Article Per Year (5 Year)
p-Index From 2020 - 2025
2.534
P-Index
This Author published in this journals
All Journal Media Statistika Jurnal Teknologi Informasi dan Ilmu Komputer BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Bumigora Information Technology (BITe) Jurnal Ilmiah Matematika Journal of Applied Data Sciences Jurnal Pengabdian kepada Masyarakat STATISTIKA Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) International Conference on Digital Advanced Tourism, Management, and Technology Radiant : Journal of Applied, Social, and Education Studies
Winita Sulandari
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

Published : 22 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 22 Documents
Search

 1   2   3 
ESTIMASI PARAMETER MODEL MIXTURE AUTOREGRESSIVE (MAR) MENGGUNAKAN ALGORITMA EKSPEKTASI MAKSIMISASI (EM) Asrini, Mika; Sulandari, Winita; Wiyono, Santoso Budi
MEDIA STATISTIKA Vol 6, No 1 (2013): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (268.444 KB) | DOI: 10.14710/medstat.6.1.21-26

Abstract

Mixture autoregressive (MAR) Model is a mixture of Gaussian autoregressive (AR) components. The mixture model is capable for modelling of nonlinear time series with multimodal conditional distributions. This paper discusses about the parameters estimation using EM algorithm. All possible models are then applied to national maize production data. In this case, the BIC is used for the MAR model selection. Keywords : Mixture Autoregressive, EM Algorithm, BIC, Maize Production
PERAMALAN PENGGUNAAN BEBAN LISTRIK JANGKA PENDEK GARDU INDUK BAWEN DENGAN DSARIMA Saptyani, Marita; Sulandari, Winita; Pangadi, Pangadi
MEDIA STATISTIKA Vol 8, No 1 (2015): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (541.279 KB) | DOI: 10.14710/medstat.8.1.41-48

Abstract

Bawen substation is a part of electrical distribution system. Forecasting load demand is required for power planning. Data used in this research are an hourly load demand of Bawen, Salatiga for 3 months, from February 2, 2013 to April 29, 2013, measured in Megawatt (MW).A half hourly load demand forecasting is needed for real time controlling and short-term maintenance schedulling. Since the data have two seasonal periods, i.e. daily and weekly seasonality with length 48 and 336 respectively, the model of double seasonal ARIMA (DSARIMA) is proposed as the most appropriate model for the case. Initial model is determined by the pattern of the data, based on the autocorrelation function plot. Some experiments was done by choosing several periods data. The most suitable model is chosen based on the outsample mean absolute percentage error (MAPE). The current study shows that the DSARIMA (0, 1, [1, 20, 47])(0, 1, 1)48(0, 1, 0)336 is the best model to forecast  336 next period. Keywords: DSARIMA, MAPE, Electricity, Bawen
PENERAPAN MODEL HYBRID ARIMA BACKPROPAGATION UNTUK PERAMALAN HARGA GABAH INDONESIA Janah, Sufia Nur; Sulandari, Winita; Wiyono, Santoso Budi
MEDIA STATISTIKA Vol 7, No 2 (2014): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (591.422 KB) | DOI: 10.14710/medstat.7.2.63-69

Abstract

Hybrid model discussed in this paper combining ARIMA and backpropagation is applied to grain price forecasting in Indonesia for period January 2008 until April 2013. The grain price time series consists of linear and nonlinear patterns. Backpropagations can recognize non linear patterns that can not be done by ARIMA. In order to find the best model, some combinations of prepocessing transformations, the number of input and hidden units, and the activation function were applied in the contruction of the network structure. Based on the experiments, it can be showed that ARIMA backpropagation hybrid model provides more accurate results than ARIMA model.  The hybrid model would rather be used in the short-term forecasting, no more than three periods. Keywords: ARIMA, Backpropagation, Hybrid, Grain Price
Retinopathy Classification using Convolutional Neural Network Method with Adaptive Momentum Optimization and Applied Batch Normalization Slamet, Isnandar; Susilotomoa, Dhestahendra Citra; Zukhronah, Etik; Subanti, Sri; Susanto, Irwan; Sulandari, Winita; Sugiyanto, Sugiyanto; Susanti, Yuliana
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.309

