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CUSTOMER CHURN PREDICTION USING THE RANDOM FOREST ALGORITHM Setiawan, Yosep; Hadiana, Asep Id; Umbara, Fajri Rakhmat
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 3 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i3.8711

Customer churn prediction plays a vital role in modern business, accurately influencing strategic and operational decisions that influence customer loyalty to a service. Customer churn focuses on customer retention being more profitable than attracting new customers because long-term customers provide lower profits and costs while losing customers increases the costs and need to attract new customers. However, customer churn still occurs frequently and cannot be predicted. If customer churn is left unchecked, it will endanger the company or banking industry because it can cause loss of income, damage reputation, and decrease market share. Random Forest, a data mining technique, was used in this research because of its ability to predict and handle many variables. This research aims to predict customer churn using the Random Forest method with datasets from Europe, especially France, Spain, and Germany, hoping to benefit the banking industry by identifying customers at high risk of abandoning services. This research is expected to benefit business people from customer churn predictions. Especially in the banking industry, it can help identify customers at high risk of abandoning service. Thus, companies can take appropriate steps to retain these customers, increase customer retention, strengthen customer loyalty and optimize their business performance. The results of this research are an accurate system for predicting customer churn in the future. The research obtained accuracy results of 87% in predicting customer churn using accuracy testing in the form of a confusion matrix.

Comparative Analysis of IndoBERT and LSTM for Multi-Label Text Classification of Indonesian Motivation Letter Setiawan, Yosep; Lili Ayu Wulandhari
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1499

The evaluation of motivation letters is a crucial step in the student admission process for one of vocational institutions in Indonesia. However, the current manual assessment method is prone to subjectivity and inconsistency, making it less reliable for fair student selection. This research presents a comparative analysis of two deep learning models, IndoBERT and Long Short-Term Memory (LSTM), for multi-label text classification of motivation letters written in Indonesian. Using a dataset of 676 motivation letters labeled with nine predefined categories, we evaluate the models based on their classification performance. The results indicate that IndoBERT outperforms LSTM, achieving an F1-score of 81%, compared to 76% for LSTM. This research provides insights into the effectiveness of IndoBERT for multi-label classification tasks in the Indonesian language and serves as a benchmark for future research in automating motivation letter evaluations.

CUSTOMER CHURN PREDICTION USING THE RANDOM FOREST ALGORITHM Setiawan, Yosep; Hadiana, Asep Id; Umbara, Fajri Rakhmat
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 3 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i3.8711

Customer churn prediction plays a vital role in modern business, accurately influencing strategic and operational decisions that influence customer loyalty to a service. Customer churn focuses on customer retention being more profitable than attracting new customers because long-term customers provide lower profits and costs while losing customers increases the costs and need to attract new customers. However, customer churn still occurs frequently and cannot be predicted. If customer churn is left unchecked, it will endanger the company or banking industry because it can cause loss of income, damage reputation, and decrease market share. Random Forest, a data mining technique, was used in this research because of its ability to predict and handle many variables. This research aims to predict customer churn using the Random Forest method with datasets from Europe, especially France, Spain, and Germany, hoping to benefit the banking industry by identifying customers at high risk of abandoning services. This research is expected to benefit business people from customer churn predictions. Especially in the banking industry, it can help identify customers at high risk of abandoning service. Thus, companies can take appropriate steps to retain these customers, increase customer retention, strengthen customer loyalty and optimize their business performance. The results of this research are an accurate system for predicting customer churn in the future. The research obtained accuracy results of 87% in predicting customer churn using accuracy testing in the form of a confusion matrix.

Pengembangan Website Skrining Kesehatan Mental Mahasiswa Berbasis IndoBERT Lite Menggunakan RAD dan Evaluasi SUS Ridha, Muhammad; Setiawan, Yosep; Abdur Rohman, Muhammad Kholil; Saputra, Muhammad Dwi; Yunior, Sheva Yudha
Jurnal Pendidikan dan Teknologi Indonesia Vol 6 No 1 (2026): JPTI - Januari 2026
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.1455

Permasalahan kesehatan mental pada mahasiswa terus meningkat seiring bertambahnya beban akademik dan tekanan sosial. Namun, keterbatasan layanan psikologis dan tingginya stigma membuat mahasiswa sering menunda atau menghindari pencarian bantuan profesional. Penelitian ini bertujuan mengembangkan sebuah platform skrining kesehatan mental berbasis Artificial Intelligence (AI) yang mampu menganalisis teks secara otomatis untuk mengidentifikasi tingkat stres, kecemasan, dan depresi pada mahasiswa. Metode Rapid Application Development (RAD) digunakan untuk mempercepat proses perancangan sistem serta memungkinkan penyesuaian prototipe secara iteratif. Novelty penelitian ini terletak pada integrasi model IndoBERT Lite sebagai mesin klasifikasi psikologis berbasis teks yang dilatih dan dievaluasi menggunakan data berlabel berdasarkan instrumen Depression Anxiety Stress Scales (DASS-21) serta melibatkan validasi dan masukan dari pakar di bidang kesehatan mental. Evaluasi performa menunjukkan bahwa model mencapai nilai Macro ROC-AUC sebesar 0,69, dengan performa terbaik pada klasifikasi depresi (ROC-AUC 0,86), yang menandakan kemampuan model yang memadai dalam mendukung skrining awal kesehatan mental. Pendekatan ini memberikan alternatif skrining yang lebih cepat, ringan, dan mudah dioperasikan dibandingkan metode asesmen tradisional yang memerlukan kehadiran profesional secara langsung. Evaluasi usability dilakukan menggunakan System Usability Scale (SUS) dengan melibatkan tujuh evaluator dari kalangan mahasiswa dan pakar IT. Sistem memperoleh skor rata-rata 89 yang termasuk dalam kategori excellent, menunjukkan tingkat kemudahan penggunaan dan pengalaman pengguna yang sangat baik. Hasil penelitian menunjukkan bahwa platform ini berpotensi menjadi alat pendukung skrining kesehatan mental yang efektif, serta berperan sebagai jembatan awal yang membantu mahasiswa mengakses layanan psikologis secara lebih terstruktur dan tidak mengintimidasi.

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