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All Journal Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer BAREKENG: Jurnal Ilmu Matematika dan Terapan SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Variance : Journal of Statistics and Its Applications Journal of Mathematics Education and Science Jurnal Pengabdian Masyarakat Indonesia MATHunesa: Jurnal Ilmiah Matematika Journal of Social Outreach Journal of Research Applications in Community Services Basis : Jurnal Ilmiah Matematika Teknikom: Teknologi Informasi, Ilmu Komputer dan Manajemen Eksponensial Georeference: Jurnal Kajian Ilmu dan Pembelajaran Geografi JMT (Jurnal Matematika dan Terapan) MILANG Journal of Mathematics and Its Applications
Fidia Deny Tisna Amijaya
Program Studi Matematika, Jurusan Matematika, FMIPA, Universitas Mulawarman
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Perbandingan Kinerja Metode Klasifikasi Chi-square Automatic Interaction Detection (CHAID) dengan Metode Klasifikasi Algoritma C4.5 pada Studi Kasus : Penderita Diabetes Melitus Tipe 2 Di Samarinda Tahun 2015 Muhammad Faisal; Yuki Novia Nasution; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 8 No 2 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

C4.5 algorithm is tree classification where tree branches can be more than two. In C4.5 algorithm, the decision tree is based on entropy and gain criterias. Chi-Squared Automatic Interaction Detection (CHAID) classification method is a methods which is used to divide data to become a smaller groups based on categorical dependent and independent variables. The purpose of this research is to determine the classification process by C4.5 algorithm and CHAID method for DM type 2 patients. Risk factors for diabetes type 2 are Decline, Age, Gender, Status of Obesity, Diet, and Sports Activity based on the availability of source data from the Hospital of Abdul Wahab Sjahranie Samarinda. The results show that factors which significantly affect the DM type 2 patients are Obesity and Sport Activity. While by using CHAID, the factors which significantly affect the patients are Decline, Obesity, Diet and Sports Activity. The Classification result accuracy of the C4.5 algorithm is 90% and 94% for CHAID method.
Klasifikasi Data Nasabah Asuransi Dengan Menggunakan Metode Naive Bayes Dyah Arumatica Novilla; Rito Goejantoro; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 10 No 2 (2019)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (534.732 KB) | DOI: 10.30872/eksponensial.v10i2.565

Abstract

Classification is the logical grouping of objects according to the characteristics of their similarities. Naive Bayes is a method for predicting future opportunities based on past experiences. This study discusses the classification of insurance customer data of PT. Prudential Life Branch of Samarinda in 2017. With the aim to know whether the method of Naive Bayes can classify data of insurance customers of PT. Prudential Life in 2017 using the R program and to determine the accuracy of the results of data testing I and data testing II. As a result, Naive Bayes method can classify data of insurance customers of PT. Prudential Life with 80% accuracy for 25 data testing I and 74.67% for 75 data testing II.
Penerapan Metode Complete Linkage dan Metode Hierarchical Clustering Multiscale Bootstrap Lisda Ramadhani; Ika Purnamasari; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 9 No 1 (2018)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Cluster analysis is an analysis that has a purpose to grouping the data (object). The multiscale bootstrap method in cluster analysis is used as a manner for looking at the validity from the result of cluster analysis. The working process of multiscale bootstrap in cluster analysis is taking a sample that has been bootstrapped and then take the one of bootstrap resampling result that has been reputed to represent the distribution in East Kalimantan 2016. The purpose of this research is looking at the result of data agglomeration in poverty indicator in East Kalimantan 2016 in using a multiscale bootstrap method that produces four cluster types. The first cluster consists of two regencies/cities who has the low percentage of poverty indicator 49,32%. Additionally, the second cluster contains of five regencies/cities with the high percentage of poverty indicator 53,39%. In addition, the third cluster involves of two regencies/cities with the percentage of poverty indicator in high sufficient 51,46%. Finally, the fourth cluster consists of a regency/city which has a percentage of poverty indicator low adequate 51,02%.
Peramalan Harga Minyak Mentah Dunia (Crude Oil) Menggunakan Metode Radial Basis Function Neural Network (RBFNN) Ayu Wulandari; Sri Wahyuningsih; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 8 No 2 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Forecasting is a technique to estimate a value in the future with past data and current data. One of the forecasting method that includes neural network is Radial Basis Function Neural Network (RBFNN). In this research, RBFNN method is used to get the best model and to forecast world crude oil price (US$) data. World crude oil prices forecasting is very important for many stakeholder, both from the government sector, business entities and investors so that all activities can go according to plan. In the RBFNN method, the network input and the number of hidden layers is very influential to get the best model from RBFNN and also the forecasting. To get the best model by using network input determination by identifying the Partial Autocorrelation Function (PACF) lag, and to determine the number of hidden layers by the K-Means cluster method. Results of the research showed that from the training data, the best model of RBFNN is using 2 network inputs Xt−1 and Xt−2 and 3 hidden layers with Mean Absolute Percentage Error (MAPE) accuracy level is 6,8150%. With the model, for the next period from June 2017 to December 2017 the world crude oil price (US $) shows a downward trend.
Aplikasi Classification and Regression Tree (CART) dan Regresi Logistik Ordinal dalam Bidang Pendididikan David Siahaan; Sri Wahyuningsih; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 7 No 1 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

