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All Journal FORUM STATISTIKA DAN KOMPUTASI Jurnal Sains dan Seni ITS Gudang Jurnal Multidisiplin Ilmu Triwikrama: Jurnal Ilmu Sosial Kohesi: Jurnal Sains dan Teknologi Mutiara : Jurnal Ilmiah Multidisiplin Indonesia Perspektif: Jurnal Pendidikan dan Ilmu Bahasa Jurnal Penelitian Teknologi Informasi dan Sains Neraca Manajemen, Akuntansi, dan Ekonomi Cendikia Pendidikan Ebisnis Manajemen Trigonometri: Jurnal Matematika dan Ilmu Pengetahuan Alam ARIMA : Jurnal Sosial Dan Humaniora Jurnal Pendidikan dan Pengajaran Zoologi: Jurnal Ilmu Peternakan, Ilmu Perikanan, Ilmu Kedokteran Hewan Switch: Jurnal Sains dan Teknologi Informasi Pentagon: Jurnal Matematika dan Ilmu Pengetahuan Alam Bilangan: Jurnal Ilmiah Matematika, Kebumian dan Angkasa Algoritma: Jurnal Matematika, Ilmu Pengetahuan Alam, Kebumian dan Angkasa Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika Jurnal Pustaka Cendekia Pendidikan IPSSJ
Sri Pingit Wulandari
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ANALISIS FAKTOR-FAKTOR KRIMINALITAS DI PROVINSI JAWA TIMUR TAHUN 2023 MENGGUNAKAN ANALISIS FAKTOR Arindah Maharani Saputri; Insania Firdausy; Sri Pingit Wulandari
Triwikrama: Jurnal Ilmu Sosial Vol. 5 No. 6 (2024): Triwikrama: Jurnal Ilmu Sosial
Publisher : Cahaya Ilmu Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.6578/triwikrama.v5i6.7048

Abstract

Kriminalitas adalah tindakan kejahatan melanggar hukum dan mengganggu keseimbangan sosial dalam masyarakat yang dapat dipengaruh oleh berbagai faktor, seperti demografi, sosial ekonomi, dan pendidikan. Kriminalitas dapat terjadi di manapun dan kapanpun, termasuk di Provinsi Jawa Timur. Banyakya jumlah penduduk di Jawa Timur dengan kepadatan penduduk mencapai 857 jiwa/km2 serta beberapa faktor lain menjadi pendorong sebagian masyarakat melakukan tindakan kejahatan demi kesejahteraan hidup. Berdasarkan data BPS, Jawa Timur merupakan provinsi di Indonesia yang paling banyak terjadi kriminalitas di tahun 2022. Berdasarkan kondisi yang ada, mengindikasikan bahwa terdapat banyak faktor yang memengaruhi seseorang melakukan tindakan kriminalitas. Luasnya cakupan faktor-faktor tindakan kriminalitas membuat perlu dilakukan reduksi data sehingga diperoleh komponen utama yang dapa tmenjelaskan sebagian besar variasi data. Data yang digunakan merupakan data sekunder yang diperoleh melalui laman resmi BPS di mana data akan diolah dan dianalisis menggunakan software SPSS. Metode yang diterapkan dalam penelitian ini yaitu analisis faktor. Sebelum itu, dilakukan analisis karakteristik dan uji asumsi yang terdiri dari uji asumsi distribusi normal multivariat, pemeriksaan nilai KMO, uji independensi, dan anti-image-correlation. Hasil analisis uji asumsi menunjukkan bahwa data berdistribusi normal multivariat, data cukup untuk difaktorkan, korelasi antar variabel dependen, serta terdapat 9 variabel yang dapat diprediksi dan dianalisis lebih lanjut. Sedangkan, hasil analisis faktor terbentuk 2 faktor yang dapat menjelaskan variabel asal yaitu faktor ketimpangan sosial ekonomi serta ketenagakerjaan dan produktivitas.
STATUS PEKERJAAN UTAMA MENURUT PENDIDIKAN TERTINGGI YANG DITAMATKAN DI JAWA TIMUR TAHUN 2023 MENGGUNAKAN ANALISIS KORESPONDENSI Anindya Putri Noliza; Hikmah Deviani; Sri Pingit Wulandari
Triwikrama: Jurnal Ilmu Sosial Vol. 5 No. 6 (2024): Triwikrama: Jurnal Ilmu Sosial
Publisher : Cahaya Ilmu Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.6578/triwikrama.v5i6.7075

