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All Journal Media Statistika MATEMATIKA Jurnal Gaussian JAIS (Journal of Applied Intelligent System) Indonesian Journal of Applied Statistics
Tatik Widiharih
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science Mathematics Other

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Implementation of Feature Selection Chi-Square to Improve the Accuracy of the Classification Model Using the Random Forest Algorithm on Coronary Artery Disease Mahendra, Ida Bagus Satya; Widiharih, Tatik; Nugroho, Fajar Agung; Sasongko, Priyo Sidik
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 1 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i1.7858

Abstract

Coronary heart disease is a disease in which the occurrence of blockages in the blood vessels in the heart. Coronary heart disease is a fatal disease, it is better to get as much information about this disease as possible. Data Mining can classify whether a person has heart disease or not based on symptoms. Data mining builds a model that can predict whether a person has heart disease or not. How well a model performs classification can be determined from its accuracy value, but this accuracy value can still be improved. Increasing the accuracy value can be done by performing Feature Selection. The research object used in this research is a dataset about coronary heart disease obtained from the Kaggle website. The classification method used in this modeling is the Random Forest algorithm to classify whether a person has coronary heart disease or not. The Random Forest Algorithm is a classification algorithm consisting of Decision Trees for classifying. The Random Forest algorithm is used because it has been proven to produce good accuracy in several previous studies. The Feature Selection method used in this modeling is the Chi-Square hypothesis test to determine whether there is an effect of each independent variable on the dependent variable. This research compared the value of modeling accuracy without using Feature Selection with modeling using Feature Selection. The result of this study is that the model without Chi-Square Feature Selection produced an accuracy value of 96,05% and the model with Chi-Square Feature Selection produced an accuracy value of 97,33%.
CLUSTERING KARAKTERISTIK INDUSTRI KECIL DAN MENENGAH DI KOTA KENDARI MENGGUNAKAN ALGORITMA k-PROTOTYPES Reihanah, Khalifah Nadya; I Maruddani, Di Asih; Widiharih, Tatik
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.12.3.340-351

Abstract

Industri Kecil Menengah (IKM) have important roles in economic development. The large number of IKM cannot be separated from various problems. The basic problems faced by IKM in Kendari are limited capital, inadequate human resources, difficulty in obtaining raw materials, and the Indonesian economy which has slumped due to the impact of the COVID-19 pandemic. This research was conducted with the aim of classifying the characteristics of the IKM with the optimal number of clusters. The method used is k-Prototypes Clustering with values of k = 2, 3, 4, ..., and 10. The k-Prototypes method is a clustering method that maintains the efficiency of the k-Means algorithm in handling large data when compared to the hierarchical clustering method. This method can group mixed type data (consisting of numeric type data and categorical type data). Based on the analysis, the optimal number of clusters is five clusters, with a Silhouette Index value of 0.461. Cluster 5 is the best IKM cluster with the highest average number of workers and the highest average investment value, while cluster 2 has the lowest average investment value and IKM in this cluster is relatively new compared to IKM in other clusters.
IMPLEMENTASI METODE NAIVE BAYES CLASSIFIER UNTUK KLASIFIKASI SENTIMEN ULASAN PENGGUNA APLIKASI NETFLIX PADA GOOGLE PLAY Rieuwpassa, Jessica Athalia; Sugito, Sugito; Widiharih, Tatik
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.12.3.362-371

Abstract

The COVID-19 pandemic has led to restrictions on activities in public places or facilities, such as cinemas. This has resulted in increased users of streaming service applications such as Netflix where users can access videos or movies online. Netflix users continue to increase from year to year, but its users began to decrease along with other streaming applications. Related to this, sentiment analysis was carried out on the classification of positive and negative reviews given by users on the Google Play website. The classification is expected to produce good accuracy and be analyzed so that it can be useful information for Netflix and potential users of streaming applications. The Naive Bayes Classifier method is a classification algorithm that is easy to apply and has high effectiveness for classifying text. This method utilizes the concept of conditional probability and has a strong assumption of independence. This study uses 2.850 Netflix application review data on Google Play which is then processed and divided into training data and test data with a ratio of 80:20. Classification with the Naive Bayes Classifier produces an accuracy value of 87,33%, a precision value of 87,6%, a recall value of 93,53%, and an F-measure value of 90,47% so it can be concluded that the performance of the Naive Bayes method is good for classifying user reviews of the Netflix.
KAJIAN SISTEM ANTRIAN PADA COUNTER KASIR DOMINO’S PIZZA MENGGUNAKAN MEAN VALUE ANALYSIS (STUDI KASUS: DOMINO’S PIZZA GAJAH MADA PEKALONGAN) Putri Milenia, Erin Novela; Sugito, Sugito; Widiharih, Tatik
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.12.3.425-433

