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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang Jiko (Jurnal Informatika dan komputer) Jurnal Onoma: Pendidikan, Bahasa, dan Sastra International Journal of Advances in Data and Information Systems ESTIMASI: Journal of Statistics and Its Application Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) International Journal of Data Science, Engineering, and Analytics (IJDASEA) Buletin Sistem Informasi dan Teknologi Islam Kohesi: Jurnal Sains dan Teknologi Parameter: Jurnal Matematika, Statistika dan Terapannya Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal PETISI (Pendidikan Teknologi Informasi) Jurnal Komtika (Komputasi dan Informatika)
Damaliana, Aviolla Terza
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Other

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ANALYSIS OF EDUCATION FUNDING ALLOCATION AND STUDENT ENROLLMENT DIFFERENCES BETWEEN SMA AND SMK STUDENTS IN INDONESIA : RM MANOVA APPROACH Zahwa, Aniq Farichatus; Ramadhani, Dafinah; Wara, Shindi Shella May; Damaliana, Aviolla Terza
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 1 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv4i1pp167-174

Abstract

The Indonesia Smart Program (PIP) is one of the government's efforts to improve access to education for underprivileged students. The purpose of this study is to examine how PIP educational aid was distributed and how successful it was in Indonesia in 2022 at the Senior High School (SMA) and Vocational High School (SMK) levels. The method used is Repeated Measures Multivariate Analysis of Variance (RM Manova) for education. The research data was obtained from the official government data portal of Indonesia (data.go.id). The results of the study do not show any significant differences in the distribution of assistance between SMA and SMK across various regions. Further research is needed to consider other factors that may have an impact.
Implementation of Bayesian Structural Time Series (BSTS) Method for Predicting Traditional Market Revenue Achievement in Surabaya Muizzadin, Muizzadin; Mohammad Idhom; Damaliana, Aviolla Terza
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.82

Abstract

Traditional markets play an important role in the regional economy, including in the city of Surabaya. However, the number of traditional markets in Surabaya has continued to decline in recent years due to competition with modern markets. In addition, the contribution of traditional markets to Regional Original Income (PAD) has fluctuated, for example 1.67% in 2013, 1.66% in 2014, and increased to 1.76% in 2015. This condition poses a challenge for the management of regional economic policies, so an accurate prediction method is needed to support strategic decision making. This study aims to predict the achievement of traditional market revenue in Surabaya using the Bayesian Structural Time Series (BSTS) method. The data used is the percentage of traditional market revenue achievement over the past fifteen years. The BSTS model is applied with various components, including Local Level, Local Linear Trend, and Seasonal, which allows flexibility in capturing trends, seasonal patterns, and structural changes in the data. Model evaluation is carried out using Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) to assess prediction accuracy. The results of the study showed that the BSTS model with Local Level and Seasonal components and 1,000 MCMC iterations provided the best performance, with a MAPE value of 4.036% and an RMSE of 5.198. This model is able to capture trend and seasonal patterns well, making it effective in predicting traditional market revenue achievements. Based on these findings, the BSTS method has proven to be a reliable approach in predicting traditional market revenue achievements. The results of this study are expected to help market managers and policy makers in designing more adaptive strategies to maintain the competitiveness of traditional markets and increase their contribution to the regional economy.
Implementasi Metode Klasifikasi LightGBM dan Analisis Survival dalam Memprediksi Pelanggan Churn Illah, Ibnu Zahy' Atha; Jauharis Sapu, Wahyu Syaifullah; Damaliana, Aviolla Terza
Jurnal Komtika (Komputasi dan Informatika) Vol 8 No 1 (2024)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v8i1.11194