Abstract

Retinopathy is a common eye disease in Indonesia, ranking fourth after cataracts, glaucoma, and refractive errors. It can be overcome by early diagnosis with optical coherence tomography (OCT), but this imaging technique takes much time. In this research, retinal imaging was carried out using an expert system. The expert system in this study was formed using the convolutional neural network (CNN or ConvNet) method. CNN is an algorithm of deep learning that uses convolution operations to process two-dimensional data, such as images and sounds. This research consisted of 4 stages: data collection, preprocessing, model design, and model testing. A CNN model was formed with three arrangements, consisting of two convolutional layers and one pooling layer. The ReLU activation function, zero padding, and batch normalization were used in all three formats. Then, the flattening process was carried out, and the Softmax activation function was used at the end of the architecture. The model was built using eight epochs, and optimization of Adaptive Momentum resulted in a 98.96% test data accuracy value. The result was considered good so that CNN could be used as an alternative in retinopathy diagnosis. Further research is suggested to use other optimizations or other model architectures.
Implementation of Scale-Invariant Feature Transform Convolutional Neural Network for Detecting Distracted Driver Fhadilla, Nahdatul; Sulandari, Winita; Susanto, Irwan; Slamet, Isnandar; Sugiyanto, Sugiyanto; Subanti, Sri; Zukhronah, Etik; Pardede, Hilman Ferdinandus; Kadar, Jimmy Abdel
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.222

Abstract

A distraction while driving a vehicle may result in fatal consequences, namely accidents that may leave road users seriously injured or even dead. In order to mitigate this risk, it is imperative to establish a distracted driver detection system that is both precise and real-time. This research focuses on the application of artificial intelligence, with a particular emphasis on deep learning, which is achieved through the utilization of the Convolutional Neural Network (CNN) model. In order to enhance the detection of inattentive drivers and produce a more precise model, a scaleinvariant feature transform (SIFT)-CNN combination is proposed. The activities of the driver while operating a vehicle are categorized into ten categories in this study. One of these categories is considered a normal condition, while the remaining nine are classified as inattentive behaviors. This study implemented Adam optimization with 64 batches, a learning rate of 0.001, and epochs of 20, 25, 50, and 100. The proposed CNNSIFT model is capable of achieving superior performance in comparison to the solitary CNN model, as evidenced by the experimental results. The CNN-SIFT model has achieved 99% accuracy and a 0.05 loss when the hyperparameter configuration is optimized for 50 epochs. The analysis indicates that the accuracy of the features obtained from CNN-SIFT can be improved by approximately 1% compared with CNN to classify the type of driver distraction behavior. The model's reliability was further enhanced by its evaluation on test data, which resulted in high accuracy, precision, recall, and F1-score values. The model's ability to accurately identify driver behavior with a high degree of reliability is demonstrated by these results, which are a positive contribution to the improvement of road safety.
Implementasi High Order Intuitionistic Fuzzy Time Series Pada Peramalan Indeks Harga Saham Gabungan Nugraha, Titis Jati; Sulandari, Winita; Slamet, Isnandar; Subanti, Sri; Zukhronah, Etik; Sugianto, Sugianto; Susanto, Irwan
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 2: April 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20241127363

Abstract

Indeks Harga Saham Gabungan (IHSG) adalah indeks yang mengukur kinerja harga semua saham yang terdaftar di Bursa Efek Indonesia (BEI) Peramalan IHSG menjadi referensi bagi investor untuk memperoleh keuntungan di pasar modal. Penelitian ini membahas penerapan metode High Order Intuitionistic Fuzzy Time Series (HOIFTS) dalam peramalan IHSG di BEI. Metode HOIFTS melibatkan tiga indikator, yaitu derajat keanggotaan, derajat non- keanggotaan, dan fungsi skor (indeks intutionistic) sehingga model yang dihasilkan mampu menangani ketidakpastian dalam data. Tahapan penting dalam pemodelan HOIFTS adalah pada fuzzifikasi intuitionistic, penentuan relasi logika fuzzy intutionistic, dan proses defuzifikasi order tinggi intuitionistic. Penelitian ini menetapkan metode Chen, baik order satu maupun order tinggi sebagai metode pembanding untuk melihat seberapa jauh keberhasilan metode HOIFTS dalam meramalkan data bulanan IHSG. Hasil perbandingan nilai RMSE (root mean square error) dan MAPE (mean absolute percentage error) yang dihasilkan oleh ketiga model menunjukkan bahwa metode HOIFTS memiliki nilai kesalahan yang paling kecil. Dengan demikian, metode HOIFTS lebih direkomendasikan dalam peramalan IHSG dibandingkan dua metode lain yang dibahas dalam penelitian ini. 
Perbandingan Algoritma Density-Based Spatial Clustering Algorithm with Noise (DBSCAN) dan Self-Organizing Map (SOM) untuk Clustering Data Gempa Bumi Wati, Rosita Kurnia; Pratiwi, Hasih; Winita Sulandari
Statistika Vol. 24 No. 2 (2024): 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.v24i2.3645

