Article Per Year (5 Year)
p-Index From 2021 - 2026
2.667
P-Index
This Author published in this journals
All Journal Media Statistika Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang Indonesian Journal of Applied Statistics
Tatik Widiharih
Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science Other

Published : 43 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 37 Documents
Search
Journal : Jurnal Gaussian

 1   2   3   4 
PENERAPAN DIAGRAM KONTROL MEWMA DALAM PENGENDALIAN KUALITAS PRODUKSI KERIPIK SINGKONG PADA UMKM DI KOTA SEMARANG Nesari Nesari; Mustafid Mustafid; Tatik Widiharih
Jurnal Gaussian Vol 11, No 3 (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.11.3.355-365

Abstract

Quality is the main thing that needs to be considered by every company. Ceriping Bintang Putra Bu Slamet is an UMKM (Usaha Mikro, Kecil dan Menengah) that produces cassava chips. During production, there are three quality characteristics, namely large crumbs defects, small crumbs, and chips sticking together. It is important to control these defects to produce quality products according to customer needs. This research was conducted from July to August 2021. The purpose of this study was to control the production quality of cassava chips using the Multivariate Exponentially Weighted Moving Average (MEWMA) control chart and multivariate process capability analysis. The MEWMA control chart is used to detect the shift in the process average which is more sensitive using weights (λ), while the process capability analysis is used to determine the process performance. The implementation of the MEWMA control chart is carried out in two stages, namely phase I control to obtain the optimal weighting and control limits so that it can be used in phase II control to monitor the average process for the next period. Based on the results of the analysis, the optimal weighting is λ =0,4 with BKA=201,7434, GT=113,538, and BKB=0 in phase I control. Then, the results of phase II control show a shift in the average process in a better direction. In addition, the results of the process capability analysis show an improvement in the performance of the production process from July 2021 to August 2021 with MCpm values of 0,535 and 1,147
KLASIFIKASI PENYAKIT HIPERTENSI MENGGUNAKAN METODE SVM GRID SEARCH DAN SVM GENETIC ALGORITHM (GA) Fithroh Oktavi Awalullaili; Dwi Ispriyanti; Tatik Widiharih
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.11.4.488-498

Abstract

Hypertension is an abnormally high pressure that occurs inside the arteries. Hypertension increased by 8.3% from 2013 based on health research in 2018. Some of the factors that cause hypertension include gender, age, salt consumption, cigarette consumption, cholesterol levels and a family history of hypertension. The data in this study are data on normal and hypertensive patients at the Padangsari Health Center for the period of July – December 2021. This study will classify blood pressure with the aim of obtaining the results of the accuracy of the classification of the methods used. The method used in this study is a support vector machine (SVM). SVM is a well-known algorithm, producing optimal solutions to classification problems. SVM uses kernel functions for separable nonlinear data. The displacement kernels used in this study are linear and RBF. SVM has the disadvantage of determining the best parameters, to overcome these weaknesses developed the method of finding the best parameters. The search for the parameters of this study used grid search and genetic algorithm (GA).  Grid search has the advantage of producing parameters that are close to the optimal value, while GA has the advantage of being easy to find global optimum values. This study will compare the classification results of the SVM grid search and SVM GA methods. The results of this study obtained the method that has the best accuracy, namely SVM grid search using a radial base function (RBF) kernel with an accuracy of 89.22%.
PERBANDINGAN SMOTE DAN ADASYN PADA DATA IMBALANCE UNTUK KLASIFIKASI RUMAH TANGGA MISKIN DI KABUPATEN TEMANGGUNG DENGAN ALGORITMA K-NEAREST NEIGHBOR Dinda Virrliana Ramadhanti; Rukun Santoso; Tatik Widiharih
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.11.4.499-505