Abstract

Increasingly tight competition in the business world causes every business sector to try to utilize relevant technology to maintain its market share. The success of a company is often measured by how strong the customer network they have. Loss of customers (customer churn) can cause a significant decrease in revenue and can even threaten the existence of the company itself. Therefore, predictive modeling and projection of customer churn is needed as a customer retention effort. This research involves the LightGBM classification algorithm for customer churn prediction and utilizes survival analysis for future projections. The results of the research can be used to prevent customer churn at companies, especially PT Kasir Pintar Internasional. LightGBM classification performance as measured by model evaluation reaches Accuracy, Precision, Recall, and F1-score values of 0.964, 0.971, 0.990, and 0.980 respectively. The LightGBM classification model also provides information on five important features that influence customer churn. Companies can use these five important features as material for designing customer retention strategies. Apart from that, the Cox Proportional Hazard survival model has a C-index evaluation value of 0.83, which means it is quite capable of projecting customer survival. The survival model also shows that currently non-churn customers have an average survival expectation of 15 months.
Analisis Sentimen Komentar Pengguna Terhadap Aplikasi Prime Video Di Google Playstore Dengan Pendekatan Machine Learning Pradipta, Alvino Hadiyan; Nugroho, Muhammad Rafli Feandika; Putri, Maretta Fairuz Luthfia Winoto; Wara, Shindi Shella May; Damaliana, Aviolla Terza
Buletin Sistem Informasi dan Teknologi Islam (BUSITI) Vol 6, No 4 (2025)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v6i4.2856

Abstract

Analisis sentimen terhadap ulasan pengguna menjadi penting dalam memahami persepsi publik terhadap sebuah aplikasi digital. Analisis ini dilakukan untuk mengklasifikasikan 1000 komentar yang terdiri dari komentar positif dan negatif dari pengguna aplikasi Prime Video yang terdapat di Google Play Store. Tujuan penelitian ini adalah untuk membantu pengembang aplikasi memahami pendapat pengguna dalam jumlah besar secara otomatis, tanpa harus membaca komentar pengguna satu per satu. Tahapan awal dilakukan melalui proses pra pemrosesan teks, yang meliputi pembersihan data, normalisasi kata, case folding, stemming, dan filtering. Selain itu, visualisasi Word Cloud digunakan untuk mengidentifikasi kata-kata yang sering muncul dalam komentar pengguna. Analisis dilanjutkan dengan penerapan metode klasifikasi untuk menentukan sentimen komentar. Dalam penelitian ini, tiga metode pembelajaran mesin yaitu Neural Network (NN), Support Vector Machine (SVM) dan Naive Bayes Classifier (NBC) digunakan dan dibandingkan untuk memperoleh hasil klasifikasi terbaik. Hasil menunjukkan bahwa metode SVM memberikan tingkat akurasi tertinggi yaitu sebesar 89,5%, disusul dengan metode NN sebesar 87% dan NBC sebesar 75% dalam mengklasifikasikan sentimen komentar pengguna. Penelitian ini menyimpulkan bahwa pendekatan berbasis machine learning efektif digunakan dalam mengidentifikasi dan mengelompokkan opini pengguna terhadap aplikasi digital secara otomatis.
Implementasi Metode Ensemble ROCK dalam Pengelompokan UMKM di Kabupaten Malang Purwadwika, Reza Sadiya; Hindrayani, Kartika Maulida; Damaliana, Aviolla Terza
JURNAL PETISI (Pendidikan Teknologi Informasi) Vol. 7 No. 1 (2026): JURNAL PETISI (Pendidikan Teknologi Informasi)
Publisher : Universitas Pendidikan Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36232/jurnalpetisi.v7i1.3396

