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Xplore: Journal of Statistics

xplore
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Published by Institut Pertanian Bogor

ISSN : 23025751 EISSN : 26552744 DOI : https://doi.org/10.29244/xplore

Core Subject : Science, Education, Engineering,

Decision Sciences, Operations Research & Management Engineering Mathematics

Xplore: Journal of Statistics diterbitkan berkala 3 (tiga) kali dalam setahun yang memuat tulisan ilmiah yang berhubungan dengan bidang statistika. Artikel yang dimuat berupa hasil penelitian atau kajian pustaka dalam bidang statistika dan atau penerapannya. ISSN: 2302-5751 Mulai Desember 2018, Xplore: Journal of Statistics mendapatkan ISSN baru untuk media online (eISSN:2655-2744) sesuai dengan SK no. 0005.26552744/JI.3.1/SK.ISSN/2018.12 - 13 Desember 2018. Maka sesuai ketentuan pada SK tersebut, edisi Xplore: Journal of Statistics mulai Desember 2018 akan dimulai menjadi Volume 7 dan No 3. eISSN: 2655-2744

Arjuna Subject : Matematika - Statistik dan Probabilitas

Articles 106 Documents

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Penerapan Binary Particle Swarm Optimization Support Vector Machine untuk Klasifikasi Komentar Cyberbullying di Instagram Dewi Fortuna; Itasia Dina Sulvianti; Gerry Alfa Dito
Xplore: Journal of Statistics Vol. 11 No. 1 (2022)
Publisher : Department of Statistics, IPB

Freedom of speech on social media is sometimes inappropriate with the ethics of communicating and has led to cyberbullying. Instagram is the most commonly used social media in cyberbullying. Cyberbullying needs to be minimized because it has many adverse effects. One way that can be done is by identifying cyberbullying comments so those comments can be deleted automatically. The method used in this study is text classification using Support Vector Machine (SVM) algorithm with the application of Binary Particle Swarm Optimization (BPSO) optimization method as features selection. The study aims to build a cyberbullying comments classification model and compare the classification model performance with and without the application of features selection. The experimental results showed that modeling with SVM produces a reasonably accurate classification performance over 72% for all classification performance on each C. The application of BPSO for features selection can improve classification performance by increasing accuracy and specificity. However, the model without features selection on C = 0,1 is chosen in this study case because it has the highest sensitivity with good accuracy and specificity that can detect cyberbullying comments more accurately.

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.

Penerapan Structural Equation Modelling-Partial Least Squares pada Faktor Kemiskinan di Jawa Tengah Arini Annisa Adi; Mohammad Masjkur; Erfiani Erfiani
Xplore: Journal of Statistics Vol. 11 No. 2 (2022):
Publisher : Department of Statistics, IPB

The number of poverty-stricken people in Central Java in March 2020 was 3.98 million people (11.41%), the second-largest in Java. The approximately high number of poverty-stricken people is a priority for the government to reduce poverty. One of the solutions to reduce poverty is knowing the factors that may affect it. The purpose of this study is to identify the factors that affected poverty in Central Java using the Structural Equation Modeling-Partial Least Squares (SEM-PLS) method. This study used data from districts/ cities in Central Java in 2020. In this case, there is one exogenous latent variable for health and three endogenous latent variables for poverty, economy, and human resources. The problem encountered that the observed data is relatively small, specifically for 35 observations and the data distribution is suspected not fulfilled the normal assumptions. In conclusion, the appropriate analysis used in this study is Structural Equation Modeling-Partial Least Squares (SEM-PLS). The results showed that the economic latent variable had a positive but not significant effect on the latent variable of poverty, Human Resources also had a positive but not significant effect, while the latent health variable had a negative and significant effect on the latent variable of poverty. The Q2 value for the latent variable of poverty is 0.333, this shows that 33.3% of the diversity of the latent variable of poverty can be explained by the latent variables of economy, health, and human resources.

PENGELOMPOKAN PROVINSI BERDASARKAN CAPAIAN INDIKATOR KESEHATAN LINGKUNGAN DI INDONESIA TAHUN 2020 Maysarah Sabariah Kudadiri; Pika Silvianti; Farit Mochamad Afendi
Xplore: Journal of Statistics Vol. 11 No. 3 (2022): Vol. 11 No. 3 (2022)
Publisher : Department of Statistics, IPB

Environmental health is part of public health in general. If each province is associated with the achievement of environmental health indicators, the achievements will not be the same. The grouping of provinces will make it easier for the government to determine priorities for environmental health development in Indonesia. The grouping of provinces in this study used cluster analysis. The method used is the k-means because it has the smallest standard deviation ratio compared to other cluster analysis methods. The grouping results obtained are four clusters. The first cluster consists of one province that has the characteristics of high Percentage of Medical Waste (PMW) indicator achievement and the lowest percentage of villages with open defecation stops indicator achievement. The second cluster consists of six provinces that have the highest achievement of the SBS indicator and the lowest achievement of the PMW indicator. The third cluster consists of 20 provinces that have the characteristics of achieving high percentage of public places and facilities that are supervised indicators and the smallest achievement of PMW indicators. The fourth cluster consists of seven provinces that have the characteristics of high achievement of the percentage of drinking water facilities supervised/checked for drinking water quality and the lowest achievement of the PMW indicator.

