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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) JURNAL MANAJEMEN INDUSTRI DAN LOGISTIK Jurnal Logistik Indonesia Dinasti International Journal of Education Management and Social Science Akbis: Media Riset Akuntansi dan Bisnis Jurnal Nuansa: Publikasi Ilmu Manajemen dan Ekonomi Syariah Land Journal Competitive Sainteks: Jurnal Sain dan Teknik Journal of Innovative and Creativity Journal of Maritime Policy Science
Syafrianita Syafrianita
Department Of Transportation Management, Universitas Logistik Dan Bisnis Internasional, Indonesia
Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Transportation Other

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Predictive Modeling of Delivery Delays in Transportation Using Machine Learning: A Comparative Study of Service Types Agus Purnomo; Nava Gia Ginasta; Syafrianita Syafrianita; Syafrial Fachri Pane
Dinasti International Journal of Education Management and Social Science Vol. 7 No. 2 (2025): Dinasti International Journal of Education Management And Social Science (Decem
Publisher : Dinasti Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/dijemss.v7i2.5736

Abstract

Traditional predictive models such as linear regression often struggle to capture the nonlinear interactions among operational factors that cause delivery delays in multi-category courier services. This study addresses that gap by developing and comparing machine learning (ML) algorithms to predict delivery delays across different service types at PT Pos Indonesia. The primary objective is to identify the most accurate predictive model and the dominant variables influencing delays across high-speed (Same Day, Next Day) and economical delivery services. A quantitative experimental design was employed using operational data from PT Pos Indonesia, consisting of 10,999 records and 12 variables. Three ML algorithms Logistic Regression, Random Forest, and XGBoost were trained and evaluated using standardized preprocessing, feature encoding, and stratified data splitting. Results show that Random Forest and XGBoost outperform Logistic Regression, each achieving approximately 65% accuracy with an AUC of 0.73, indicating moderate yet consistent predictive capabilities. Feature importance analysis reveals that Discount_offered, Weight_in_gms, and Prior_purchases are the most influential predictors of delivery timeliness.This study provides theoretical and practical contributions by introducing the first comparative ML framework for delay prediction in a national logistics context. The findings offer actionable insights for optimizing scheduling, load balancing, and promotional strategies, while advancing the integration of AI-based predictive analytics within postal logistics operations.
Optimization Disaster Logistics by Determining the Optimal Location and Number of Evacuation Centers Syafrianita Syafrianita; Agus Purnomo; Mohamed Ibrahim Abdul Mutalib
Jurnal Manajemen Industri dan Logistik Vol. 9 No. 2 (2025): 10 original research articles, were authored/co-authored by 33 authors from 2 c
Publisher : Politeknik APP Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30988/jmil.v9i2.1627

Abstract

Indonesia, particularly Bandung Regency, faces significant flood risks that disrupt livelihoods and damage infrastructure. This study identifies the optimal locations and number of evacuation centers using the Set Covering Problem (SCP) model, integrating geographic data, population density, accessibility, and infrastructure capacity. The study applied constraints including a 1,000-meter maximum service distance, minimum road width of 6 meters for Class IIIB and IIIC access, shelter capacity limits, and full coverage of demand points. Using ArcGIS 10.2.1, candidate locations were evaluated by overlaying flood vulnerability maps with accessibility and facility data. Environmental sustainability was addressed by selecting sites with minimal ecological disruption, avoiding sensitive zones, and reusing existing structures to reduce land conversion. Results show that five centralized shelters in high-density, well-connected areas can cut evacuation travel time by up to 20% compared to dispersed locations. This integrated approach improves response efficiency, ensures access for vulnerable populations, and supports sustainable site planning. The findings contribute to disaster logistics theory and offer practical, replicable guidance for policy in other flood-prone regions.
MDI and PI XGBoost regression-based methods: regional best pricing prediction for logistics services Agus Purnomo; Aji Gautama Putrada; Roni Habibi; Syafrianita Syafrianita
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 5: October 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i5.26037

Abstract

The logistics industry in Indonesia, with PT Pos Indonesia as the dominant player, is confronted with intense price competition. The challenge lies in establishing the most favorable price for regional logistics services in every region, with the aim of gaining a competitive edge and augmenting revenue. This intricate task encompasses local market conditions, competition, customer preferences, operational costs, and economic factors. To address this complexity, this study proposes the utilization of machine learning for price prediction. The price prediction model devised incorporates the extreme gradient boosting regression (XGBR), support vector machine (SVM), random forest, and logistics regression algorithms. This research contributes to the field by employing mean decrease in impurity (MDI) and permutation importance (PI) to elucidate how machine learning models facilitate optimal price predictions. The findings of this study can assist company management in enhancing their comprehension of how to make informed pricing decisions. The test results demonstrate values of 0.001, 0.005, 0.458, 0.009, and 0.9998. By employing machine learning techniques and explanatory models, PT Pos Indonesia can more accurately determine optimal prices in each region, bolster profits, and effectively compete in the expanding regional market.
Selection of 3PL Vendors for The Distribution of Garment Products Using The ANP: A Case at An Indonesia Garment Company Wafi Hudaya; Anggi Widya Purnama; Syafrianita Syafrianita
Jurnal Logistik Indonesia Vol. 9 No. 2: Oktober 2025
Publisher : Institut Ilmu Sosial dan Manajemen Stiami