Abstract

Indonesia merupakan negara yang memiliki penduduk terbanyak ke-4 di dunia dengan total penduduk mencapai 279 juta jiwa. Disamping itu, pembahasan mengenai topik sektor industri sangat marak pada saat ini. Banyak faktor yang mempengaruhi kemajuan industri sangat banyak, salah satunya adalah ketenagakerjaan. Status pekerjaan utama juga dipengaruhi oleh tingkat pendidikan tertinggi yang ditamatkan. Total tenaga kerja berdasarka provinsi di Indonesia yang paling tinggi adalah provinsi Jawa Barat dan disusul oleh provinnsi Jawa Timur di urutan kedua. Analisis yang tepat untuk menganalisis hubungan antara status pekerjaan utama dengan pendidikan tertinggi yang ditamatkan di provinsi Jawa Timur adalah analisis korespondensi. Kesimpulan dari ppenelitian ini adalah mayoritas penduduk Jawa Timur memiliki pekerjaan utama sebagai buruh/karyawan/pegawai dengan pendidikan tertinggi SMP, terdapat hubungan antara status pekerjaan utama dengan pendidikan tertinggi yang ditamatkan di Jawa Timur tahun 2023, kecenderungan penduduk dengan pendidikan tertinggi SMP dengan status pekerjaan utama sebagai pekerja keluarga/tidak dibayar, berusaha sendiri, pekerja bebas, dan berusaha dibantu buruh tidak tetap/buruh tidak dibayar. Serta hasil dari analisis korespondensi diperoleh 3 dimensi maka keragaman data yang dapat dijelaskan dan dipetakan adalah sebesar 100%.
ANALISIS CLUSTER PROVINSI DI INDONESIA BERDASARKAN FAKTOR POTENSI EKONOMI LAUT TAHUN 2022 Vira Angelina; Elsa Amelia Nur Arimba; Sri Pingit Wulandari
Kohesi: Jurnal Sains dan Teknologi Vol. 5 No. 3 (2024): Kohesi: Jurnal Sains dan Teknologi
Publisher : CV SWA Anugerah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.3785/kohesi.v5i3.6975

Abstract

Pertumbuhan ekonomi secara global maupun nasional mengalami penurunan pada pasca pandemi Covid-19. Kondisi tersebut membuat Indonesia harus segara mencari solusi alternatif untuk memulihkan perekonomian. Salah satu alternatif dengan potensi yang besar adalah ekonomi biru. Hal ini karena Indonesia memiliki wilayah lautan yang luas. Ekonomi biru merupakan pemanfaatan sumber daya hayati secara berkelanjutan pada industri kelautan dan maritim. Penerapan konsep ini dapat memberikan dampak positif untuk pertumbuhan ekonomi jangka Panjang. Indonesia telah melakukan beberapa upaya dalam sistem ekonomi biru. Hasilnya, Indonesia berhasil mengekspor hasil perikanan sebesar USD 6,2 miliar pada tahun 2022. Namun, kontribusi tersebut hanya menyumbangkan 2,58% terhadap PDB nasional. Hal tersebut disebabkan oleh perbedaan potensi ekonomi kelautan di setiap provinsi yang ada di Indonesia. Berdasarkan karakteristiknya, pemerintah dapat mengelompokkan provinsi-provinsi tertentu untuk memudahkan dalam meningkatkan ekonomi biru secara merata. Salah satu metodenya adalah analisis cluster. Analisis clustermerupakan metode untuk mengelompokkan objek-objek berdasarkan karakteristik yang serupa. Pada penelitian ini, dilakukan analisis cluster pada provinsi di Indonesia berdasarkan faktor potensi ekonomi laut tahun 2022. Faktor-faktor tersebut terdiri dari luas lahan budidaya perikanan, produksi budidaya perikanan, persentase kontribusi perikanan terhadap PDRB, dan volume ekspor hasil perikanan. Berdasarkan hasil analisis, karakteristik luas lahan, produksi budidaya, volume ekspor hasil perikanan sangat beragam antara satu provinsi dengan provinsi yang lain. Hasil cluster hierarki menggunakan single lingkage diperoleh jumlah cluster optimum sebesar 5, sedangkan hasil cluster non hierarki menggunakan K-means diperoleh jumlah cluster optimum sebesar 3.
Pengelompokkan Kabupaten/Kota di Provinsi Jawa Timur berdasarkan Indikator Ketenagakerjaan Naufalia Alfi Fuadah; Zahra Afifah Nafisah; Sri Pingit Wulandari
Ebisnis Manajemen Vol. 2 No. 4 (2024): Ebisnis Manajemen
Publisher : Fakultas Ekonomi & Bisnis, Universitas Nusa Nipa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59603/ebisman.v2i4.589

