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All Journal Media Statistika Jurnal Sains dan Seni ITS TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Chemical Reaction Engineering & Catalysis Jurnal Biometrika dan Kependudukan (Journal of Biometrics and Population) Inferensi Journal of Applied Sciences, Management and Engineering Technology (JASMET) Proceeding ISETH (International Summit on Science, Technology, and Humanity) Sewagati Journal of Research on Community Engagement
Santi Wulan Purnami
Department Of Statistic, Faculty Of Science And Analytical Data, Institut Teknologi Sepuluh Nopember, Surabaya
Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Model Evaluation for Logistic Regression and Support Vector Machines in Diabetes Problem Baiq Siska Febriani Astuti; Neni Alya Firdausanti; Santi Wulan Purnami
Inferensi Vol 1, No 2 (2018): Inferensi
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (557.911 KB) | DOI: 10.12962/j27213862.v1i2.6728

Abstract

Machine learning is a method or computational algorithm to solve problems based on data that already available from the database. Classification is one of the important methods of supervised learning in machine learning. Support Vector Machine and Logistic Regression are some supervised learning methods that can be used both for classification and regression. In datamining process, Preprocessing is an important part before doing further analysis. In preprocessing data, feature selection and deviding training and testing data are important part of preprocessing data. In this research will be compared some evaluation model of deviding method for training and testing data, namely Random Repeated Holdout, Stratified Repeated Holdout, Random Cross-Validation, and Startified Cross-Validation. Evaluation model would be implying in logistic regression and Support Vector Machines (SVMs). From the analysis, can be concluded that by selecting features can improve the accuracy of classification with logistic regression, but opposite of Support Vector Machines (SVMs). For training and testing data pertition method can not be sure what method is better, because each method of partition training and testing data using the concept of random selection. Model evaluation cannot sure influence to increase best perform for SVMs model in particular this case.
Indeks Prognostik Pada Pasien Kanker Serviks di RSUD dr. Soetomo Surabaya Menggunakan Model Regresi Cox Extended Nur Arifiyani; Santi Wulan Purnami
Inferensi Vol 3, No 1 (2020): Inferensi
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v3i1.6880

Abstract

Saat ini masalah yang rentan dihadapi oleh wanita adalah timbulnya sel kanker dalam tubuh. Tak lain adalah kanker leher Rahim atau biasa disebut kanker serviks. Kanker serviks merupakan kanker yang disebabkan oleh infeksi virus HPV (Human Papillomavirus). Virus tersebut menyerang dalam tubuh wanita dikarenakan beberapa factor. Peningkatan kejadian kanker serviks semakin bertambah namun diagnosis dini atau inisiasi melakukan pengobatan kurang diperhatikan oleh masyarakat, sehingga berpengaruh pada prognosis buruk pasien kanker serviks. Dalam penelitian ini digunakan metode regresi Cox Extended untuk mengetahui prognosis pasien dimasa datang yang disebut prognostik indeks. Hasil penelitian menunjukkan model yang signifikan terhadap ketahanan hidup pasien kanker serviks dengan metode regresi Cox Extended adalah variabel jenis pengobatan lainnya atau pasien kanker serviks tidak melakukan pengobatan kemoterapi, operasi, tranfusi PRC, dan kombinasi antar ketiga jenis pengobatan tersebut. Prognosis pasien yang tidak melakukan jenis pengobatan tersebut memiliki resiko terjadinya meninggal sangat tinggi dibandingkan pasien yang melakukan kemoterapi, operasi, tranfusi PRC, dan kombinasi antar ketiga jenis pengobatan tersebut yang memiiliki resiko terjadinya meninggal rendah.
Regresi Cox Proportional Hazard Untuk Analisis Survival Pasien Kanker Otak di C-Tech Labs Edwar Technology Tangerang Izdiharti Noni Pertiwi; Santi Wulan Purnami
Inferensi Vol 3, No 2 (2020): Inferensi
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v3i2.7727

