Article Per Year (5 Year)
p-Index From 2021 - 2026
6.064
P-Index
This Author published in this journals
All Journal TEKNIK INFORMATIKA Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIKO (Jurnal Informatika dan Komputer) JPM (Jurnal Pemberdayaan Masyarakat) SMARTICS Journal Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURIKOM (Jurnal Riset Komputer) Journal on Education Jurnal Telematika JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Informatika Global Jurnal Review Pendidikan dan Pengajaran (JRPP) INTI Nusa Mandiri Jurnal Sistem Informasi dan Informatika (SIMIKA) Vocatech : Vocational Education and Technology Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Darmabakti : Junal Pengabdian dan Pemberdayaan Masyarakat Jurnal Pendidikan dan Teknologi Indonesia Decode: Jurnal Pendidikan Teknologi Informasi Indonesian Community Journal Jurnal Algoritma Jurnal Bisnis Kolega Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics INOVTEK Polbeng - Seri Informatika
Mula Agung Barata
Universitas Nahdlatul Ulama Sunan Giri
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Public Health Social Sciences Other

Published : 47 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 47 Documents
Search

 1   2   3   4   5 
Komparasi Algoritma Machine Learning untuk Deteksi Review Palsu dan Rekomendasi Pembelian Pada Platform Lazada Affan Agung Prabowo; Mula Agung Barata; Ita Aristia Sa'ida
VOCATECH: Vocational Education and Technology Journal Vol 8, No 1 (2026): April
Publisher : Akademi Komunitas Negeri Aceh Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38038/vocatech.v8i1.307

Abstract

AbstractThe rapid growth of e-commerce has increased the potential for the emergence of fake reviews that can mislead consumers and reduce the credibility of online purchasing decisions. This study aims to evaluate the performance of several machine learning algorithms in distinguishing fake and genuine reviews, as well as to develop a purchase recommendation model that considers review authenticity. The dataset used consists of 2,644 product reviews from the Lazada platform, which were labeled using a rule-based approach, followed by text preprocessing, normalization, and feature extraction using TF-IDF. The classification methods applied include Support Vector Machine (SVM), Decision Tree, Random Forest, Naive Bayes, and C4.5. The results show that Random Forest and C4.5 achieved the highest accuracy of 99.81%, followed by Decision Tree (99.62%), SVM (98.30%), and Naive Bayes (93.01%). In addition, a purchase recommendation score was developed by combining rating, sentiment, helpfulness, and purchase status to classify products into recommended and not recommended categories. The findings indicate that most reviews identified as fake still result in positive recommendations, which may introduce bias in conventional recommendation systems. Therefore, integrating fake review detection with sentiment analysis and multi-criteria evaluation is essential to improve the reliability of recommendation systems in e-commerce platforms. AbstrakMaraknya perkembangan e-commerce meningkatkan potensi munculnya ulasan palsu yang dapat menyesatkan konsumen dan menurunkan kredibilitas dalam pengambilan keputusan pembelian secara daring. Penelitian ini bertujuan untuk mengevaluasi kinerja beberapa algoritma machine learning dalam membedakan ulasan palsu dan asli, serta mengembangkan model rekomendasi pembelian yang mempertimbangkan keaslian ulasan. Dataset yang digunakan terdiri dari 2.644 ulasan produk pada platform Lazada yang diberi label menggunakan pendekatan rule-based, kemudian melalui tahapan preprocessing teks, normalisasi, dan ekstraksi fitur menggunakan TF-IDF. Metode klasifikasi yang diterapkan meliputi Support Vector Machine, Decision Tree, Random Forest, Naive Bayes, dan C4.5. Hasil pengujian menunjukkan bahwa Random Forest dan C4.5 mencapai akurasi tertinggi sebesar 99,81%, diikuti oleh Decision Tree (99,62%), SVM (98,30%), dan Naive Bayes (93,01%). Selain itu, dikembangkan skor rekomendasi pembelian dengan menggabungkan rating, sentimen, tingkat helpful, dan status pembelian untuk mengelompokkan produk ke dalam kategori direkomendasikan dan tidak direkomendasikan. Temuan menunjukkan bahwa sebagian besar ulasan yang terdeteksi sebagai palsu tetap menghasilkan rekomendasi positif, sehingga berpotensi menimbulkan bias pada sistem rekomendasi konvensional. Oleh karena itu, integrasi deteksi ulasan palsu dengan analisis sentimen serta penilaian multi-kriteria menjadi penting untuk meningkatkan keandalan sistem rekomendasi pada platform e-commerce.  
Attention-Enhanced Multivariate Forecasting for Intelligent Microservice Autoscaling Nur Saifuddin; Mula Agung Barata; Ifnu Wisma Dwi Prastya
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12662

