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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
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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

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IMPLEMENTATION OF CROSS-VALIDATION ON HANG SENG INDEX FORECASTING USING HOLT’S EXPONENTIAL SMOOTHING AND AUTO-ARIMA METHOD Sucipto, Christy Sheldy; Sulandari, Winita; Susanti, Yuliana
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp13-24

Abstract

This study applies a rolling window cross-validation to evaluate the multi-step forecasts instead of using the traditional single split for Hang Sheng Index (HSI) forecasting. The forecasting methods discussed in this study are Holt's Exponential Smoothing and auto ARIMA, chosen because of their ability to model trend data as in the daily HSI. This research aims to evaluate up to five step forecast values obtained by the two forecasting methods built in the training data with rolling window cross-validation. In the experiment, each of the 21 auto ARIMA and Holt's models was constructed from 84 observations (as in-sample data) obtained from the rolling window cross-validation. The one to five step forecast values of daily HSI are then calculated using those models, and the accuracy of each forecast value is evaluated based on Mean Absolute Percentage Error (MAPE). The results show that the Auto ARIMA model produces a lower MAPE value than Holt's model, namely 2.9196%, 4.6553%, 6.4012%, 8.3083%, and 10.3781%, respectively, for one to five steps ahead. Therefore, auto ARIMA is more recommended for forecasting HSI values up to five steps ahead than Holt's method.
Forecasting the Composite Stock Price Index Using Fuzzy Time Series Type 2: Peramalan Indeks Harga Saham Gabungan dengan menggunakan Metode Fuzzy Time Series Tipe 2 Pramesti, Arsita Anggraeni; Sulandari, Winita; Subanti, Sri; Yudhanto, Yudho
RADIANT: Journal of Applied, Social, and Education Studies Vol. 4 No. 2 (2023): RADIANT: Journal of Applied, Social, and Education Studies
Publisher : Politeknik Harapan Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52187/rdt.v4i2.167

Abstract

The movement and fluctuation of the IHSG are one of the references for investors in making investment decisions for buying, selling, or holding share ownership. Forecasting the value of the IHSG can assist investors in making this decision. This study used the fuzzy time series type 2 method to predict the IHSG. This study uses monthly IHSG data for 2017-2021 with 3 variables, namely close prices, high prices, and low prices. In fuzzy time series forecasting, the length of the interval affects the prediction results. This study uses a distribution and average-based method in determining the length of the interval to obtain optimal forecasting results. Based on the calculation of MAPE, the forecasting using average-based and distribution-based interval lengths had errors of 2.56% and 2.46%. The MAPE value shows that the forecasting results from the two methods of taking the length of the interval are very good. The IHSG forecasting results in January 2022 use a distribution-based interval length is 6450 while the IHSG forecasting results in January 2022 use an average-based interval length is 6510. The results of this study indicate that the interval length affects the forecasting results in fuzzy time series. Keywords: IHSG, fuzzy time series type 2, length of the interval, MAPE
IS THE BOX-COX TRANSFORMATION NEEDED IN MODELING TELKOM’S STOCK PRICE USING NNAR AND DESH METHODS? Noven, Michela Sheryl; Respatiwulan, Respatiwulan; Sulandari, Winita
MEDIA STATISTIKA Vol 17, No 2 (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.17.2.185-196

