Article Per Year (5 Year)
p-Index From 2021 - 2026
2.973
P-Index
This Author published in this journals
All Journal Media Statistika Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang
Tatik Widiharih
Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro
Decision Sciences, Operations Research & Management Other

Published : 45 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 45 Documents
Search

 1   2   3   4   5 
PENERAPAN TEXT MINING DAN FUZZY C-MEANS CLUSTERING UNTUK IDENTIFIKASI KELUHAN UTAMA PELANGGAN PDAM TIRTA MOEDAL KOTA SEMARANG Genisia Pramestiloka Aulia; Tatik Widiharih; Iut Tri Utami
Jurnal Gaussian Vol 12, No 1 (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.12.1.126-135

Abstract

Customer complaints can be handled effectively by identifying the main complaints that cause customers to be dissatisfied. Many customer complaints cause difficulty for PDAM Tirta Moedal Semarang to identify problems, which are frequently the primary complaints of customers. Grouping complaints that have similarities using Fuzzy C-Means Clustering will make the identification of the main customer complaints easier. Fuzzy C-Means uses fuzzy models, allows data to be a member of all formed clusters with membership level between 0-1. Fuzzy C-Means Clustering can also introduce more flexible patterns and show results in more accurate cluster placement. Text mining is used to convert textual data into numerical data. Customer complaints received through all contacts in PDAM Tirta Moedal Semarang from October–December 2021 were used as data. The clustering process forms 6 clusters,with the number of clusters tried being 3, 4, 5, and 6, which are seen by the smallest Xie-Beni Index. The main complaints from PDAM Tirta Moedal Semarang customer that seen through Word cloud in each cluster are that the water stops running in clusters 1 and 6 and the pipes leak in clusters 4 and 5. Complaints in clusters 2 and 3 are complaints related to water meters and water flow.
ANALISIS VOLATILITAS BITCOIN MENGGUNAKAN MODEL ARCH DAN GARCH Dheanisa Widyanti; sudarno sudarno; Tatik widiharih
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.12.2.254-265

Abstract

The popularity of Bitcoin increased significantly in 2021. Bitcoin is considered to deliver high returns in a relatively short period, indicating that bitcoin has high volatility. Data with high volatility usually violates the Autoregresstive IntegratedinMovinginAverage (ARIMA)in homoscedasticity assumption. The Autoregressive Conditional Heteroscedasticity (ARCH) and General Autoregressive Conditional Heteroscedasticity (GARCH) model is often used to overcome the problem of heteroscedasticity in thelARIMA model. The ARCH and GARCH models canfbe used to model thefvolatilityfof data. This Research uses ARCH and GARCH models to overcome the heteroscedasticity problem caused by the high volatility of Bitcoin data for the period 30th June 2018 to 30th June 2022. The results of this study suggest that there might be a heteroscedasticity problem in Bitcoin data. The bestffiimodel for Bitcoin data ismiARIMA(1,0,[4])-GARCH(1,1) with an AIC value of -1,4263 at a 95% confidence level
PENERAPAN KLASIFIKASI REGRESI LOGISTIK BINER DAN ADAPTIVE BOOSTING MENGGUNAKAN CLASSIFICATION AND REGRESSION TREES PADA PREDIKSI PENYAKIT HEPATITIS C Ellina Dhiya Ulhaq Oktaviani; Rukun Santoso; Tatik Widiharih
Jurnal Gaussian Vol 15, No 1 (2026): 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.15.1.120-130

Abstract

Chronic liver disease is primarily attributed to the hepatitis C virus. Disorders of liver function can inhibit metabolism and threaten health. Hepatitis C disease must be detected earlier to reduce the risk of spreading it. Data processing using the Binary Logistic Regression and Adaptive Boosting classification methods to predict the category of patients with positive or negative hepatitis C status. Problems with unbalanced data are found in the classification process. Data imbalance can be overcome with the Synthetic Minority Over-Sampling Technique (SMOTE). Data retrieval was obtained from the 2020 UCI (University of California Irvine) Machine Learning Repository regarding data on predictions of hepatitis C patients which were downloaded on October 25, 2022. The results for the accuracy of the classification show that the Binary Logistic Regression method produces an accuracy value of 97,44%, the value sensitivity of 100%, and specificity of 97,17%. The accuracy of the classification produced by the Adaptive Boosting method with an accuracy value of 92,31%, a sensitivity value of 63,64%, and specificity of 100%. Binary Logistic Regression is the best method that can classify hepatitis C status of patients with the highest sensitivity of 100%.
KLASIFIKASI SENTIMEN KASUS ONLINE TRADING BINOMO PADA TWITTER MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) Elva Nadia Nugroho; Tatik Widiharih; Arief Rachman Hakim
Jurnal Gaussian Vol 15, No 1 (2026): 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.15.1.46-56