Abstract

Proactive autoscaling in cloud-native microservices requires anticipatory decisions because reactive controllers often lag under abrupt workload shifts. This study aims to improve autoscaling decision quality through a two-stage machine learning pipeline. The research adopts an experimental design using production-grade microservice traces, with strict time-respecting train-validation-test splits and training-only fitting for preprocessing and oracle-threshold estimation to prevent leakage. In the first stage, multivariate forecasting models predict future CPU and memory utilization from engineered temporal features. In the second stage, the predicted signals are combined with observed features to classify three autoscaling actions: scale down, hold, and scale up. Benchmarking shows recurrent neural models are strong baselines, while an attention-enhanced encoder-decoder performs best. The best Bahdanau-attention model with residual connection reduces test CPU RMSE from 0.030977 to 0.028924 and memory RMSE from 0.010322 to 0.005452 relative to the strongest BiLSTM baseline. For decision learning, the optimized Extreme Gradient Boosting model using prediction-augmented features achieves an accuracy of 0.950602 and an F1 score of 0.951026. Supporting downstream validation also yields lower SLO violation rates than horizontal and vertical baselines while maintaining zero downtime in the evaluated scenarios. These findings indicate that improving forecasting quality and explicitly transferring predictive signals to the decision stage strengthens proactive autoscaling performance.
Comparison of the Performance of K-Nearest Neighbor and Naive Bayes Algorithms for Sentiment Analysis of PinjamYuk Application User Reviews Using SMOTE and TF-IDF Lindya Rossita Handoko; Amelia Faza; Mula Agung Barata; Afril Efan Pajri
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13398

Abstract

The growth of online lending services has driven the increasing adoption of digital financial applications, providing users with convenient access to financial services. This growing number of users has generated a large volume of reviews on the Google Play Store, which can serve as a valuable source for understanding users’ perspectives on the benefits and performance of these applications. This study aims to compare the performance of the K-Nearest Neighbor (KNN) and Naive Bayes algorithms in classifying the sentiment of user reviews of the PinjamYuk application. The study uses a secondary dataset obtained from Kaggle, consisting of 500 user reviews of the PinjamYuk application on the Google Play Store during the 2023–2024 period. The reviews were categorized into three sentiment classes: positive, neutral, and negative, based on their rating scores. Because the class distribution in the dataset was imbalanced, the Synthetic Minority Oversampling Technique (SMOTE) was applied to balance the classes before the classification process. The research procedure included data preprocessing, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), data balancing using SMOTE, and KNN parameter optimization using GridSearchCV. The models were evaluated using accuracy, precision, recall, F1-score, confusion matrix, stratified K-fold cross-validation, and the McNemar test. The results show that the Naive Bayes algorithm outperformed KNN. The stratified K-fold cross-validation results yielded an average accuracy of 81.0% for Naive Bayes and 80.8% for KNN. Furthermore, the McNemar test produced a p-value of 0.014 (p < 0.05), indicating that the performance difference between the two algorithms was statistically significant. These findings demonstrate that the Naive Bayes algorithm is more effective for analyzing user sentiment in reviews of the PinjamYuk application.
KOMPARASI METODE SVM DAN C4.5 DENGAN BACKWARD ELIMINATION UNTUK KLASIFIKASI STRES Ervina Putri Efendi; Mula Agung Barata; Aprillia Dwi Ardianti
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 3 (2025): Oktober 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i3.2130

