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All Journal IPTEK The Journal for Technology and Science Techno.Com: Jurnal Teknologi Informasi BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Statistika dan Aplikasinya Journal of Multidisciplinary Applied Natural Science Makara Journal of Science Javanologi: International Journal of Javanese Studies STATISTIKA Society
Yekti Widyaningsih
Universitas Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Analisis Faktor-Faktor yang Menjelaskan Tingkat Kematian Akibat Bunuh Diri pada Negara-Negara di Benua Asia dan Eropa Yekti Widyaningsih; Setjiadi, Nerissa Netanaya
Jurnal Statistika dan Aplikasinya Vol. 7 No. 2 (2023): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07207

Abstract

Many Indonesians still view mental health as a taboo subject and people with mental disorders are treated like a disgrace. As a result, they have difficulty getting the help they need and can end in suicide. The objects of research are countries in Asia and Europe. The purposes of this research are to analyze factors explaining death rate due to suicide and to work out the grouping results of Asian and European countries. The methods used are multiple linear regression, Ward’s method clustering, and Biplot mapping. Based on the analysis result, it is obtained that factors of having no religion, alcohol consumption, and psychiatrists’ availability have significant positive relationships with suicide rate. Factors of income and unemployment have significant negative relationships with suicide rate. Factor of education level has no significant effect with suicide rate. Two groups of countries are formed, namely group 1 consisting of 46 countries and group 2 consisting of 44 countries. Result of mapping based on the groups using the Biplot method is able explain 63,7% of data diversity. Group 1 is a group of countries that have a high unemployment rate and low values in: suicide rate, proportion of irreligious people, Gross Domestic Product (GDP) per capita, number of psychiatrists, and education level. Group 2 is a group of countries that have high values in: suicide rate, proportion of irreligious people, GDP per capita, number of psychiatrists, and education level while the unemployment rate is low.
Relationship Pattern of Fatherless Impacts to Internet Addiction, Suicidal Tendencies and Learning Difficulties for Students at SMAN ABC Jakarta: Relationship Pattern of Fatherless Impacts to Internet Addiction, Suicidal Tendencies and Learning Difficulties for Students at SMAN ABC Jakarta Bunga Maharani Yasmin Wibiharto; Rianti Setiadi; Yekti Widyaningsih
Society Vol 9 No 1 (2021): Society
Publisher : Laboratorium Rekayasa Sosial, Jurusan Sosiologi, FISIP Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/society.v9i1.275

Abstract

Fatherless is the absence of a father figure. Some impacts of fatherlessness are loneliness, openness, depression, self-control, and self-esteem. These factors can influence internet addiction and suicidal tendencies. It also can cause difficulty in the learning process for students. This study aims to determine the significant impacts caused by fatherlessness and the relation to internet addiction, suicidal tendencies, and learning difficulties. The method used is Partial Least Square. The results showed that the significant impacts caused by fatherlessness are loneliness, depression, and self-esteem. The impacts of fatherless that influence internet addiction are loneliness and depression. The impact of fatherlessness that influences suicidal tendencies is depression. Internet addiction and suicidal tendencies influence learning difficulties.
ANALYSIS OF BICLUSTERING ITERATIVE SIGNATURE ALGORITHM ON POVERTY DATA IN SULAWESI ISLAND IN 2022 Yekti Widyaningsih; Safitri, Nabila
Jurnal Statistika dan Aplikasinya Vol. 8 No. 2 (2024): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.08206

