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All Journal Jurnal Ilmiah Mahasiswa FEB SAINTIFIK@ : Jurnal Pendidikan MIPA Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Agroteksos Proceedings Series on Physical & Formal Sciences Sejahtera: Jurnal Inspirasi Mengabdi Untuk Negeri Nanggroe: Journal Of Scholarly Service Jurnal Manajemen Riset Inovasi Maeswara: Jurnal Riset Ilmu Manajemen dan Kewirausahaan Nanggroe: Journal of Scholarly Service Faedah: Jurnal Hasil Kegiatan Pengabdian Masyarakat Indonesia JEAS Jurnal Penelitian Teknologi Informasi dan Sains Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Jurnal Inovasi Ekonomi Syariah dan Akuntansi Jurnal Bisnis Inovatif dan Digital Router : Jurnal Teknik Informatika dan Terapan Repeater: Publikasi Teknik Informatika dan Jaringan Algoritma: Jurnal Matematika, Ilmu Pengetahuan Alam, Kebumian dan Angkasa Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Karya Nyata: Jurnal Pengabdian Kepada Masyarakat Router : Jurnal Teknik Informatika dan Terapan
Andy Hermawan
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ANALISIS PENGARUH UMK DALAM PENGENTASAN KEMISKINAN DI JAWA TIMUR HERMAWAN, ANDY
Jurnal Ilmiah Mahasiswa FEB Vol. 7 No. 2
Publisher : Fakultas Ekonomi dan Bisnis Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penelitian bertujuan untuk mengetahui pengaruh dari Upah Minimum Kabupaten/Kota(UMK) di provinsi Jawa Timur pda tahun 2012 dan 2016. Kemiskinan merupakan isu nasionaldimana tiap provinsi memiliki strategi yang berbeda beda untuk menyikapinya. Jawa timurmerupaka salah satu provinsi dengan kepadatan penduduk yang tinggi yaitu pada posisi kelimadan posisi kedua menurut jumlah pnduduk tiap provinsi di Indonesia. keadaan demograi tersebutmenyebabkan Jawa Timur menjadi Provinsi yang sangat potensial dalam pengembangan ekonomidikarnakan melimpahnya Sumber Daya Manusia yang tersedia. Namun pada kenyataannya diProvinsi Jawa Timur pada satu dekade terakhir menjadi salah satu penyumbang angkakemiskinan nasional, yaitu 10.85 persen pada tahun 2018 dan UMK provinsi Jawa Timur masihberada dibawa rata rata nasional padahal pertumbuhan cenderung selalu berada diatas nasional.Penelitian menggunakan data panel 38 kabupaten/kota pada tahun 2012-2016. Hasil penelitianmenunjukkan ditemukan bukti bahwa kenaikan upah minimum dan investasi dapat menurunkantingkat kemiskinan yang ada di Jawa Timur, namun terdapat elastisitas daerah dengan IPM yangtinggi cenderung memerlukan kenaikan yang tinggi untuk dapat menurunkan kemiskinandibandingkan daerah dengan IPM rendah. Maka dari itu, peningkatan UMK masih dapatdilakukan namun dengan mempertimbangkan karakteristik tiap tiap daerah.Kata kunci: Kemuskinan, UMK, IPM, Pertumbuhan Ekonomi, Investasi. 
Implementasi Algoritma Apriori pada Market Basket Analysis terhadap Data Penjualan Produk Supermarket Andy Hermawan; Bayu Wicaksono; Tigfhar Ahmadjayadi; Bagas Surya Prakasa; Jasico Dacomoro Aruan
Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa Vol. 2 No. 5 (2024): Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/algoritma.v2i5.137

Abstract

Market Basket Analysis (MBA) is an analytical technique used to identify relationships between items in purchasing transactions. This notebook uses retail transaction datasets and the Apriori algorithm to discover hidden associations and patterns that retailers can leverage in optimizing marketing strategies, store layouts, and product recommendations. Through initial data processing, data exploration, and application of the Apriori algorithm, this analysis succeeded in identifying various significant associations between items that are frequently purchased together. The results provide valuable insights for retailers to develop targeted promotions and improve customer shopping experiences, while emphasizing the importance of selecting the right parameters to obtain accurate and relevant results.
Deep Learning as an Implementation of Mathematical Theory for Modeling Sentiment Dynamics: The Case of Pertamina’s “BBM Oplosan” Issue Hermawan, Andy; Cindana, Adinda Prilly; Nainggolan, Dian Margaretha; Safryan, Rizky Jemal
SAINTIFIK@: Jurnal Pendidikan MIPA Vol 10, No 2 (2025): SAINTIFIK@: Jurnal Pendidikan MIPA EDISI OKTOBER 2025
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/saintifik.v10i2.10869

Abstract

Public sentiment dynamics provide a quantitative reflection of how societal trust and perception evolve during crises. This study implements mathematical theory through deep learning techniques to model changes in public sentiment surrounding Pertamina’s “BBM Oplosan” (fuel adulteration) issue, which went viral in Indonesia in early 2025. Twitter (X) data containing the keyword “Pertamina” were collected across two temporal windows—before and after the issue’s emergence. Sentiment was classified into positive, neutral, and negative categories using both lexicon-based analysis (InSet Lexicon) and deep learning architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN-LSTM model. From a mathematical standpoint, deep learning serves as a functional approximation framework that minimizes loss through gradient-based optimization—an implementation of multivariable calculus and linear algebra principles. Results show that negative sentiment increased from 23.5% to 48.2%, while positive sentiment declined from 44.6% to 26.2%, indicating a significant erosion of public trust. The CNN model achieved the highest validation accuracy (~63%), though it exhibited signs of overfitting. This research demonstrates how mathematical models underlying deep learning can be effectively applied to analyze real-world social phenomena, offering a robust quantitative framework for monitoring and interpreting public opinion dynamics during corporate crises.
Predicting Hotel Booking Cancellations Using Machine Learning for Revenue Optimization Andy Hermawan; Aji Saputra; Nabila Lailinajma; Reska Julianti; Timothy Hartanto; Troy Kornelius Daniel
Router : Jurnal Teknik Informatika dan Terapan Vol. 3 No. 1 (2025): Maret: Router : Jurnal Teknik Informatika dan Terapan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/router.v3i1.400

