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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang Jiko (Jurnal Informatika dan komputer) Jurnal Onoma: Pendidikan, Bahasa, dan Sastra bit-Tech Jurnal Sistem informasi dan informatika (SIMIKA) International Journal of Advances in Data and Information Systems Journal of Advanced in Information and Industrial Technology (JAIIT) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) Djtechno: Jurnal Teknologi Informasi International Journal of Data Science, Engineering, and Analytics (IJDASEA) Jurnal Impresi Indonesia Journal of Renewable Energy, Electrical, and Computer Engineering Buletin Sistem Informasi dan Teknologi Islam Journal of Information Systems and Technology Research Kohesi: Jurnal Sains dan Teknologi Parameter: Jurnal Matematika, Statistika dan Terapannya Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Komtika (Komputasi dan Informatika)
Damaliana, Aviolla Terza
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Pendekatan Time Series Decomposition (STL) Dalam Prediksi Kecelakaan Berbasis Kepadatan Lalu Lintas Sebagai Dasar Kebijakan Di Tol Surabaya-Gempol Rizky Mahendra, Rakha; Damaliana, Aviolla Terza; Diyasa, I Gede Susrama Mas
Jurnal Impresi Indonesia Vol. 4 No. 5 (2025): Indonesian Impression Journal (JII)
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jii.v4i5.6491

Abstract

Kecelakaan lalu lintas di jalan tol tetap menjadi masalah kritis yang mempengaruhi keselamatan publik dan stabilitasekonomi. Penelitian ini mengusulkan penggunaan dekomposisi Seasonal-Trend menggunakan LOESS (STL) untukmemprediksi risiko kecelakaan berdasarkan data volume lalu lintas di jalan tol Surabaya-Gempol. Data dari Januari 2022hingga Desember 2023, termasuk volume lalu lintas harian dan laporan kecelakaan, diuraikan menjadi komponen tren,musiman, dan residu untuk mengidentifikasi pola. Korelasi positif sedang (r = 0,4882) ditemukan antara volume lalulintas dan frekuensi kecelakaan. Analisis STL mengungkapkan puncak musiman mingguan yang konsisten di akhir pekan,terutama hari Sabtu. Model prediktif yang dikembangkan berhasil mengidentifikasi 11 hari berisiko tinggi pada Januari2024. Berdasarkan temuan tersebut, delapan rekomendasi kebijakan berbasis waktu dirumuskan, termasuk manajemenlalu lintas dinamis, pemantauan real-time, dan peningkatan pengawasan selama periode puncak. Penelitian ini menyumbangkan kerangka kerja berbasis data baru untuk manajemen keselamatan lalu lintas, menggabungkandekomposisi deret waktu dengan panduan kebijakan yang dapat ditindaklanjuti. Tidak seperti penelitian sebelumnya yanghanya berfokus pada prediksi volume, atau pada konteks jalan non-tol, penelitian ini memajukan penerapan STL untukidentifikasi risiko real-time di jalan tol Indonesia. Implikasinya menekankan integrasi sistem lalu lintas cerdas dan potensiprakiraan berbasis STL sebagai fondasi strategi keselamatan jalan nasional.
PERBANDINGAN ALGORITMA HDBSCAN DAN AGGLOMERATIVE HIERARCHICAL CLUSTERING DALAM MENGELOMPOKKAN DATA KETENAGAKERJAAN YANG OUTLIERS Permadani, Citra Amelia Intan; Damaliana, Aviolla Terza; Idhom, Mohammad
Djtechno: Jurnal Teknologi Informasi Vol 6, No 2 (2025): Agustus
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v6i2.7237