Abstract

Kanker otak adalah pertumbuhan sel-sel otak yang tidak terkendali yang terjadi di otak. Di Indonesia kanker otak merupakan salah satu kanker terbanyak pada anak. Meskipun demikian, tumor ini dapat terjadi pada umur berapapun. Risiko kanker otak meningkat seiring dengan bertambahnya usia. Berbagai treatment dilakukan sebagai usaha untuk memperpanjang ketahanan hidup pasien kanker otak, seperti operasi, kemoterapi, radioterapi, pengobatan herbal, dan ECCT. ECCT merupakan metode untuk mengobati kanker menggunakan sumber gelombang elektrostatis intensitas rendah (<30Vpp) dan frekuensi rendah (<100KHz) yang dipasang pada pakaian yang dipakai setiap hari oleh pasien. Pasien disarankan melakukan konsultasi untuk memeriksa kinerja alat dan perkembangan penyebaran sel kanker. Penelitian ini bertujuan untuk mendapatkan faktor yang mempengaruhi model survival pasien kanker otak berdasarkan faktor treatment dan faktor resiko seperti usia dan jenis kelamin. Model regresi Cox PH digunakan karena semua variabel telah memenuhi asumsi PH. Data yang digunakan yaitu pasien yang melakukan konsultasi lebih dari 6 bulan. Berdasarkan pemodelan dengan menggunakan regresi Cox PH menghasilkan variabel yang berpengaruh terhadap waktu survival pasien kanker otak yaitu frekuensi konsultasi dan radioterapi. Didapatkan bahwa setiap bertambahnya 1 kali konsultasi resiko untuk mengalami kematian semakin turun sebesar 1,15 kali dan pasien kanker otak yang memiliki riwayat radioterapi memiliki resiko untuk meninggal 3 kali lebih besar daripada pasien yang tidak memiliki riwayat.
Predicting Popularity of Movie Using Support Vector Machines Dwi Rantini; Rosyida Inas; Santi Wulan Purnami
Inferensi Vol 2, No 1 (2019): Inferensi
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (411.243 KB) | DOI: 10.12962/j27213862.v2i1.6806

Abstract

There are many movies performed, from low until high rating, which is the movie maybe popular or not popular. If many people watched that movie maybe it is popular, in other hand if a movie is watched by a little person so that movie can called as not popular movie. Popularity of movie can determined by several factors, such as likes, ratings, comments, etc. To determine popular or not popular of movie based on features, will use two classification methods that is logistic regression and Support Vector Machine (SVM). In this research, the data are Conventional and Social Media Movies Dataset 2014 and 2015. To get the best model and without ignoring the principle of parsimony, will do feature selection. The selected features are genre, sentiment, likes, and comments. That features will be used to classify the popularity of movies. This research used two classification methods namely logistic regression and Support Vector Machine (SVM). When used logistic regression, the accuracy is 77.29%, while used SVM the accuracy is 83.78%. Based on the accuracy of both methods, it is found that SVM gives the highest accuracy for CSM dataset. The highest accuracy is obtained from the SVM method with non-stratified holdout training-testing strategy. 
Support Vector Machine optimization with fractional gradient descent for data classification Dian Puspita Hapsari; Imam Utoyo; Santi Wulan Purnami
Journal of Applied Sciences, Management and Engineering Technology Vol 2, No 1 (2021)
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.jasmet.2021.v2i1.1467

Abstract

Data classification has several problems one of which is a large amount of data that will reduce computing time. SVM is a reliable linear classifier for linear or non-linear data, for large-scale data, there are computational time constraints. The Fractional gradient descent method is an unconstrained optimization algorithm to train classifiers with support vector machines that have convex problems. Compared to the classic integer-order model, a model built with fractional calculus has a significant advantage to accelerate computing time. In this research, it is to conduct investigate the current state of this new optimization method fractional derivatives that can be implemented in the classifier algorithm. The results of the SVM Classifier with fractional gradient descent optimization, it reaches a convergence point of approximately 50 iterations smaller than SVM-SGD. The process of updating or fixing the model is smaller in fractional because the multiplier value is less than 1 or in the form of fractions. The SVM-Fractional SGD algorithm is proven to be an effective method for rainfall forecast decisions.
Pengelolaan Sampah sebagai Kompos di Wisata Gronjong Wariti Berbasis Pemberdayaan Masyarakat dengan Media Bata Terawang Santi Wulan Purnami; Harmin Sulitiyaning Titah; Diah Puspito Wulandari; Yoyok Setyo Hadiwidodo; Bambang Widjanarko Otok; Purhadi Purhadi; Jerry D. T. Purnomo; Achmad Choiruddin; Shofi Andari; Abima Aunur Rochman
Sewagati Vol 7 No 3 (2023)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (6282.262 KB) | DOI: 10.12962/j26139960.v7i3.501