IDENTIFIKASI FAKTOR-FAKTOR YANG MEMENGARUHI PRESTASI MAHASISWA PROGRAM SARJANA DI INSTITUT PERTANIAN BOGOR MENGGUNAKAN METODE CHAID Ragsa Endahas Ahmad; Akbar Rizki; Mohammad Masjkur
Xplore: Journal of Statistics Vol. 11 No. 2 (2022):
Publisher : Department of Statistics, IPB

IPB University (IPB) is one of the best universities in Indonesia, based on the Ministry of Education and Culture (Kemendikbud) clustering in 2020. As the best university, IPB requires efforts to improve the quality of its education. One of these efforts is to improve student achievement. This study aims to identify the factors that influence the competition and non-competition achievements of undergraduate students at IPB. The data used are achievement data (academic year 2016/2017 to 2020/2021) from the Directorate of Student Affairs and Career Development (Ditmawa) of IPB and demographic data of undergraduate level IPB students (entry year 2016/2017 to 2019/2020) from the Directorate of Administration and Education (Dit-Ap) IPB. The analytical method used in this study is the Chi-square Automatic Interaction Detection (CHAID) classification method. There was an imbalance of data on the Student Achievement response variable. Therefore, in this study, unbalanced data handling was also carried out by resampling in the form of oversampling, undersampling, and over-undersampling methods. The results showed that the classification using CHAID analysis with resampling in the form of oversampling with a balance accuracy of 73.7% resulted in the best classification performance. The factors that influence student achievement are 11 variables, and the 3 most influential variables are variables of year of admission, department, and last GPA.

Penerapan Metode CART pada Pengklasifikasian Bekerja dan Pengangguran di Kabupaten Subang Ilma Nabila; I Made Sumertajaya; Mulianto Raharjo
Xplore: Journal of Statistics Vol. 11 No. 2 (2022):
Publisher : Department of Statistics, IPB

Unemployment is a complex problem faced by developing countries, including Indonesia. The high unemployment rate in Indonesia impacts poverty, so that the government seeks to carry out economic development. Subang is one of the districts that contributed 8,68 percent of the open unemployment rate in 2019 and increased by 9,48 percent in 2020. The incessant growth of industrial estates and smart city program development in Subang is one of the efforts to reduce unemployment. This study used a classification and regression tree (CART) to determine the factors that influenced unemployment status in Subang Regency. The advantage of the CART method is easy to interpret the results of the analysis. However, the accuracy of the classification tree is relatively low due to data imbalance. Therefore, this study used SMOTE method to deal with this problem. The optimal classification tree was formed from 17 terminal nodes and 6 explanatory variables. 7 terminal nodes represent work as work, and 10 terminal nodes represent unemployment as unemployment. The 6 explanatory variables consist of marital status (X3), attending job training (X5), the position in the family (X4), the education level (X2), gender (X1), and age (X6).

Analisis Tingkat Kepuasan Pelanggan dan Loyalitas Pelanggan terhadap Cafe Infinity Coffee Muhammad Nuruddin Prathama; Muhammad Nur Aidi; Agus Mohamad Soleh
Xplore: Journal of Statistics Vol. 11 No. 2 (2022):
Publisher : Department of Statistics, IPB

Cafe and restaurant businesses are some of the most competitive businesses and have a sizeable market in Jakarta. In this case, the restaurant owner must know the wishes and preferences of the buyer. This research was conducted in one of the cafes in Jakarta "Infinity coffee", this study was conducted to identify consumer characteristics, customer satisfaction, and consumer loyalty. Applying customer satisfaction analysis in the Infinity coffee business can increase understanding of what Infinity coffee consumers want and improve the quality of Infinity coffee services based on researchâ€™s results. The analytical methods used in this study are descriptive analysis, Important Performance Analysis (IPA), and the Consumer Satisfaction Index (CSI) as well as correspondence analysis. The results of this study indicate that the entire Infinity coffee service satisfaction index for all aspects is above 80%, which means that the value is included in the satisfied category. However, the IPA scatter diagram shows that there are attributes with a high level of importance that need to be improved in terms of service quality. One of the most important attributes that become a priority for improvement is the attribute of completeness of supporting facilities and adequate cutlery. The Method that used was proven to be successful in examine level of consumer satisfaction also to know more about the characteristic of the consumer.

