4) on students interpersonal intelligence high, medium and low, the cooperative learning model of Jigsaw type gives a better mathematics achievement than cooperative learning model of NHT type and direct learning model, and the cooperative learning model of NHT type gives a better mathematics achievement than direct learning model.
Mengubah Limbah Kayu Menjadi Produk Bernilai Tinggi di Industri Kreatif Nughthoh Arfawi Kurdhi; Saputro, Dewi Retno Sari; Widyaningsih, Purnami; Sutanto, Sutanto; Setiyowati, Ririn; Sudibyo, Nugroho Arif
PaKMas: Jurnal Pengabdian Kepada Masyarakat Vol 4 No 2 (2024): November 2024
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/pakmas.v4i2.3252

Abstract

The UMKM involved in this community partnership program is engaged in the production of teak wood furniture. The issues currently faced are (1) a limited range of furniture products and (2) traditional management and marketing methods. This study aimed to implement technological induction to enhance productivity, creativity, and competitiveness within these UMKMs. The technological induction involved providing modern production tools for creating innovative products and developing an application boot program and server computer to enhance marketing efforts. A mixed-methods approach was used, combining case study observations and user-centred design principles to create technological solutions. The program's outcomes were assessed through partner training and mentoring, employing a problem-solving approach to address post-implementation challenges. The study found that introducing advanced production tools and digital marketing strategies significantly increased the variety of products, streamlined production processes, and expanded market reach, positioning the UMKMs for improved competitiveness in the national market. This research underscores the importance of integrating technology into small-scale industries for sustainable growth. Future efforts should focus on scaling these practices to other creative sectors for broader economic impact.
Dekomposisi Transformasi Wavelet Kontinu dengan Filter Wavelet Morlet Adzakie, Haabi Luckmanoor; Saputro, Dewi Retno Sari; Sutanto, Sutanto; Widyaningsih, Purnami; Khomariah, Nurul
NUCLEUS Vol 6 No 01 (2025): NUCLEUS
Publisher : Neolectura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37010/nuc.v6i01.2008

Abstract

Analyzing high-dimensional data often presents unique challenges, necessitating dimension reduction methods, one of which is the wavelet approach. Wavelets function as a transformation that automatically separates data into several components, then analyzes each component based on a resolution appropriate to its time scale. The Continuous Wavelet Transform (CWT) is a dimension reduction technique that relies on multiresolution decomposition to address modeling problems by generating local signal representations in the time and frequency domains (scales) continuously. Through multiresolution decomposition, trends in time series data can be separated. This transformation enables the transfer of data from the original domain into the wavelet domain for further analysis, as well as facilitating the separation of signals at both low and high frequencies more accurately. This study revisits the use of CWT, which divides data into various scales or frequency components and analyzes each part with the appropriate resolution. In this context, the Morlet wavelet filter is used. The results of the study indicate that the Morlet wavelet in CWT has advantages in detecting transient frequency components and local oscillation phenomena, making it highly effective in analyzing complex signals.
Analysis of the Hankel Matrix in Embedding Using the Singular Spectrum Analysis (SSA) Method Ni’am, Dafi’ Ichsani Aysar; Saputro, Dewi Retno Sari; Sutanto, Sutanto
Indonesian Journal of Advanced Research Vol. 4 No. 5 (2025): May 2025
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/ijar.v4i5.14340

