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All Journal Jurnal Simantec Jurnal Ilmiah Kursor TEKMAPRO Journal of Industrial Engineering and Management Scan : Jurnal Teknologi Informasi dan Komunikasi International Journal of Advances in Intelligent Informatics International Journal of Artificial Intelligence Research Teknika: Engineering and Sains Journal JIEET (Journal of Information Engineering and Educational Technology) IJEBD (International Journal Of Entrepreneurship And Business Development) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Penelitian SEINASI-KESI Journal of Information Systems and Informatics bit-Tech Journal of Robotics and Control (JRC) JATI (Jurnal Mahasiswa Teknik Informatika) e-NARODROID Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Journal of Applied Data Sciences International Journal of Data Science, Engineering, and Analytics (IJDASEA) ITIJ Jurnal ilmiah teknologi informasi Asia Jurnal Pengabdian Masyarakat SENSASI Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
I Gede Susrama Mas Diyasa
Department Of Informatics, Faculty Of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Transportation

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Journal : bit-Tech

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Performance Comparison of Gaussian Mixture Model, Hierarchical Clustering, and K-Medoids in Passenger Data Clustering Thalita Syahlani Putri; I Gede Susrama Mas Diyasa; Achmad Junaidi
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3013

Abstract

The rapid growth of urban populations and increasing reliance on public transportation in Indonesia present challenges in managing passenger demand effectively. In Surabaya, the steady rise in Suroboyo Bus passengers underscores the need for data-driven strategies to optimize fleet allocation, scheduling, and infrastructure development. Identifying passenger density patterns through clustering provides a systematic basis for decision-making. This study aims to address a local research gap by comparing three clustering algorithms Agglomerative Hierarchical Clustering (AHC), Gaussian Mixture Model (GMM), and K-Medoids on empirical passenger data. Unlike previous studies that emphasize route optimization or demand forecasting, this research highlights a comparative evaluation to determine the most effective method for handling fluctuating and outlier-prone transportation data. The dataset was obtained from the Surabaya City Transportation Office for the Purabaya–Perak route during a two-week period in 2024. Data preprocessing included attribute selection, transformation of time into numerical format, outlier detection using the Interquartile Range (IQR), and Z-Score normalization. Clustering results were assessed with the Silhouette Score and visualized using scatter plots and histograms. Findings show that K-Medoids achieved the highest Silhouette Score (0.4222), surpassing AHC (0.3657) and GMM (0.3024). K-Medoids produced more balanced clusters and stronger resilience to outliers, while AHC provided interpretable hierarchical structures, and GMM modeled complex patterns but with weaker separation. In conclusion, K-Medoids is recommended as the most suitable approach for passenger density clustering. Academically, this study contributes a comparative framework for clustering in transportation research, while practically offering insights to support data-driven public transport management in developing cities.
Bus Passenger Demand Forecasting Using A Hybrid ARIMA–MLP Model Naufal Baihaqi Moerrin; Aviolla Terza Damaliana; I Gede Susrama Mas Diyasa
bit-Tech Vol. 8 No. 3 (2026): bit-Tech - IN PROGRESS
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3549