Abstract

Poverty in Indonesia is still a problem that must be addressed every year. According to the March 2022 Susenas report, Sulawesi Island ranks third among the six major islands in Indonesia in terms of the percentage of the population living in poverty. This shows that there are still many people living in poverty in Sulawesi. Therefore, the government needs to make the right policies to address this problem. One potential approach is to cluster districts or cities in Sulawesi based on poverty-related variables. The objective of this research is to group the data in two directions: first, by districts or cities and, second, by its variables simultaneously. The formation of these groupings will facilitate the development of the right government policies to address poverty. The appropriate method for these groupings is the biclustering method, which can group observations and characteristics simultaneously so that biclusters formed can be characterized differently. One of the biclustering algorithms is the Iterative Signature Algorithm (ISA), which requires an upper threshold value and a lower threshold value. The threshold value is the value used to determine whether a district or city and variables can be included in a bicluster. The best result is selected based on the average Mean Square Residue (MSR) per volume. Biclustering analysis of poverty data in Sulawesi in 2022 using ISA produced 2 biclusters. Based on these results, the government is expected to make the right policy to overcome poverty problems in Bicluster 1 and Bicluster 2.
Analisis Performa Deep Embedded Clustering untuk Pendeteksian Topik Cahyadi, Danu Julian; Murfi, Hendri; Satria, Yudi; Abdullah, Sarini; Widyaningsih, Yekti
Techno.Com Vol. 24 No. 1 (2025): Februari 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i1.11841

Abstract

Pendeteksian topik adalah solusi untuk mengungkap struktur laten dalam sebuah dokumen. Kerangka umum pendeteksian topik berbasis clustering terdiri dari dua langkah: pembelajaran representasi dan pendeteksian topik melalui clustering. Dalam penelitian ini, Bidirectional Encoder Representations from Transformers (BERT) digunakan untuk pembelajaran representasi karena BERT mampu menangkap konteks setiap kata berdasarkan kata-kata di sekitarnya. Representasi teks yang diperoleh dari BERT digunakan untuk pendeteksian topik dengan clustering. Deep Embedded Clustering (DEC) dan Improved DEC (IDEC) adalah model clustering berbasis deep learning yang digunakan dalam penelitian ini untuk pendeteksian topik. DEC dan IDEC mampu mengubah data ke dalam ruang dimensi yang lebih rendah serta mengoptimalkan cluster secara simultan. Output dari teknik clustering berupa kata-kata kunci yang menggambarkan setiap topik cluster. Setelah mendapat kata kunci yang mewakili topik, evaluasi model dilakukan dengan melakukan perbandingan nilai topic coherence menggunakan Topic Coherence - Word2Vec (TC-W2V) sebagai analisis kuantitatif. Penelitian ini merupakan perluasan dari penerapan DEC dan IDEC pada pendeteksian topik dengan menambahkan analisis visualisasi dan kata kunci. Simulasi menunjukkan bahwa DEC dan IDEC mengungguli Uniform Manifold  Approximation and Projection (UMAP)-based k-means (UKM) dan Eigenspace-Based Fuzzy C-Means (EFCM) dari segi nilai TC-W2V, hasil visualisasi, dan kata kunci.   Kata kunci: analisis teks, deep clustering, pemrosesan teks
Performance of Ridge Regression, Least Absolute Shrinkage and Selection Operator, and Elastic Net in Overcoming Multicollinearity Saputro, Dewi Retno Sari; Wahyu, Nugroho Lambang; Widyaningsih, Yekti
Journal of Multidisciplinary Applied Natural Science Vol. 5 No. 2 (2025): Journal of Multidisciplinary Applied Natural Science
Publisher : Pandawa Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47352/jmans.2774-3047.251

Abstract

Multicollinearity is a violation of assumptions in multiple linear regression analysis that can occur if there is a high correlation between the independent variables. Likewise, the variants of multiple linear regression models such as the Geographically Weighted Regression model (GWR). Multicollinearity causes parameter estimation using the Quadratic Method (QM) unstable and produces a large variance. On the other hand, what is expected in the estimation parameters is an estimate with a minimum variance, even though it is biased. Thus, one way to overcome multicollinearity can be to use biased estimators, such as Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (LASSO), and Elastic Net (EN). In RR, the Least Square Method (LSM) coefficient is reduced to zero but it can’t select the independent variable. However, the parameter model obtained from the Ridge Regression is biased, and the variance of the resulting regression coefficients is relatively tiny. In addition, the RR is increasingly difficult to understand if a huge number of independent variables are used. Meanwhile, LASSO is a computational method that uses quadratic programming and can act out the RR principles and perform variable selection. The LASSO method became known after discovering the Least-Angle Regression (LARS) algorithm. The LASSO method can reduce the LSM coefficient to zero to perform variable selection. LASSO also has a weakness, so EN is used. In this article, the performance of the three methods is compared from the mathematical aspect. The performance of each is written as follows, RR is helpful for clustering effects, where collinear features can be selected together; LASSO is proper for feature selection when the dataset has features with poor predictive power and EN combines LASSO and RR, which has the potential to lead to simple and predictive models.
COMPARISON BETWEEN BICLUSTERING AND CLUSTER-BIPLOT RESULTS OF REGENCIES/CITIES IN JAVA BASED ON PEOPLE’S WELFARE INDICATORS Widyaningsih, Yekti; Nisa, Alfia Choirun
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1009-1022

