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All Journal PROSIDING SEMINAR NASIONAL Media Statistika MATEMATIKA Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer AdBispreneur Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) Ilomata International Journal of Management Ilomata International Journal of Management International Journal of Economics, Management, Business, and Social Science JURNAL ILMIAH GLOBAL EDUCATION Jurnal Manajemen dan Bisnis International Journal of Integrative Sciences Indonesian Journal of Advanced Research (IJAR) Technopreneurship and Educational Development Review (TENDER)
Rukun Santoso
Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Journal : Jurnal Gaussian

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PREDIKSI TINGKAT TEMPERATUR KOTA SEMARANG MENGGUNAKAN METODE LONG SHORT-TERM MEMORY (LSTM) Rahmatul Akbar; Rukun Santoso; Budi Warsito
Jurnal Gaussian Vol 11, No 4 (2022): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.11.4.572-579

Abstract

Temperature is one of the most important attributes of climate, temperature affects life in many different ways such as in agriculture, aviation, energy, and life in general. Temperature prediction is needed to make the right step to prevent the negative impact of climate change. Long Short-Term Memory (LSTM) is the method that can predict time series data, using the unique design of neural networks, LSTM can help to prevent vanishing gradient from happening which allows LSTM model to use more data from the past to predict the future. Hyperparameters like LSTM unit, epochs, and batch size are used to make the best model, the best model is the one with the lowest loss function. This research used climate data from 1 January 2019 until 31 December 2021 consist of 1096 data in total. The best prediction in this research is made by the model with 70% training data, 0,009 learning rate, 128 LSTM unit, 16 batch size, and 100 epochs with the lowest loss function of 0,013, this model gives MAPE value of 1,896016% and RMSE value of 0,725.
K-NEAREST NEIGHBOR DENGAN ADAPTIVE BOOSTING DAN SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE UNTUK KLASIFIKASI DATA TIDAK SEIMBANG Ria Sulistyo Yuliani; Agus Rusgiyono; Rukun Santoso
Jurnal Gaussian Vol 12, No 2 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.2.231-241

Abstract

Breast cancer is non-skin cancer that is caused by several factors, including glandular ducts, cells, and breast support tissue, except for the skin of the breast. Breast cancer if not treated immediately will be fatal for the sufferer, so early detection of breast cancer is important for the patient's safety. The success of breast cancer detection depends on the right diagnosis. Measurement of the accuracy of a breast cancer diagnosis can be assisted by statistical methods, namely classification. K-Nearest Neighbor is a classification algorithm based on the nearest neighbor that is easy to implement. In the classification process, there are several problems including when faced with imbalanced data. Imbalanced data can cause classification algorithms to tend to focus on the majority class. Data imbalance can be overcome by using Synthetic Minority Oversampling Technique (SMOTE). Ensemble methods can be applied to improve the performance of imbalanced data classification, one of which is Adaptive Boosting. This study applies K-Nearest Neighbor combined with Adaptive Boosting and SMOTE for handling imbalanced data classification. The results of this study are, SMOTE can handle the problem of imbalanced data and the application of K-Nearest Neighbor with Adaptive Boosting can produce an accuracy of 80%, a sensitivity of 83,33%, a specificity of 66,67%, and a G-Mean value of 74,54%. So it can be concluded that K-Nearest Neighbor combined with Adaptive Boosting and SMOTE can be applied for handling imbalanced data classification. 
PERBANDINGAN METODE DOUBLE EXPONENTIAL SMOOTHING HOLT DAN FUZZY TIME SERIES CHENG PADA PERAMALAN HARGA EMAS DI INDONESIA DILENGKAPI GUI R Fauziyyah, Fida; Sugito, Sugito; Santoso, Rukun
Jurnal Gaussian Vol 12, No 4 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.4.509-519

Abstract

Investment is placing a certain amount of money at this time to get some profit in the future. Investments are divided into three based on the period, namely short-term investments, medium-term investments, and long-term investments. Gold is an example of a good long-term investment. Gold price forecasting is an important thing to know when investing in gold. In this study, gold price data is divided into two parts, namely training data consisting of 674 data from 1 September 2020 to 6 July 2022 and testing data consisting of 75 data from 7 July 2022 to 19 September 2022. The data indicates that there is a trend element so it is suitable for analysis using the Double Exponential Smoothing Holt and Fuzzy Time Series Cheng. Data processing using the Double Exponential Smoothing Holt and Fuzzy Time Series Cheng methods is complemented by the creation of a Graphical User Interface (GUI) which can facilitate the process of selecting the best method. The analysis's findings indicate that Double Exponential Smoothing Holt (0.5427603%), which has a reduced MAPE value than Fuzzy Time Series Cheng (0.6053103%), is the best method.
ANALISIS SENTIMEN VAKSIN COVID-19 PADA TWITTER MENGGUNAKAN RECURRENT NEURAL NETWORK (RNN) DENGAN ALGORITMA LONG SHORT-TERM MEMORY (LSTM) Maharani, Chintya Ayu; Warsito, Budi; Santoso, Rukun
Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.3.403-413

