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All Journal Dinamik Dimas: Jurnal Pemikiran Agama untuk Pemberdayaan Jurnal Transformatika At-Taqaddum Wahana Akademika: Jurnal Studi Islam dan Sosial Sawwa: Jurnal Studi Gender Isamic Communication Journal InComTech: Jurnal Telekomunikasi dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING SINTECH (Science and Information Technology) Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) JSiI (Jurnal Sistem Informasi) JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) RELIGIA Jurnal Ilmu Dakwah EDUMATIC: Jurnal Pendidikan Informatika Jurnal Sistem Komputer dan Informatika (JSON) Jifosi Aiti: Jurnal Teknologi Informasi Wahana Akademika: Jurnal Studi Islam dan Sosial Jurnal Teknik Informatika (JUTIF) Journal La Multiapp JURNAL ILMIAH GLOBAL EDUCATION JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Algoritma Religia : Jurnal Ilmu-Ilmu Keislaman Jurnal Informatika: Jurnal Pengembangan IT JUTEKOM - Jurnal Teknologi dan Ilmu Komputer
Maya Rini Handayani
Universitas Islam Negeri Walisongo Semarang
Religion Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering 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 Mathematics Public Health Social Sciences Other

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Perbandingan Klasifikasi Single-Label dan Multi-Label Ulasan Pengguna Lapangan Futsal di Semarang Menggunakan SVM Syifa, Achrijal Shohib Arya; Umam, Khotibul; Handayani, Maya Rini; Aini, Siti Nur
InComTech : Jurnal Telekomunikasi dan Komputer Vol 15, No 2 (2025)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v15i2.33905

Abstract

Futsal merupakan cabang olahraga yang semakin populer di seluruh Indonesia, termasuk di Semarang. Penelitian ini bertujuan untuk melakukan klasifikasi sentimen ulasan pengguna mengenai lapangan futsal di Kota Semarang menggunakan metode Support Vector Machine. Data penelitian diperoleh melalui scraping ulasan Google Maps dengan ekstensi Chrome “Instant Data Scraper” dan terdiri dari 1.189 ulasan. Proses penelitian mencakup pengumpulan data, Cleaning dan pre-processing (normalisasi teks, modifikasi data, tokenisasi, stop word filtering, stemming), pelabelan (single label dan multi label), pembagian data (80% pelatihan dan 20% pengujian), pemodelan menggunakan SVM (single label dengan GridSearchCV dan multi label dengan One-vs-Rest Classifier), serta evaluasi model dengan metrik presisi, recall, dan F1-Score. Hasil menunjukkan pemodelan Support Vector Machine single-label mencapai presisi 0,84, recall 0,73, dan F1-Score 0,78. Sementara pemodelan Support Vector Machine multi-label mencapai presisi 0,96, recall 0,88, dan F1-Score 0.92. Dari ulasan yang dinalisis, sebaran data pada single-label maupun multi-label menunjukan dominasi ulasan kategori Fasilitas, menegaskan bahwa Fasilitas merupakan kategori yang paling sering dikomentari oleh pengguna. Temuan ini tidak hanya memberikan wawasan praktis bagi pengelola lapangan futsal, tetapi juga berkontribusi pada pengembangan metode klasifikasi ulasan berbasis machine learning dalam domain analisis opini, khususnya dalam membandingkan performa pendekatan single-label dan multi-label pada data multi-kategori di bidang teknologi informasi.
Identification of Buzzers in Skincare Reviews Using a Lexicon-Based Sentiment Analysis Method Pramesti, Arfiana Diah; Umam, Khothibul; Handayani, Maya Rini
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.11005

Abstract

Along with the rapid development of digital technology, social media has become the main platform for consumers to share experiences about products, including skincare products. However, it is not uncommon for reviews provided by users to not reflect authentic experiences, but rather reviews created by certain parties, or buzzers, to manipulate public perception. The presence of buzzers in skincare reviews is important to consider, as they can affect consumer trust and influence purchasing decisions. This study aims to identify the presence of buzzers in skincare product reviews using a lexicon dictionary-based sentiment analysis. Of the 529 comments analyzed, 75 comments showed negative sentiment and 454 comments showed positive sentiment. The classification results revealed that 85.8% of the comments belonged to the non-buzzer category, while 14.2% were indicated as buzzers. Evaluation of the classification model showed high accuracy, reaching 93%, but performance in detecting buzzers was limited, with a recall metric of only 0.50. This shows that while the model managed to classify non-buzzer comments well, there are still difficulties in identifying buzzer comments, mostly due to data imbalance. This research emphasizes the importance of a proper analytical approach in detecting inauthentic reviews to ensure the information consumers receive remains accurate, transparent, and accountable.
Mapping the Polarity of Tourist Opinions on Indonesian Destinations through Google Maps Reviews Using Supervised Learning Methods Sa’adah, Siti Miftahus; Umam, Khothibul; Handayani, Maya Rini; Mustofa, Mokhammad Iklil
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.9836