Abstract

Labor is one of the drivers of the regional economy because it can increase productivity and community welfare. Employment indicators are often used to assess the condition and dynamics of the labor market in a region, one of which is in East Java Province. To measure the condition and dynamics of the workforce in East Java Province, a grouping of districts/cities in East Java Province was carried out using the cluster analysis method. This method is used to group objects or data into groups that have similar characteristics in the observed data. This method is used by analyzing data starting from data collection, testing cluster analysis assumptions, determining methods and conducting hierarchical and non-hierarchical cluster analysis, interpreting the results of the analysis and drawing conclusions and suggestions. So the analysis that can be obtained from this study is the best hierarchical cluster method for classifying districts/cities in East Java Province is the complete linkage method with an optimum number of clusters of 4 clusters while for the non-hierarchical cluster method with the K-Means method with the best clusters of 4 clusters while for the non-hierarchical cluster method with the K-Means method with the best clusters of 3 clusters.
Pengelompokan Indikator Kemiskinan di Kabupaten/Kota Aceh Tahun 2021 Menggunakan Analisis Klaster Naomi Gloria Pasaribu; Famita Wibi Wulandari; Sri Pingit Wulandari
Bilangan : Jurnal Ilmiah Matematika, Kebumian dan Angkasa Vol. 2 No. 6 (2024): Desember : Jurnal Ilmiah Matematika, Kebumian dan Angkasa
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/bilangan.v2i6.306

Abstract

Poverty in Aceh Province is a significant challenge with variation between districts/cities due to differences in access to education, health, job opportunities, and infrastructure. This study aims to group districts/cities in Aceh based on poverty indicators in 2021 in order to produce a more targeted policy basis. The research data consists of 23 poverty indicators obtained from secondary sources. Cluster analysis is applied using hierarchical (average linkage) and non-hierarchical (K-Means) methods to identify poverty patterns between regions. The results of the hierarchical cluster show that there are two main groups, namely the first cluster has low poverty rates, higher education, strong purchasing power, and low unemployment, while the second cluster has the opposite characteristics. The non-hierarchical analysis (K-Means) produced five clusters with significant differences in poverty levels, labor force participation, education, and economy. These findings provide a basis for the Aceh government to design poverty alleviation policies that focus on the specific needs of each cluster to accelerate the improvement of welfare in all districts/cities in Aceh Province.
Analisis Diskriminan pada Indikator yang Memengaruhi Indeks Kerentanan Pangan Menurut Provinsi di Indonesia Tahun 2023 Melita Handayani; Natasya Liana Putri; Sri Pingit Wulandari
Bilangan : Jurnal Ilmiah Matematika, Kebumian dan Angkasa Vol. 2 No. 6 (2024): Desember : Jurnal Ilmiah Matematika, Kebumian dan Angkasa
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/bilangan.v2i6.319

Abstract

Indonesia is committed to achieving zero hunger as one of the goals of fulfilling the Sustainable Development Goals (SDGs) where this commitment focuses on addressing the problem of food availability but also ensuring that every individual has access to sufficient, nutritious, and safe food throughout the year for everyone. However, reviewing the current conditions in Indonesia, there is still an imbalance in food availability that will cause food vulnerability. Therefore, a prediction of food vulnerability in the future is needed where discriminant analysis is one of the appropriate statistical methods to analyze qualitative dependent and quantitative independent variables. This study uses secondary data from the official website of the food agency and the central statistics agency. The results of the study show that the characteristics of the data have small variations, asymmetric distribution, and there are outliers in several categories. The assumptions of multivariate normality, the suitability of the dependent variables, and the identity of the variance-covariance matrix have been met. Through discriminant analysis, the variables of the percentage of poverty and the percentage of households with access to clean drinking water are proven to significantly affect the IKP category. The discriminant model produces one significant function that is able to group the IKP category with a model accuracy rate of 86.8% and a classification accuracy of 64.7%.
ANALISIS PENGARUH JENIS NYERI DADA TERHADAP RATA-RATA TEKANAN DARAH DAN USIA MENGGUNAKAN MANOVA Helvy Tiana Rosa Nabila; Annisa’ Riskika Hayyu; Sri Pingit Wulandari
Jurnal Pendidikan dan Pengajaran (JUPEJA) Vol. 2 No. 2 (2024): November
Publisher : Merwin Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69820/jupeja.v2i2.196