Abstract

Singular Spectrum Analysis (SSA) is an effective method of decomposition of time series for separating key components in data, such as trends, seasonality, and noise. This study aims to analyze the role of Hankel matrix in the SSA embedding process and how window length (L) selection can affect the effectiveness of component separation in data time series. In this study, the data used includes public data that can be influenced by seasonal factors and unexpected events, such as natural disasters or regulatory changes. The research process begins with the data preprocessing stage, followed by the embedding stage to form a matrix used in decomposition with Singular Value Decomposition (SVD). To evaluate the similarity of separate components, w-correlation is used. The results show that the selection of optimal window lengths, in the range of N/4 < L < N/2 is very important to maintain a balance between temporal information and matrix dimensions. With the right window selection, the embedding process in SSA can be more effective in separating the trending, seasonal, and noise components in the data pattern. By understanding the structure of the Hankel matrix and selecting the right parameters, the embedding process in SSA can be more effective in separating the components of the time series and preserving temporal information.
Co-Authors Ade Susanti Adhitama, Ria Puan Adzakie, Haabi Luckmanoor Agung Nugroho, Tri Wahyu Agung Nugroho, Tri Wahyu Ahmad Faqihi, Ahmad Aji Hamim Wigena Al Barra, Andre Fajry Alfa Lutfiananda, Immas Metika Anik Djuraidah Antoni Wibowo Antoni Wibowo Ariati, Lia Arif Rahman Arrazaq, Khamid Muhammad Astutiningsih, Tiyas ‘Aini, Addin Zuhrotul Baharum, Aslina Budi Usodo Budi Usodo BUDIYONO Budiyono Budiyono Budiyono Budiyono Budiyono, Budiyono Cahyono, Heri Christy, Alexander Yonathan Dewi, Noviana Sukma Doni Susanto dwi hidayati Gustiasih, Restuning Harun Al Rasyid Heri Cahyono Ikawati, Nur Ikhsan Abdul Latif Indriati, Sela Putri Joko Domas, Joko Kananta, Ghaitsa Shafa Cinta Khairina, Fadiah Khamsatul Faizati, Khamsatul Khayati, Fitrotul Khomariah, Nurul Kiki Riska Ayu Kurniawati, Kiki Riska Ayu Kusuma, Nunung Fajar Kusumo, Fahri Aimar M Mardiyana, M Maharani, Swasti Marchamah Ulfa, Marchamah Mardiyana Mardiyana Mardiyana Mardiyana Mardiyana Mardiyana Mardiyana, Mardiyana Mardiyana, Mardiyana Muhamad Safa’udin, Muhamad Muslikhah, Muslikhah Musmiratul Uyun Musta'in, Ghufron Nanang Nabhar Fakhri Auliya, Nanang Nabhar Nanda Noor Fadjrin, Nanda Noor Ningrum, Hanifah Listya Ni’am, Dafi’ Ichsani Aysar Nughthoh Arfawi Kurdhi, Nughthoh Arfawi Nugroho Arif Sudibyo Nurul Khairiatin Nida Pambudi, Pangesti Arum Paryatun, Suji Paryatun, Suji Pradipta Annurwanda, Pradipta Prasetyo, Heri Pratama, Rizcka Indah Hani Prihastini Oktasari Putri Primasari, Dessy Marlinda Purnami Widyaningsih Purwaningsih, Tri Purwaningsih, Tri Putera Khano, Muhammad Nazhif Abda Putri, Diah Purwaning Putri, Matin Enggar Rahman, Arif Ramadhanti, Fajhria Budi Ririn Setyowati Riyadi Riyadi Riyansyah, Husnun Nur Ghiffari Putri Rizky Anggar Kusuma Wardani Safa’udin, Muhamad Santika, Putri Aura Sena, Arya Bima Setiyowati, Ririn Sidiq, Krisna Sujadi, Imam Sulistyaningsih Sulistyaningsih Suprapto, Suprapto Suryani, S Susanti, Ika Sutanto sutanto sutanto Sutanto Sutanto Tambunan, Nicolas Ray Amarco Tanjung, Andjani Ayu Cahaya Ummu Salamah Utami, Dwi Sari Utin Desy Susiaty Wahyu, Nugroho Lambang Wibowo, Gusti Ngurah Adhi Widiyaningsih, Purnami Winarno, Bowo YAFITA ARFINA MUTI Yekti Widyaningsih Yumaroh, Siti Roqhilu Zaidah Nurul Hasanah