Abstract

Accurate stock price forecasting requires appropriate preprocessing, particularly for time series data with high variability and nonlinear patterns. This study investigates whether applying the Box-Cox Transformation (BCT) improves forecasting performance when modeling Telkom Indonesia's stock price using Neural Network Autoregressive (NNAR) and Double Exponential Smoothing Holt (DESH) methods. The NNAR model architecture is selected based on nonlinearity testing of lag variables, while DESH parameters are optimized by minimizing mean square error. Forecasting accuracy is evaluated using Mean Absolute Percentage Error (MAPE), root Mean Square Error (RMSE), and Mean Percentage Error (MPE), comparing models built with and without BCT. Results show that BCT does not enhance forecasting accuracy for either NNAR or DESH. Moreover, the NNAR model without BCT outperforms DESH, producing approximately 50% lower MAPE, RMSE, and MPE values on the testing dataset. These findings suggest that BCT may not be necessary for time series modeling in this case, and NNAR without transformation is recommended for forecasting Telkom's stock price.
PERAMALAN HARGA SAHAM PT UNILEVER INDONESIA MENGGUNAKAN METODE HIBRIDA ARIMA-NEURAL NETWORK Setiawan, Crisma Devika; Sulandari, Winita; Susanti, Yuliana
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 7, No 1 (2023): SEMNAS RISTEK 2023
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v7i1.6270

Abstract

Saham merupakan salah satu instrumen investasi yang diminati oleh banyak investor dan memiliki tingkat keuntungan yang menarik. Saham dari PT Unilever merupakan salah satu saham yang aktif diperjual belikan dalam BEI dan tergabung dalam LQ45. Kinerja perusahaan ditunjukkan melalui harga saham dari perusahaan tersebut dan para investor perlu memprediksi harga sebuah saham untuk mengurangi resiko kerugian. Harga saham yang selalu berfluktuasi memungkinkan data historisnya memiliki hubungan linier dan nonlinier. Penelitian ini menggunakan metode hibrida ARIMA – Neural Network untuk memprediksi harga saham PT Unilever periode Januari hingga Desember 2019, karena metode ini digunakan untuk memprediksi runtun waktu yang linier maupun non linier. Hasil akhir penelitian ini menunjukkan bahwa model ARIMA terbaik adalah ARIMA (3,1,2) dengan nilai MAPE data latih 1.04% dan data uji 0.86%, sedangkan model hibrida terbaik adalah ARIMA (3,1,2) – NN (4,9,1) dengan nilai MAPE data latih dan data uji berturut adalah 1,03% dan 0,82%. Model hibrida memiliki nilai MAPE lebih kecil dibandingkan model ARIMA, tetapi tidak memberikan perbedaan hasil peramalan yang signifikan. Meskipun demikian model hibrida dapat menambah tingkat keakuratan peramalan pada harga saham unilever.
SOLUSI CERDAS PENGELOLAAN SAMPAH KEPADA IBU-IBU PKK DI RT 23, RW 06 TLOBONGAN BENTAK, SIDOHARJO, SRAGEN Sri Subanti; Isnandar Slamet; Etik Zukhronah; Sugiyanto, Sugiyanto; Irwan Susanto; Winita Sulandari
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 4 No. 1: Juni 2024
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53625/jabdi.v4i1.7961

Abstract

Pengelolaan sampah adalah proses yang terstruktur, komprehensif, dan berkelanjutan yang mencakup pengelolaan dan proses reduksi sampah. Tata kelola sampah harus dilakukan secara terintegrasi dari asal hingga ke akhir agar dapat menghasilkan keuntungan secara ekonomi, perlindungan kesehatan lingkungan, dan perubahan perilaku masyarakat. Berdasarkan survei lapangan tim pengabdian Hibah Grup Riset (HGR), ibu-ibu PKK yang berlokasi di Tlobongan RT 23 RW 06 Bentak, Sidoharjo, Sragen mengalami permasalahan berkaitan dengan pengelolaan sampah. Permasalahan yang ada antara lain: banyaknya produksi sampah harian, kurangnya edukasi mengenai cara mengelola sampah, dan sampah yang terkumpul tidak dipilah. Berdasarkan analisis permasalahan mitra, tim pengabdian HGR telah memberikan solusi cerdas yaitu melalui pembuatan tempat penampungan sampah/bank untuk pemilahan sampah organik dan sampah anorganik dengan tujuan meminimalisir pencemaran lingkungan dan kegiatan pendampingan penyusunan tata prosedur pemilahan sampah organik dan anorganik
PENGELOLAAN SAMPAH DI GEDUNG TPA QURROTA A'YUN DUKUH TLOBONGAN, BENTAK, SIDOHARJO, SRAGEN Isnandar Slamet; Winita Sulandari; Irwan Susanto; Etik Zukhronah; Sugiyanto, Sugiyanto; Sri Subanti; Adigama Tri Nugraha; Aji Susanto
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 4 No. 4: September 2024
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53625/jabdi.v4i4.8472