Abstract

The success of a country's development can be known from the well-being of its people. Improving the welfare of the population is the main goal of the development activities carried out by the government. To ensure that development is effective and targeted, grouping is needed to understand the characteristics of the region. This study discusses the grouping of regencies/cities in Java Island based on the people's welfare indicators in 2022. The measured welfare is material well-being. Variables used in this study are the percentage of the poor population, GDP per capita at current prices, average length of schooling, expected length of schooling, percentage of per capita expenditure on food, open unemployment rate, population, population density, and life expectancy. There are two approaches used in grouping regencies/cities along with their variables. The first approach is to simultaneously group regencies/cities and their variables using Plaid Model biclustering. The second approach is to group regencies/cities using the Ward clustering method followed by the biplot method. This study aims to compare the results of these two approaches, namely the biclustering and cluster-biplot results, on data from 119 regencies/cities in Java Island in 2022 based on people's welfare indicators. Based on the results of this study, the number of groups from each approach is 2, with group 1 being more prosperous than group 2. Judging from the standard deviation values, the Plaid Model biclustering result groups have lower standard deviation values than the cluster-biplot result groups. Therefore, in general the first approach produces better groups as they are more homogeneous than the second approach.
Spatial Clustering Analysis of Hand, Foot, and Mouth Disease in Jakarta using Local Indicator of Spatial Association Cluster Map and K-Means Clustering Sabila, Fatsa Vidyaningtyas; Widyaningsih, Yekti
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 3 (2025): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i3.30339

Abstract

Hand, Foot, and Mouth Disease (HFMD) is a infectious disease characterized by ulcers and blisters, primarily affecting children. The objective of this quantitative study is to identify areas with the highest HFMD cases (hotspot areas) in Jakarta in 2024 and to classify areas (districts) based on the number of HFMD cases and variables associated with the disease. The analysis employs the Local Indicator of Spatial Association (LISA) Cluster Map to detect spatial hotspots and K-Means Clustering to group districts by HFMD cases and related variables. LISA is a univariate method for detecting hotspots based on the local Moran’s Index that measures spatial dependence, whereas K-Means Clustering is a multivariate method for grouping individuals based on multiple variables. This study uses data from official government sources, including the number of HFMD cases, population density, average number of students per kindergarten, and average number of students per elementary school. The results of this study show that the LISA clustering reveals Kalideres and Cengkareng as High-High (H-H) clusters, while Tanah Abang, Menteng, and Senen form Low-Low (L-L) clusters. Makasar is classified as a Low-High (L-H) cluster. In contrast, the K-Means clustering groups districts into four clusters based on HFMD cases and related demographic factors, sorted in ascending order of HFMD cases. Areas with the lowest HFMD cases tend to have a moderate population density and fewer average number of students per kindergarten, while areas with the highest cases tend to have a lower population density but a higher average number of students per kindergarten. Areas classified as high cases HFMD by both methods, such as Cengkareng, should be prioritized for intervention. Cengkareng represents a district with the highest HFMD cases despite having a relatively low population density, along with a high average number of students per kindergarten and per elementary school. 
IDENTIFICATION OF LOCATION ALLOWANCE ZONE FOR BANK SYARIAH "X" OUTLETS USING ORDINAL LOGISTIC REGRESSION Aldinda Albanna; Yekti Widyaningsih
Jurnal Statistika dan Aplikasinya Vol. 9 No. 1 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09110

