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All Journal HAYATI Journal of Biosciences FORUM STATISTIKA DAN KOMPUTASI JURNAL KIMIA SAINS DAN APLIKASI Jurnal Manajemen Teknologi The Journal of Pure and Applied Chemistry Research Indonesian Journal of Chemistry Indonesian Journal of Statistics and Its Applications Martabat: Jurnal Perempuan dan Anak Xplore: Journal of Statistics

Utami Dyah Syafitri

Department Of Statistics, Faculty Of Mathematics And Natural Sciences, IPB University Bogor

Author-ID : 398549

Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Engineering Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Social Sciences Other

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Discrimination of cassava, taro, and wheat flour using near-infrared spectroscopy and chemometrics Mohamad Rafi; Sifa Awalul Fikriah; Rifki Husnul Khuluk; Utami Dyah Syafitri
Jurnal Kimia Sains dan Aplikasi Vol 23, No 10 (2020): Volume 23 Issue 10 Year 2020
Publisher : Chemistry Department, Faculty of Sciences and Mathematics, Diponegoro University

There is a difference in the selling price for cassava, taro, and wheat flour, with taro flour having a higher price. It could be a reason for adulterating the taro flour from the other two flours and reducing quality. This study aims to distinguish the three types of flour using the near-infrared (NIR) spectra combined with chemometrics. The NIR spectra of all samples were measured at a wavelength of 1000-2500 nm. The multivariate analysis used was principal component analysis (PCA), and PCA followed with discriminant analysis (DA). The preliminary process of the signal using area normalization was carried out before the multivariate analysis. The PCA results showed that most of the samples were grouped in their respective groups except for two samples, namely 1 sample of taro flour and 1 sample of cassava flour. Meanwhile, the PCA-DA results using seven main components showed that the three samples were grouped well. DA validation was carried out using the cross-validation method, showing that the samples could be identified into their respective groups. Therefore, a combination of NIR spectrum and chemometric analysis can be used to differentiate cassava, taro, and wheat flour

K-prototypes Algorithm for Clustering Schools Based on The Student Admission Data in IPB University Sri Sulastri; Lismayani Usman; Utami Dyah Syafitri
Indonesian Journal of Statistics and Applications Vol 5 No 2 (2021)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v5i2p228-242

The new student admissions was regularly held every year by all grades of education, including in IPB University. Since 2013, IPB University has a track record of every school that has succeeded in sending their graduates, even until they successfully completed their education at IPB University. It was recorded that there were 5,345 schools that included in the data. It was necessary to making every school in the data into the clusters, so IPB could see which schools were classified as good or not good in terms of sending their graduates to continue their education at IPB based on the characteristics of the clusters. This study using the k-prototypes algorithm because it can be used on the data that consisting of categorical and numerical data (mixed type data). The k-prototypes algorithm could maintain the efficiency of the k-means algorithm in handling large data sizes, but eliminated the limitations of k-means. The results showed that the optimal number of clusters in this study were four clusters. The fourth cluster (421 school members) was the best cluster related to the student admission at IPB University. On the other hand, the third cluster (391 school members) was the worst cluster in this study.

Segmentasi Mahasiswa S1 IPB terhadap Sistem Peminjaman Sepeda Tania Amalia Darsono; Utami Dyah Syafitri; Aam Alamudi
Xplore: Journal of Statistics Vol. 2 No. 1 (2018): 30 Juni 2018
Publisher : Department of Statistics, IPB

IPB is the one campus that realize the Green Campus program. One of the elements in Green Campus is Green Transportation. In realizing this Green Transportation, IPB has several programs that include the Green Bike program. There are rules in implementation the Green Bike program related to the borrowing system. Because of the borrowing system, it is necessary to make the segmentation of S1 IPB students on bicycle borrowing system. Segmentation of respondent's characteristic used two step clustering method and the result is 3 optimal clusters. Then segmentation on respondent's preference to bicycle borrowing system used k-means method and the result is 2 optimal clusters. Segmentation of bicycle borrowing system based on respondent's characteristic and respondent's preference is 6 combinations of cluster using cross tabulation.

Segmentasi Mahasiswa S1 IPB terhadap Sistem Peminjaman Sepeda Tania Amalia Darsono; Utami Dyah Syafitri; Aam Alamudi
Xplore: Journal of Statistics Vol. 7 No. 3 (2018): 31 Desember 2018
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar

Green Campus is one program of IPB. One element of Green Campus is Green Transportation. There are programs in Green Transportation, one of the programs is Green Bike. There are rules in Green Bike program which were related to the system of borrowing. Based on the rules, so it was required to make segmentation of undergraduate students IPB on bicycle borrowing system. This research used data of undergraduate students IPB on bicycle borrowing systemâ€™s preferences and characteristics of respondents. Segmentation on characteristics of respondents using two step cluster method. The distance that was used in two step cluster is log-likelihood and to determinate the optimal clusters using BIC. There are 3 optimal clusters formed and quality of clustering is fair (coefficient Silhouette = 0.3). Then segmentation on bicycle borrowing systemâ€™s preferences using kmeans method. The distance that was used in k-means is euclid and there are 2 optimal clusters formed (based on the Pseudo-F value). Based on segmentation on bicycle borrowing system by combining characteristics and preferences of respondents, there are 6 cluters formed.