Abstract

ABSTRAK Gempa bumi merupakan bencana alam yang kerap melanda Indonesia karena letak geografisnya berada pada batas pertemuan tiga lempeng aktif dunia. Dampak kerusakan yang timbul akibat gempa bumi bergantung pada magnitudo dan kedalamannya. Oleh karena itu, perlu upaya mitigasi bencana dan manajemen risiko bencana melalui pengolahan data untuk mengetahui karakteristik dari data gempa tersebut. Penelitian ini bertujuan untuk clustering data gempa bumi di Indonesia berdasarkan magnitudo dan kedalaman dengan menerapkan algoritma Density-Based Spatial Clustering Algorithm With Noise (DBSCAN) dan Self-Organizing Map  (SOM) dengan validasi kebaikan cluster menggunakan koefisien silhouette. Penerapan algoritma DBSCAN dengan nilai Eps dan MinPts optimal sebesar 1,6 dan 12 membentuk dua cluster dan 23 data diidentifikasi sebagai noise, sedangkan menggunakan algoritma SOM dengan learning rate 0,05 dan maksimal epoch 1.000 membentuk dua cluster. Pada analisis ini SOM mampu  melakukan clustering yang lebih baik jika dibandingkan dengan DBSCAN karena memberikan  nilai koefisien silhouette yang lebih besar, yaitu sebesar 0,717 sedangkan DBSCAN sebesar  0,677. Hasil clustering terbaik memiliki karakteristik yaitu cluster 1 dikategorikan sebagai gempa sedang berkekuatan sedang dan cluster 2 dikategorikan sebagai gempa dangkal berkekuatan sedang. ABSTRACT Earthquakes are natural disasters that occur frequently in Indonesia because of the geographical location at the convergence of three active tectonic plates. The severity of an earthquake's impact is influenced by magnitude and depth. Therefore, disaster mitigation efforts and disaster risk management through data mining are needed to understand the characteristics of earthquakes. This research aims to cluster earthquake data in Indonesia based on magnitude and depth by applying a Density-Based Spatial Clustering Algorithm with Noise (DBSCAN) and Self-Organizing Map (SOM) algorithms and cluster results are evaluated using the silhouette coefficient. Using the DBSCAN algorithm with optimal Eps and MinPts values of 1.6 and 12 formed two clusters and 23 data were identified as noise while using the SOM algorithm with a learning rate of 0.05 and a maximum epoch of 1000 formed two clusters. SOM can perform clustering better than DBSCAN because it provides a larger silhouette coefficient value, which is 0.717 while DBSCAN is 0.677. The clustering results obtained show that cluster 1 is categorized as moderate earthquakes of moderate intensity and cluster 2 is categorized as shallow earthquakes of moderate intensity.
IMPLEMENTATION OF PROPHET IN AMERICAN ELECTRICITY FORECASTING WITH AND WITHOUT PARAMETER TUNING Sulandari, Winita; Yudhanto, Yudho; Hapsari, Riskhia; Wijayanti, Monica Dini; Pardede, Hilman Ferdinandus
MEDIA STATISTIKA Vol 17, No 1 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.1.93-104