Abstract

The Coronavirus, also known as the Covid-19 pandemic, has reached every country worldwide, including Indonesia. Covid-19 is still prevalent and has killed many people in Indonesia. This makes it impossible to stop Covid-19 from spreading. The government's attempt to stop the Covid-19 pandemic is acquiring the vaccine. The administration of the Covid-19 vaccine has generated much discussion on social media, particularly Twitter. Tweets displaying public opinion on Twitter can be used for sentiment analysis and categorizing public opinion on the Covid-19 vaccine. 20,000 tweets were collected by Twitter crawling between January 10 and January 15, 2022. 3.290 tweets were left after pre-processing and meaningless tweets were eliminated. The data were processed using the Recurrent Neural Network method with the Long Short-Term Memory algorithm to determine its accuracy and identify topics often discussed by the public on Twitter. The LSTM method is capable of storing old information/data. A model with 70% training data, a learning rate of 0.01, 100 LSTM units, 32 batch sizes, 100 epochs, a cross-entropy loss function, and Adam optimizers was used to build the classification in this study. The accuracy value obtained from the performance evaluation of the Long Short-Term Memory model research was 80.34%.
PEMODELAN DATA LONGITUDINAL MENGGUNAKAN REGRESI POLINOMIAL LOKAL PADA KELOMPOK SAHAM PERUSAHAAN PENYEDIA JASA TELEKOMUNIKASI DENGAN GUI R Noer Rachma, Gustyas Zella; Suparti, Suparti; Santoso, Rukun
Jurnal Gaussian Vol 12, No 3 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.3.352-361

Abstract

The economic development of a country can be seen based on the capital market that was growing and developing. One of the most popular capital market instruments is stocks. Stocks based on market capitalization groups include longitudinal data. One of the statistical methods for longitudinal data modelling is nonparametric regression which has no modelling assumptions requirement. This research models monthly stock prices using a nonparametric local polynomial method with the selection of the best model which has minimum value of Mean Square Error (MSE). The data was divided into 2 parts, namely in sample data from November, 2018 to June, 2021 to form a model and out sample data from July, 2021 to February, 2022 used for evaluation of model performance by Mean Absolute Percentage Error (MAPE) values. The best model is the local polynomial model with Biweight kernel function of degree 5, local point of 4, bandwidth of 37, and MSE value of 0.03481085. MAPE out sample of data value is 31.13%, which indicating that the model has sufficient forecasting. In this research arrange Graphical User Interface (GUI) by using R software with shiny package is built to make display output data analyzing more easy and more interactive.
PEMODELAN JUMLAH KASUS PNEUMONIA PADA BALITA DI JAWA TIMUR MENGGUNAKAN METODE REGRESI POISSON INVERSE GAUSSIAN DILENGKAPI GUI-R Utami, Krisdiana Nur; Sugito, Sugito; Santoso, Rukun
Jurnal Gaussian Vol 12, No 4 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.4.539-548

Abstract

Reducing toddler mortality is one of the desire of sustainable development programs.Modeling count data may be analyzed the usage of Poisson regression.The assumption that must be met in Poisson regression is that the mean and variance values must be equal, often in count data there is a violation of this assumption. This is indicated by the variance value which is greater than the mean value (overdispersion). Poisson Inverse Gaussian (PIG) regression is one form of mixed Poisson regression to model data that experience overdispersion cases. The MLE method is used to estimate the PIG regression parameters and hypothesis testing using the MLTR method. The best model of the PIG regression form is based on the smallest AIC value. The results of hypothesis testing concluded that the percentage of under-fives who received exclusive breast feeding had a significant effect on the number of pneumonia cases among toddler. Data modeling using the PIG regression method in this study is complemented by the creation of a Graphical User Interface (GUI) that can facilitate the process of selecting the best model.
ANALISIS SENTIMEN PENGGUNA ONLINE TRAVEL AGENT (OTA) PADA PERUSAHAAN PEGIPEGI.COM MENGGUNAKAN RANDOM FOREST Lestari, Ayu; Santoso, Rukun; Suparti, Suparti
Jurnal Gaussian Vol 12, No 4 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.4.616-624

