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All Journal SMARTICS Journal The IJICS (International Journal of Informatics and Computer Science) Jurnal Algoritma Buffer Informatika
Fathoni
Universitas Sriwijaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Analisis UX E-PPT Universitas XYZ dengan Metode User Experience Questionnaire Kevin Agustin Purba Purba; Umar Rahman Zidan; Muhammad Azyumardi Azra; Fathoni
SMARTICS Journal Vol 11 No 2 (2025): Journal SMARTICS (Oktober 2025)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v11i2.11916

Abstract

Digital transformation in academic administrative services demands systems that are efficient and user-experience oriented. This study aims to analyze the user experience of the e-PPT website of the Faculty of Computer Science at XYZ University, which functions as an academic administrative service platform. The research employs the User Experience Questionnaire (UEQ) method, which measures six core aspects of user experience: attractiveness, efficiency, perspicuity, dependability, stimulation, and novelty. A total of 40 active students from the Faculty of Computer Science at XYZ University participated by completing the UEQ questionnaire. The analysis results indicate that all UEQ dimensions received low scores, with the novelty dimension scoring negatively. Benchmarking against similar systems revealed that the e-PPT website falls within the lowest quartile (bottom 25%) across all scales. These findings underscore the urgent need for comprehensive improvements in the website’s design and functionality to enhance the quality of digital academic services in the future.
Penilaian Risiko Fraud Transaksi Digital menggunakan Hybrid Machine Learning dengan Clustering dan Klasifikasi Hendra Wijaya; Naek Parulian Hutagalung; Mira Afrina; Ali Ibrahim; Fathoni
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3398

Abstract

Credit card transaction fraud detection is commonly treated as a binary classification problem, whereas operational risk management requires more detailed risk-level information to support investigation prioritization. This study proposes a hybrid machine learning framework for transaction risk stratification. In the first stage, the K-Means algorithm was applied to the training set to discover latent risk structures and generate cluster-based risk labels. Subsequently, a Random Forest model was trained to predict risk levels for new transaction data. To maintain evaluation objectivity, the dataset was divided into training, validation, and testing sets, and data leakage prevention mechanisms were implemented. The testing results show that the model was able to consistently classify two levels of risk with stable precision, recall, and F1-score values. In the binary fraud detection scenario, the model achieved an accuracy of 0.8831. These findings indicate that separating latent risk exploration from predictive classification can produce a more informative risk representation compared to conventional binary approaches. However, this study is still limited to a single public dataset and one classification model. Therefore, the generalizability and potential performance improvements of the model still need to be evaluated by experimenting with other algorithms.
Deteksi Dini Churn Pelanggan E-Commerce Berbasis CRM di Indonesia Menggunakan Algoritma Random Forest Alifa Putri Shahabiyah; Zakirah Sabrina Putri Pasha; Aliya Faiza; Ali Ibrahim; Fathoni
Buffer Informatika Vol. 12 No. 1 (2026): Buffer Informatika
Publisher : Department of Informatics Engineering, Faculty of Computer Science, University of Kuningan, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/buffer.v12i1.566

Abstract

Customer churn merupakan salah satu permasalahan utama dalam industri e-commerce karena berdampak langsung terhadap penurunan jumlah pelanggan dan pendapatan perusahaan. Meskipun berbagai penelitian telah dilakukan untuk meningkatkan akurasi prediksi churn, sebagian besar masih berfokus pada aspek teknis tanpa mengaitkannya secara langsung dengan implementasi dalam Customer Relationship Management (CRM). Oleh karena itu, penelitian ini bertujuan untuk membangun model deteksi dini churn pelanggan berbasis machine learning serta mengkaji pemanfaatannya dalam mendukung strategi CRM.Penelitian ini menggunakan dataset ulasan pelanggan e-commerce yang diolah melalui tahap preprocessing, transformasi data, dan pembentukan label churn. Model dibangun menggunakan algoritma Random Forest dengan pendekatan Cross-Validation. Hasil pengujian menunjukkan model berjalan sangat baik dengan akurasi mencapai 99,03%, presisi 76,92%, dan recall 84,42%.Hasil penelitian menunjukkan bahwa data ulasan pelanggan efektif dalam merepresentasikan perilaku pelanggan dan dapat digunakan untuk mendeteksi potensi churn sejak tahap awal interaksi. Model yang dihasilkan dapat diintegrasikan ke dalam sistem CRM untuk mendukung strategi retensi pelanggan secara proaktif melalui deteksi dini pelanggan berisiko churn.
Classification of Depression Severity Using a Random Forest Algorithm Based on Lifestyle, Demographic, and Psychological Factors haniyah faizah; Oktavio Theonady; Syalwa Salsabillah S; Fathoni; Ali Ibrahim
Buffer Informatika Vol. 12 No. 1 (2026): Buffer Informatika
Publisher : Department of Informatics Engineering, Faculty of Computer Science, University of Kuningan, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/buffer.v12i1.569