CART method is a nonparametric statistical methods which is for obtaining accurate data group in the classification analysis. CART main goal is to get an accurate data as a group identifier of a classification. CART can be applied in three main steps, namely the establishment of a classification tree, trimming the classification tree, and determination of optimal classification tree. Ordinal logistic regression is a statistical method for analysis response variables that have an ordinal scale consisting of three or more categories. Predictor variables that can be included in the model can be either continuous or categorical data consisting of two or more variables. This study wanted to know the classification results FMIPA UNMUL predicate graduation, the main factor that affect the predicate graduation FMIPA UNMUL who graduated in 2014, and a comparison of the accuracy of the classification results between CART and ordinal logistic regression. The results showed that gender (X1), region origin (X2), major (X3), the status of secondary school (X4), and duration of the study period (X5) is the primary identifier graduation predicate FMIPA UNMUL, whereas gender (X1 ) and duration of the study period (X5) is a factor that affects the predicate graduation. Ordinal logistic regression model was able to predict with 65% accuracy, while the CART method has a predictive accuracy of 54.9%
Pengendalian Kualitas Produk Menggunakan Diagram Kontrol Multivariat p Bayu Iswahyudi Noor; Ika Purnamasari; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 10 No 1 (2019)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Every company competes with other companies in similar industries. One way to win the competition or at least stay in the competition is to give full attention to the quality of the products, so it can outperform the products produced by a competitors company. Quality control is done at the stage of production process, in order to get the standard or quality as expected. Multivariate p chart is one of the methods used for quality control which is the development of the control chart p. This research is conducted in the newspaper company Kaltim Post, with the characteristics quality of color blur, not symmetrical and dirty. The research is conducted in two phases; phase I is conducted for the period of July 2017 and phase II is conducted for the period of August 2017. The purpose of this research is to know the result of the controlling production of Kaltim Post newspaper by using multivariate p chart , knowing the types of defects that often occur and the cause of the defects types. The result of controlling production of Kaltim Post newspaper using multivariate diagram p is controlled in phase I with upper control limit of 0.002736, center line of 0.0024224 and lower control limit of 0.0021087. So the limits in phase I are appropriate for use in phase II. The most common types of defects are colors blur the caused by machine, method, material, human, and environmental factors.
Optimasi Klasifikasi Batubara Berdasarkan Jenis Kalori dengan menggunakan Genetic Modified K-Nearest Neighbor (GMK-NN) Nanang Wahyudi; Sri Wahyuningsih; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 10 No 2 (2019)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

The K-Nearest Neighbor (K-NN) method is one of the oldest and most popular Nearest Neighbor-based methods. The researchers developed several methods to improve the performance of the K-NN algorithm by using the Genetic Modified K-Nearest Neighbor (GMK-NN) algorithm. This method combines the genetic algorithm and the K-NN algorithm in determining the optimal K value used in the classification prediction. The GMK-NN algorithm will greatly facilitate the examination of coal classification in the laboratory without having to do a lot of chemical and physics testing that takes a long time only with the data already available. In this research, K value optimization is done to predict the classification of coal based on calories owned by PT Jasa Mutu Mineral Indonesia in 2017. Based on the research, using the proportion of training and testing data 90:10, 80:20 and 70:30 obtained the value of K the most optimal is at K = 1. The highest prediction accuracy was obtained by using 90:10 proportion data which is 100%, then with the proportion of 80:20 data obtained prediction accuracy of 91.67% and with the proportion of 70:30 data obtained prediction accuracy of 94.44%.
Aplikasi Data Mining Market Basket Analysis untuk Menemukan Pola Pembelian di Toko Metro Utama Balikpapan Nadya Rahmawati; Yuki Novia Nasution; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 8 No 1 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

The development of information technology in the transaction process in supermarkets compete to improve the quality and utility in order to achieve dissemination of information easily and quickly which is accurate and effective. This situation encourages the development of techniques that automatically find the relationship between item in the database. This study aims to analyzing and knowing association rules formed by using apriori algorithm. Market basket analysis’s steps are doing descriptive analysis, grouping the data transactions, applying apriori algorithm on the data, calculating the value of support and calculating the value of confidence. With the value of the minimum support 10% and minimum value of confidence 40%, the results obtained are one rule of association on the first day, four rules of association on the second day, one rule of association on the third day, four rules of association on the fourth day, six rules of association on the fifth day, nine rules of association on the sixth day, and four rules of association on the seventh day.
Penerapan Metode If-Then dari Rough Set Theory dalam Menangani Kecelakaan Lalu Lintas di Kota Samarinda Tahun 2016 Martua Tri Januar Sinaga; Rito Goejantoro; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 8 No 2 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Traffic accidents have caused many victims and lost materials, so itbecomes one of casesneed special attention every year. Therefore, it required a serious treatment to avoid the incidence of traffic accidents, so it can reduce the number of victimsbe inflicted. The aim ofthis study to determine the greatest factor/conditioncausing the fatality rate of traffic accidents and to determine the rules of decision rules from data that has been collected. The data used was secondary data taken from the report of traffic accidents recapitulation at Laka Lantas Unit, Satlantas Samarinda City. The analytical methods used to analyze the data are descriptive statistics analysis and Rough Set Theory. Based on the result, it can be seen the largest frequency of the victim who died is in traffic accidents that occur in sunny conditions. Moreover it is obtained 53 decision rules from the fatalities of victims by the traffic accidents in Samarinda City. The most powerful rule is "if a male student involved in a traffic accide nt at residential area and the road condition feasible passed by vehicles then the victim is likely to get serious injuries" with weight of 0.80.
Penentuan Percepatan Penyelesaian Proyek Pada Metode Jalur Kritis dengan Program Crash Wasono Wasono; Fidia Deny Tisna Amijaya; Moch Nurul Huda
EKSPONENSIAL Vol 10 No 1 (2019)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