Abstract

This study aims to analyze the effect of chest pain type on mean blood pressure and age using the Multivariate Analysis of Variance (MANOVA) method. Chest pain is a common symptom that can indicate various health conditions, including heart disease. The data used in this study was taken from the "Heart Failure Prediction ” dataset downloaded from Kaggle, which included variables such as chest pain type, blood pressure, and patient age. MANOVA analysis was performed to test whether there were significant differences in blood pressure and age based on the type of chest pain (Typical Angina, Atypical Angina, Non-Anginal Pain, and Asymptomatic). MANOVA test results showed that at least one type of chest pain significantly affected blood pressure and age. Multiple comparison tests were conducted to identify differences between chest pain groups. This study concluded that chest pain type significantly affected blood pressure, but no significant difference was found in age based on chest pain type. These findings may help in the diagnosis and management of patients with chest pain, and provide important information for the prevention of cardiovascular complications.
Analisis Faktor Tingkat Pendidikan di Jawa Tengah Tahun 2021 Maziyah Mufidah Wahyudiono; Puspa Damai Kukuh Hati; Sri Pingit Wulandari
Perspektif : Jurnal Pendidikan dan Ilmu Bahasa Vol. 2 No. 4 (2024): Desember : Jurnal Pendidikan dan Ilmu Bahasa
Publisher : STAI YPIQ BAUBAU, SULAWESI TENGGARA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59059/perspektif.v2i4.1897

Abstract

Nation-building requires long-term investment focused on improving human resource (HR) quality, where higher education levels play a crucial role in shaping superior HR. In Indonesia, the government continuously strives to expand access to and improve the quality of education across regions, including in Central Java Province. However, disparities in educational attainment remain a challenge, influenced primarily by social, economic, and demographic factors. This study aims to analyze the factors affecting education levels in Central Java in 2021, a post-pandemic year, using the Principal Component Analysis (PCA) approach. Suspected influential factors include poverty rates, gender ratios, population growth rates, gross enrollment rates, net enrollment rates, expected years of schooling, and average years of schooling. The analysis results show that the data characteristics of these factors indicate asymmetrical distributions and high variability, as observed from wide boxplots for most factors. Only the expected years of schooling exhibit lower variability, with the presence of outliers. Assumption tests reveal that the data follow a multivariate normal distribution, are sufficient for factor analysis, and are dependent. The principal component analysis results indicate that two components are sufficient to explain overall data variability. The factor analysis forms two new components, identified as the welfare and education factor and the education participation factor.
Analisis Faktor-faktor Volume Ekspor Hasil Perikanan Menurut Provinsi di Indonesia Tahun 2021 menggunakan Analisis Faktor Byrlianty Tsabita El Haqq; Arum Antika; Sri Pingit Wulandari
Zoologi: Jurnal Ilmu Peternakan, Ilmu Perikanan, Ilmu Kedokteran Hewan Vol. 3 No. 1 (2025): Januari : Zoologi: Jurnal Ilmu Peternakan, Ilmu Perikanan, Ilmu Kedokteran Hewa
Publisher : Asosiasi Riset Ilmu Tanaman Dan Hewan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/zoologi.v2i2.89

