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TIN: TERAPAN INFORMATIKA NUSANTARA
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27227987     DOI : -
Core Subject : Economy, Health, Science, Education, Art, Social, Engineering,
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other
Jurnal TIN: TERAPAN INFORMATIKA NUSANTARA memuat tentang Kajian Bunga Rampai dari berbagai ide dan hasil penelitian para peneliti, mahasiswa, dan dosen yang berkompeten di bidangnya dari berbagai disiplin ilmu seperti: Komputer, Informatika, Industri, Elektro, Telekomunikasi, Kesehatan, Agama, Pertanian, Pembelajaran, Pendidikan, Teknologi Pendidikan, Ekonomi dan Bisnis, Manajemen, Akuntansi, dan Hukum
Arjuna Subject : Umum - Umum
Articles 613 Documents
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SMOTE-Based Oversampling for Imbalanced Digital Fraud Risk Classification Fitriana, Ika Nur Laily; Leviany, Fonda; Kasmiarno, Kurnia Sari; Mabruri, Mohammad Okky
TIN: Terapan Informatika Nusantara Vol 6 No 11 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i11.9589

Abstract

Digital fraud risk among university students is an important issue, yet classification using survey-based indicators is complicated by class imbalance. This study examined whether Synthetic Minority Over Sampling Technique (SMOTE) improves Digital Fraud Risk classification among Universitas Terbuka students. This research used primary survey data from 498 respondents and modeled using five predictors representing financial literacy, digital financial literacy, monthly gross income, age, and job tenure. The evaluated models were Gaussian Naive Bayes, Random Forest, calibrated linear Support Vector Machine (SVM), Radial Basis Function SVM, and XGBoost. The performance of model was evaluated using confusion matrix, accuracy, balanced accuracy, precision, recall, F1 score, ROC-AUC, PR-AUC, MCC and Kappa. This research revealed that without oversampling, some models showed higher nominal accuracy but zero recall for High risk. It means that accuracy is insufficient for model selection under imbalance. In contrast, SMOTE increased recall for the High risk class across all models and improved PR AUC in several cases. The SMOTE based Random Forest achieved the highest test PR AUC (0.415), whereas the SMOTE based RBF SVM achieved the highest recall (0.659). Diagnostic analyses for the selected SMOTE based Random Forest provided evidence of non-random predictive signal, although overall discriminative performance remained moderate.
Klasifikasi Teks Komentar Penggunaan Listrik Gratis di Youtube Menggunakan Metode Naïve Bayes Harahap, Mikho Alfatih; Hasugian, Abdul Halim
TIN: Terapan Informatika Nusantara Vol 6 No 12 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i12.9731

Abstract

The growth of social media has made YouTube one of the platforms used by the public to express opinions regarding government policies, including the free electricity program. The large number of comments makes manual analysis difficult; therefore, a text classification method is needed to automatically categorize comments. This study aims to classify YouTube user comments related to the free electricity program using the Naïve Bayes algorithm. The research data were obtained through a crawling process from ten YouTube videos discussing the free electricity policy, resulting in 910 comments, which were reduced to 906 comments after data cleaning. The data processing stages included cleaning, case folding, tokenizing, normalization, stopword removal, stemming, and term weighting using TF-IDF. Furthermore, the data were classified into four categories: Public Discussion and Information, Policy Support and Appreciation, Complaints and Technical Issues, and Non-Electricity. Model evaluation was conducted using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results showed that the Naïve Bayes algorithm provided fairly good classification performance with an accuracy of 70.9%, precision of 0.62, recall of 0.80, and F1-score of 0.70. The Non-Electricity category achieved the best performance with precision of 0.77, recall of 0.90, and F1-score of 0.83. Based on these findings, the Naïve Bayes method is considered effective for classifying public opinion from social media comment data.
Implementasi Algoritma K-Means Clustering untuk Pengelompokan Produk E-Commerce Berdasarkan Harga, Diskon, dan Total Revenue Pasaribu, Rinaldi; Siregar, Saidi Ramadan
TIN: Terapan Informatika Nusantara Vol 6 No 12 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i12.9872

Abstract

The rapid growth of e-commerce has generated a large volume of transactional data; however, this data has not been fully utilized to support strategic decision-making, particularly in product segmentation. The main problem addressed in this study is the absence of a systematic product grouping approach based on key attributes such as price, discount, and revenue, which leads to less effective pricing and promotional strategies. Therefore, this study aims to analyze product sales patterns and cluster e-commerce products based on the characteristics of price, discount_percent, and total_revenue. The dataset used is an Amazon-style e-commerce dataset consisting of 50,000 transaction records and 13 attributes, with the analysis focusing on the three main attributes as the basis for clustering. The method applied in this research is K-Means Clustering, which involves data preprocessing, normalization using Min-Max Scaling, and determining the optimal number of clusters using the Elbow Method and Silhouette Score. The results indicate that the optimal number of clusters is three clusters, supported by the highest Silhouette Score of 0.354 and a clear elbow pattern in the Elbow graph. Additional evaluation using the Davies-Bouldin Index of 0.9335 indicates that the clustering quality is fairly good, although not yet optimal. The clustering results produce three main groups: premium product cluster (high price, low discount, high revenue), discount product cluster (moderate price, high discount, moderate revenue), and low-performance product cluster (low price, low discount, low revenue). In conclusion, the K-Means algorithm is capable of effectively clustering e-commerce products based on relevant numerical attributes and generating insights that can support business strategies such as pricing and promotional decisions.

Page 62 of 62 | Total Record : 613

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