Garuda - Garba Rujukan Digital

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

Author-ID : 2977112

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

Articles

Title

1 2 3 4

PENERAPAN STRUCTURAL EQUATION MODELLING (SEM) UNTUK MENGANALISIS FAKTOR – FAKTOR YANG MEMPENGARUHI KINERJA BISNIS (STUDI KASUS KAFE DI KECAMATAN TEMBALANG DAN KECAMATAN BANYUMANIK PADA JANUARI 2019) Ade Irma Pramudita; Tatik Widiharih; Rukun Santoso
Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

This research is done to examine the effect of quality of service and product attractiveness toward business strategies based on service in order to improving business performance. The sample of this study were Cafe owners in Tembalang Subdistrict and Banyumanik Subdistrict, total are 116 respondents. In this Final Project, the processing of Structural Equation Modeling (SEM) is AMOS software. The results of the analysis show that service quality has a positive effect on business strategies based on service to improving business performance. The most significant factor that affecting business performance is quality of service. Quality of service is important in the performance of a café business. Cafe owners must always pay attention to the quality of café service to customers, because the quality of service is the main consideration for customers to visit cafes.

GRAFIK PENGENDALI MULTIVARIATE EXPONENTIALLY WEIGHTED MOVING COVARIANCE MATRIX (MEWMC) PADA DATA SAMPEL ZAT KANDUNGAN BATU BARA (Studi Kasus : PT Bukit Asam (Persero) Tbk. Tahun 2016) Sensiani Sensiani; Tatik Widiharih; Rita Rahmawati
Jurnal Gaussian Vol 9, No 1 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The progress of industrial business in the midst of global competition increased rapidly. A businessman should have special treatment for their products to compete of market quality. The quality of product is an important factor in choosing a product or service, particularly for the costumers. In technological development, the factors of failure in the product can be minimized by Statistical Quality Control. Besides to reducing diversity in product characteristics, statistical quality control can increase business income. The data source of this research is sekunder sample data of coal products of PT Bukit Asam (Persero) Tbk. with seven variables, the variables is Total Moisture (TM), Inherent Moisture (IM), Ash Content (ASH), Volatile Matter (VM), Fixed Carbon (FC), Total Sulfur (TS), and Calorific Value (CV). The analytical method is the controlling chart of Multivariate Exponentially Weighted Moving Covariance Matrix (MEWMC) which is one of the multivariate charts that serves to detect small shift in covariance matrix and the development of Multivariate Exponentially Weighted Moving Average (MEWMA) charts. Based on the results of the analysis, the MEWMA control chart is statistically controlled with a weighting value λ=0,2 while the MEWMC chart with λ=0,2 is not controlled statistically and detected small shift in covariance matrix . In a controlled process, the capability value of multivariate process is 0,83222 < 1 which means the process is not capable.Keywords: MEWMA control chart, MEWMC control chart, Process capability analysis.

PENGENDALIAN KUALITAS PRODUK MINO DI HOME INDUSTRY “SARANG SARI” BANYUMAS Winahyu Handayani; Tatik Widiharih; Budi Warsito
Jurnal Gaussian Vol 6, No 4 (2017): 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.v6i4.30386

Mino is Banyumas’s signature souvenir that is fancied by the public. High competitiveness makes mino manufacturers are prosecuted to improve the quality of their products. One of the ways to ascertain whether a product has a good quality is by looking at the number of defective products, the less the number of defective products the better the quality. The objective of the study is to minimize broken and burnt products and also size faultiness of the mino. Control Charts and R are used to view defectiveness data from mino’s diameter and mino’s weight respectively, where as Control Chart p is used to see the data of burnt and broken mino. Furthermore, the value of process capability (Cpk) used to review whether the process is considered capable or not capable. The result and analysis at “Sarang Sari” Nopia and Mino’s Home Industry Banyumas show attribute data in the form of broken and burned defects is restrained after eliminating seven observations data. Thereupon, the variable data in the form of mino’s weight data is restrained after omitting the three observations data with Cpk value is 1.1180, and for mino’s diameter data process has been restrained with Cpk value of 0.9559. Factors that are affecting mino’s defectiveness are equipment, method and measurement. Meanwhile, the profit value of this mino home industry business is Rp 9.276.110 per month. Keywords: Mino, Chart Control, Process Capability, Economic Analysis