Identifikasi Cepat Segmentasi Konsumen Susu Cair dalam Kemasan Fadhila Hijryani; Bagus Sartono; Utami Dyah Syafitri
Xplore: Journal of Statistics Vol. 7 No. 3 (2018): 31 Desember 2018
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/xplore.v7i3.128

Consumer segmentation is the process of grouping customers into some segments based on some shared similar characteristics. Consumer segmentation allows companies to understand the customer's characteristics in each segment, thus make them easier to establish suitable marketing strategies for each segment's characteristics.Companies tend to use marketing strategies with demographical and consumer behavioural based scheme of consumer segmentation therefore make them easier to identify customer as the characteristics are easily measured. This research uses k-means method for segmenting 419 customers of packaged liquid milk. The life style pattern of the customers are used as the basis of the segmentation. Furthermore, this research uses decision tree algorithm to classify characteristics of the new customer. According to Hartigan index alteration (26.2433), ideal number of segments is 4. After tree pruning step, classification modelling with CART method yielded 54.61% accuracy.

Penerapan SMOTE dalam Pemodelan CHAID pada Data Keberhasilan Mahasiswa PPKU IPB Ririn Fara Afriani; Mohammad Masjkur; Utami Dyah Syafitri
Xplore: Journal of Statistics Vol. 8 No. 1 (2019): 30 April 2019
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/xplore.v8i1.154

Bogor Agricultural University (IPB) as the third rank of Indonesian non polytechnic universities in 2017 requires new students to join the General Competency Education Program (PPKU) for two semesters to improve the quality of human resources. Student achievement success can be determine from the student's academic status, where the student's academic status is divided into two, which are Drop Out (DO) and not DO. Only 1% of PPKU students who are drop out.. This means there is a data imbalance. One of the method used to handled that is Synthetic Minority Oversampling Technique (SMOTE) method. Classification analysis used is the Chi-Square Automatic Interaction Detection (CHAID) method to identify the factors that influence the success of PPKU students. The application of SMOTE to the 2016/2017 PPKU student data was able to improve the ability of classiï¬cation trees with the average values â€‹â€‹of accuracy, sensitivity, and specificity to 0.718, 0.575, and 0.72. The factors that influence the success of IPB's PPKU students are the entry point, gender, and regional origin.

Evaluasi Produk Multivitamin Baru Berdasarkan Penilaian Responden Noer Endah Islami; Utami Dyah Syafitri; Cici Suhaeni
Xplore: Journal of Statistics Vol. 10 No. 2 (2021)
Publisher : Department of Statistics, IPB

In order to lead in the market, companies should have an innovation product. Before the innovation product lauch to the market, the marketing research should be done. The goal of the reasearch is to determine whether the new product is accepted or rejected in the market. This study was to identify the characteristics of the new product based on organoleptic point of view and performance the three type of new multivitamin products based on location and social economic classes (SEC) of respondents. MANOVA and biplot analysis were used in this research. Based on MANOVA, there were differences on the organoleptic point of view of respondents among three type of new multivitamin products. The three products had differences on the assessment of aroma, sour taste, and sour after taste. In addtion with biplot analysis, it was concluded that each product had different location for sale and the target of respondents based on sosial economic classes. According to respondents, product A was too sweet taste and too sour after taste in the mouth compared to others. This product was preferred by respondents who reside in South Jakarta with social economic classes (SEC) A2 and C1. Unlike product A, product B was too hard with a bit of bitter after taste in the mouth. This product was relatively preferred by respondents in various residential with social economic classes (SEC) B. Product C was strong aroma with smooth texture and more bitter taste than others. This product was preferred by respondents who reside in North Jakarta and Depok with social economic classes (SEC) A1. Overall, product B was preferred by respondents compared to other products.

Faktor-Faktor yang Memengaruhi Keberhasilan Studi Mahasiswa IPB Jalur Ketua OSIS dengan Metode Pohon Regresi Novia Yustika Tri Lestari. YR; Utami Dyah Syafitri; Mulianto Raharjo
Xplore: Journal of Statistics Vol. 11 No. 2 (2022):
Publisher : Department of Statistics, IPB

