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All Journal JPTK: Jurnal Pendidikan Teknologi dan Kejuruan Scan : Jurnal Teknologi Informasi dan Komunikasi Knowledge Engineering and Data Science JMKSP (Jurnal Manajemen, Kepemimpinan, dan Supervisi Pendidikan) Jurnal Penelitian Transportasi Laut Jurnal Praksis dan Dedikasi Sosial Journal of Vocational Education Studies (JOVES) International Journal of Advances in Data and Information Systems Dinamika Bahari: Journal of Maritime Dynamic TEKIBA : Jurnal Teknologi dan Pengabdian Masyarakat Journal of Dinda : Data Science, Information Technology, and Data Analytics Journal of Practical Computer Science (JPCS) Maritime Park: Journal Of Maritime Technology and Socienty Knowbase : International Journal of Knowledge in Database Journal Research of Social Science, Economics, and Management Innovative: Journal Of Social Science Research Bulletin of Social Informatics Theory and Application Information Technology International Journal (ITIJ) Jurnal Riset & Teknologi Terapan Kemaritiman Warta Penelitian Perhubungan
Setiawan, Ariyono
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences Transportation Other

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Uneven Transitions in Container Ship Capacity Across Indo-Pacific Economies (2010–2022): Integrating PCA, ANOVA, and Clustering Evidence Setiawan, Ariyono; Otok, Bambang Widjanarko; Handoko, Wisnu; Hadi, Abdul Razak Abdul; Onn, Choo Wou; Arli, Denni
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1442

Abstract

We examine uneven transitions in container ship capacity (TEU per ship) across five Indo-Pacific economies  China, Singapore, Australia, Vietnam, and Indonesia  during 20102022 using an integrated statistical framework that combines ANOVA, Welch ANOVA, GamesHowell post-hoc tests, Principal Component Analysis (PCA), and clustering. Results reveal persistent divergence: China and Singapore maintain high-capacity fleets (>10,000 TEU/ship), Australia stabilizes in the mid-tier range (~7,000 TEU/ship), while Indonesia and Vietnam experience rapid but low-level growth (<6,000 TEU/ship). ANOVA confirms significant cross-country differences (F=28.33; p<0.001; 0.65), with Welch ANOVA yielding consistent results under unequal variances (p<0.01). PCA indicates one dominant component (PC199.5%) explaining most variance, forming three readiness clusters: high, medium, and low capacity economies. These patterns suggest that policy inertia, infrastructure bottlenecks, and green transition constraints drive the uneven capacity development. The study contributes by introducing TEU per ship as a cross-national indicator for maritime readiness, linking statistical divergence to SDG targets 8, 9, 10, 13, and 14, and offering empirical guidance for low-carbon fleet transition and port modernization in emerging economies..
PREDICTION OF SEA LEVEL MEASUREMENT IN PANGPANG BAY FOR SEAPLANE LANDING SEAPLANE LANDING USING ID CONVOLUTIONAL NEURAL NETWORK Setiawan, Ariyono; Islam, Fajar; Efendi, Efendi; Globalisasi, Safitri Era; Hammad, Jehad A.H
Jurnal Praksis dan Dedikasi Sosial Vol. 7 No. 2 (2024)
Publisher : Universitas Negeri Malang

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This research investigates the relationship between sea level height and various environmental factors in Pangpang Bay, Indonesia, using Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) modeling techniques. Daily data on sea level height, weather, and oceanography were collected from April 1 to April 15, 2024. An analysis was conducted on the factors affecting sea level height and the evaluation of predictive model performance. The findings reveal historical patterns of sea level height changes influenced by the variability of meteorological and oceanographic conditions. Although ANN and CNN models have varying degrees of accuracy, both show potential in predicting sea level height by considering environmental factors. Recommendations include the development of more advanced predictive models, deeper data observation, integration of multidisciplinary information, continuous environmental monitoring, and stakeholder collaboration. This research is expected to contribute to the understanding and management of environmental risks related to sea level height in Pangpang Bay.
IMPLEMENTASI MACHINE LEARNING UNTUK MENINGKATKAN KUALITAS OPERASIONAL SERVICE KENDARAAN DENGAN METODE RANDOM FOREST DAN LOGISTIC REGRESSION Mandenni, Ni Made Ika Marini; Wiratama, I Putu Bayu Adhya; Setiawan, Ariyono; Putri, Gusti Agung Ayu; Dana, I Putu Ngurah Krisna
Jurnal Praksis dan Dedikasi Sosial Vol. 7 No. 2 (2024)
Publisher : Universitas Negeri Malang