Abstract

Companies need good human resources to achieve their goals, one of which is by providing rewards, such as location allowances. Bank Syariah "X" is one of the institutions that provides location allowance, which is an allowance based on the employee's work location. This policy was last established in 2021, therefore adjustments are needed. This study aims to analyze the factors that explain the determination of location allowance zoning and predict the zoning of new outlet location allowances. Location allowance zoning is determined based on the factors of cost, remoteness, and location access. Factors that are thought to represent these three factors and influence the determination of location allowance zoning are the consumer price index (CPI), human development index (HDI), construction cost index (CCI), infrastructure pillar index (IPI), outlet distance to the nearest health center (ODHC), and outlet distance to the nearest primary school (ODPS). The location allowance zoning consists of three categories with an ordered nature. Based on the research objectives and the type of dependent variable, the method used was ordinal logistic regression. This research produces factors that explain the determination of location allowance zoning, namely CCI, IPI, and ODHC with 70% accuracy and balanced accuracy for Zone 1, Zone 2, and Zone 3 & 4, respectively 81.2%, 70.8%, and 76.7%. Based on the initial policy data of Bank Syariah "X", the model misclassified 35.6% of outlets.
Analisis Variabel-Variabel yang Menjelaskan Tingkat Prokrastinasi Akademik pada Mahasiswa FMIPA Universitas XYZ Widyaningsih, Yekti; Rahmawati, Aprilia; Soemartojo, Saskya Mary
Statistika Vol. 24 No. 1 (2024): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v24i1.3054

Abstract

ABSTRAK Mahasiswa diharapkan untuk menempuh pendidikan sarjananya dengan baik dan selesai dalam tepat waktu. Sebagai mahasiswa mempunyai aktivitas cukup banyak di luar rutinitas kuliah sudah menjadi hal yang lazim. Dengan banyaknya rutinitas, mahasiswa seringkali menunda belajar atau menyelesaikan tugas yang diberikan oleh dosennya inilah yang disebut dengan prokrastinasi akademik. Prokrastinasi akademik pada mahasiswa dapat berdampak pada penurunan prestasi akademiknya. Tujuan penelitian ini adalah mengetahui variabel-variabel yang menjelaskan tingkat prokrastinasi akademik pada mahasiswa, mengetahui profil mahasiswa yang mempunyai tingkat prokrastinasi akademik yang tinggi, dan mengetahui perbedaan antara kedua metode yang digunakan berdasarkan urutan variabel-variabel yang signifikan menjelaskan tingkat prokrastinasi akademik pada mahasiswa. Variabel yang diduga menjelaskan tingkat prokrastinasi akademik adalah jenis kelamin, tempat tinggal, kondisi fisik, kondisi psikologis, kondisi lingkungan, motivasi belajar, persepsi mahasiswa, dukungan sosial orang tua, dan dukungan sosial teman sebaya. Penelitian ini menggunakan metode Analisis Regresi Linier Berganda dan Classification and Regression Tree (CRT). Penelitian ini memanfaatkan data primer yaitu 660 mahasiswa FMIPA Universitas XYZ yang dipilih melalui metode purposive sampling. Hasil penelitian menyimpulkan bahwa variabel-variabel yang secara signifikan menjelaskan tingkat prokrastinasi akademik mahasiswa FMIPA Universitas XYZ adalah jenis kelamin, kondisi fisik, kondisi psikologis, motivasi belajar, persepsi mahasiswa, dukungan sosial orang tua, dan dukungan sosial teman sebaya. Profil mahasiswa yang memiliki tingkat prokrastinasi akademik yang tinggi yaitu mahasiswa dengan kondisi fisik dan kondisi psikologis yang buruk, serta dukungan sosial orang tua yang rendah. Dan juga adanya perbedaan urutan variabel-variabel yang signifikan antara metode Regresi Linier Berganda dan CRT, tetapi variabel kondisi fisik berada pada urutan pertama kedua metode tersebut. ABSTRACT Students are expected to be able to undertake their undergraduate studies satisfactorily and graduate as scheduled.  As a student, it is normal having with numerous activities outside academic routine. Consequently, students often delay studying and completing the tasks given by their lecturers. This is called academic procrastination. Academic procrastination may lead to a declining academic achievement. This study aimed to determine variables that affect academic procrastination levels, to find out the profile of students with high levels of academic procrastination, and to the difference between the two methods on the sequence of significant variables explains the level of academic procrastination of students. The variables considered to affect the level of academic procrastination include gender, living place, physical conditions, psychological conditions, environmental conditions, learning motivation, student perception, parental social support, and peer social support. The methods used are Multiple Linear Regression and Classification and Regression Tree (CRT). This study used primary data, namely 660 FMIPA students of University of XYZ obtained through purposive sampling.  The results showed that the variables that significantly affect the level of academic procrastination of FMIPA students of University of XYZ include gender, physical conditions, psychological conditions, learning motivation, student perception, parental support, and peer support. Students who demonstrate a high level of academic procrastination are characterized by poor physical and psychological conditions, as well as low parental support. In addition, there is a significant difference in the sequence of variables between the Multiple Linear Regression method and CRT, but both have one thing in common, that is, the highest variable is physical condition.
APLIKASI K-FOLD CROSS VALIDATION DALAM PENENTUAN MODEL REGRESI BINOMIAL NEGATIF TERBAIK Widyaningsih, Yekti; Arum, Graceilla Puspita; Prawira, Kevin
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 15 No 2 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (424.732 KB) | DOI: 10.30598/barekengvol15iss2pp315-322