The success of IPB student's study can be seen from the achievement index obtained at the end of each semester. Meanwhile, the success rate of one's study is generally influenced by two factors, internal factors and external factors. Internal factors consist of intelligence (intellectual), physical, attitudes, interests, talents, and motivations, while external factors consist of family circumstances, school conditions, and the community environment. Therefore, this study uses the analysis method of classification and regression trees (CART) to find out what factors influenced the success of the Student Council (OSIS) university students. Regression tree it is one of the methods of classification and regression trees (CART) to perform classification analysis on both categorical and continuous response variables. Continuous response variables will produce a regression tree or hierarchical data group that starts at the root and ends with a relatively homogeneous small group. The response variable used in this study is the Achievement Index of first semester students. The results obtained from the analysis showed that there are several different variables in each class in influencing the success of the student council (OSIS) university students, but if we look further, there are two variables that are the same in influencing the success of the student council (OSIS) university students, which are variables from high school province and student study programs. This study uses secondary data from 493 IPB students track the chairman of the student council of the year 2018-2020 which is still active until now. Furthermore, the analysis of the regression tree is performed against four different models, for each of the force and the overall force by adjusting the variables available. The formation of tree regression performed 10 repetitions and the results of regression trees is taken from a tree which has the approximate value of the smallest risk. Then, the final results obtained from the analysis showed that there are several different variables in each class in influencing the success of the student council (OSIS) university students, but if we look further, there are two variables that are the same in influencing the success of the student council (OSIS) university students, which are variables from high school province and student study programs.

Analisis Regresi Logistik dan Cart untuk Credit Scoring dengan Penanganan Kelas Tak Seimbang Siwi Haryu Pramesti; Indahwati Indahwati; Utami Dyah Syafitri
Xplore: Journal of Statistics Vol. 11 No. 3 (2022): Vol. 11 No. 3 (2022)
Publisher : Department of Statistics, IPB

The absence of collateral for a type of credit will increase the bank's credit risk (failed to pay). Banks apply the precautionary principle by managing their credit portfolios so that potential hazards that occur can be measured and controlled in a model. Credit scoring describes how likely a debtor will fail with payments. This study aimed to compare logistic regression analysis and Classification and Regression Trees (CART) in classifying debtors to evaluate credit policies. One of the problems in classification is unbalanced data. Synthetic Minority Oversampling Technique (SMOTE) is a technique to handle the unbalanced problem in classification. The results show that the logistic regression model with SMOTE has higher sensitivity than the CART model, and there was no difference in Area Under Curve (AUC). The variables that have significant effects on the classification of debtors (good, bad) are level of education, homeownership status, and income.

Klasifikasi Sekolah dalam Penerimaan Mahasiswa Baru Vokasi IPB Jalur USMI Menggunakan Metode CART Erlinda Widya Widjanarko; Utami Dyah Syafitri; Aam Alamudi
Xplore: Journal of Statistics Vol. 11 No. 3 (2022): Vol. 11 No. 3 (2022)
Publisher : Department of Statistics, IPB

The selection of new student admissions for the IPB vocational school consists of several routes, one of which is the USMI route. To improve its performance, it is necessary to evaluate the USMI new student admission system. Previously, research with the same objective had been carried out using the clustering method. The study resulted in three clusters in which schools were differentiated based on commitment and quality. This study aim to create a classification model of the clusters obtained using the CART method. Classification and Regression Tree (CART) is a nonparametric classification technique that produces a single decision tree. The CART method can involve mixed-type data. The classification model generated from the 2019 data yields an accuracy of 98.52%. However, the results of the 2019 model evaluation with the 2020 data are still not good enough to predict with an accuracy of 57.22%, so the 2020 data is re-clustered and produces three clusters. Furthermore, the classification model was remade with 2020 data, resulting in an accuracy of 97.47%. However, the results of the 2020 model evaluation with the 2021 data are still not good enough to predict with an accuracy of 44.34%, so the clustering in the previous year cannot be used for predictions of the following year's data. The grouping of schools for USMI applicants needs to be done by grouping schools every year.

Co-Authors Aam Alamudi Aam Alamudi Abdul Rohman Agustin Faradila Aji Hamim Wigena Anang Kurnia Andrew Donda Munthe Anggraini Sukmawati Anik Djuraidah Antonio Kautsar Azkiya, Azka Al Bagus Sartono Bagus Sartono Bambang Prajogo Eko Wardoyo Bartho Sihombing Cici Suhaeni Dwi Putri Kurniasari Eka Dewi Pertiwi Eka Winarni Sapitri Erfiani Erfiani Erlinda Widya Widjanarko Eti Rohaeti Eti Rohaeti Evita Choiriyah Fadhila Hijryani Fatimah, Zahra Nurul Hari Wijayanto I Made Sumertajaya Indahwati Indahwati Indonesian Journal of Statistics and Its Applications IJSA Intan Lukiswati Irmanida Batubara Isti Rochayati Lismayani Usman M. Iqbal Mohamad Rafi Mohammad Masjkur Muhammad Bachri Amran Mulianto Raharjo Muslim, Muhammad Irfai Nariswari Karina Dewi Noer Endah Islami Novia Yustika Tri Lestari. YR Nurhajawarsi Nurhajawarsi Nursifa Mawadah Rifki Husnul Khuluk Ririn Fara Afriani Sifa Awalul Fikriah Siwi Haryu Pramesti Soni Yadi Mulyadi Sri Sulastri Sri Wahyuningsih Syifa Muflihah Tania Amalia Darsono Topan . Ruspayandi Vega, Iliana Patricia Wulan Tri Wahyuni Yenni Angraini Yuan Millafanti Yuni Suci Kurniawati Yuniar Istiqomah

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