Abstract

Queuing is the phenomenon that occurs when a service needs more than it can handle. This phenomenon is common in many places, such as restaurants. Attempts to analyze the behavior of queuing systems are called queuing system studies, one of which is the use of mean analysis (MVA). MVA can be used when arrival and service times do not follow an exponential distribution. The case study is the queuing system of Domino's Pizza Gajah Mada Pekalongan, which has two counters and took seven days to observe. This study aims to apply MVA and determine performance measures for queuing systems. In this study, MVA can be used because the arrival-to-service time does not follow an exponential distribution. The resulting cue model is (Gamma/GEV/2). (GD/∞/∞) and utilization is 0.43045. The average customer queuing and in the system are at most one customer. The average time to queue is 31.80336 seconds, the average time to complete a service is 321.0971 seconds, and the probability that the system isn’t busy 0.39816 or 39.8%.
Klasifikasi Menggunakan Algoritma K-Nearest Neighbor pada Imbalance Class Data dengan SMOTE. (Studi Kasus: Nasabah Bank Perkreditan Rakyat “X”) Ardhana, Salsabilla Rizka; Widiharih, Tatik; Saputra, Bagus Arya
Indonesian Journal of Applied Statistics Vol 6, No 2 (2023)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v6i2.79389

Abstract

Rural Banks (Bank Perkreditan Rakyat/BPR) provide financial services to micro-businesses and low repayment communities, especially in rural areas. The main activity of the bank is lending. Customer credit classification is expected to assist BPR in anticipating potential bad loans. K-Nearest Neighbor classify current and potential bad credit status based on customer data from BPR “X” in Central Java in October 2022. K-Nearest Neighbor is effective against a large amount of training data and works based on the nearest neighbor. There is an imbalance class data which causes the classification process to focus more on the majority class. Imbalance class data is handled using Synthetic Minority Oversampling Technique (SMOTE) as an oversampling approach. Classification with the addition of SMOTE can improve the evaluation of classification accuracy, especially G-mean. G-mean is the most comprehensive measurement in term of  accuracy, sensitivity and specificity in evaluating classification performance on imbalance class data. The results of this research were able to increase g-mean to 58.55% and sensitivity to 45.46% by implementing SMOTE. Based on the classification results, it is concluded that K-Nearest Neighbor with SMOTE at k = 19 and a proportion of training data to test data of 70:30 is a more appropriate classification model to use for customer credit status. Keywords: Credit Status; K-Nearest Neighbor; Imbalance Class Data; SMOTE
Co-Authors Aan Rosiatun Abdul Hoyyi Alan Prahutama Ardhana, Salsabilla Rizka Arsyil Hendra Saputra Budi Warsito Deby Fakhriyana, Deby Di Asih I Maruddani Diah Safitri Dwi Ispriyanti Eugenia Septri Hutahayan, Eugenia Septri Fajar Agung Nugroho Finisa, Husnul Hendi Septianto Mahendra, Ida Bagus Satya Moch. Abdul Mukid Nabila, Eva Salsa Nahdliyah, Milla Alifatun Nasichah Siska Andriani Nurcahyanti, Tri Meida Priyo Sidik Sasongko Putri Milenia, Erin Novela Rahmaniar, Ratna Reihanah, Khalifah Nadya Rieuwpassa, Jessica Athalia Rismia, Erysta Risky Rita Rahmawati Robiah Peni Raharjanti Raharjanti Rukun Santoso Saputra, Bagus Arya Sayekti Dewi Anggraini Sugito - Triastuti Wuryandari Wiwin Mardjiyati Yuciana Wilandari