Abstract

Poverty is a global problem that has occurred in various countries with various impacts. Poverty conditions are characterized by the inability of a person or household to meet the basic needs of life. Socio-economic problems, such as poverty, can be handled using machine learning, one of which is classification. The classification of households based on poverty criteria is expected to assist the government in preparing programs that are right on target. K-Nearest Neighbor is one of the easy-to-use classification algorithms. this classification is based on the closest neighborliness. The problem that can be experienced when classifying is if the data used is imbalanced. The data imbalance will causing the classification process to focus more on the majority class. SMOTE and ADASYN are used to solve the problem of imbalanced data. This study resulted in the addition of  SMOTE and ADASYN to imbalanced data can improve classification performance, especially on the G-mean value. G-mean is a performance measure that is widely used in the case of imbalanced data. The result of this study is that SMOTE can increase the G-mean value to 58.5%, while ADASYN is 57.3%. Therefore, it can be concluded that SMOTE-KNN is the best classification model for household poverty classification.
ANALISIS PENGARUH KUALITAS PELAYANAN TERHADAP KEPUASAN PENUMPANG BRT TRANS SEMARANG MENGGUNAKAN PARTIAL LEAST SQUARE (PLS) Irma Dwi Tyana; Tatik Widiharih; Iut Tri Utami
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.11.4.591-604

Abstract

BRT Trans Semarang is an integrated bus transportation system that operates in Semarang City and parts of Semarang Regency. This transportation provides service facilities such as the availability of bus stops, air-conditioned rooms to travel route information. The facility is expected to be able to provide service satisfaction for its passengers. This study was conducted to determine the effect of service quality on the satisfaction of Trans Semarang BRT passengers using Partial Least Square (PLS), with a case study of Diponegoro University students. PLS is an alternative approach from covariance-based SEM to variance-based. The advantage of PLS is that it is able to handle covariance-based SEM problems such as small sample numbers, abnormal data and the presence of multicholinearity. The quality of this service is measured through the variables of Direct Evidence, Reliability, Responsiveness, Empathy and Guarantee. Passenger satisfaction is measured through a sense of pleasure, a positive impression and the absence of complaints. The results showed that the variables that had a significant effect on the satisfaction of Trans Semarang BRT passengers were the variables of Direct Evidence, Reliability and Responsiveness. Variables that do not have a significant effect on the satisfaction of Trans Semarang BRT passengers are the empathy and guarantee variables. The Adjusted R-Square value is included in the medium category with a value of 0.414, means that the variables of Direct Evidence, Reliability and Responsiveness affect the satisfaction of Trans Semarang BRT passengers by 41.4%. 
ANALISIS SENTIMEN PADA ULASAN APLIKASI INVESTASI ONLINE AJAIB PADA GOOGLE PLAY MENGGUNAKAN METODE SUPPORT VECTOR MACHINE DAN MAXIMUM ENTROPY Fath Ezzati Kavabilla; Tatik Widiharih; 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.11.4.542-553

Abstract

Investment is money or asset to earn profits in the future. Online investment applications are already available, one of which is Ajaib. A review of Ajaib’s application is needed to find out reviews given are positive or negative. Sentiment analysis in Ajaib is used to see the user's response to Ajaib’s performance which is divided into positive and negative classes. Sentiment analysis of the Ajaib’s reviews classification can be used with the Support Vector Machine and Maximum Entropy methods. Support Vector Machine on non-linear problems inserts the kernel into a high-dimensional space, to find a hyperplane that can maximize the distance between classes. The kernel used in SVM is the Radial Basis Function (RBF) kernel with gamma parameters of 0.002 and Cost (C) of 0.1; 1; 10. Maximum Entropy is a classification technique that uses the entropy value to classify data with the evaluation model used, namely 5-fold cross-validation. The algorithm which has the highest accuracy and kappa statistics is the best algorithm for classifying the sentiments of Ajaib users. The results using the Support Vector Machine algorithm show the overall accuracy is 85.75% and the kappa accuracy is 58.07%. The results using the Maximum Entropy algorithm show an overall accuracy of 83% and kappa accuracy of 50.5%. This shows that sentiment using the Support Vector Machine has a better performance than Maximum Entropy.
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
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 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 Salsabilla Rizka Ardhana 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