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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang Jiko (Jurnal Informatika dan komputer) Jurnal Onoma: Pendidikan, Bahasa, dan Sastra bit-Tech International Journal of Advances in Data and Information Systems ESTIMASI: Journal of Statistics and Its Application Indonesian Journal of Data and Science 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 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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Religion 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 Social Sciences Other

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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): Jurnal Impresi Indonesia
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.
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.
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.
Application of the DeepSurv Model to Predict Survival in Patients with Kidney Failure Undergoing Hemodialysis Amanda, Rizki; Damaliana, Aviolla Terza; Idhom, Muhammad
Indonesian Journal of Data and Science Vol. 7 No. 1 (2026): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i1.389

Abstract

This study aims to improve survival prediction in patients with kidney failure undergoing hemodialysis, given their high mortality risk. Traditional models such as Cox Proportional Hazards (Cox PH) have limitations in capturing complex and nonlinear relationships in clinical data. Therefore, this study applies DeepSurv, a deep learning–based survival model, and compares its performance with Cox PH and Cox PH Spline. A total of 300 patients were included, with 165 events and 135 censored observations. The data were split into training and testing sets. DeepSurv was implemented using two hidden layers (64 and 32 neurons), a dropout rate of 0.2, and a learning rate of 1e-3. The model was trained for up to 1000 epochs with early stopping at epoch 435. Performance was evaluated using the concordance index (C-index) and time-dependent AUC at 365, 544, and 730 days. Patients were stratified into low-, medium-, and high-risk groups based on predicted scores. Results showed that Cox PH achieved a C-index of 0.913 and average AUC of 0.964, while Cox PH Spline reached 0.917 and 0.971. DeepSurv achieved a C-index of 0.920 and average AUC of 0.969. Performance differences were small, but DeepSurv provided consistent individual risk estimates. In conclusion, DeepSurv is a flexible approach with performance comparable to Cox-based models. Further external validation and clinical evaluation are needed before wider application
Comparison of the Effectiveness IndoBERT and mBERT for Sentiment Analysis of SME Customer Reviews Afandy, Selena Nurmanina; Hindrayani, Kartika Maulida; Damaliana, Aviolla Terza
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3501

Abstract

This study presents a structured comparative evaluation of IndoBERT and Multilingual BERT (mBERT) for three-class sentiment classification of customer reviews from Pawonkoe Banyuwangi, an Indonesian small and medium-sized enterprise (SME). Motivated by the limited transferability of IndoNLU-style benchmarks to real SME feedback, the central question is whether monolingual versus multilingual transformers remain reliable when fine-tuned on small, domain-specific, and operationally noisy datasets. A total of 365 survey-based reviews (January–December 2024), which is substantially smaller than typical transformer fine-tuning corpora, served as the empirical basis. Models were fine-tuned under matched hyperparameters and evaluated using a single stratified hold-out train–test split (not cross-validation), reporting accuracy, precision, recall, and F1-score. To reflect the deployed pipeline, mBERT additionally incorporates the original 1–5 rating as an auxiliary numeric signal alongside the review text, whereas IndoBERT is trained on text only. The results reveal a substantial performance gap: mBERT achieved 81% test accuracy, whereas IndoBERT reached 48% under the same evaluation setting. Because the label distribution is strongly imbalanced (with very few negative instances), these aggregate scores should be interpreted as overall effectiveness rather than minority-class robustness. Overall, the findings indicate that multilingual representations combined with auxiliary rating information can generalize more effectively in low-resource SME scenarios, while IndoBERT appears more sensitive to data scarcity in this context. The study offers practical guidance for model selection in resource-constrained Indonesian sentiment analytics and contributes evidence on transformer behavior beyond curated benchmarks.
Co-Authors Afandy, Selena Nurmanina Amanda, Rizki Ananta, Aditya Putra Anas Ahmadi Anugrah, Muhammad Cahya Raka Azzah, Alyssa Amorita Bey Lirna, Cagiva Chaedar Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Fahrudin, Tresna Maulana Febriyanti, Dea Putri Pascha Hindrayani , Kartika Maulida idhom, Mohammad Idhom, Muhammad Illah, Ibnu Zahy' Atha Ilmatus Sa'diyah J. S., Wahyu Syaifullah Jauharis Sapu, Wahyu Syaifullah Kaffi, Laisal Kartika Maulida Hindrayani Kurniawan, Muhammad Erlangga Lestari, Amanda Ayu Dewi Maulana, Mohamad Ibnu Fajar Maulana, Mohammad Hikmal Maulidiyyah, Nova Auliyatul Moerrin, Naufal Baihaqi Mohammad Idhom Muhaimin, Amri Muizzadin, Muizzadin Nugroho, Muhammad Rafli Feandika Oktaviani, Sheny Eka Permadani, Citra Amelia Intan Pradipta, Alvino Hadiyan Purwadwika, Reza Sadiya Putri, Adinda Rusdianti Maulani Putri, Irma Amanda Putri, Maretta Fairuz Luthfia Winoto Putri, Nabila Rizky Amalia Ramadhani, Dafinah Rizky Mahendra, Rakha Saputra, Wahyu Syaifullah Jauharis Shindi Shella May Wara Trimono, Trimono Wara, Shindi Sheila May Wiyono, Farhan Syah Putra Zahwa, Aniq Farichatus