Abstract

Ketenagakerjaan merupakan indikator penting dalam mendukung pembangunan ekonomi nasional. Namun, distribusi tenaga kerja di Indonesia masih menunjukkan ketimpangan antarprovinsi. Beberapa provinsi memiliki kontribusi ekonomi dan tingkat pekerjaan formal yang tinggi, sementara yang lain tertinggal. Penelitian ini bertujuan mengidentifikasi pola distribusi ketenagakerjaan antarprovinsi dengan menerapkan analisis klaster menggunakan delapan variabel dari data BPS. Mengingat adanya pencilan dalam data, deteksi outlier dilakukan menggunakan metode Local Outlier Factor (LOF) yang mengidentifikasi enam provinsi sebagai outlier yaitu Jawa Barat, Jawa Tengah, Jawa Timur, DKI Jakarta, Banten, dan Sumatera Utara. Selanjutnya, data dianalisis menggunakan dua pendekatan klasterisasi, yaitu Agglomerative Hierarchical Clustering (Single, Complete, Average Linkage, dan Ward) dan HDBSCAN untuk membandingkan ketahanan metode terhadap data outlier. Validasi kualitas klaster dilakukan dengan Silhouette Coefficient. Hasil menunjukkan bahwa metode Single Linkage memiliki nilai koefisien tertinggi, namun kurang konsisten dalam memisahkan outlier. Sebaliknya, HDBSCAN lebih adaptif terhadap data yang mengandung noise dan pencilan dengan Silhouette Coefficient sebesar 0.546. Dengan demikian, HDBSCAN dinilai lebih efektif dalam analisis klasterisasi data ketenagakerjaan yang kompleks, sementara metode AHC lebih unggul dalam membentuk klaster yang jelas jika pencilan dapat ditangani secara terpisah.
Multinomial Logistic Application on Factors Affecting Poor Population in East Java Isyanto, Aisyah Kirana Putri; Trimono; Damaliana, Aviolla Terza
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2400

Abstract

Poverty is still one of the major problems in East Java, even though this province has an important role in supporting the national economy. This condition shows that development in each district/city has not been evenly distributed, so a data-based analysis is needed to determine the factors that influence the poverty rate. This study aims to analyze the influence of socioeconomic variables on the poverty rate category in East Java using a multinomial logistic regression model. The data used is secondary data from the Central Bureau of Statistics (BPS) in 2023 which covers 38 districts/cities. The independent variables analyzed consisted of life expectancy, average years of schooling, open unemployment rate, labor force participation rate, expenditure per capita, human development index, and gross regional domestic product (GRDP) per capita. The analysis process involved data exploration, multicollinearity test, multinomial logistic regression modelling, simultaneous and partial parameter significance test, and model performance evaluation. The results show that per capita expenditure is the only variable that has a significant effect on poverty level classification. The model is able to classify the data with an accuracy of 81% and a McFadden R² value of 0.6483, which means the model has a fairly good performance. This finding shows the importance of increasing people's purchasing power as an effort to reduce poverty. This research is expected to be a reference for local governments in formulating more targeted and data-based policies.            
LONGITUDINAL MODELING OF E-COMMERCE CHOICE USING LATENT GROWTH CURVE TO ASSESS INFLUENCING FACTORS AMONG LATE ADOLESCENTS Agustina, Fadlila; Prasetya, Dwi Arman; Damaliana, Aviolla Terza
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/hv8z4172

Abstract

The rapid growth of e-commerce in Indonesia has significantly influenced consumer behavior, particularly among late adolescents aged 18–21 years. This study examines the dynamic factors affecting e-commerce preferences, including price, service quality, and customer loyalty, using Latent Growth Curve Modeling (LGCM). This method was chosen for its ability to analyze variable changes longitudinally, allowing the identification of growth patterns and factors influencing shifts in consumer behavior over time. Data were collected through an online survey involving 400 respondents over three time periods. The study’s findings reveal that price is the most stable variable (intercept 0.5302, slope 0.0811), whereas service quality (intercept 0.8127, slope -0.0285) and loyalty (intercept 0.8508, slope -0.0188) show slight declines. Innovation, functioning as a covariate, significantly affects the intercept of all variables, particularly loyalty, although its impact on growth rates varies. The model demonstrates a good fit, with RMSEA (0.0730), CFI (0.9844), and TLI (0.9402), confirming its validity. Visualizations indicate that loyalty evolves more dynamically than service quality, highlighting the crucial role of innovation in customer engagement. This study emphasizes the need for e-commerce platforms to prioritize innovation and service quality improvements to foster long-term loyalty. These findings provide valuable insights into consumer behavior dynamics and offer strategic recommendations for achieving competitive advantage in the digital marketplace.
KLASTERING WILAYAH DI JAWA TIMUR BERDASARKAN FAKTOR UNMET NEED MENGGUNAKAN FUZZY GUSTAFSON-KESSEL Windyadari, Chrysilla Citra; Damaliana, Aviolla Terza; Idhom, Mohammad
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/5b2g7x82

