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All Journal FORUM STATISTIKA DAN KOMPUTASI Jurnal Pendidikan Matematika Media Statistika Journal of Education and Learning (EduLearn) AKSIOMA: Jurnal Program Studi Pendidikan Matematika Proceedings Education and Language International Conference Journal of Mathematics and Mathematics Education (JMME) BAREKENG: Jurnal Ilmu Matematika dan Terapan Specta Journal of Technology Budimas : Jurnal Pengabdian Masyarakat NUCLEUS: Jurnal Sains dan Teknologi Journal of Multidisciplinary Applied Natural Science Jurnal Pengabdian Kepada Masyarakat Aptekmas : Jurnal Pengabdian Kepada Masyarakat Journal of Community Service Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Indonesian Journal of Advanced Research (IJAR) Equivalent: Jurnal Ilmiah Sosial Teknik Javanologi: International Journal of Javanese Studies Equiva Mathematics Education Journal
Dewi Retno Sari Saputro, Dewi Retno
Jurusan Matematika FMIPA UNS
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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.
IDENTIFIKASI MODEL SELF-EXCITING THRESHOLD AUTOREGRESSIVE DENGAN SWITCHING TWO REGIME (KASUS PADA DATA EKSPOR AGRIKULTUR DI INDONESIA) Riyansyah, Husnun Nur Ghiffari Putri; Saputro, Dewi Retno Sari; Winarno, Bowo
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 14 No 4 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (724.939 KB) | DOI: 10.30598/barekengvol14iss4pp511-522

Abstract

A time series model that explain the structural changes associated with data in a certain time period is the Threshold Autoregressive (TAR) model. The basic of the TAR model there are some different usage regimes in autoregressive analysis. One model based on TAR is a self-exciting threshold autoregressive (SETAR) model with the same delay parameters for each regimen. The SETAR model has a linear nature in each regime but being nonlinear if the models of each regime are combined. In addition, this model can improve jump data that cannot be captured by linear time series models. This means that the SETAR model has high-level parameters through an appropriate switching regime that is applied to agricultural export data in Indonesia. The purpose of this reseach is to test the estimated SETAR parameter model and apply it to Indonesian agricultural export data. There are three methods that can be done for estimating of parameter of SETAR model, namely the conditional quadratic sequential method, ordinary least square (OLS) and nonlinear least square (NLS). In this research, the two stage parameter estimation method is used with OLS and the second stage parameter estimation is used to optimisze the parameter values ​​that are not significant in the model. In its application, the SETAR model (2,1,1) was obtained to model agricultural export data in Indonesia and the MAPE value was 25%.
HILL CLIMBING ALGORITHM ON BAYESIAN NETWORK TO DETERMINE PROBABILITY VALUE OF SYMPTOMS AND EYE DISEASE Adhitama, Ria Puan; Saputro, Dewi Retno Sari; Sutanto, Sutanto
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (577.687 KB) | DOI: 10.30598/barekengvol16iss4pp1271-1282

Abstract

One of the five human senses referred to as photoreceptors is the eye because the eye is very sensitive to light stimuli. Refractive abnormalities in the eyes are often experienced, which are abnormalities that occur when the eyes cannot see clearly in the open or blurred vision. An unhealthy lifestyle is a trigger for an increase in individuals who experience complaints of eye diseases. In diagnosing a disease, doctors need patient information in the form of symptoms experienced so that patients can be treated immediately. Information in the form of symptoms and types of eye diseases can be used to make conjectures about eye diseases through the structure of BN. The symptom information and type of the disease are represented through nodes, while the relationships are represented through the edge. BN is one of the Probabilistic Graphical Models (PGM) consisting of nodes and edges. BN is also known as a direct acyclic graph (DAG), which is a directed graph that does not have a cycle. The approach method used is scored based on the evaluation process with the bic scoring function. The algorithm used in this study is the HC algorithm. The research data used consisted of 52 symptoms and 15 eye diseases. The results of the study were obtained by the final structure of BN formed by the HC algorithm produced 93 edges and 65 connected nodes, and the probability value of the disease and the symptoms of the disease in the eye.
CABLE NEWS NETWORK (CNN) ARTICLES CLASSIFICATION USING RANDOM FOREST ALGORITHM WITH HYPERPARAMETER OPTIMIZATION Saputro, Dewi Retno Sari; Sidiq, Krisna
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp0847-0854

Abstract

The growth of news articles on the internet occurs in a short period with large amounts so necessary to be grouped into several categories for easy access. There is a method for grouping news articles, namely classification. One of the classification methods is random forest which is built on decision tree. This research discusses the application of random forest as a method of classifying news articles into six categories, these are business, entertainment, health, politics, sport, and news. The data used is Cable News Network (CNN) articles from 2011 to 2022. The data is in form of text and has large amounts so good handling is needed to avoid overfitting and underfitting. Random forest is proper to apply to the data because the algorithm works very well on large amounts of data. However, random forest has a difficult interpretation if the combination of parameters is not appropriate in the data processing. Therefore, hyperparameter optimization is needed to discover the best combination of parameters in the random forest. This research uses search cross-validation (SearchCV) method to optimize hyperparameters in the random forest by testing the combinations one by one and validating those. Then we obtain the classification of news articles into six categories with an accuracy value of 0.81 on training and 0.76 on testing.
BIBLIOMETRIC ANALYSIS OF NEURAL BASIS EXPANSION ANALYSIS FOR INTERPRETABLE TIME SERIES (N-BEATS) FOR RESEARCH TREND MAPPING Saputro, Dewi Retno Sari; Prasetyo, Heri; Wibowo, Antoni; Khairina, Fadiah; Sidiq, Krisna; Wibowo, Gusti Ngurah Adhi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp1103-1112

Abstract

Bibliometrics is the statistical analysis of articles, books, and other forms of publication. The bibliometrics analysis is performed with data on the number and authorship of scientific publications and articles, and citations to measure the work of individuals or groups of researchers, organizations, and countries to identify national and international networks and map developments in new multidisciplinary fields of science and technology. In addition, bibliometrics assesses and maps the research, organization, and country of researchers at a given time period. The Bibliometric analysis also has advantages which include mapping relationships between concepts, mapping research directions or trends, mapping state of the art (the novelty of the results of research conducted), and providing insights related to fields, topics, and research problems for future works. This study aims to determine the growth and development of N-BEATS publications, their distribution, variable keywords, and author collaboration using a bibliometric network. The research method used in this paper, through screening of articles obtained from the Scopus database page in 2008-2022, is used for citations in the form of metrics. At the same time, they are visualizing the metadata with VOSviewer. Data was collected from the direct science database with the keyword N-BEATS. The results show that 2022 has the highest number of publications, reaching 310 publications (14.90%). The distribution of research publications on N-BEATS shows a perfect distribution. Terms in the N-BEATS variable that often appear and are associated with other variables.
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 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 Ikawati, Nur Ikhsan Abdul Latif Imam Sujadi 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 Muhamad Safa’udin, Muhamad Muslikhah, Muslikhah Musmiratul Uyun Musta'in, Ghufron Mu’ti, Yafita Arfina 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 Reyga Ferdiansyah Putra 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 Sulistyaningsih Sulistyaningsih Suprapto Suprapto 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