Perbandingan CART dan SMOTE CART dalam Mengklasifikasikan Kebutuhan KB Tidak Terpenuhi di Indonesia Ulfa Afilia Shofa; Muhammad Nur Aidi; Budi Susetyo
Xplore: Journal of Statistics Vol. 11 No. 2 (2022):
Publisher : Department of Statistics, IPB

Indonesia is ranked fourth in the world as the country with the largest population. The high population growth in Indonesia can cause problems in several fields. The government seeks to suppress the rate of population growth through the Family Planning (KB) program. In Indonesia, the number of unmet needs for family planning is still relatively high and has not yet reached the BKKBN target. Therefore, it is necessary to identify the characteristics of unmet need for family planning among married women or living with partner. This study used the Classification and Regression Trees (CART) method. This study handling unbalanced data by Synthetic Minority Oversampling Technique (SMOTE). This study aims to compare the performance of the CART and SMOTE CART classification methods in classifying unmet need for family planning and to identify the characteristics of unmet need for family planning among married women or living with partner in Indonesia. The SMOTE CART model has better performance than the CART model, with the percentages of balanced accuracy, sensitivity, and specificity being respectively 54.83%, 34.96%, and 74.70%. In general, the characteristics of unmet need for family planning among married women or living with partner in Indonesia are having 1-4 living children, not getting information from mass media, not accessing the internet in the last month, having a primary or secondary education level, a husband with no education or with a primary or secondary education level, and aged more than 30 years old.Â Â Â Keywords: CART, SMOTE CART, unmet need for family planning

Perbandingan Perbandingan Pengklasifikasian Metode Support Vector Machine dan Random Forest (Kasus Perusahaan Kebun Kelapa Sawit) Nabila Destyana Achmad; Agus M Soleh; Akbar Rizki
Xplore: Journal of Statistics Vol. 11 No. 2 (2022):
Publisher : Department of Statistics, IPB

Palm oil is one of the leading commodities that support the economy in Indonesia. One of the companies engaged in the oil palm plantation sector has 146 units of oil palm plantations. It is very important to optimize oil palm production, so it is necessary to classify the status of plantation units. Classification aims to predict new plantation units and find the most important variables in the modeling process. The variables used were the status of the garden as a response variable and nine explanatory variables, namely harvested area, rainfall, percentage of normal fruit, fresh fruit bunches production, oil palm loose fruits, production, harvest job performance, harvesting rotation, and farmers. The classification process is carried out using the Support Vector Machine and Random Forest methods to find which method is the best. The data is divided into 80% training data and 20% test data with ten iterations so that ten models are produced for each method. Comparing accuracy value, F1 score, and Area Under Curve (AUC) to evaluate the model. The modeling results show that the random forest method has better performance than the SVM method. The random forest has an average occuracy, F1 score, and AUC, respectively, 90%, 86%, and 89%. Variables of harvest job performance, oil palm loose fruits, harvested area, rainfall, and harvesting rotation are important variables that contribute more than 10% of the model. The results of the research are used for the evaluation and development process of oil palm companies by taking into account the result of important variables that affect productivity and predictive results of new plantation units.

Kajian Metode Pohon Model Logistik (Logistic Model Tree) dengan Penanganan Ketakseimbangan Data Akmala Firdausi; Aam Alamudi; Kusman Sadik
Xplore: Journal of Statistics Vol. 11 No. 2 (2022):
Publisher : Department of Statistics, IPB

Logistic model tree is a nonparametric modelling method that combines decision tree with linear logistic regression. Logistic model tree handles multicollinearity well, but is not immune to problems that arise due to data imbalance. This study was carried to compare the performance of undersampling, SMOTE, and ROSE in handling imbalanced data when used in tandem with logistic model tree. The data used in the simulation was obtained by generating random numbers following the Bernoulli distribution as the response variable and the Bivariate Normal distribution as the explanatory variables, based on five different imbalance levels. Comparisons done on the AUC value showed that logistic model trees built with methods to handle imbalanced data performed better than logistic model trees built without applying any such method on every level of tested data imbalance in classifying objects. Among those, logistic model trees built with ROSE performed better than logistic model trees built with other methods. On datasets with low level of imbalance, the performance of logistic model trees built with ROSE and undersampling do not significantly differ.

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Home Page

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Contact Name Akbar Rizki

Contact Email akbar.ritzki@apps.ipb.ac.id

Phone +628111144470

Journal Mail Official akbar.ritzki@apps.ipb.ac.id

Editorial Address Departemen Statistika, IPB Jl. Meranti Kampus IPB Darmaga Wing 22, Level 4 Bogor 16680

Location Kota bogor, Jawa barat INDONESIA

Abstract

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Contact Name
Akbar Rizki

Contact Email
akbar.ritzki@apps.ipb.ac.id

Phone
+628111144470

Journal Mail Official
akbar.ritzki@apps.ipb.ac.id

Editorial Address
Departemen Statistika, IPB Jl. Meranti Kampus IPB Darmaga Wing 22, Level 4 Bogor 16680

Location
Kota bogor,

Jawa barat

INDONESIA