Abstract

The Family Planning Program is an effort to control the rate of population growth by regulating desired pregnancies. In its realization, the family planning program faces challenges in the form of unmet need (couples of childbearing age who do not use contraception). East Java Province in 2023 was recorded as the province with the third highest number of unmet need cases in Java. One method that can be used to analyze the phenomenon of unmet need is clustering analysis. Clustering analysis will help identify areas in East Java based on the priority level of the family planning program. Fuzzy Gustafson-Kessel (FGK) is one of the clustering methods developed as a refinement of the Fuzzy C-Means method. This study implements the Fuzzy Gustafson-Kessel (FGK) method with and without Principal Component Analysis (PCA) to cluster regions in East Java based on unmet need and determinant factors such as the availability of family planning facilities and resources. The results showed that the best model was obtained when using FGK with PCA, with the highest FSI value of 0.668 and XB of 0.235 at configuration c = 4 and m = 3.5. The clusters formed consist of 5 medium priority areas, 12 low priority areas, 9 high priority areas, and 12 developing priority areas. The results of this clustering can be used as a basis for policy makers in designing more effective intervention strategies to address unmet need in East Java.
OPTICS-Based Clustering of East Java Regencies and Cities by Divorce Factors Nurhalizah, Cesaria Deby; Damaliana, Aviolla Terza; Prasetya, Dwi Arman
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1227

Abstract

Divorce is a social phenomenon that occurs when a married couple decides to legally end their marriage. This decision is influenced by various factors such as conflict, economic pressure, domestic violence, and deviant behavior. The aim of this study is to group regencies and cities in East Java Province that share similarities in the main causes of divorce, in order to understand the patterns that emerge across regions. The OPTICS (Ordering Points to Identify the Clustering Structure) clustering method was chosen for its ability to identify cluster structures with varying densities. The modeling process was conducted using a proportion-based approach for each causal factor, with optimal parameters obtained through manual grid search using min_samples = 2, xi = 0.05, and min_cluster_size = 0.1. The analysis identified three main clusters, each dominated by conflict, economic hardship, and deviant behavior, respectively. The quality of the clustering was evaluated using a Silhouette Score of 0.588, indicating reasonably good results. These findings are expected to serve as an initial understanding of divorce causes in East Java and can be used as input for the formulation of more targeted social policies.
Integrasi Metode Pembelajaran Project Based Learning, Outcame Based Education, dan Bermain Peran dengan Model Webinar Mini untuk Meningkatkan Keterampilan Berbicara Mahasiswa Ilmatus Sa'diyah; Ahmadi, Anas; Damaliana, Aviolla Terza; Putri, Adinda Rusdianti Maulani; Febriyanti, Dea Putri Pascha
Jurnal Onoma: Pendidikan, Bahasa, dan Sastra Vol. 11 No. 1 (2025)
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/onoma.v11i1.5144

