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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Teknik Informatika (JUTIF)
Theijer, Jessica
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Computer Science & IT Control & Systems Engineering

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Optimization of Electric Vehicle Charging Stations Recommendation for Intercity Travel in Bali Using K-NN Algorithm Theijer, Jessica; Tanamal, Rinabi
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 2 (2026): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.113372

Abstract

The problem of range anxiety among electric vehicle (EV) users is caused by the uneven distribution of Public Charging Stations (Indonesian: Stasiun Pengisian Kendaraan Listrik Umum, or SPKLU) in the Bali region often occurs. Currently, existing navigation applications provide SPKLU locations, but still lack route-based, battery-aware and vehicle connector type recommendations.To address this limitation, an SPKLU recommendation system was developed using the K-Nearest Neighbors (KNN) algorithm, specifically designed for intercity travel across Bali Island. The proposed method applies a two-stage filtering mechanism: Geodesic Distance for initial candidate selection, followed by the Google Maps Directions API for route-accurate distance validation. The research data were obtained through manual collection from the PLN Mobile application, containing geographic coordinate locations and connector type information. User inputs parameters include origin, destination, current EV range, maximum travel capacity, and vehicle connector type.Experimental results show that the system can provide accurate SPKLU suggestions aligned with planned routes and optimal charging intervals. The findings indicate that the proposed model is lightweight, adaptive, and effective in supporting EV users, thereby reducing range anxiety while contributing to the promotion of sustainable transportation in Indonesia.
Hybrid Unsupervised-Supervised Learning for Housing Submarket Segmentation and Price Prediction in Surabaya Urban Areas Tanamal, Rinabi; Nugraha, Satria Adi; Rasyid Jr, Nathalia Minoque Kusuma Salma; Dendy, Livanty Efatania; Theijer, Jessica
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5517

Abstract

Surabaya’s rapid population growth, reaching 3.02 million residents, has intensified housing affordability challenges and increased structural variability in residential markets. This study proposes a hybrid machine learning framework that combines unsupervised clustering with supervised classification to identify submarket segments and predict housing price categories. A dataset of 490 properties containing structural, land, ownership, and contextual features was preprocessed and analyzed using K-Means. Cluster quality assessment through elbow inspection and a silhouette score of 0.45 indicated the presence of five meaningful market segments. These segments served as targets for a supervised classification stage that evaluated seven models, optimized via randomized hyperparameter search within a standardized preprocessing pipeline. The RBF-SVM achieved the strongest performance, reaching 97 percent accuracy and a macro-F1 score of 0.97, representing an 8 percent improvement over non-hybrid baselines and outperforming boosted ensembles such as XGBoost. Permutation importance analysis identified number of floors, building orientation, position rank, and ownership status as dominant drivers of segment differentiation. The integration of clustering and classification enhances predictive reliability while improving interpretability, offering a transparent analytical toolkit for housing market assessment. The proposed framework provides actionable insights for developers, appraisers, and policymakers in Surabaya, enabling data-driven identification of submarkets and supporting more equitable housing strategies aligned with SDG 11 on sustainable urban development. The approach is scalable to other Indonesian cities and establishes a foundation for future work incorporating spatial, socioeconomic, or temporal predictors.
Co-Authors Dendy, Livanty Efatania Rasyid Jr, Nathalia Minoque Kusuma Salma Rinabi Tanamal Satria Adi Nugraha