Abstract

Hotel booking cancellations pose significant challenges to the hospitality industry, affecting revenue management, demand forecasting, and operational efficiency. This study explores the application of machine learning techniques to predict hotel booking cancellations, leveraging structured data derived from hotel management systems. Various classification algorithms, including Random Forest, XGBoost, and LightGBM were evaluated to identify the most effective predictive model. The findings reveal that XGBoost model outperforms other models, achieving F2-score of 0.7897. Key influencing factors include deposit type, total number of special requests, and marketing segment. The results underscore the potential of predictive modeling in optimizing hotel revenue strategies by enabling proactive measures such as dynamic pricing, targeted customer engagement, and improved overbooking policies. This study contributes to the ongoing advancements in data-driven decision-making within the hospitality industry, offering insights into how machine learning can mitigate financial risks associated with booking cancellations.
Leveraging the RFM Model for Customer Segmentation in a Software-as-a-Service (SaaS) Business Using Python Andy Hermawan; Nila Rusiardi Jayanti; Aji Saputra; Army Putera Parta; Muhammad Abizar Algiffary Thahir; Taufiqurrahman Taufiqurrahman
Maeswara : Jurnal Riset Ilmu Manajemen dan Kewirausahaan Vol. 2 No. 5 (2024): OKTOBER : Maeswara : Jurnal Riset Ilmu Manajemen dan Kewirausahaan
Publisher : Asosiasi Riset Ilmu Manajemen Kewirausahaan dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/maeswara.v2i5.1283

Abstract

Customer segmentation plays a pivotal role in driving marketing strategies and improving customer retention across various industries. This study explores the application of the RFM (Recency, Frequency, Monetary) model for customer segmentation in a Software-as-a-Service (SaaS) business, using Python for efficient data processing and analysis. By analyzing one year of customer purchase data, we segmented customers into key groups such as "Champions," "Loyal Customers," and "At Risk." The results highlight that targeted discount strategies significantly affect profitability, especially for high-value customer segments. Furthermore, the research builds upon existing methodologies, demonstrating how Python-based implementations streamline RFM analysis and allow for scalable solutions in business contexts, as illustrated in prior works by Hermawan et al. (2024). This study offers actionable recommendations, including tailored discounting, loyalty programs, and personalized engagement strategies, to enhance customer retention and business profitability. The findings underscore the importance of data-driven marketing approaches for customer segmentation and engagement, reinforcing the relevance of the RFM model in modern business environments.
Co-Authors Adam Praharsya Rahmadian Agatha Christy Situru Aji Saputra Aji Saputra Aji, Krishna Amaliasyifa Agustina Amira Afdhal Angga Sukma Budi Darmawan Army Putera Parta Ayi Satria Yuddha B Hilda Nida Alistiqlal Bagas Dio Hanggoro Bagas Surya Prakasa Bayu Wicaksono Biky, Muhammad Amir cahaya Tambunan Chumidach Roini Cindana, Adinda Prilly Dewi Amiroh Dzaky Muhammad Baihaqi Erwin Surya Fachmi Aditama Fatika Rahma Sanjaya Fatma Hamid Ferdiana Ferdiana Firmansyah Firmansyah Gregorius Aldo Primantono Hijrasil Hijrasil Hilya Wildana Sofia Hutri Handayani Isra Indah Kristiani Siringo Ringo Ismi Musdalifah Darsan Iwan Abdy Jasico Dacomoro Aruan Jayanti, Nila Rusiardi Kerin Aurelia Khoironi Fanana Akbar Lilik Kartika Sari, Lilik Kartika Limatahu, Iqbal Lintang Rizki Ramadhani Masrifah, Masrifah Mirda Prisma Wijayanto Mochammad Rivan Akhsa Muhamad Fauzi Hakim Muhammad Abizar Algiffary Thahir Muhammad Alif Syahreza Muhammad Dhika Rafi Muhammad Hafizh Bayhaqi Muhammad Iqbal Muhammad Mustofa Muhammad Syahirul Alim, Muhammad Syahirul Nabila Lailinajma Nainggolan, Dian Margaretha Nasrun Balulu Nila Rusiardi Jayanti Nur Fajrhi Nurdin Abdul Rahman Nurlaela Muhammad Nurul Ainun Tangge Nurul Hidayah Nuur Muhammad Ilham Palti Maretto Caesar Manalu Ravli Avdala Kahfi Reska Julianti Riris Idiawati Risky Hidayat Roni Kurniawan Rusandry Rusandry Safryan, Rizky Jemal Sandy, Agung Ferdinan Saprudin, Saprudin Sartika Putri Sailuddin Sheila Kusumaningrum Sitti Mukarramah Sumarni Sahjat, Sumarni Syafiq Basmallah Syahrial Maulana Taufiqurrahman Taufiqurrahman Tigfhar Ahmadjayadi Timothy Hartanto Troy Kornelius Daniel Ulfatul Aini Usman Sambiri Vita Mayastinasari Wafa Nabila Wakhidati, Yusmi Nur Yilmaz Trigumari Syah Putra Zacharia Bachtiar Zandy Pratama Zain Zidan Amukti Rajendra