Abstract

Pengelolaan sampah di Indonesia menghadapi tantangan besar akibat urbanisasi dan pertumbuhan populasi yang pesat, dengan sebagian besar sampah dibuang tanpa proses pengolahan yang memadai. Penelitian ini bertujuan meningkatkan kesadaran dan perilaku positif dalam pengelolaan sampah di TPA Qurrota A'yun melalui pendidikan karakter dan solusi pintar. Metode yang digunakan mencakup survei awal, penyusunan materi, sosialisasi, penyediaan fasilitas tempat sampah, serta pelatihan dan praktek langsung. Hasil survei awal menunjukkan rendahnya pengetahuan dan kesadaran peserta tentang pengelolaan sampah. Setelah sosialisasi, terjadi peningkatan signifikan dalam pemahaman dan perilaku peserta, dengan 50% membuang sampah di tempat yang sesuai dan 87,5% mengetahui cara memilah sampah. Kesadaran lingkungan juga meningkat menjadi 96,9%. Dampak positif terlihat dari lingkungan yang lebih bersih dan peningkatan kesadaran lingkungan serta pendidikan karakter. Hasil ini menegaskan pentingnya pendidikan dan fasilitas yang memadai dalam pengelolaan sampah yang berkelanjutan.
The Autoregresiive Integrated Moving Average and Fuzzy Time Series Cheng Hybrid for Predicting Stock Price Neyun, Ignasia N.G.; Sulandari, Winita; Slamet, Isnandar
Jurnal Bumigora Information Technology (BITe) Vol. 5 No. 2 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v5i2.2972

Abstract

Background: PT Telkom Indonesia Tbk is the largest company in the telecommunications sector in Indonesia. PT Telkom's share price always rises every year, attracting investors to invest. In investing, it is very important to analyze shares in order to know the situation and condition of the shares. Objective: This research aims to predict the share price of PT Telkom Indonesia Tbk. Methods: The method used is the Autoregressive Integrated Moving Average (ARIMA)-Fuzzy Time Series Cheng hybrid method. Cheng's FTS model is able to overcome nonlinearity problems in ARIMA model residuals. In this research, the first modeling uses the ARIMA model, where the data is divided into two, namely January to November 2019 data used as training data, and December 2019 data used as testing data. Next, residual modeling was carried out with FTS Cheng. Hybrid forecasting is obtained by adding up the results of ARIMA and FTS Cheng forecasts. Result: Model evaluation is based on MAPE values and in this study the MAPE value of the ARIMA-FTS Cheng hybrid model was obtained at 1.03\% for training data and 1.09\% for testing data. Conclusion: The hybrid model has a MAPE value of less than 10\%, so it can be concluded that the ARIMA-FTS Cheng hybrid model can predict PT Telkom Indonesia Tbk stock closing price data accurately.
Peramalan curah hujan di kota bandung menggunakan singular spectrum analysis Febrianti, Tri Kartika; Sulandari, Winita; Pratiwi, Hasih
Jurnal Ilmiah Matematika Vol 8, No 2 (2021)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/konvergensi.v0i0.21461

