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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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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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Social Sciences Other

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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.
A Hybrid Neural Network-Time Series Regression Model for Intermittent Demand Forecasting Data Amri Muhaimin; Damaliana, Aviolla Terza; Muhammad Nasrudin; Riyantoko, Prismahardi Aji; Nabilah Selayanti; Putri, Shafira Amanda
Journal of Advances in Information and Industrial Technology Vol. 7 No. 2 (2025): Nov
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v7i2.704

Abstract

Forecasting is a vital tool that helps us make informed decisions by predicting future events based on past data. For forecasts to be accurate, it is important that the data is reliable, complete, and consistent. Yet, the intermittent data is a unique data that is challenging to forecast. Intermittent data contains a characteristic that the data has a lot of long zeros in some periods. The zero value will influence the model to generate a forecasting model. This study aims to tackle those problems by applying a hybrid approach. We integrate the regression model and neural network to create a novel approach for forecasting intermittent data. The dataset used for this data is from Kaggle, sales at Walmart supermarket for one category only. The sales data always produce an intermittent demand pattern, because not every day are the items always sold to customers. This irregular pattern makes the data difficult to forecast using a naïve approach, such as the Croston method, exponential smoothing, and ARIMA. To evaluate the performance of our model, some metrics were calculated. We use mean squared error, root mean squared error, and root mean squared scaled error. The result shows that our proposed method outperforms the benchmark model, with an RMSSE of 0.98, which is the lowest compared to other benchmark models in the root mean squared scaled error value. This result shows promise as an exciting solution for overcoming the challenges posed by irregular data in future forecasting tasks.
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