Abstract

Publikasi ilmiah merupakan salah satu indikator penilaian terhadap kualitas akademisi. Tetapi tidak dapat dipungkiri pembuatan publikasi ilmiah bukanlah suatu hal yang mudah, karena membutuhkan proses pembuatan dan proses penelaahan yang rumit. Tujuan dari penelitian ini adalah untuk mengetahui faktor-faktor yang memengaruhi banyaknya publikasi ilmiah yang dihasilkan oleh mahasiswa PhD Biokimia tahun 1997. Karena variabel dependen merupakan count data, metode analisis yang digunakan adalah Regresi Poisson. Namun karena data mengalami overdispersi, akan digunakan Regresi Binomial Negatif. Perbandingan beberapa model Regresi Poisson dan Binomial Negatif dilakukan untuk menentukan model terbaik dengan k-fold cross validation sebagai validasi model. Hasil penelitian menunjukkan bahwa model terbaik yang didapatkan adalah model Regresi Binomial Negatif dengan variabel independen jenis kelamin, status pernikahan, banyaknya anak dibawah 5 tahun, prestise, dan banyaknya artikel oleh mentor dalam 3 tahun terakhir.
Co-Authors Abdullah, Sarini Abraham Raja Swara Darwanto Agustin, Natania Aji H. Wigena Aldinda Albanna Anis Dela Desela Antoni Wibowo Arum, Graceilla Puspita ASEP SAEFUDDIN Budiawan, Zalfa Alifah Bunga Maharani Yasmin Wibiharto Cahyadi, Danu Julian Curie Nabilah Nasution Dewi Retno Sari Saputro, Dewi Retno Dian Lestari Geraldine Immanuel Tangyong Hendri Murfi Ivana, Rugun Khairil Anwar Notodiputro Melia Rizki Fitrianingrum Mohammad Zahran Pratomo Natasha Latifatu Soliha Nisa, Alfia Choirun Pin, Tjiong Giok Prawira, Kevin Rahmawati, Aprilia Rianti Setiadi Rianti Setiadi Sabila, Fatsa Vidyaningtyas SAFITRI, NABILA Setiadi, Rianti Setjiadi, Nerissa Netanaya Silvia Soemartojo, Saskya Mary Taza Luzia Viarindita Tjiong Giok Pin Wahyu, Nugroho Lambang Wibiharto, Bunga Maharani Yasmin Yudi Satria