Abstract

Indonesia has significant fisheries potential due to its vast waters. Its abundant fishery resources have strong export potential. However, export activities often face challenges that cause export volumes to fluctuate. This fluctuation is influenced by various factors. These factors can be minimized using statistical methods such as Principal Component Analysis (PCA) and Factor Analysis. This study includes data characterization for each variable and testing PCA and factor analysis assumptions, including multivariate normality testing, independence testing (Bartlett's test), sampling adequacy (KMO test), anti-image correlation testing, PCA testing, and factor analysis. The results indicate that the percentage contribution of fisheries to GDP, the number of coastal villages with disaster mitigation facilities, and the average daily per capita calorie consumption from fish are relatively less dispersed and not highly variable around the mean. Additionally, all data meet the assumptions, and the sample size is adequate. Factors such as aquaculture pond production and the percentage contribution of fisheries to GDP sufficiently explain the data variations.
Analisis Pengelompokkan Provinsi di Indonesia Berdasarkan Indikator Kinerja Sektor Peternakan Sapi Tahun 2022 Arsa Saladine; Endita Prastyansyach; Sri Pingit Wulandari
Zoologi: Jurnal Ilmu Peternakan, Ilmu Perikanan, Ilmu Kedokteran Hewan Vol. 3 No. 1 (2025): Januari : Zoologi: Jurnal Ilmu Peternakan, Ilmu Perikanan, Ilmu Kedokteran Hewa
Publisher : Asosiasi Riset Ilmu Tanaman Dan Hewan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/zoologi.v3i1.93

Abstract

Indonesia, based on natural resource potential, has great potential to achieve beef self-sufficiency. The contribution of this sector is not only limited to meeting food needs in the form of beef, but also includes economic aspects such as providing employment opportunities, industrial raw materials, and increasing the income of local farmers. This shows that the development of this sector has great potential in supporting food security and improving community welfare. Therefore, research was conducted on performance indicators that could influence the performance of the cattle farming sector in Indonesia in 2022 using cluster analysis. Cluster analysis is a statistical method that identifies groups of samples based on similar characteristics. Cluster analysis has two methods, namely hierarchical and non-hierarchical. This research focuses on classifying regions in Indonesia into groups based on similar characteristics. In this research, cluster analysis assumptions will be tested, namely the multivariate normal distribution test, conducting cluster analysis using hierarchical and non-hierarchical methods, characterizing the data in each cluster, then drawing conclusions and suggestions from the research results. Based on the research results obtained on data characteristics, it was found that variables tend to have a variety of data. Hierarchical cluster analysis uses the single linkage method which has an optimum number of clusters of 4. The highest number of cluster members is in cluster 1. Then cluster 1 shows the highest performance in the cattle farming sector. In non-hierarchical cluster analysis using the k-means method which has an optimum number of clusters of 5. The highest number of cluster members is in cluster 4. Then clusters 2, 3 and 4 show higher performance in the cattle farming sector compared to clusters 1 and 5 .
Co-Authors Abghaza Bayu Kusuma Wardhana Aisyatul Kamilah Ramadina Albertus Eka Amelia Kurnia Fitri Amelya Putri, Veronica Ammaar Razaan Yahya Anindya Putri Noliza Anjani Lestari Hartanty Annisa’ Riskika Hayyu Arindah Maharani Saputri Arsa Saladine Arum Antika Bintang Amirul Mukminin Brodjol Sutijo Byrlianty Tsabita El Haqq Damayanti Zulvita Aisyah Daniel Wicaksono Nugroho Dentina Dewi Amaliana Elsa Amelia Nur Arimba Endita Prastyansyach Famita Wibi Wulandari Farhan Bramhatchi Feny Ulil Amrina Hanni Putria Hartanti Helvy Tiana Rosa Nabila Hikmah Deviani Ika Rahmawati Insania Firdausy Khurotaayun Subagja Latifah Sekar Hanin Marcella Pramesti Putri Maziyah Mufidah Wahyudiono Melita Handayani Monica Putri Natalia Muhammad Hasan Alwi Abu Sifa Naomi Gloria Pasaribu Natasya Liana Putri Naufalia Alfi Fuadah Nisrina Aulia Salsabila Nur Fitri Mustika Ayu Nurfajriyani Nurfajriyani Ovi Wahyu Adriani Purhadi Purhadi Puspa Damai Kukuh Hati Putri Buana, Avinda Lutfi Anggraini Putri Dita Prameswari Rakha Maheswara Rida Dwi Lestari Rindy Retno Prihastari Riska Aisyah Widyaningsari Salsabila Aulia Azizah Sa’diyyah Divamia Amanda Siti Nabila Sri Mumpuni Retnaningsih Syefa Ilmi Beandita Putri Vira Angelina Virda Wulandari Zahra Afifah Nafisah _ Haryono