Abstract

Mejono merupakan sebuah desa yang terletak di Kecamatan Plemahan, Kabupaten Kediri, Provinsi Jawa Timur. Di Desa Mejono terdapat sebuah sungai bernama Gronjong Wariti yang dijadikan tempat wisata dengan wahana yang tersebar di sepanjang sungai. Tingginya aktivitas pengunjung dan banyaknya pohon bambu yang tumbuh disepanjang Sungai Gronjong Wariti menimbulkan peningkatan timbunan sampah yang memberikan dampak buruk bagi kesehatan, lingkungan, ekonomi, serta mengurangi nilai estetika. Dari observasi permasalahan yang ada pada mitra, tim pengabdian masyarakat Institut Teknologi Sepuluh Nopember menawarkan solusi yakni edukasi pemilahan sampah dan mengelola sampah organik menjadi kompos menggunakan media bata terawang. Edukasi pemilahan sampah menjadi sampah organik dan anorganik dilakukan kepada pengelola, warga sekitar dan pengunjung wisata Gronjong Wariti. Tempat pilah sampah diberikan di tempat yang mudah dijangkau di area Wisata. Pembangunan Bata Terawang dengan warna yang menarik ditempatkan di titik yang menghasilkan sampah organik terbanyak tiap harinya. Proses pengomposan media Bata Terawang dimulai dengan pengisian sampah organik, penyemprotan EM4, pengadukan dan pemanenan kompos. Waktu yang dibutuhkan mulai dari tahap pengisian sampah sampai tahap pemanenan diperkirakan memakan waktu 40 hari.
Edukasi Peningkatan Kemampuan Pengenalan dan Pengelolaan Stress, Kecemasan, dan Depresi di Kalangan Mahasiswa di Surabaya Timur Wulandari, Diah Puspito; Purnami, Santi Wulan; Rahmawati, Novi Agung; Andari, Shofi; Suprapto, Yoyon Kusnendar; Boedinugroho, Hanny; Juniastuti, Susi; Zaini, Ahmad; Kurniawan, Arief; Fandiantoro, Dion Hayu
Sewagati Vol 8 No 4 (2024)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v8i4.1068

Abstract

Jiwa merupakan salah satu unsur yang membentuk kesehatan pada manusia. Jiwa yang sehat membuat seorang individu mampu mengembangkan dirinya secara fisik, mental, spiritual dan sosial. Ketika seseorang memasuki usia produktif, yaitu 15-64 tahun, seharusnya ia bisa memaksimalkan pengembangan potensi dirinya. Akan tetapi justru di rentang usia ini bermunculan permasalahan yang bersumber dari jiwa yang tidak sehat, mulai dari tingkat ringan hingga yang paling berat, yaitu tindakan bunuh diri. Sebagaimana laiknya penyakit yang lain, gangguan kesehatan jiwa bisa tertangani dengan baik atau tingkat harapan kesembuhannya meningkat jika permasalahannya bisa terdeteksi sejak dini. Pada umumnya pasien tidak mendapatkan penanganan yang baik karena dia tidak mengetahui bahwa gejala-gejala yang dimilikinya mengindikasikan penyakit kejiwaan, cenderung meremehkan, atau khawatir mendapat stigma yang buruk dari masyarakat. Melalui sebuah seminar tentang pengelolaan stress dan deteksi dini, mahasiswa sebagai bagian dari angkatan usia produktif mendapatkan pembekalan tentang tanda-tanda gangguan kesehatan mental, penyebab dan penanganannya.
SEM-PLS Training at Universitas Islam Negeri Maulana Malik Ibrahim Otok, Bambang Widjanarko; Astuti, Cindy Cahyaning; Mulyanto, Angga Dwi; Purhadi, Purhadi; Andari, Shofi; Choiruddin, Achmad; Purnami, Santi Wulan
JRCE (Journal of Research on Community Engagement) Vol 7, No 1 (2025): Journal of Research on Community Engagement
Publisher : Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrce.v7i1.32959

Abstract

At Universitas Islam Negeri Maulana Malik Ibrahim Malang in 2024 SEM-PLS training will develop data analysis capabilities for lecturers and students to enhance their work on quality scientific publications. The Department of Mathematics at Faculty of Science and Technology conducted the session on May 21, 2024, where 40 people participated. Training and mentoring stands as the service method which features instruction about SEM-PLS theory alongside practical utilization of SmartPLS software for implementation. Observation activities together with documentation assessment and satisfaction questionnaire responses determine the program's outcome. Participant satisfaction reached an exceptional level because they showed positive feedback about the material presented. Time constraints together with a constrained space area negatively affected  this event. This training achieved success in providing extensive SEM-PLS understanding to students and lecturers. The activity builds campus research capacity. The organization of similar consecutive training courses is highly suggested because it will boost academic knowledge in data analysis fields.
RANDOM EFFECTS META-REGRESSION USING WEIGHTED LEAST SQUARES (CASE STUDY: EFFECTIVENESS OF ACCEPTANCE AND COMMITMENT THERAPY IN REDUCING DEPRESSION) Arumningtyas, Felinda; Otok, Bambang Widjanarko; Purnami, Santi Wulan
MEDIA STATISTIKA Vol 18, No 1 (2025): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.18.1.49-60

