Article Per Year (5 Year)
p-Index From 2021 - 2026
2.255
P-Index
This Author published in this journals
All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of New Media Technology MULTINETICS Building of Informatics, Technology and Science Journal of Innovation and Future Technology (IFTECH) Jurnal Sistem Informasi dan Sistem Komputer Jurnal AbdiMas Nusa Mandiri Jurnal Abdimas Ekonomi dan Bisnis Journal of Accounting Information System Jurmas Azam Insan Cendikia
Purwandani, Indah
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Mathematics Physics Social Sciences Other

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
Analisis Sentimen Terhadap Game Clash of Clans Berdasarkan Ulasan Pemain Menggunakan Metode Support Vector Machine Agustian, Satria Bayu; Tengku Pasyah, Ahmad Dani; Vinaro, Lahenda; Santoso, Rame; Purwandani, Indah
Jurnal Sistem Informasi dan Sistem Komputer Vol 11 No 1 (2026): Vol 11 No 1 - 2026
Publisher : STIMIK Bina Bangsa Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51717/simkom.v11i1.1163

Abstract

Popularitas game Clash of Clans menghasilkan volume ulasan yang besar pada platform Google Play Store hingga saat ini. Studi ini mengevaluasi opini pemain menggunakan pendekatan Support Vector Machine (SVM) terhadap 3.287 data ulasan yang dihimpun pada periode April-Mei 2025. Serangkaian tahapan preprocessing diterapkan, mulai dari pembersihan data hingga stemming. Selanjutnya, ulasan dikategorikan ke dalam label sentimen positif dan negatif. Data tersebut kemudian diproses melalui pembobotan teks TF-IDF untuk selanjutnya diklasifikasikan menggunakan algoritma SVM. Hasil pengujian menunjukkan dominasi sentimen positif dengan tingkat akurasi mencapai 89%. Temuan ini memberikan wawasan bagi pengembang dalam memetakan preferensi serta aspirasi pemain, sekaligus mengonfirmasi keandalan teknik machine learning untuk analisis sentimen yang presisi.
Triangulation Approach Using K-Means, Hierarchical Clustering, and DBSCAN for Beef Production Analysis Syamsiah, Nurfia Oktaviani; Purwandani, Indah; Rosmiati, Mia; Nurwahyuni, Siti
IJNMT (International Journal of New Media Technology) Vol 12 No 2 (2025): Vol 12 No 2 (2025): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v12i2.4481

Abstract

This study implements a methodological triangulation approach for clustering highly skewed data using three algorithms with different paradigms: K-Means (partitional-based), Agglomerative Hierarchical Clustering with Ward Linkage (hierarchical-based), and DBSCAN (density-based). Applied to beef production data from 38 Indonesian provinces in 2024, the dataset exhibited extreme characteristics with a coefficient of variation of 171.89%, skewness of 2.87, and a maximum-minimum ratio of 664:1. Data were standardised using Z-score transformation to address scale differences. Evaluation using the Silhouette Score for K-Means and Hierarchical Clustering, alongside qualitative outlier detection with DBSCAN, revealed high consistency across all algorithms in identifying k=2 as the optimal structure, with a Silhouette Score of 0.9155. K-Means and Hierarchical Clustering produced identical groupings, separating three observations (7.89%) from 35 observations (92.11%), while DBSCAN confirmed this by explicitly labelling the three provinces as outliers. Robustness analysis via bootstrap resampling (100 iterations) demonstrated clustering stability with membership consistency of 99.7-100% and standard deviation of 0.0089. Sensitivity analysis validated the stability of outlier detection across the epsilon range 0.5-0.55. This research demonstrates that algorithmic triangulation provides robust cross-validation for data with extreme outliers, yielding consistent and stable clustering structures across sampling variation and parameter changes.
Co-Authors Agustian, Satria Bayu Devy Sofyanty Lila Dini Utami Nurfia Oktaviani Syamsiah Nurhayaty, Ety Nurwahyuni Rame Santoso Rosmia, Mia Rosmiati, Mia Rosmita Rosmita, Rosmita sismadi, sismadi Siti Masripah, Siti Siti Nurwahyuni Syamsiah, Nufia Oktaviani Tengku Pasyah, Ahmad Dani Vinaro, Lahenda