Article Per Year (5 Year)
p-Index From 2021 - 2026
2.461
P-Index
This Author published in this journals
All Journal JSI: Jurnal Sistem Informasi (E-Journal) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab INTECOMS: Journal of Information Technology and Computer Science METIK JURNAL Jurnal Pengabdian Kepada Masyarakat (Mediteg) KLIK: Kajian Ilmiah Informatika dan Komputer Aptekmas : Jurnal Pengabdian Kepada Masyarakat The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika
Endang Lestari Ruskan
Unknown Affiliation
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 14 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 14 Documents
Search

 1   2 
Sistem Pendukung Keputusan Pemilihan Sunscreen Untuk Remaja Menggunakan Kombinasi Metode SAW dan ROC Yesi Handayani; Endang Lestari Ruskan
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1622

Abstract

Ultraviolet (UV) rays can negatively affect the skin and increase the risk of developing skin cancer especially if it occurs throughout childhood and adolescence. For teenagers, especially those who are often active outdoors, use of sunscreen is very important in order to avoid premature aging and various harmful effects of UV radiation. The use of inappropriate products will hamper the sunscreen's ability to protect skin thoroughly and may cause side effects such as acne or irritation. There are many types and brands of sunscreen products for each skin type, as well as various product criteria that are also considered by teenagers in choosing the best sunscreen product. Role of information technology can help the process of determining the best sunscreen products quickly and precisely, one of which is Decision Support System. The method used is ROC method to determine the weight value of criteria and sub-criteria, and SAW method to determine the final value in ranking alternatives. The data source of this research was obtained by distributing questionnaires to 103 respondents who were teenagers in Palembang City. This study uses 8 alternatives and 8 criteria, namely registered BPOM (C1), acne skin condition (C2), skin type suitability (C3), composition (C4), price (C5), weight (C6), weight suitability to price (C7), and texture (C8). Based on the ranking results, the highest preference value for oily skin type is 0.802, dry skin type is 0.782, normal skin type is 0.788, sensitive skin type is 0.790, and combination skin type is 0.788. All of these skin types have the same alternative with the highest preference value for each skin type, namely alternative A1 (Azarine Calm My Acne Sunscreen Moisturizer SPF 35 PA+++).
Peningkatan Pengetahuan Pemuda Desa dalam Pemasaran Online Hasil Tani Menggunakan Media Sosial dan Platform E-commerce Indah, Dwi Rosa Indah; Mgs Afriyan Firdaus; M. Husni Syahbani; Yoppy Sazaki; Ardina Ariani; Endang Lestari Ruskan; Fathoni; Iin Seprina; Naretha Kawadha Pasemah Gumay
Jurnal Pengabdian Kepada Masyarakat (MEDITEG) Vol. 10 No. 2 (2025): Jurnal Pengabdian Kepada Masyarakat (MEDITEG)
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat (P3M) Politeknik Negeri Tanah Laut (Politala)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/mediteg.v10i2.369

Abstract

Sektor pertanian dan perkebunan merupakan mata pencaharian utama di Desa Ibul Besar 1 Provinsi Sumatera Selatan. Berdasarkan analisis situasi, diketahui bahwa petani kurang memperoleh keuntungan akibat tingginya biaya produksi dan kurangnya pendapatan dari hasil panen. Pelibatan pemuda desa dalam pemasaran digital diharapkan dapat meningkatkan pendapatan petani dengan memangkas rantai distribusi yang selama ini masih bergantung pada koperasi atau tengkulak. Tantangannya adalah rendahnya pengetahuan pemuda desa dalam memanfaatkan media sosial dan Platform e-commerce sebagai sarana pemasaran produk. Upaya Peningkatan Pengetahuan Pemuda Desa dalam Pemasaran Online Hasil Tani Menggunakan Media Sosial dan Platform E-commerce dilaksanakan dengan teknis pelatihan dan praktik Pemasaran menggunakan Media Sosial (Instagram dan Tiktok) dan E-commerce (Shopee dan Tokopedia) serta Editing Foto dan Video (PixelLab, Snapseed, dan CapCut) menggunakan perangkat smartphone. Kegiatan berhasil meningkatkan pengetahuan 15 pemuda dan 2 perangkat desa dibuktikan dengan kenaikan nilai rata-rata, perangkingan data dan uji wilcoxon matched-pairs terhadap hasil pre-test dan post-test peserta.
COMPARATIVE STUDY OF MACHINE LEARNING MODELS FOR CLASSIFYING SENTIMENT IN GOOGLE GEMINI APP REVIEWS Muhammad Dzaky Alifayoezra; Ali Ibrahim; Yadi Utama; Endang Lestari Ruskan; Dwi Rosa Indah
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7201

