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All Journal Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Sistemasi: Jurnal Sistem Informasi Jurnal Teknik Komputer AMIK BSI Industri Inovatif : Jurnal Teknik Industri JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Peduli Masyarakat Teknika Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) International Journal of Social Science Prosiding Seminar Nasional Program Pengabdian Masyarakat Indonesian Journal on Data Science Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen Teknomatika: Jurnal Informatika dan Komputer Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Herdiesel Santoso
Program Studi Sistem Informasi, STMIK El Rahma Yogyakarta
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Medicine & Pharmacology Public Health Social Sciences Other

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Analisis Sentimen Berbasis Aspek Pada Ulasan Produk Fashion Shopee  Dengan Normalisasi Bahasa Slang Herdiesel Santoso; Wahyuni Nareswari
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 24 No 2 (2026): Mei 2026
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v24i2.201

Abstract

The rapid growth of e-commerce in Indonesia, particularly within Shopee’s fashion category, has generated a large volume of customer reviews that can serve as valuable sources of business insights. However, sentiment classification of these reviews is challenged by the extensive use of informal slang expressions and imbalanced sentiment distributions. This study develops an Aspect-Based Sentiment Analysis (ABSA) model by comparing the performance of Multinomial Naïve Bayes (MNB) and Support Vector Machine (SVM), while integrating slang normalization and the Synthetic Minority Oversampling Technique (SMOTE). A dataset of 5,877 customer reviews was analyzed using an 80:20 train–test split and evaluated through a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results indicate that, without preprocessing, SVM achieved an accuracy of 0.852, outperforming MNB with an accuracy of 0.722. After applying slang normalization and SMOTE, the performance of both models improved substantially. MNB achieved an accuracy of 0.853, while SVM attained the highest performance with an accuracy of 0.938, precision of 0.939, recall of 0.938, and F1-score of 0.937. These findings demonstrate that integrating slang normalization and SMOTE effectively enhances aspect-based sentiment classification, with SVM providing the best performance on Shopee fashion product reviews.
Optimasi K-Means++ Menggunakan Principal Component Analysis (PCA) pada Klasterisasi Profil Kelulusan Mahasiswa Herdiesel Santoso; Hana Solikatun
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 24 No 2 (2026): Mei 2026
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v24i2.202

Abstract

Timely graduation is a key indicator of student success and institutional effectiveness in higher education. However, clustering student academic records containing mixed data types (numerical and categorical) using the conventional K-Means algorithm often leads to distance bias and reduced clustering quality due to the curse of dimensionality. This study proposes an optimized K-Means++ approach integrated with One-Hot Encoding and Principal Component Analysis (PCA) to improve clustering performance. The model was evaluated using 200 graduate records from STMIK El Rahma Yogyakarta. The results show that reducing the dataset to two principal components significantly enhances cluster quality. Validation metrics indicate that the Silhouette Score increased from 0.3275 to 0.4979, the Davies–Bouldin Index decreased from 1.405 to 0.871, and the Calinski–Harabasz Index improved from 70.448 to 168.035. The optimized model identified two distinct groups: Academically Stable Students (157 students) and At-Risk Working Students (43 students), the latter predominantly consisting of part-time employed students. These findings provide valuable insights for developing data-driven Academic Early Warning Systems (EWS) that enable higher education institutions to identify students at risk of delayed graduation and implement targeted intervention strategies.
Co-Authors Ahmad Arif Nurrahman Aina Musdholifah Aksan, Azzikra Alamiyah, Resda Amalia Beladinna Arifa Anggraini, Deviana Dyah Bagas Dwi Santosa Dini Siskasari Edi Iskandar Edi Iskandar Hana Solikatun Harimanto Istika Dwi Kusumaningrum Istika Dwi Kusumaningrum Istika Dwi Kusumaningrum Khasanah, Setiyani Nur Kholidun Kholidun LINDA PRATIWI Minarwati Mita Aprilia Damayanti Muhammad Alvian Rizky Muhammad Nur Falak Minhar Lukman Netania Indi Kusumaningtyas Nurul Fatimah Ridwan, Muhamad Fikry Maulana Rizky, Muhammad Alvian Rois Ali Fernandi Sanuri, Rachmad Sari, Aurellya Fadhilla Tunjung Suahati, Ajrina Febri Sur Yanti Sur Yanti Sur Yanti Swastyani, Resa Syarief Hidayatullah Ulfi Saidata Aesyi Untung Subagyo Wahyu Widodo Wahyu Widodo Wahyuni Nareswari Zilfana Falahi