Abstract

Curah hujan merupakan fenomena alam yang selalu terjadi di Indonesia setiap tahunnya. Fenomena ini bisa saja menyebabkan bencana seperti banjir dan tanah longsor. Adanya peramalan sangat dibutuhkan sebagai bentuk peringatan dini mengenai kondisi di waktu yang akan datang. Singular Spectrum Analysis (SSA) merupakan suatu teknik analisis deret waktu dan peramalan. SSA bertujuan untuk menguraikan deret waktu asli menjadi sejumlah kecil komponen yang dapat diinterpretasikan menjadi tren, osilasi dan noise. Tujuan dari penelitian ini yaitu menyajikan model peramalan curah hujan di Kota Bandung menggunakan metode Singular Spectrum Analysis (SSA). Berdasarkan penelitian ini, diketahui bahwa data curah hujan di Kota Bandung memiliki pola musiman. Penentuan window length (L) dilakukan dengan trial and error, yang dalam kasus ini diperoleh window length 17. Melalui dekomposisi dan rekonstruksi dengan window length 17 diperoleh 4 pengelompokan, yaitu satu kelompok tren dan tiga kelompok musiman. Pada penelitian ini digunakan RMSE untuk mengukur kesalahan hasil peramalan. Berdasarkan hasil pengujian dengan metode Singular Spectrum Analysis (SSA) diperoleh RMSE sebesar 167,510.
Comparative Analysis Of Performance Levels Of Svm And Naïve Bayes Algorithm For Lifestyle Classification On Twitter Social Media Fadlila Nurwanda; Winita Sulandari; Yuliana Susanti; Zakya Reyhana
International Conference On Digital Advanced Tourism Management And Technology Vol. 1 No. 1 (2023): International Conference on Digital Advanced Tourism, Management, and Technolog
Publisher : Sekolah Tinggi Ilmu Ekonomi Pariwisata Indonesia Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56910/ictmt.v1i1.65

Abstract

Lifestyle is how individuals express themselves through their activities and interests and utilize their financial resources and available time. Twitter is a social network platform that allows people to express opinions and directly criticize various topics, including the recently widely discussed lifestyle topics. Topic classification on Twitter is central in facilitating the search, recommendation, and management of relevant content for users. This research aims to analyze public sentiment regarding lifestyle using 11,000 pieces of data with the keywords "concert", "watching films", "smoking", and others related to lifestyle. Research data is labeled according to the sentiment of public opinion towards lifestyle. Negative polarity for data that has the context of "underestimating", "insulting", "sarcastic", and "feeling sad". Positive polarity for data that has the context of "grateful", "praying", "feeling happy", and "encouraging". Neutral polarity for data that has the contexts “ask”, “predict”, and “feel surprised”. Next, the data enters the pre-processing stage, which consists of case-folding, tokenization, stopword removal, and lemmatizing. The analysis continues by dividing the data into training and test data with a ratio of 70%:30%. Sentiment analysis uses an algorithm Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC). The analysis results show that the SVM algorithm provides better classification than NBC. In this case, the SVM algorithm produces accuracy, precision, recall, and value F1-Score the same, namely 61%.
Optimizing Train-Test Splits for LSTM and MLP Models in Bitcoin Price Forecasting Accuracy Kamisan, Nur Arina Bazilah; Lee, Muhammad Hisyam; Sulandari, Winita
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.v25i2.6989

Abstract

Abstract. This study investigates the application and efficiency of two machine learning models, Long-Short Term Memory (LSTM) and Multilayer Perceptron (MLP), for cryptocurrency price forecasting, using Bitcoin as a case study. MLP is a feedforward neural network that learns patterns from independent data, while LSTM is a recurrent network that remembers past information to handle sequential or time-series data. The rapid growth and volatility of cryptocurrencies underscore the need for accurate price predictions to support investor’s and trader’s decision-making. The study aims to identify the optimal train-test splitting ratio for each machine learning model and to forecast Bitcoin prices over a 120 days. The daily Bitcoin price data is obtained from the Bitcoin website recorded from January 2018 until March 2021. Model performance was evaluated using Akaike Information Criterion (AIC), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Experimental results demonstrate that both models exhibit strong predictive capabilities; the LSTM model consistently outperforms MLP in accuracy and reliability, achieving lower MAE, MAPE, and AIC values. These findings highlight LSTM’s effectiveness for forecasting volatile financial data and provide insights into selecting appropriate data-splitting ratios to improved model performance.
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