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

Abstract

Decision-making for the selection of a 3PL vendor needs to look at many factors, currently PT BASIL has not determined a vendor for the new delivery route as well as previously the selection of a vendor based on price and fleet availability. This study uses the Analytical Network Process (ANP) method assisted by super decision which provides value information in the form of weight, alternatives that have high weight are recommended recommendations. The results of data processing from the highest order for the criteria are delivery 19%, service 16%, experience 15%, quality 14%, responsive 12%, performance 11%, insurance 7%, price 5%. For the sub-criteria, namely the reputation of the vendor, the availability of the car carrier when there is demand and the ability to fulfill the specified schedule when there is a demand 12.50%, the safety of goods 7.54%, payment 6.48%, good complaint handling 6.39%, low price 6.02%, the condition of the goods 4.96%, good communication 4.84%, liability 4.80%, late goods insurance 4.75%, damaged goods insurance 4.26%, flexible 4.24%, lost goods insurance 3.49%, punctuality 3.06%, ability to respond to defective and surviving goods claims 2.86%, non-defective goods 2.11%, accuracy of the quantity of units delivered and units received in good condition 2.10%, goods not lost 2.01%, fleet condition 1.40%, accurate 1.12%, documents 0.48%, paying attention to K3 in operational activities 0%. For the alternative with the highest weight, Lion Parcel is 36%, because Lion Parcel is a vendor that matches the priority needs of the company.
Public Management Model in Marine Pollution Control; A Case Study of Bintan Regency Fitri Kurnianingsih; Syafrianita Syafrianita; Armauliza Septiawan
Journal of Maritime Policy Science Vol. 2 No. 2 (2025): August, 2025
Publisher : Center for Maritime Policy and Governance Studies. Universitas Maritim Raja Ali Haji. Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31629/jmps.v2i2.7718

Abstract

Marine pollution is one of the most pressing environmental challenges in coastal areas, particularly in areas with intensive shipping and tourism activities such as Bintan Regency, Indonesia. Recurrent oil spills, accumulation of marine debris and microplastics, and degradation of mangrove and coral reef ecosystems highlight the need for an integrated governance model capable of ensuring both ecological sustainability and the socio-economic resilience of coastal communities. This study aims to assess the performance of public management in controlling marine pollution in Bintan and propose a governance model that emphasizes strengthening institutional coordination, preventive measures, and accountability mechanisms. The method used is a case study with a qualitative-descriptive approach, based on secondary data obtained from government documents, environmental regulations, media reports, and international scientific articles. Data were analyzed using thematic content analysis techniques to identify key issues, institutional roles, and the effectiveness of policy instruments. The results show that although regulatory frameworks such as PP No. 19/1999, DIKPLHD Bintan, and MARPOL obligations are in place, implementation in the field remains fragmentary and reactive, reflected in the recurrence of black oil pollution and limited ship waste reception facilities. This study emphasizes the importance of a polycentric governance model that integrates Integrated Coastal Zone Management (ICZM), collaborative governance between state and non-state actors, and economic instruments such as indirect fee schemes for wastewater treatment facilities. In conclusion, strengthening monitoring capacity, participatory evaluation, and multi-level actor integration are key to reducing the rate of recurrent pollution while protecting Bintan's coastal ecosystems and communities.
Co-Authors Agus Purnomo Aji Gautama Putrada Anggi Widya Purnama Armauliza Septiawan Fachmi Rachmat Chaidar, Diar Fitri Kurnianingsih Handoko, Kokoh Indra Batara Siahaan Lestiani, Melia Eka M. Yogi Riyantama Isjoni Mohamed Ibrahim Abdul Mutalib Muhammad Ardhya Bisma Nava Gia Ginasta Nurlaela Kumala Dewi Nurlaela Kumala, Dewi Pakpahan, Hartati Mediyanti Roni Habibi Shabrina, Fitriana Nur Suryo Mukti Rury, Febrianbaqi Syafrial Fachri Pane Wafi Hudaya Zahrayni, Hafizhah