Abstract

Kesehatan mental mahasiswa menjadi perhatian penting dalam dunia pendidikan, terutama terkait tingkat stres yang dialami selama proses akademik yang dapat berdampak pada prestasi dan kesejahteraan mereka. Penelitian ini bertujuan untuk membandingkan kinerja algoritma Support Vector Machine (SVM) dan C4.5 dalam mengklasifikasikan tingkat stres mahasiswa guna menentukan metode yang lebih optimal. Penelitian ini menggunakan dataset dari Kaggle yang berisi 1.100 data mahasiswa dengan 20 atribut penyebab stres. Data tersebut diproses melalui tahapan normalisasi menggunakan MinMax Scaling dan seleksi fitur dengan metode Backward Elimination untuk mengoptimalkan model. Klasifikasi tingkat stres dibagi ke dalam tiga kategori: ringan, sedang, dan berat. Evaluasi model dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil analisis menunjukkan bahwa algoritma SVM memberikan akurasi tertinggi sebesar 91% dengan nilai presisi, recall, dan F1-score yang konsisten, sementara algoritma C4.5 menghasilkan akurasi 90% dengan hasil evaluasi yang serupa. Temuan ini menegaskan bahwa SVM lebih unggul dalam mengklasifikasikan tingkat stres mahasiswa dibandingkan C4.5. Kesimpulan dari penelitian ini adalah bahwa penerapan machine learning, khususnya SVM, dapat menjadi pendekatan efektif untuk deteksi dini tingkat stres mahasiswa dan berpotensi digunakan sebagai dasar pengembangan sistem pendukung keputusan dalam upaya pencegahan masalah kesehatan mental di lingkungan pendidikan.
IMPLEMENTASI ALGORITMA MULTIPLE LINEAR REGRESSION DALAM MENGESTIMASI HASIL PANEN TANAMAN TEMBAKAU Diah nawang wulan; Mula Agung Barata; Ita Aristia Sa&#039;ida
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.3662

Abstract

Penilitian ini bertujuan untuk menaksir panen tembakau petani di Desa Balongrejo menggunakan algoritma regresi linier khusus. Data yang digunakan terdiri dari empat variabel dasar: jumlah bit, jumlah pembelian, jumlah transaksi, dan jumlah jam. Analisis dilakukan secara manual dan dengan bantuan alat statistik. Hasil analisis data menunjukkan bahwa model regresi dapat menjelaskan 95,5% varians dalam data. Selain itu, uji F menunjukkan semua variabel memiliki pengaruh yang signifikan secara bersamaan, sedangkan uji t mengidentifikasi tiga variabel yang memiliki pengaruh signifikan secara terpisah.
Analisis Klasifikasi Hepatitis Menggunakan Synthetic Minority Oversampling Technique, Support Vector Machine, dan Random Forest Amalia Nur Laily; Mula Agung Barata; Denny Nurdiansyah
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 6 No. 1: MARET 2026
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v6i1.1630

Abstract

Hepatitis akibat infeksi virus masih menjadi masalah kesehatan masyarakat yang serius sehingga deteksi dini berbasis data klinis penting untuk mencegah kerusakan hati lebih lanjut. Penelitian ini menganalisis kinerja algoritma Support Vector Machine (SVM) dan Random Forest pada klasifikasi hepatitis serta mengkaji dampak penerapan Synthetic Minority Over-sampling Technique (SMOTE). Dataset yang digunakan adalah HepatitisCdata.csv dari Kaggle dengan 615 data pasien yang memuat atribut demografis dan parameter biokimia hati. Tahapan penelitian meliputi preprocessing data, penanganan outlier, transformasi atribut kategorikal, serta pembangunan model baseline dan SMOTE. Evaluasi dilakukan menggunakan 10-fold cross-validation dengan metrik akurasi, presisi, recall, dan F1-score. Hasil menunjukkan bahwa SMOTE meningkatkan performa kedua algoritma, dengan Random Forest + SMOTE memberikan hasil terbaik (akurasi 98,85%) dibandingkan SVM + SMOTE (98,50%). Kontribusi penelitian ini terletak pada penggunaan pipeline preprocessing dan evaluasi yang seragam untuk membandingkan dampak SMOTE secara langsung pada dua algoritma klasifikasi hepatitis.
ANALISIS SENTIMEN PENGGUNA TWITTER TERHADAP SKINCARE DENGAN METODE SUPPORT VECTOR MACHINE (SVM) Dwi Tiyas Novitasari; Mula Agung Barata; Pelangi Eka Yuwita
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6297