Abstract

Accurate passenger demand forecasting is crucial for operational planning and service reliability in public transportation systems. Despite the effectiveness of traditional models, existing approaches often struggle with nonlinear fluctuations in demand, which limits their ability to adapt to real-world variability. This study proposes a hybrid forecasting framework that combines the Autoregressive Integrated Moving Average (ARIMA) model with a Multi-Layer Perceptron (MLP) neural network for short-term passenger demand prediction. By using ARIMA to capture linear components like trend, seasonality, and autocorrelation, and MLP to model the residuals that contain nonlinear patterns, the proposed approach integrates the strengths of both models. This hybrid method addresses gaps in current forecasting techniques by improving adaptability and precision. Empirical analysis was conducted using daily passenger count data from Bus Trans Jatim during 2023–2024. Data preprocessing included exploratory time series analysis, variance stabilization, and outlier assessment to ensure compatibility with the modeling assumptions. Forecast performance was evaluated using the Mean Absolute Percentage Error (MAPE). The results show that the hybrid ARIMA–MLP model achieved a MAPE of 4.95%, outperforming the standalone ARIMA model in providing more adaptive and accurate short-term forecasts. These findings have practical implications for public transportation planning, enabling more responsive and efficient operations, particularly for forecasting demand fluctuations.
Co-Authors Achmad Junaidi Achmad Junaidic Adiwidyatma, Afdhal Reshanda Ahmad Naufal Mumtaz Akmal, Mohammad Faizal Alfiatun Masrifah Alhamda, Denisa Septalian Amanullah , Nurkholis Anak Agung Diah Parami Dewi Ardianto, Taruna Ariyono Setiawan Aryananda, Rangga Laksana Aurelia, Cenditya Ayu Aviolla Terza Damaliana Awaludin W., Moh. Haydir Awang, Mohd Khalid Azizah, Nabila Wafiqotul Bambang Trigunarsyah Bambang Trigunarsyah Budi Nugroho Cahyani Kuswardhani, Hajjar Ayu Dewi, Deshinta Arrova Dewi, Deshinta Arrowa Dwi Arman Prasetya Dwi Kusuma, Irma Erma Suryani Etniko Siagian, Pangestu Sandya Eva Yulia Puspaningrum Fara Disa Durry Fatmah Sari, Allan Ruhui Firmansyah, Taufik Nur Firya Nadhira Gideon Setya Budiwitjaksono Gideon Setya Budiwitjaksono Gunawan, Ellexia Leonie Hadi, Surjo Hafidz Amarul Ma’rufi Halim, Christina Hamawi, Moch. Hawin Humairah, Sayyidah I Nyoman Dita Pahang Putra I Nyoman Dita Pahang Putra Ilham Ade Widya Sampurno Ilham Ade Widya Sampurno Intan Yuniar Purbasari Jauharis Saputra, Wahyu Syaifullah Jojok Dwiridotjahjono Kraugusteeliana Kraugusteeliana Mandeni, Ni Made Ika Marinni Mandyartha, Eka Prakarsa Moch. Hatta Mohamad Nur Amin Mohammad Idhom Mohammad Rafka Mahendra A Mohammad Rafka Mahendra Ariefwan Mudjahidin Muhammad Rif'an Dzulqornain Mumtaz, Ahmad Naufal Munoto Mustika, Agung Nadhira, Firya Nahusuly, Barep J. A. I. Naufal Baihaqi Moerrin Ni Made Ika Marini Mandenni Ni Made Ika Marini Mandenni NYOMAN DITA PAHANG PUTRA, NYOMAN Prabowo, Aris Prasetyo, Galih Novian Putri, Fitri Aulia Yuliandi Raditya, Askara Rangga Laksana A Rangga Laksana Aryananda Rheza Rizqi Ahmadi Ridho Syahdindo Rizal Harjo Utomo Sabrina Charya Floribunda Santoso, Sri Fuji Senny Meliyan Setiawan, Ariyono Setiawan, Ariyono Shodiq, Ja’far Slamet Winardi Sri Wibawani, Sri Sugeng Purwanto Sugiarto S Sugiarto Sugiarto Sugiarto, Sugiarto Sukri, Hanifudin Sulianto Bhirawa Sunarko, Victor Immanuel Suryani, Dedik Taruna Ardianto Terza Damaliana, Aviolla Thalita Syahlani Putri Trimono, Trimono Wafiqotul Azizah, Nabila Wahyu Caesarendra Wahyu Dwi Lestari Wahyu S.J. Saputra Wan Awang, Wan Suryani Wardhani, Naritha Cahya Widianto, Purwito Ridho Widiastuty, Riana Retno Wijaya, Pandu Ali Yisti Vita Via