Abstract

Meta-analysis is a statistical method for synthesizing quantitative data from multiple related studies, yet heterogeneity among studies often complicates interpretation. Meta-regression extends this approach by incorporating study-level covariates to explain variations in outcomes. With the global increase in depression, Acceptance and Commitment Therapy(ACT) has attracted attention as an effective psychological intervention. Therefore, a deeper understanding of the factors that influence its effectiveness across studies is needed. However, to date, only a few meta-analyses have quantitatively examined moderator variables that influence ACT outcomes using a random effects meta-regression approach. This study aims to fill this gap. This study estimated the model parameters using the Weighted Least Squares (WLS) method. Thirty-three published studies testing the effectiveness of ACT in reducing depression were collected from PubMed, Google Scholar, and Science Direct. The homogeneity test results showed significant heterogeneity, supporting the use of a random effects model. The combined effect size of -0.321 indicates that ACT significantly reduces depression levels compared to the control group. Meta-regression analysis revealed that variation in effect size was significantly influenced by differences in the average age of patients and duration of therapy. These findings provide new insights into the conditions and characteristics that make ACT therapy more effective.
The Theoretical Study of Rare Event Weighted Logistic Regression for Classification of Imbalanced Data Sulasih, Dian Eka Apriana; Purnami, Santi Wulan; Rahayu, Santi Puteri
Proceeding ISETH (International Summit on Science, Technology, and Humanity) 2015: Proceeding ISETH (International Conference on Science, Technology, and Humanity)
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/iseth.2376

Abstract

One of the problems in data classification is imbalanced data. In two-class classification, imbalance problem occurs where one of the two classes has more samples than another class. In such situation, most of the classifier will be biased towards the major class, while the minor class will be subordinated eventually which leads to inaccurate classification. Therefore, a method to classify the imbalanced data is required. Rare Event Weighted Logistic Regression (RE-WLR) which is developed by Maalouf and Siddiqi is a method of classification applied to large imbalanced data and rare event. This study showed the review of RE-WLR for the classification of imbalanced data. It explicated the steps to obtain the estimator specifically, particularly for IRLS. RE-WLR is a combination of Logistic Regression (LR) rare events corrections and Truncated Regularized Iteratively Re-weighted Least Squares (TR-IRLS). Rare event correction in LR is applied to Weighted Logistic Regression (WLR). Regularization was added to reduce over-fitting. The estimation of ߚ is performed by using the method of maximum likelihood (ML), while WLR maximum likelihood estimates (MLE) were obtained by using IRLS method of Newton-Raphson algorithm. In order to solve large optimization problems, Truncated-Newton method is applied.
Co-Authors Abima Aunur Rochman Achmad Choiruddin Achmad Sjafii Ahmad Zaini Almuhayar, Mawanda Angga Dwi Mulyanto Anindya Apriliyanti Pravitasari Arief Kurniawan Arina Nur Afifah Arumningtyas, Felinda Ayu Mayliawati Baiq Siska Febriani Astuti Bambang Widjanarko Otok Boedinugroho, Hanny Choiruddin, Achmad Cindy Cahyaning Astuti Diah Puspito Wulandari Diah Puspito Wulandari Dian Puspita Hapsari Didik Prasetyoko Diyah Herowati Dwi Rantini Epa Suryanto Fandiantoro, Dion Hayu Farizi Rachman Ferriastuti, Widiana Fiscy Aprilia Rahmanika Fithriasari, Kartika Fitria Nur Aida Fourina Ayu Novianti H. Holilah Harmin Sulitiyaning Titah Hasliza Bahruji Ida Ayu Sevita Intansari Ika Wuryanti Febrina Wuryanti Imam Utoyo Irhamah - Izdiharti Noni Pertiwi Jerry D. T. Purnomo Juniastuti, Susi Kurnia Dwi Inayati Millatur Rodliyah Mukti Ratna Dewi Neni Alya Firdausanti Nur Arifiyani Nur Iriawan Nurcahyati Akbar Kusumawardani Nurfain Nurfain Nurul Qomariyah Purhadi Purhadi Rahmawati, Novi Agung Riza Inayah Rosyida Inas Santi Puteri Rahayu Setyo Purnomo Shofi Andari Shofi Andari Siska P Wibisono Siti Holis Sumartini Sri Hartati Selviani Handayani Sri Pingit Wulandari Sulasih, Dian Eka Apriana Sutikno Sutikno Tannassia Winda Adharina Taufik Azmi Titie Prapti Oetami Utoyo, Imam Yoyok Setyo Hadiwidodo Yoyon Kusnendar Suprapto Zeni Rahmawati