Abstract

UMKM memiliki peran penting dalam perekonomian nasional, namun masih menghadapi berbagai permasalahan seperti rendahnya pemanfaatan teknologi, keterbatasan akses permodalan, dan lemahnya daya saing. Kompleksitas karakteristik data UMKM yang mencakup variabel numerik dan kategorikal menjadi tantangan dalam analisis dan pemetaan yang akurat. Penelitian ini bertujuan untuk mengelompokkan UMKM di Kabupaten Malang berdasarkan karakteristik usaha dan pelaku usahanya dengan pendekatan ensemble clustering menggunakan algoritma ROCK. Data terdiri dari 75 entri UMKM yang mencakup variabel numerik (omset, modal, tenaga kerja) dan kategorikal (jenis usaha, penggunaan aplikasi transportasi daring). Clustering dilakukan secara terpisah dengan Agglomerative Hierarchical Clustering untuk data numerik dan ROCK untuk data kategorikal. Hasil kedua metode digabungkan menggunakan pendekatan ensemble untuk memperoleh klaster yang lebih stabil dan representatif. Parameter optimal diperoleh pada theta = 0,05 dan k = 4 dengan nilai Clustering Purity (CP*) sebesar 0,8148 dan Davies-Bouldin Index sebesar 0,3817, menunjukkan pemisahan cluster yang baik. Cluster akhir menunjukkan perbedaan signifikan dalam skala usaha, pemanfaatan teknologi digital, dan performa ekonomi. Temuan ini diharapkan menjadi dasar dalam merancang kebijakan pengembangan UMKM yang lebih tepat sasaran dan berbasis data.
Segmentasi Wilayah Jawa Timur Berdasarkan Ketersediaan Fasilitas dan Tenaga Kesehatan Kurniawan, Muhammad Erlangga; Ananta, Aditya Putra; Anugrah, Muhammad Cahya Raka; Damaliana, Aviolla Terza; Wara, Shindi Sheila May
ESTIMASI: Journal of Statistics and Its Application Vol. 7, No. 1, Januari, 2026 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v7i1.44767

Abstract

Health is a fundamental indicator in measuring societal well-being, where the equitable distribution of healthcare facilities and personnel plays a critical role. This study aims to segment regions in East Java Province based on the availability of healthcare facilities (community health centers, general/special hospitals, pharmacies, integrated health posts, and primary clinics) and healthcare personnel (doctors, midwives, nurses, pharmacists). The methods used include Principal Component Analysis (PCA) for dimensionality reduction, followed by K-Means and Agglomerative Hierarchical Clustering (AHC) algorithms using Average Linkage and Cosine Similarity. The analysis results show that AHC provides more optimal outcomes, with a silhouette score of 0.75, compared to K-Means which only achieved 0.51. The segmentation produced three main clusters: low (Pacitan, Ponorogo, Madura), medium (Bojonegoro, Jember, Banyuwangi), and high (Surabaya, Malang, Sidoarjo). These findings reveal disparities in the distribution of healthcare services in East Java and can serve as a foundation for more targeted policy formulation to improve equitable access to healthcare, particularly in underserved regions.
Co-Authors Ananta, Aditya Putra Anas Ahmadi Anugrah, Muhammad Cahya Raka Bey Lirna, Cagiva Chaedar Dwi Arman Prasetya Fahrudin, Tresna Maulana Febriyanti, Dea Putri Pascha Hindrayani , Kartika Maulida Illah, Ibnu Zahy' Atha Ilmatus Sa'diyah Jauharis Sapu, Wahyu Syaifullah Kaffi, Laisal Kartika Maulida Hindrayani Kurniawan, Muhammad Erlangga Lestari, Amanda Ayu Dewi Maulana, Mohammad Hikmal Maulidiyyah, Nova Auliyatul Mohammad Idhom Muhaimin, Amri Muizzadin, Muizzadin Nugroho, Muhammad Rafli Feandika Oktaviani, Sheny Eka Pradipta, Alvino Hadiyan Purwadwika, Reza Sadiya Putri, Adinda Rusdianti Maulani Putri, Irma Amanda Putri, Maretta Fairuz Luthfia Winoto Ramadhani, Dafinah Shindi Shella May Wara Trimono, Trimono Wara, Shindi Sheila May Wiyono, Farhan Syah Putra Zahwa, Aniq Farichatus