Abstract

The Originote Hyalucera Moisturizer skincare product has attracted public attention because it offers superior quality at an affordable price. Social media, especially Twitter, is used by consumers to express opinions regarding this product, whether positive, negative, or neutral. However, the large number of reviews with various sentiments can confuse potential consumers in assessing product quality. Therefore, this study aims to understand user perception through sentiment analysis and evaluate the effectiveness of the Support Vector Machine (SVM) algorithm in sentiment classification. A total of 1,820 tweets were collected using the crawling technique with Python. The data undergoes preprocessing, including text cleaning, tokenization, stopword removal, and stemming, reducing it to 902 tweets. Key text features are extracted using Term Frequency-Inverse Document Frequency (TF-IDF). For sentiment classification, this study used the SVM algorithm, which is known as an effective method in text processing. Model evaluation showed good results with an accuracy of 87%, precision of 89%, and recall of 87%. This study provides insight into public perception of The Originote Hyalucera Moisturizer and measures the effectiveness of SVM in social media-based sentiment analysis. The results of the study can be utilized by manufacturers for more targeted marketing strategies, product quality improvement, and more effective communication in responding to opinions on social media. In addition, this study contributes to the development of machine learning-based sentiment analysis methods in the context of skincare products.
Co-Authors Abdul Aziz Affan Agung Prabowo Afril Efan Pajri Alfianto Faidatul Aldi Yumardiansyah Alvinatul Hidayah Amalia Nur Laily Amalia, Salsabila Dani Amelia Faza Andiyani, Putri Aprillia Dwi Ardianti Buyung Panigoro Deni Reskianto Deni Denny Nurdiansyah Diah nawang wulan Dina Selvi Rahmadani Dina, Intan Rachma Distira, Riski Putra Ayu Dwi Irnawati Dwi Issadari Hastuti Dwi Syafi'i, Ahmad Dwi Tiyas Novitasari Dwi Tiyas Novitasari Edi Noersasongko Eka Wahyu Andriyani Elok Fathiyatul Laili Ervina Putri Efendi Fannisa Salsabila Pratiwi Fina Indri Silfana Guruh Putro Dirgantoro Hidayah, Alvinatul Ifnu Wisma Dwi Prastya Ilmiyah, Miftakhul Indra Dharma Wijaya Indra Dharma Wijaya, Indra Dharma Ita Aristia Sa&#039;ida Ita Aristia Sa&#039;ida Ita Aristia Sa'ida Ita Aristia Sa'ida Jauhar Vikri, Muhammad Lambang, Rahmat Tegar Patriot Hari Levia, Zachdyna Aurelya Lindya Rossita Handoko M. Khoirul Risqi M. Ridlwan Hambali Maulani, Vicka Rizqi Moch Arief Soeleman Moh. Miftahul Choiri Moh. Muhajir Moh. Yusuf Efendi Munir, Ach Sirojul Muzakka, Moch. Arifuddin Naili Nafa Khatirokimmah Nasirudin, M. Nirma Ceisa Santi Nisa, Siti Khoirun Novitasari, Dwi Tiyas Nur Mahmudah Nur Mahmudah Nur Mahmudah Nur Saifuddin Pelangi Eka Yuwita Pradema Sanjaya, Ucta Purwanto Purwanto Putri Amelia Reza Anggapratama Rheyna Anggri Setyani Rochmatin, Novia Nur Roihatur Rohmah Roihatur Rohmah Sahri Sahri Sahri Sahri Saputra, Agus Bima Shafa Kirana Aralia Shofiatuz Zulfia Shofiatuz Zulfia Silfana, Fina Indri Sinta Ningrum Taufik Hidayat Teguh Pribadi Ucta Pradema Sanjaya Usman Nurhasan Viki Mei Adi Saputra Vita Dwi Rahmawati Wulan, Diah Nawang Yaqin, Ahmad Ainul Zainul Abidin Zakki Alawi Zulfiana Nur’aini