GRAFIK PENGENDALI MIXED EXPONENTIALLY WEIGHTED MOVING AVERAGE – CUMULATIVE SUM (MEC) DALAM ANALISIS PENGAWASAN PROSES PRODUKSI (Studi Kasus : Wingko Babat Cap “Moel”) Aulia Resti; Tatik Widiharih; Rukun Santoso
Jurnal Gaussian Vol 10, No 1 (2021): 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.v10i1.30938

Quality control is an important role in industry for maintain quality stability. Statistical process control can quickly investigate the occurrence of unforeseen causes or process shifts using control charts. Mixed Exponentially Weighted Moving Average - Cumulative Sum (MEC) control chart is a tool used to monitor and evaluate whether the production process is in control or not. The MEC control chart method is a combination of the Exponentially Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) charts. Combining the two charts aims to increase the sensitivity of the control chart in detecting out of control. To compare the sensitivity level of the EWMA, CUSUM, and MEC methods, the Average Run Length (ARL) was used. From the comparison of ARL values, the MEC chart is the most sensitive control chart in detecting out of control compared to EWMA and CUSUM charts for small shifts. Keywords: Grafik Pengendali, Exponentially Weighted Moving Average, Cumulative Sum, Mixed EWMA-CUSUM, Average Run Lenght, EWMA, CUSUM, MEC, ARL

METODE K-HARMONIC MEANS CLUSTERING DENGAN VALIDASI SILHOUETTE COEFFICIENT (Studi Kasus : Empat Faktor Utama Penyebab Stunting 34 Provinsi di Indonesia Tahun 2018) Silvy ‘Aina Salsabila; Tatik Widiharih; Sudarno Sudarno
Jurnal Gaussian Vol 11, No 1 (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.v11i1.34003

The k-harmonic means method is a method of using the cluster center point value, which is to determine each cluster from its center point based on the calculation of the harmonic average. The k-harmonic means determines the existence of each data point based on the membership function and weighting function by using a distance measure. in the clustering, which aims to increase the importance of data that is far from each central point. This causes the k-harmonic means to be insensitive in initialization in determining the cluster center point and significantly improves the quality of clustering compared to k-means. In determining the level of similarity, the determination of the level of similarity uses the distance measure and the distance measure used is the Euclidean distance measure. The distance measure used in cluster analysis can affect the cluster results obtained. Thus, to determine the quality of the results of the cluster analysis, validation tests were carried out using an internal criteria approach, namely silhouette coefficient. In this study, the k-harmonic means used to classify provinces in Indonesia based on the causes of stunting in 2018. The stunting in children under five in Indonesia has exceeded the limit set by WHO. In 2016-2017 there was an increase in the prevalence of stunting by 27.5% to 29.6%. The k-harmonic means method is used so that the four main factors causing stunting in every province in Indonesia can be seen and the prevention and cure of stunting can run optimally. This method is also used because the data on the four factors that cause stunting show a significant rate of change and as a measure of central tendency in 34 provincial objects in Indonesia. Four factors that cause stunting are used, namely the percentage of households that do not have access to clean drinking water, the percentage of exclusive breastfeeding, the percentage of Low Birth Weight Babies (LBW) 2,500-grams born safely and the percentage of households that do not have proper sanitation facilities. The results obtained by the cluster which is optimal at k= 3 using the Euclidean, where the silhouette coefficient = 0,3040722675 ≈ 0,3. Based on the results of the cluster analysis, it is known that in cluster one, the main factor that stands out the most is the percentage of exclusive breastfeeding. In cluster two, the main factor that stands out the most is the percentage of Low Birth Weight Babies (LBW) 2,500-grams born safely. In cluster three, the most prominent main factors are the percentage of Low Birth Weight Babies (LBW) 2,500-grams born safely and the percentage of households that do not have proper sanitation facilities with the highest average centroid among other clusters. Keywords: Clustering, K-Harmonic Means, Euclidean distance, Silhouette Coefficient, Stunting