A project requires scheduling so that project completion time can be completed at the targeted time. Critical Path Method (CPM) is one of the scheduling methods that is able to provide solutions to scheduling problems. This method has several project acceleration methods to get the fastest turnaround time with a minimal increase in costs. The acceleration method is the program crashes by not using free float time. Case studies of project scheduling at the bus terminal administration office building in city X have been carried out. Analysis is carried out to obtain a critical path at normal times. At normal times, the implementation time is 385 days with a total cost of Rp. 488,488,000.00. After that the project was accelerated by using a crash program by not using the free float time and the implementation time being 289 days with a direct total project cost of Rp. 520,239,992.00. Based on the time of the acceleration of the crash by not using the free float time, the reduction time was 96 days with the addition of a total direct cost of Rp. 14.252.008.00
Co-Authors A'yun, Qonita Qurrota Adawiyah, Rabbiatul Akbar Rizky Wardani Aminah, Esse Andri Azmul Fauzi Ardyanti, Hesti Asmaidi Asmaidi Asmaidi Auliya Rahman Ayu Wulandari A’yun, Qonita Qurrota Bayu Iswahyudi Noor Br Tarigan, Agnes Janitarian Cahyadi, Aldy Fradana Mahaputra Clemensius Arles Dala, Maria Alensia Deltin David Siahaan Desi Febriani Putri Desi Febriani Putri Dewi Erla Mahmudah Dewi Erla Mahmudah, Dewi Erla Dimas Raditya Dimas Raditya Sahputra Dimas Raditya Sahputra Dwi Indra Yunistya Dyah Arumatica Novilla Elvita, Melati Erlina Erlina Fahreza, Ilham Farha, Izzaty Fauzi, Andri Azmul Fiqri, Muhammad Dul Gunsyang, Grassella Hardina Sandariria Husna Novia Husna Novia Ramadhanty Ibrahim, Rizky Nur Ika Purnamasari Ika Purnamasari Imasdiani, Imasdiani Indriasri Raming Itsar Mangngiri Izzaty Farha Karina Putri Korompot Naufal Fahrezi Kurniawan Noor Bilal Kusrahman, Nanda Yopan Laila Nur Qamara Latifah Uswatun Khasanah Lestari, Nur Aini Ayu Lisda Ramadhani M. Sabransyah Mahmudi Mahmudi Martua Tri Januar Sinaga Meiliyani Siringoringo Melati Elvita Memi Nor Hayati Moch Nurul Huda Moh Khoridatul Huda, Moh Khoridatul Moh. Nurul Huda Muhammad Faisal Munfaati, Rafika Husnia Mushalifah, Mushalifah Mustika, Anggi Winda Nadya Rahmawati Nanang Wahyudi Neni Rahayu Nola Febriana Saputri Nur Amah Nur Aminah Pasarella, Muhammad Danil Pasia Rande Putri Pita Mutia Putri, Annisa Amalia Putri, Desi Febriani Qonita Qurrota A'yun Qonita Qurrota A’yun Rabbiatul Adawiyah Rachel Cornelia Simanjuntak Rachman, Dezty Adhe Chajannah Rachmawati, Amalia Raka Putra Pridiptama Rakhmawaty, Nurul Raming, Indriasri Ratna Dwi Christyanti, Ratna Dwi Rito Goejantoro, Rito Sahputra, Dimas Raditya Said Said, Said Sandariria, Hardina Sifriyani, Sifriyani Sri Wahyuningsih Sri Wahyuningsih Sri Wigantono Stefania Sesilia G. Witin Suciati, Rara Syaripuddin Syaripuddin Syaripuddin Syaripuddin Taqriri Kamal Mulyadi Tulzahrah, Shanaz Tumilaar, Rinancy Vika Novitasari Wasono Wasono Wasono Wasono Wasono, Wasono Welly Dona Permatasari Wigantono, Sri Yoki Novia Nasution Yuki Novia Nasution Yuki Novia Nasution Yuki Novia Nasution Yuki Novia Nasution Yuki Novia Nasution, Yuki Novia Yuliasari, Pratiwi Dwi