Abstract

Investment in futures trading and technology is growing in Indonesia, making many domain sites of online trading companies appear that are easy for everyone to access. The Binomo app is so viral among the public because of an ad that displays professional traders who can earn 1000 USD a day without leaving their homes. Binary options trading ultimately causes losses for some people, allegedly caused by affiliates. The Binomo case is widely discussed on social media, especially Twitter. Tweets displaying public opinion on Twitter can be used for sentiment analysis and categorizing public opinion on the Binomo case. 2.686 tweets were collected by Twitter scraping between January 1 and April 1, 2022. 1.519 tweets were left after pre-processing. The data were processed using the Convolutional Neural Network algorithm with the Word2Vec method to determine their accuracy and identify topics often discussed by the public on Twitter. A CNN model with 70% training data, 3, 4, 5, kernel sizes, 4 batch sizes, 30 epochs, and Adam optimizers was used to build the classification in this research. The accuracy value obtained from the performance evaluation of the Convolutional Neural Networ model research was 89%.
ANALISIS KLASIFIKASI MENGGUNAKAN REGRESI LOGISTIK BINER DAN K-NEAREST NEIGHBOR PADA DATA IMBALANCE Eva Fitriyani; Tatik Widiharih; Bagus Arya Saputra
Jurnal Gaussian Vol 15, No 1 (2026): 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.15.1.154-165

Abstract

Savings and Loan Cooperative or (KSP) is a cooperative that conducts its business activities only saving and borrowing. KSP members come from various different backgrounds so that they can affect their behavior in carrying out their obligations. To find out the status of current or bad customer payments, a classification process is carried out. The division of KSP customer data is carried out in the classification process into two, namely training data and test data. In the classification process, there are often cases of data imbalance, so it is necessary to handle data imbalance in training data with SMOTE and ADASYN. SMOTE and ADASYN were chosen because these methods handle imbalance data by generating data from minor classes so as not to eliminate important parts of the data. Classification was performed with Binary Logistic Regression and K-Nearest Neighbor. Binary Logistic Regression is a regression where the dependent variable is binary. While K-Nearest Neighbor is a grouping method based on the closeness of the distance of a data with other data as many as k nearest neighbors. The results of this study indicate that the ADASYN Binary Logistic Regression method is the best method that can classify and predict the payment status of KSP customers because it produces the highest accuracy and G-mean, namely the accuracy value of 70.67% and G-Mean 67.63%.
Co-Authors Abdiyasti Nurul Arifa Abdul Hoyyi Ade Irma Pramudita Agus Rusgiyono Amalina Sari Dewi Anestasya Nur Azizah Arief Rachman Hakim Aulia Resti Bagus Arya Saputra Bramesa Winanda Nugraha Budi Warsito Chalimatus Sa'diah Cylvia Evasari Margaretha Delsy Nurutsaniyah Denny Jales Manalu Dheanisa Widyanti Di Asih I Maruddani Diah Aliyatus Saidah Diah Safitri Dilla Retno Deswita Dinda Virrliana Ramadhanti Dwi Ispriyanti Eka Lestari Ellina Dhiya Ulhaq Oktaviani Elva Nadia Nugroho Eva Fitriyani Farisiyah Fitriani Fath Ezzati Kavabilla Fithroh Oktavi Awalullaili Genisia Pramestiloka Aulia Ghina Nabila Saputro Putri Gunardi . Hasbi Yasin Immawati Ainun Habibah Irma Dwi Tyana Isna Wulandari Iut Tri Utami Moch. Abdul Mukid Muhamad Syukron Mustafid Mustafid Nesari Nesari Nisa Afida Izati Puspita Ayu Utami Puspita Kartikasari Qorina Rara Sartika Rita Rahmawati Rizki Pradipto Widyantomo Rukun Santoso Ryan Anugrah Salma Farah Aliyah Salma Innassuraiya Sensiani Sensiani Setya Adi Rahmawan Silvy ‘Aina Salsabila Sintia Rizki Aprilianti Sisca Indah Pratiwi Sri Haryatmi Sudarno Sudarno sudarno sudarno Sugito Sugito Suparti Suparti Syavhana Yusricha Zuhri Putri Tita Aulia Edi Putri Winahyu Handayani