Kernel K-Means Clustering untuk Pengelompokan Sungai di Kota Semarang Berdasarkan Faktor Pencemaran Air Anestasya Nur Azizah; Tatik Widiharih; Arief Rachman Hakim
Jurnal Gaussian Vol 11, No 2 (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.v11i2.35470

K-Means Clustering is one of the types of non-hierarchical cluster analysis which is frequently used, but has a weakness in processing data with non-linearly separable (do not have clear boundaries) characteristic and overlapping cluster, that is when visually the results of a cluster are between other clusters. The Gaussian Kernel Function in Kernel K-Means Clustering can be used to solve data with non-linearly separable characteristic and overlapping cluster. The difference between Kernel K-Means Clustering and K-Means lies on the input data that have to be plotted in a new dimension using kernel function. The real data used are the data of 47 rivers and 18 indicators of river water pollution from Dinas Lingkungan Hidup (DLH) of Semarang City in the first semester of 2019. The cluster results evaluation is used the Calinski-Harabasz, Silhouette, and Xie-Beni indexes. The goals of this study are to know the step concepts and analysis results of Kernel K-Means Clustering for the grouping of rivers in Semarang City based on water pollution factors. Based on the results of the study, the cluster results evaluation show that the best number of clusters K=4

ANALISIS KLASIFIKASI MENGGUNAKAN METODE REGRESI LOGISTIK BINER DAN BOOTSTRAP AGGREGATING CLASSIFICATION AND REGRESSION TREES (BAGGING CART) (Studi Kasus: Nasabah Koperasi Simpan Pinjam Dan Pembiayaan Syariah (KSPPS)) Salma Innassuraiya; Tatik Widiharih; Iut Tri Utami
Jurnal Gaussian Vol 11, No 2 (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.v11i2.35458

The Save Loan and Sharia Financing Cooperatives (KSPPS) is a financial institution that offers deposits, loans, and financing to its members while adhering to Islamic sharia rules. Customers payment behaviour is influenced by their background differences, such as age, gender, occupation, and so on. The classification method is used to determine the characteristics of members who are currently in arears or are stuck in arears. Binary Logistic Regression and Bootstrap Aggregating Classification and Regression Trees were utilized as classification methods (BAGGING CART). A Logistic Regression with binary response variables is known as a Binary Logistic Regression. By resampling 50 times, the technique with the BAGGING process is used to improve the performance of the classification using CART. Customer data from one of the KSPPS in Central Java in 2021 was used in this investigation. Gender, age, marital status, employment, education level, time period, and income were the independent variables in this study, whereas payment status was the dependent variable (not stuck and stuck). The Binary Logistic Regression approach had an accuracy of 78.67 percent with an APER 21.33 percent, a Press's Q of 24.65, and a specificity of 98.30 percent, according to the classification accuracy statistics. The accuracy of the classification produced by CART with an accuracy value of 77.33 percent with an APER 22.67 percent, the value of Press's Q is 22,413, and specificity is 94.91 percent, then approached by BAGGING process the accuracy of the resulting classification by predicting data testing accuracy value of 78.67 percent with an APER 21.33 percent, press's Q value of 24.65, and specificity of 96.61 percent. Based on these findings, it can be inferred that using the BAGGING process can increase the CART method's performance to the point where it is nearly as good as Binary Logistic Regression, which has a slightly higher classification accuracy