Abstract

The presence of the internet makes online applications increasingly attractive to the public in supporting their daily activities. Online applications have developed rapidly, including online travel agent (OTA) companies such as Pegipegi. Pegipegi is a platform designed to meet the community's tertiary needs, such as providing accommodations for vacations. Pegipegi has an application that can be downloaded through the Google Playstore. Google Playstore provides a review feature as a medium for communication between application owners and consumers to express opinions that felt when using the application. The reviews submitted can be used as data to carry out sentiment analysis. Data collection was carried out on 11 December 2021 – 11 December 2022. A total of 2926 reviews obtained. Sentiment analysis was able to proceed by a classification method. This research used Random Forest to classify opinions on positive and negative sentiments. Random Forest is a classification model based on the majority vote of all decision trees. Classification using Random Forest produces an accuracy of 92.27% and AUC-ROC of 82.35%. Based on this accuracy and AUC-ROC value, the Random Forest algorithm has a good model performance in classifying the opinions of Pegipegi application users because it has a good accuracy and AUC-ROC value.
Co-Authors Abdiel Pandapotan Manullang Abdiyasti Nurul Arifa Abdul Hoyyi Achmad Soleh Ade Irma Pramudita Ade Irma Prianti Agum Prafindhani Putri, Agum Prafindhani Agus Rusgiyono Agustian, Kresnawidiansyah Aini Nurul Al Qarani, Muhammad Aqajahs Alan Prahutama Alan Prahutama Alika Ramadhani Alvita Rachma Devi Arief Rachman Hakim Aris Sugiharto Aukhal Maula Fina Aulia Resti Avida Anugraheni AYU LESTARI Bahtiar Ilham Triyunanto Brahim Abdullah Brahim Abdullah Budi Warsito Chrisentia Widya Ardianti Dhimas Bayususetyo Di Asih I Maruddani Di Asih I Maruddani Diah Aliyatus Saidah Diah Safitri Dinda Virrliana Ramadhanti Dwi Nooriqfina Emyria Natalia br Sembiring Endang Saefuddin Mubarok Erwin Permana Fauziyyah, Fida Fuadah, Alfi Gina Rosalinda Hadi, Bawa Mulyono Hana Hayati Hanum, Cholida Hasbi Yasin Hasbi Yasin Infan Nur Kharismawan Iryanto, Rivaldo Kurniawan Iyan Antono Jenesia Kusuma Wardhani Johanes Roisa Prabowo Khansa Amalia Fitroh Krismayadi Krismayadi Kurniawati, Galuh Nurvinda Laili Rahma Khairunnisa Lia Safitri Maharani, Chintya Ayu Mamuki, Emiliyan Margo Purnomo Mifta Fara Sany Mubarok, Endang Saefuddin Mubarok, Endang Saifuddin Muchammad Aziz Chusen Muhamad Syukron Muhammad Akhir Siregar Mustafid Mustafid Noer Rachma, Gustyas Zella Nor Hamidah Permana, Erwin Puspita Kartikasari Rahmat Hidayat Rahmatul Akbar Ratih Ayu Sekarini Ratna Kurniasari Ria Epelina Situmorang Ria Sulistyo Yuliani Rima Nurlita Sari Rismia, Erysta Risky Rita Rahmawati Rita Rahmawati Rosinar Siregar Saepudin, Yunus Sahara Sahara Sekarini, Ratih Ayu Setiani, Eri Shinta Karunia Permata Sari Siti Munawaroh Subagja, Asep Zamzam Subari Sudarno Sudarno Sudarno Sudarno Sudarno Sudarno Sugito - Sugito Sugito Suparti Suparti Suparti Suparti Syazwina Aufa Syiva Multi Fani Tamura Rolasnirohatta Siahaan Tarno Tarno Tasrif, Mohammad Jon Tatik Widiharih Tatik Widiharih Ta’fif Lukman Afandi Thea Zulfa Adiningrumh Tina Diningrum Tita Aulia Edi Putri Tomi Ardi Uswatun Hasanah Utami, Krisdiana Nur Via Risqiyanti Wahyu Tiara Rosaamalia wardhana, galih wisnu Wijayanto, Ahmad Windianingsih, Agustin Wiwin Wiwin Wiwin, Wiwin Yuciana Wilandari Zen, Agustian