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All Journal Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Asian Journal of Logistics Management Informasi interaktif : jurnal informatika dan teknologi informasi
Tampinongkol, Felliks Feiters
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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One-hot encoding feature engineering untuk label-based data studi kasus prediksi harga mobil bekas Herdian, Cevi; Kamila, Ahya; Tampinongkol, Felliks Feiters; Kembau, Agung Stefanus; Budidarma, I Gusti Agung Musa
Informasi Interaktif : Jurnal Informatika dan Teknologi Informasi Vol 9 No 1 (2024): JII Volume 9, Number 1, Januari 2024
Publisher : Program Studi Informatika Fakultas Teknik Universitas Janabadra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37159/jii.v9i1.41

Abstract

Penggunaan machine learning telah meluas di berbagai industri untuk analisis tren dan prediksi. Untuk memprediksi harga mobil bekas yang fluktuatif, penelitian ini menerapkan salah satu teknik Feature Engineering yaitu One-Hot Encoding, sebuah teknik Feature Engineering yang fokus kepada data-data label atau non-numeric. Studi ini mengeksplorasi data harga penjualan mobil bekas sebagai target variabel dan beberapa fitur seperti produsen, tahun keluaran, tipe mesin, jumlah pintu, dan popularitas. Hasil dari proses Feature Engineering ini sangatlah bagus, dimana R-Squared untuk data validasi adalah 0.85 dan untuk data testing adalah 0.86. Hasil penelitian ini memberikan informasi yang berharga bagi para peneliti dan profesional bisnis yang ingin membuat sebuah model prediksi khususnya bagaimana menangani sebuah data yang bentuknya adalah label kategori atau non-numeric.
Why Do Indonesian Users Remain Loyal to Digital Subscriptions? Examining Endowment Effect, Commitment Bias, Hedonic Motivation, and Switching Costs Kembau, Agung Stefanus; Mandey, Nancy Henrietta Jessamine; Tampinongkol, Felliks Feiters; Makarawung, Reynard Justino Nehemia
Asian Journal of Logistics Management Vol 4, No 1 (2025): Asian Journal of Logistics Management
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ajlm.2025.27028

Abstract

Subscription‐based platforms have become pivotal to digital business models, yet little is known about the psychological forces that bind customers to these services in emerging markets. Drawing on behavioral economics, this study theorizes that endowment effect and commitment bias build consumer loyalty by strengthening users’ perceived ownership and rationalizing prior choices. We further propose that hedonic motivation (enjoyment derived from service use) and switching costs (economic and psychological barriers to change) intensify these effects. Data from 224 digital-service subscribers in Greater Jakarta were analyzed with covariance-based structural equation modeling in SmartPLS. All six hypotheses are supported: both endowment effect (β = .32, p < .001) and commitment bias (β = .29, p < .001) directly enhance loyalty, while their impacts are amplified by hedonic motivation (interaction β = .14, p < .01) and switching costs (interaction β = .18, p < .01). The findings extend loyalty theory by positioning endowment and commitment as dual psychological anchors in subscription contexts and by identifying boundary conditions that magnify their influence. For managers, the results stress the value of fostering user ownership feelings, designing engaging service experiences, and deliberately increasing perceived costs of defection to boost retention. Collectively, the study offers a nuanced framework for understanding—and managing—the drivers of sustained patronage in rapidly growing digital economies.
Implementation of Random Forest Classification and Support Vector Machine Algorithms for Phishing Link Detection Tampinongkol, Felliks Feiters; Kamila, Ahya Radiatul; Wardhana, Ariq Cahya; Kusuma, Adi Wahyu Candra; Revaldo, Danny
Journal of INISTA Vol 7 No 1 (2024): November 2024
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v7i1.1588

Abstract

This research compares two machine learning methods, Support Vector Machine (SVM) and Random Forest Classification (RFC), in detecting phishing links. Phishing is an attempt to obtain sensitive information by masquerading as a trustworthy entity in electronic communications. Detecting phishing links is crucial in protecting users from this cyber threat. In this study, we used a dataset consisting of features extracted from URLs, such as URL length, the use of special characters, and domain information. The dataset was then split into training and testing data with an 80:20 ratio. We trained the SVM and RFC models using the training data and evaluated their performance based on the testing data. The results show that both methods have their respective advantages. SVM, known for handling high-dimensional data well and providing optimal solutions for classification problems, demonstrated a high accuracy rate in detecting phishing links. However, SVM requires a longer training time compared to RFC. On the other hand, RFC, an ensemble method known for its resilience to overfitting, showed performance nearly comparable to SVM in terms of accuracy but with faster training time and better interpretability. This comparison indicates that RFC is more suitable for scenarios requiring quick results and easy interpretation, while SVM is more appropriate for situations where accuracy is critical, and computational resources are sufficient. In conclusion, the choice of phishing link detection method should be tailored to specific needs and available resource constraints. This research provides valuable insights for developing more effective, efficient, and relevant phishing detection systems.
Co-Authors Ariq Cahya Wardhana Herdian, Cevi I Gusti Agung Musa Budidarma Budidarma Kamila, Ahya Kamila, Ahya Radiatul Kembau, Agung Stefanus Kusuma, Adi Wahyu Candra Makarawung, Reynard Justino Nehemia Mandey, Nancy Henrietta Jessamine Revaldo, Danny