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All Journal Media Statistika MATEMATIKA Jurnal Gaussian JAIS (Journal of Applied Intelligent System)
Tatik Widiharih
Jurusan Statistika FSM Undip
Decision Sciences, Operations Research & Management Mathematics Other

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Journal : jurnal gaussian

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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 SENTIMEN KASUS ONLINE TRADING BINOMO PADA TWITTER MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) Nugroho, Elva Nadia; Widiharih, Tatik; Hakim, Arief Rachman
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%.
PENERAPAN KLASIFIKASI REGRESI LOGISTIK BINER DAN ADAPTIVE BOOSTING MENGGUNAKAN CLASSIFICATION AND REGRESSION TREES PADA PREDIKSI PENYAKIT HEPATITIS C Oktaviani, Ellina Dhiya Ulhaq; Santoso, Rukun; Widiharih, Tatik
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%.
Co-Authors Aan Rosiatun Abdul Hoyyi Alan Prahutama Arief Rachman Hakim 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 Nugroho, Elva Nadia Nurcahyanti, Tri Meida Oktaviani, Ellina Dhiya Ulhaq 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 Sayekti Dewi Anggraini Sugito - Triastuti Wuryandari Wiwin Mardjiyati Yuciana Wilandari