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All Journal CCIT (Creative Communication and Innovative Technology) Journal
Henderi Henderi
Universitas Raharja, Indonesia
Computer Science & IT

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Implementation of the KNearest Neighbor Algorithm for Classifying Immigration Residence Permit Applicants at the Class I Special Immigration Office TPI SoekarnoHatta Nur Azizah; Henderi Henderi; Berisno Hendro Pardamean Manik Raja
CCIT Journal Vol 18 No 2 (2025): CCIT JOURNAL
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v18i2.3577

Abstract

This study aims to apply the K-Nearest Neighbor (KNN) algorithm in classifying immigration residence permit applicants at the Class I Special Immigration Office TPI Soekarno-Hatta, focusing on the algorithm's effectiveness and accuracy in categorizing residence permit applicants based on the types of residence permits: Visit Stay Permit (ITK), Limited Stay Permit (ITAS), and Permanent Stay Permit (ITAP). This study employs a quantitative, experiment-based approach utilizing a dataset of 17,212 residence permit applicant records consisting of 11 key attributes, such as nationality, visa type, residence permit type, gender, and age group.The research process began with data preprocessing stages, including data cleaning, normalization, and dataset splitting into training and testing sets with 80:20 and 70:30 partitioning scenarios. The KNN algorithm was implemented using a parameter of k=5k = 5k=5, chosen based on experimentation to achieve optimal performance. The model's performance evaluation was conducted using accuracy, precision, and recall metrics derived from a confusion matrix. The findings reveal that the KNN algorithm successfully classifies data with the highest accuracy of 96.95% in the 80:20 dataset partition scenario and 96.84% in the 70:30 scenario. The Visit Stay Permit (ITK) class demonstrated the best performance with a precision of 97.46% and a recall of 99.97%, whereas the Permanent Stay Permit (ITAP) class showed the lowest performance with a recall of 59.79%, indicating challenges in recognizing patterns for this class. This study also identifies the advantages of the KNN algorithm, including its simplicity of implementation, flexibility in handling multiclass data, and effectiveness for low-dimensional datasets. However, the algorithm has limitations, such as sensitivity to imbalanced data distributions and high computational time for large datasets.
Sentiment Analysis of Students Reviews on Campus Curriculum for the Workplace Using Bidirectional Encoder Representations from Transformers BERT Algorithm Ilamsyah Ilamsyah; Henderi Henderi
CCIT Journal Vol 19 No 1 (2026): CCIT JOURNAL
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v19i1.4189

Abstract

Higher education curricula play a crucial role in preparing graduates for the workforce; however, a gap often exists between theoretical instruction and the practical demands of industry. This study evaluates curriculum effectiveness through sentiment analysis of students reviews using the Bidirectional Encoder Representations from Transformers (BERT) model. A total of 250 students reviews were collected via open-ended questionnaires and processed using Python with libraries such as NLTK, Transformers, and Pandas for text cleaning, normalization, and tokenization. Twenty percent of the data were manually labeled, achieving a Cohen’s Kappa coefficient of 0.8405, indicating excellent inter-annotator agreement. The IndoBERT model—implemented using the Torch and Scikit-learn libraries—was trained to classify sentiments into positive, negative, and neutral categories. Results show that 82.7% of reviews were positive, praising the curriculum’s relevance to job readiness and technical skills; 12.7% were negative, highlighting insufficient practical content and soft skills development; and 4.5% were neutral. This research demonstrates that BERT-based sentiment analysis using Python is effective for curriculum evaluation, providing data-driven insights to help academic institutions enhance curriculum alignment with industry needs
Co-Authors Berisno Hendro Pardamean Manik Raja Ilamsyah Ilamsyah Nur Azizah