Abstract

Prophet is one of the machine learning approximation methods that accommodate trends, seasonality, and holiday impacts in time series data. Generally, the performance of machine learning models can be improved by implementing hyperparameter tuning. This study investigates whether hyperparameter tuning can improve the model's performance. To show its effectiveness, the Prophet model constructed by parameter tuning is compared to the one with fixed parameter values (namely the default model) for both the original series and the Box-Cox transformed series in terms of mean absolute percentage error (MAPE). Based on the experimental results of the twenty-four daily electricity load time series in American Electric Power (AEP). This shows that parameter tuning successfully reduces the MAPE of the default model in the range of about 3-8% for training data. However, there is no guarantee for testing data. Although, in some cases, parameter tuning can reduce the MAPE value of the default model by up to 38%, in other cases, it actually increases the MAPE of the default model by almost 15%. The experiments on testing data also show that models built from transformed data do not necessarily produce more accurate forecast values than those built from the original data.
PENINGKATAN LITERASI STATISTIKA : MEWUJUDKAN SANTRI CERDAS SEBAGAI UPAYA OPTIMALISASI ZAKAT DAN PEMBERDAYAAN POTENSI UMMAT Slamet, Isnandar; Zukhronah, Etik; Sulandari, Winita; Subanti, Sri; Sugiyanto, Sugiyanto; Susanto, Irwan; Isnaini, Bayutama; Afanyn Khoirunissa, Husna; Adi Wicaksono, Nanda; Indra Raditya , Dionisius
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 5 No. 3: Agustus 2025
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pemberdayaan Potensi Ummat”. Tujuan utama kegiatan adalah membekali peserta dengan pengetahuan dasar statistika sebagai alat berpikir rasional dan analitis, serta memperkuat kesadaran akan kewajiban dan keutamaan (fadhilah) zakat dalam kehidupan sosial-keagamaan. Kegiatan diikuti oleh 114 peserta, terdiri dari 102 santri dan 12 ustadz. Materi yang disampaikan meliputi statistika dasar, konsep kewajiban zakat menurut syariat Islam, serta fadhilah zakat dalam rangka pemberdayaan umat. Tim pengabdian berasal dari Grup Riset Statistika dan Sains Data Bidang Industri dan Ekonomi, Universitas Sebelas Maret (UNS). Metode pelaksanaan meliputi pre-test, penyampaian materi secara interaktif, praktik pengolahan data sederhana, diskusi aplikatif, dan post-test. Hasil evaluasi menunjukkan peningkatan signifikan pada pemahaman peserta terhadap materi yang disampaikan. Kegiatan ini diharapkan menjadi langkah awal dalam membentuk generasi santri yang cerdas secara statistik, sadar zakat, dan siap berkontribusi dalam penguatan ekonomi umat berbasis pesantren.
HYBRID MODEL OF SINGULAR SPECTRUM ANALYSIS WITH AUTOREGRESSIVE INTEGRATED MOVING AVERAGE AND FUZZY TIME SERIES FOR INDONESIAN CRUDE PRICE FORECASTING Zukhronah, Etik; Sulandari, Winita; Ilahi, Esa Permata Sari Putri
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/barekengvol18iss3pp1519-1526

Abstract

This study discusses a hybrid model of Singular Spectrum Analysis (SSA) with Autoregressive Integrated Moving Average (ARIMA) and Fuzzy Time Series (FTS) for forecasting the Indonesian Crude Price (ICP). SSA is considered to capture the deterministic component of the data while the ARIMA and FTS are to represent the stochastics one. The data that used in this study are ICP per month from January 2017 to May 2023. The data from January 2017 to December 2022 are used as insample data, while the data from January to May 2023 are used as outsample data. The insample data is firstly modeled by SSA and the residuals are then modeled by ARIMA, referred to as the hybrid SSA-ARIMA. By the same procedure, the hybrid SSA-FTS model is also constructed to the insample data. Based on the experiment, the hybrid SSA-ARIMA produces Mean Absolute Percentage Error values 8.08% for an insample and 7.10% for an outsample data. These values are less than those obtained by hybrid SSA-FTS. Therefore, the hybrid SSA-ARIMA is recommended for forecasting the monthly ICP.
Co-Authors Adi Wicaksono, Nanda Adigama Tri Nugraha Aji Susanto Ardiyani, Faradilla Fadlila Nurwanda Febrianti, Tri Kartika Fhadilla, Nahdatul Hapsari, Riskhia Husna Afanyn Khoirunissa Ilahi, Esa Permata Sari Putri Indra Raditya , Dionisius Irwan Susanto Irwan Susanto Isnaini, Bayutama Isnandar Slamet Isnandar Slamet, Isnandar Kadar, Jimmy Abdel Kamisan, Nur Arina Bazilah Kayyisa, Alfien Diva Lee, Muhammad Hisyam Marita Saptyani, Marita Mika Asrini Neyun, Ignasia N.G. Noven, Michela Sheryl Nugraha, Titis Jati Pangadi Pangadi Pardede, Hilman Ferdinandus Pramesti, Arsita Anggraeni pratiwi, hasih Respatiwulan, Respatiwulan Santoso Budi Wiyono Setiawan, Crisma Devika Sri Subanti Sucipto, Christy Sheldy Sufia Nur Janah Sugianto Sugianto Sugiyanto - Susilotomoa, Dhestahendra Citra Wati, Rosita Kurnia Wijayanti, Monica Dini Yudho Yudhanto, Yudho Yuliana Susanti Yuliana Susanti, Yuliana Zakya Reyhana Zukhronah, Etik