Abstract

Depression among college students is a mental health issue that impacts quality of life and academic performance. However, factors influencing depression levels such as lifestyle, demographics, and psychological factors have not yet been analyzed in an integrated manner. This study aims to develop a depression severity classification model using the Random Forest algorithm based on these factors. The dataset consists of 1,998 records with 16 features selected through the Knowledge Discovery in Database (KDD) process. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The results show that the Random Forest model achieved an accuracy of 97.88% and an AUC of 0.998. Feature importance analysis indicates that the variables Symptoms, Nervous Level, and Employment Status are dominant factors in determining depression levels. Based on these results, the model is capable of effectively classifying depression levels and has the potential to serve as the basis for an early detection system in the university setting.
Analisis Pola Keputusan Pembelian di Tokopedia berbasis Machine Learning untuk Customer Relationship Management Andin Sabilla Janna; Tamara Juliyanti; Muhammad Bayu Samudra; Ali Ibrahim; Fathoni
Buffer Informatika Vol. 12 No. 1 (2026): Buffer Informatika
Publisher : Department of Informatics Engineering, Faculty of Computer Science, University of Kuningan, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/buffer.v12i1.571

Abstract

Pertumbuhan pesat platform e-commerce telah memengaruhi keputusan pembelian konsumen, khususnya pada marketplace seperti Tokopedia. Penelitian ini bertujuan menganalisis pola keputusan pembelian produk makanan dan minuman serta merumuskan implikasinya terhadap strategi Customer Relationship Management (CRM) berbasis data. Metode yang digunakan adalah pendekatan kuantitatif dengan dataset sebanyak 9.711 ulasan pelanggan yang diperoleh melalui Kaggle. Analisis dilakukan menggunakan Naive Bayes untuk klasifikasi keputusan pembelian ulang dan FP-Growth untuk menemukan pola asosiasi antara rating, harga, dan keputusan pembelian. Tahapan praproses meliputi pembersihan data, transformasi atribut, diskretisasi, serta pemrosesan teks menggunakan TF-IDF. Hasil penelitian menunjukkan bahwa Naive Bayes menghasilkan akurasi 60,18% dengan precision kelas Y sebesar 94,30%, namun recall masih dipengaruhi oleh ketidakseimbangan data. FP-Growth menunjukkan bahwa rating bintang 5 merupakan faktor paling dominan dalam pembelian ulang, dengan kombinasi rating bintang 5 dan harga sedang menghasilkan confidence tertinggi sebesar 98,6%. Temuan ini menegaskan pentingnya kepuasan pelanggan dan mendukung perumusan strategi CRM yang lebih terarah.
The Impact of Dominant Color in the Shopee Application's User Interface Design on User Focus Levels Fathimah Fadiyah Salimah; Septi Mulia Putri; Badia Inaya Sazrade; Fathoni
The IJICS (International Journal of Informatics and Computer Science) Vol. 10 No. 2 (2026): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v10i2.9686

Abstract

The design of mobile application interfaces plays a crucial role in shaping user experience, where color influences not only aesthetics but also user cognition and focus. This study aims to analyze the effect of the dominant orange color in the Shopee application interface on user focus levels. A quantitative correlational approach was employed, involving 32 respondents who completed a Likert-scale questionnaire, with data analyzed using SPSS. The results show a strong and statistically significant correlation between color perception and user focus (r = 0.681; p < 0.05), indicating that better evaluation of the dominant color is associated with higher focus levels. Furthermore, the coefficient of determination (R² = 0.464) reveals that nearly half of the variation in user focus is explained by color alone. These findings confirm that the dominant orange color enhances attention, supports sustained concentration, and improves navigation efficiency. This study highlights color as a key functional factor in optimizing user focus in mobile commerce applications.
Co-Authors Ali Ibrahim Alifa Putri Shahabiyah Aliya Faiza Andin Sabilla Janna Badia Inaya Sazrade Fathimah Fadiyah Salimah haniyah faizah Hendra Wijaya Kevin Agustin Purba Purba Mira Afrina Muhammad Azyumardi Azra Muhammad Bayu Samudra Naek Parulian Hutagalung Oktavio Theonady Septi Mulia Putri Syalwa Salsabillah S Tamara Juliyanti Umar Rahman Zidan Zakirah Sabrina Putri Pasha