Abstract

Penelitian ini dilakukan untuk melakukan uji efektivitas pada penggunaan metode pembelajaran project based learning, outcame based education, dan bermain peran terhadap peningkatan keterampilan berbicara mahasiswa. Metode yang digunakan adalah metode penelitian tindakan kelas dengan menguji coba integrasi tiga metode secara bersamaan di dalam pembelajaran. Data kemudian diolah secara kualitatif dan kuantitatif. Dalam pelaksanaannya, metode ini mengajak mahasiswa untuk secara langsung berbicara di depan publik. Melalui project based learning, mahasiswa dipandu untuk mengadakan kegiatan webinar mini. Secara langsung, mahasiswa bisa mendapatkan pengalaman nyata berbicara di depan publik menghasilkan video materi webinar dan modul materi secara singkat sebagai luaran pembelajaran di kelas. Sementara itu, melalui bermain peran, mahasiswa menjadi narasumber, moderator, MC, pembaca doa, dan pengarah kuis dalam kegiatan webinar. Mahasiswa juga mencari peserta di luar kelas untuk hadir dalam acara webinar. Setelah uji coba, nilai rata-rata sebelum pelaksanaan metode adalah 65, sementara setelah metode diterapkan, nilai rata-rata meningkat menjadi 85. Kemudian, integrasi metode ini efektif dilaksanakan karena mahasiswa menyatakan bahwa kegiatan pembelajaran dapat meningkatkan keterampilan berbicara mereka.
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.
COMPARISON OF DECISION TREE AND RANDOM FOREST METHODS IN THE CLASSIFICATION OF DIABETES MELLITUS Maulidiyyah, Nova Auliyatul; Trimono, Trimono; Damaliana, Aviolla Terza; Prasetya, Dwi Arman
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8316

Abstract

Diabetes mellitus is a deadly disease caused by the failure of the pancreas to produce enough insulin. Indonesia ranks fifth in the world with the number of people with diabetes in 2021 at around 19.47 million, and this number continues to increase. One of the main challenges in diabetes management is to make the right classification between type 1 and type 2 diabetes, as misdiagnosis can result in inappropriate treatment and worsen the patient's condition. This study uses a machine learning approach to compare Decision Tree and Random Forest methods in classifying type 1 and type 2 diabetes mellitus. The goal is to identify the most effective model in predicting the type of diabetes based on medical record data. The comparison was done using k-fold cross validation and confusion matrix. The results showed that Random Forest provided an average accuracy of 94%, while Decision Tree reached 93% during cross validation testing. Although both models were able to perform well in classification, Random Forest showed a more stable performance and a slight edge in accuracy over Decision Tree. Evaluation with the confusion matrix showed that the Decision Tree model achieved 93% accuracy compared to Random Forest's 91%. In addition, the Decision Tree model also had a lower number of prediction errors, 7, compared to 9 for Random Forest. The most influential variables in classification also differed between the two models, showing the unique advantages and characteristics of each approach.
Co-Authors Agustina, Fadlila Amri Muhaimin Ananta, Aditya Putra Anas Ahmadi Anugrah, Muhammad Cahya Raka Bey Lirna, Cagiva Chaedar Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Fahrudin, Tresna Maulana Febriyanti, Dea Putri Pascha Hindrayani , Kartika Maulida idhom, Mohammad Illah, Ibnu Zahy' Atha Ilmatus Sa'diyah Isyanto, Aisyah Kirana Putri Jauharis Sapu, Wahyu Syaifullah Kaffi, Laisal Kurniawan, Muhammad Erlangga Lestari, Amanda Ayu Dewi Maulana, Mohammad Hikmal Maulidiyyah, Nova Auliyatul Mohammad Idhom Muhaimin, Amri Muhammad Nasrudin Muizzadin, Muizzadin Nabilah Selayanti Nugroho, Muhammad Rafli Feandika Nurhalizah, Cesaria Deby Oktaviani, Sheny Eka Permadani, Citra Amelia Intan Pradipta, Alvino Hadiyan Putri, Adinda Rusdianti Maulani Putri, Irma Amanda Putri, Maretta Fairuz Luthfia Winoto Putri, Nabila Rizky Amalia Putri, Shafira Amanda Ramadhani, Dafinah Riyantoko, Prismahardi Aji Rizky Mahendra, Rakha Trimono Trimono, Trimono Wara, Shindi Shella May Windyadari, Chrysilla Citra Wiyono, Farhan Syah Putra Zahwa, Aniq Farichatus