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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Proceedings of KNASTIK TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnas Nasional Teknologi dan Sistem Informasi Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Seminar Nasional Teknologi Informasi Komunikasi dan Industri JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI ILKOM Jurnal Ilmiah SISFO International Journal of Social Science and Business Data Science: Journal of Computing and Applied Informatics ILKOMNIKA: Journal of Computer Science and Applied Informatics Journal of Social Research Sewagati Jurnal Teknik Elektro dan Komputer Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika
Ahmad Muklason
Institut Teknologi Sepuluh Nopember
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Penerapan Metode Hybrid LSTM dan Seasonal Decomposition untuk Peramalan Permintaan Tiket Harian PT KAI Yunianto, Rahmanizar Maksum; Muklason, Ahmad
ILKOMNIKA Vol 7 No 2 (2025): Volume 7, Number 2, August 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i2.733

Abstract

Peramalan permintaan tiket harian berperan penting dalam mendukung efisiensi operasional dan peningkatan kualitas layanan transportasi, termasuk pada kereta api. Penelitian ini membangun model peramalan permintaan tiket Kereta Api 1 Argo Bromo Anggrek dengan teknik machine learning yang menggabungkan pendekatan hibrida metode dekomposisi deret waktu dan algoritma Long Short-Term Memory (LSTM). Metode dekomposisi deret waktu memisahkan data menjadi komponen trend, seasonal, dan residual yang dianalisis secara terpisah guna memahami pola permintaan secara lebih mendalam. Hasil menunjukkan bahwa komponen trend memiliki korelasi kuat terhadap permintaan dan berperan penting dalam model peramalan. Model LSTM yang dikembangkan menunjukkan performa baik dengan nilai Mean Absolute Percentage Error (MAPE) sebesar 7,53% pada data uji. Model yang dihasilkan dapat dimanfaatkan untuk mendukung perencanaan operasional PT Kereta Api Indonesia (PT KAI). Pengembangan selanjutnya disarankan untuk mempertimbangkan variabel eksternal seperti hari libur, cuaca, dan kebijakan harga, serta diarahkan untuk mendukung sistem manajemen tarif dinamis (dynamic pricing) guna mengoptimalkan pendapatan dan efisiensi layanan.
Utilization of Business Intelligence Dashboards for Continual Improvement of It Services and Efficient Workforce Demand Prediction Based on Service Desk Ticket Data Chalid Sahuri, Afandi; Muklason, Ahmad
Journal of Social Research Vol. 5 No. 4 (2026): Journal of Social Research
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/josr.v5i4.3077

Abstract

In the digital age, organizations must provide efficient and reliable IT services to ensure business continuity. The implementation of ITIL 4 as a framework for IT service management has become essential in many organizations, including PT XYZ. However, challenges persist in service performance reporting, particularly due to manual, time-consuming processes and the absence of predictive analytics. This research focuses on the design and development of a Business Intelligence (BI) dashboard that integrates ITIL 4 principles to automate reporting, track SLA trends, and predict IT workforce needs. Using historical ticket data, this study employs ARIMA forecasting to predict future ticket volumes and optimize workforce planning. The BI dashboard provides a visual, real-time overview of service performance, ticket status, and SLA compliance, replacing traditional manual processes. Interviews with IT managers from various regions of PT XYZ inform the dashboard's design, ensuring it meets operational and strategic needs. The results indicate that the BI dashboard significantly improves reporting efficiency, enhances SLA monitoring, and supports data-driven decision-making. The integration of descriptive and predictive analytics provides a robust decision-support framework, promoting continual improvement in IT service management. Future research will enhance the system by incorporating external variables and hybrid forecasting models.
Co-Authors Adi Suryani, Adi Aelisa Nailin Nabila Agung Premananda, I Gusti Ahlan Robbani, Shof Rijal Alda Muhammad Sulaiman Amalia Utamima Amalinda Jayanty Angelita Depari, Helena Arif Djunaidy Arif Nuriman, Muhammad Berto Mulia Wibawa Chalid Sahuri, Afandi Diaz Perdana Emral Hakim Endarko Endarko Eskalalita, Eskalalita Fabroyir, Hadziq Faizal Mahananto Fajar Kurnia Malays Feby Artwodini Muqtadiroh Firdaus, Anang Grandys Frieska Prassida Hanif Mitsal Mahatta Hardhika Propitadewa I Gusti Agung Premananda I Gusti Agung Premananda Indraswari, Rarasmaya Ita Ulfin Jeremy Diaz Prasetyo Jihaan Harvi Habibah Lailatul Qadariyah Laura Wilhelmina Theresia M. Fiqri Ananda Marom, Ahsanul Mashuri Mashuri Mochamad Zidan Aqila Kamil Muhammad Ali Fikri Muhammad Aliffiro Naufal Muhammad Arif Nuriman Muhammad Daffa Maulana Muhammad Hisyam Muhammad Reza Pahlawan Naufal Rafiawan Basara Nazriyah Deni Tsaniyah Nur Aini Rakhmawati Nur Khofifah Nur Laila Nur Nindya Risnina Premananda, I Gusti Agung Putri, Malfa Liya Rahadiantino, Lienggar Rahma Fauzia Rahmawati, Deti Rahmawati, Deti Raras Tyas Nurita Raras Tyasnurita Retno A. Vinarti Retno Aulia Vinarti Riksakomara, Edwin Rizal Risnanda Hutama Septica Tiara Indah Permatasari Setiawan Setiyo Gunawan Soedarso Soedarso Soedarso Soedarso Soedarso, Soedarso Supoyo, Vicha Azthanty Supoyo, Vicha Azthanty Sutikno Tendio, Yusnardo Tri Achmadi Tsani Nahdliyah Ulya, Athiyatul Vicha Azthanty Supoyo Wiwik Anggraeni Wiwik Anggraeni Yunianto, Rahmanizar Maksum