Abstract

The rising number of reviews for the Google Gemini app on the Google Play Store reflects diverse user opinions regarding the performance of this AI-based application. To identify sentiment patterns, this research conducted a comparative study of three classification algorithms—Support Vector Machine (SVM), Naive Bayes, and Random Forest—using 14,479 raw reviews collected through scraping. These reviews then went through several preprocessing steps, including case folding, text cleaning, tokenization, normalization, stopword removal, and stemming. After being labeled based on ratings, the dataset formed a highly imbalanced class distribution, consisting of 11,252 positive reviews and 1,571 negative reviews, and was subsequently split using the Hold-Out method with an 80% training and 20% testing ratio. Evaluation using the Confusion Matrix along with accuracy, precision, recall, and F1-score metrics showed that SVM achieved the best performance, producing 91% accuracy, 93% precision, 97% recall, and a 95% F1-score, outperforming Random Forest and Naïve Bayes, which each reached 90% accuracy. Overall, these results highlight SVM as the most effective algorithm for classifying sentiment in Google Gemini reviews, while the predominance of positive feedback suggests a relatively high level of user satisfaction, although model performance on the minority (negative) class remains a challenge due to data imbalance.
Comparison of SVM and Naive Bayes Algorithms in Sentiment Analysis of User Reviews on Bukalapak M Yasir Alghifari; M. Rudi Sanjaya; Dwi Rosa Indah; Endang Lestari Ruskan
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/dqhpkb12

Abstract

Indonesia’s rapid e-commerce growth has produced a vast volume of user reviews, yet their use for insight extraction remains limited—particularly for the Bukalapak platform. This study compares the performance of Naïve Bayes and Support Vector Machine for sentiment classification on 10,000 Bukalapak reviews. The workflow includes text preprocessing (cleaning, case folding, tokenization, stopword removal, and stemming) and feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF; max_features = 10,000). Evaluation employs 10-fold cross-validation with accuracy, precision, recall, and F1-score, complemented by a paired t-test for significance. Results show SVM outperforming NB (accuracy 84.48% vs. 83.96%; F1 0.8253 vs. 0.8205) with better consistency (standard deviation ±1.08% vs. ±1.24%). The t-test confirms a significant difference (p = 0.019), with SVM’s advantage most evident for the negative class (precision 0.80 vs. 0.78). Both models underperform on the neutral class due to severe class imbalance. These findings provide empirical evidence for algorithm selection in Indonesian e-commerce sentiment analysis and open avenues for future research using deep learning and class-imbalance handling techniques.
Co-Authors Ali Ibrahim Ali Ibrahim Allsela Meiriza, Allsela Ardina Ariani Ariansyah Saputra Budiman S, M. Arif Deka Septa Nora Lisa Dwi Rosa Indah Fathoni Fauzan Ramli Saputra Hanny Caroline Iin Seprina Indah Dwi Rosa Indah, Dwi Rosa Indah Khairunnisa Khoiriah, Annisa M Rudi Sanjaya Sanjaya M Yasir Alghifari M. Husni Syahbani M. Rudi Sanjaya M. Rudi Sanjaya Mgs Afriyan Firdaus Muhammad Argabzi Muhammad Dzaky Alifayoezra Muhammad Rafie Chautie Muhammad Raihan Udda Rahmany Naretha Kawadha Pasemah Gumay Novitas Sari Pacu Putra Rafliandi Ardana Rahma Destriani RR. Ella Evrita Hestiandari Sanjaya, M. Rudi Tharisa Antya Perdani Tri Wulandari, Dinda Yadi Utama Yesi Handayani Yusak Maryunianta