PENGELOMPOKAN PROVINSI DI INDONESIA BERDASARKAN INDIKATOR KESEHATAN LINGKUNGAN MENGGUNAKAN METODE PARTITIONING AROUND MEDOIDS DENGAN VALIDASI INDEKS INTERNAL Diah Aliyatus Saidah; Rukun Santoso; Tatik Widiharih
Jurnal Gaussian Vol 11, No 2 (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.v11i2.35478

Environmental health is an important aspect in efforts to achieve public health. The condition of environmental health in Indonesia is varies in each province, so the priorities for increasing environmental health are also different. This study aims to grouping provinces in Indonesia based on environmental health indicators in order to know the high/low environmental quality in each province to assist the government in optimizing environmental health efforts. The grouping of provinces is done partitioning around medoids method which is robust to data containing outliers. The measure of similarity objects is calculated using the Euclidean and Manhattan distances, the selection of the best number of clusters is done by validating the internal index, namely the Calinski-Harabasz index, Baker-Hubert index, silhouette index, C-index, and Davies-Bouldin index. The result of this study is that the best number of clusters are two clusters using the Manhattan distance measurement method, with the largest Calinski-Harabasz index value = 24.10072, the largest Baker-Hubert index = 0.8466251, the largest silhouette index = 0.4246581, the smallest C-index = 0.07290109, and the smallest Davies-Bouldin index = 1.094805.

GUI R UNTUK ANALISIS KERANJANG BELANJA DENGAN ALGORITMA APRIORI PADA SUATU PERUSAHAAN E-COMMERCE Ryan Anugrah; Tatik Widiharih; Sugito Sugito
Jurnal Gaussian Vol 11, No 2 (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.v11i2.35475

Technological developments help people live easier. One of the technological developments is being able to trade digitally or it can be called e-commerce. To increase revenue, e-commerce companies collect consumer sales history data that can be analyzed and obtain information about consumer habits. One of the analyzes that can be used is shopping basket analysis which aims to find a pattern in transaction data. In data processing and analysis is done using the R program computation and GUI R is made with a recommendation system simulation. The results of the shopping cart analysis produce as many as 22 rules using a minimum support of 0.06 and a confidence of 0.5. The greater the support value, the more often the product or rule is purchased by consumers from all data transactions and vice versa. Meanwhile, the greater the trust value, the more often the products purchased under the regulation are purchased together. Thus, the information can be used to help carry out promotions to increase sales by the company.

PENERAPAN TUNING HYPERPARAMETER RANDOMSEARCHCV PADA ADAPTIVE BOOSTING UNTUK PREDIKSI KELANGSUNGAN HIDUP PASIEN GAGAL JANTUNG Tita Aulia Edi Putri; Tatik Widiharih; Rukun Santoso
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.11.3.397-406

Heart failure is the number one cause of death every year. Heart failure is a pathological condition characterized by abnormalities in heart function, which results in the failure of blood to be pumped to supply metabolic needs of tissues. The application of data mining and computational techniques to medical records can be an effective tool to predict each patient's survival who has heart failure symptoms. Data mining is a process of gathering important information from big data. The collection of important information is carried out through several processes, including statistical methods, mathematics, and artificial intelligence technology. The AdaBoost method is one of the supervised algorithms in data mining that is widely applied to make classification models. Hyperparameter Optimization is selecting the optimal set of hyperparameters for a learning algorithm. AdaBoost has hyperparameters requiring a classification process set, namely learning rate and n_estimators. RandomSearchCV is a random combination method of selected hyperparameters used to train the model. This research uses heart failure patient data collected at the Faisalabad Institute of Cardiology and at the Allied Hospital in Faisalabad (Punjab, Pakistan) from April to December 2015. The research uses learning rate: [-2.2] (log scale), n_estimators start from 10 to 776, and Kfold=5 and produces the best hyperparameters in learning rate=0.01 and n_estimators=443 with an accuracy value of 0.85 and AUC value of 0.897.

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

Title Search

Found 37 Documents Search Journal : Jurnal Gaussian

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 37 Documents
Search
Journal : Jurnal Gaussian