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IMPLEMENTATION OF MACHINE LEARNING TO IMPROVE THE QUALITY OF VEHICLE SERVICE OPERATIONS WITH RANDOM FOREST AND LOGISTIC REGRESSION METHODSVehicle motor repairs (service) are an important aspect for motor vehicle owners to undertake. This activity is carried out by automotive workshops to ensure that the customer's vehicle is in prime condition. To boost sales, some automotive workshops offer various promotional packages to attract customer interest. However, in practice, this is done manually by workshop staff, resulting in suboptimal performance in offer presentations (customer calls). This research aims to build a recommendation system for package deals and offer dates to enhance the quality of customer calls in the operations of automotive workshops using Random Forest and Logistic Regression. The dataset used is operational data from customer calls at one automotive workshop in Bali. The Random Forest model achieves 91 percent accuracy, while Logistic Regression achieves 72 percent accuracy. The system developed can be used to recommend good package deals and offer dates to customers.Perbaikan kendaraan bermotor (service) merupakan hal penting untuk dilakukan bagi pemilik kendaraan bermotor. Kegiatan ini dilakukan oleh bengkel otomotif untuk memastikan kondisi kendaraan customer dalam kondisi prima. Untuk meningkatkan penjualan, beberapa bengkel otomotif menawarkan berbagai paket promo untuk menarik minat customer. Namun dalam pelaksanaannya, hal ini dilakukan secara manual oleh staff bengkel yang mengakibatkan performa penawaran (customer call) kurang optimal. Penelitian ini bertujuan untuk membangun sistem rekomendasi paket dan tanggal penawaran untuk meningkatkan kualitas customer call pada operasional bengkel otomotif menggunakan Random Forest dan Logistic Regression. Dataset yang digunakan adalah data operasional customer call salah satu bengkel otomotif di Bali. Model Random Forest mencapai akurasi 91 persen dan Logistic Regression mencapai akurasi 72 persen. Sistem yang dibangun dapat digunakan untuk merekomendasikan paket dan tanggal penawaran yang baik untuk ditawarkan kepada customer.
Random Forest Algorithm to Measure the Air Pollution Standard Index Setiawan, Ariyono; Wibowo, Untung Lestari; Mubarok, Ahmad; Larasati, Khoirunnisa
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

This study uses the Random Forest algorithm to measure and predict the Air Pollution Standard Index (APSI) at Blimbing Banyuwangi Airport. Air pollution data, including concentrations of O3, CO, NO2, SO2, PM2.5, and PM10, were collected from air monitoring stations at the airport from April 15-30, 2024. APSI measurement followed established formulas by relevant authorities. Data analysis utilized statistical approaches and computational algorithms. The findings reveal that air quality at the airport is generally "Moderate," with occasional "Good" days. The Random Forest algorithm effectively predicts APSI based on existing pollution data. These results provide insights for improving air pollution management at the airport and surrounding areas, emphasizing the need for continuous air quality monitoring. Days classified as "Moderate" suggest health risks for sensitive groups, indicating the need for targeted mitigation strategies. Recommendations include increasing green spaces, optimizing flight schedules to reduce peak pollution, and raising public awareness about air quality. The effectiveness of the Random Forest algorithm suggests its potential application in other airports for proactive air quality management. Future research could integrate real-time data and advanced machine learning models for more accurate and timelier APSI predictions.
Co-Authors Abdul Razak Abdul Hadi, Abdul Razak Abdul Razak Bin Abdul Hadi Ahmad Bahrawi, Ahmad Aji Prasetya Wibawa Antoni Arif Priadi Ardian, Dede Arli, Denni Arrova Dewi, Deshinta Bambang Widjanarko Otok Bin Abdul Hadi, Abdul Razak Dana, I Gusti Ngurah Krisna Daniel Dewantoro Rumani Dewi Hidayati Dewi, Deshinta Arrova Dwi Kusuma, Irma Efendi Efendi Eppy Yundra, Eppy Erwin Faller Fajar Islam Faller, Erwin Fatmawati Sabur Febiyanti, Herlina Globalisasi, Safitri Era Gusti Agung Ayu Putri Hadi, Abdul Razak Abdul Hammad, Jehad A. H Hammad, Jehad A.H Handoko, Wisnu Harianto, Bambang Bagus I Gede Susrama Mas Diyasa Ika, Ni Made Ikhwanul Qiram Islam, Fajar Larasati, Khoirunnisa Luthfiyah Nurlaela M. Anjar Pamungkas Masdiyas, I Gede Susrama Meini Sondang Mubarok, Ahmad Nahusuly, Barep J. A. I. Ni Made Ika Marini Mandenni Onn, Choo Wou Pambudiyatno, Nyaris Patrisia Mintje, Quirina Ariantji Prasetyo, Kukuh Tri Putra, Iwansyah Putri, Fitri Aulia Yuliandi Raditya, Askara Rusdyansyah, Arief Sazali, Abdul Muti SRI RAHAYU Sri Wibawani Sutoyo Sutoyo Sutralinda, Dety Suwondo, Iie Terza Damaliana, Aviolla Tirtanto, Jadon Pieter Elia Tri Prasetyo, Kukuh Utomo, Tri Rizki Wahyuni, A. A. Istri Sri Wibowo, Untung Lestari Widjanarko Otok , Bambang Widyaningsih , Upik Widyaningsih, Upik Wiratama, I Putu Bayu Adhya Wou Onn, Choo Yudianto, Prima Yudha Yuniar, Dwi Candra Yuyun Suprapto