Abstract

The advancement of information technology has transformed how individuals seek information and plan their travels, notably through online reviews of tourist attractions on platforms like Google Maps. However, these reviews do not always align with visitors' expectations, necessitating further analysis to comprehend the underlying sentiments. The objective of this research is to inspect the performance of multiple machine learning algorithms in executing sentiment analysis on user generated reviews related to tourist attractions in Indonesia. The algorithms examined include Multinomial Naïve Bayes, Random Forest Classifier, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Extra Trees Classifier. The research process encompasses data collection and labeling, data preprocessing, exploratory data analysis (EDA), Word Cloud visualization, feature extraction, classification implementation, and performance evaluation. Experimental results indicate that the K-Nearest Neighbors (KNN) algorithm attain the most accuracy and F1-score of 97%, indicating its effectiveness in categorizing text-based sentiment reviews sourced from the Google Maps platform.
THE PERCEPTIONS OF SEMARANG FIVE STAR HOTEL TOURISTS WITH SUPPORT VECTOR MACHINE ON GOOGLE REVIEWS Aufan, Muhammad Haikal; Handayani, Maya Rini; Nurjanna, Afifah Basmah; Wibowo, Nur Cahyo Hendro; Umam, Khotibul
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2025

Abstract

Travelers on the road sometimes need a hotel to rest. In choosing a hotel, they refer to the ratings or reviews written by users through reviews on Google. This is because not all star hotels provide facilities in accordance with user assessments. This study discusses the analysis of the opinions of tourists who have stayed in 5-star hotels in Semarang through a review of commentary data on Google. The 5-star hotels used as the research are Padma, Gumaya, Tentrem, Grand Candi, Ciputra, and PO. The dataset of the six hotels was obtained through a scraping process then followed by data pre-processing. The data was retrieved from Google Maps using the Chrome Instant Data Scrapper extension. Data preprocessing begins with case folding, tokenizing, filtering, and ends with stemming. Support Vector Machine (SVM) is implemented for sentimen classification process. The results from this study are the majority of 5-star hotel reviews in Semarang tend to have positive rather than negative sentimens. Our model was able to produce an accuracy of 0.87 to 0.98. The highest accuracy was achieved by Ciputra Hotel at 0.98 with 543 positive reviews.
Identifikasi Polaritas Sikap Pengguna Aplikasi X terhadap Coretax di Indonesia Menggunakan Algoritma Naïve Bayes Prasilda, Dina Rahma; Yuniarti, Wenty Dwi; Handayani, Maya Rini; Umam, Khothibul
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8548

Abstract

The Core Tax Administration System (Coretax) was launched by the Directorate General of Taxes (DGT) in January 2025 as a technology-based integrated tax system. While its initial goal was to improve tax efficiency and compliance, Coretax faced technical challenges, including system errors, slow processing speed, and criticism from the public. The main platform used to address these challenges is the X app (formerly known as Twitter). This research aims to understand the public's views and responses to Coretax's services by analyzing user sentiment patterns seen on social media. The research identifies the polarity of user attitudes by utilizing natural language processing (NLP) and Naïve Bayes algorithms, applied to a dataset of 1,628 tweets collected between January and March 2025. The analyzed data reflects a wide range of public reactions that include both positive and negative opinions towards the Coretax implementation, both in terms of functionality and ease of use. The results show that the model has an accuracy rate of 93.07%, a precision value of 95%, a recall value of 96%, and an F1-Score value of 96%. The results of this study are expected to be able to provide precise mapping related to changes in public opinion towards Coretax, so that it can be a valuable source of information for application developers, policy makers in the field of taxation, and analysis in the technology sector in responding to the needs and expectations of society in the digital era.
Deteksi Dark patterns Biaya Layanan E-commerce Berdasarkan Perspektif Konsumen Menggunakan Algoritma Support Vector Machine Salmalina, Divana Taricha; Umam, Khothibul; Handayani, Maya Rini
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 6 No. 4 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v6i4.8563

Abstract

Perkembangan industri e-commerce di Indonesia belakangan ini dibayangkan pada fenomena meningkatnya keluhan konsumen terkait kebijakan biaya layanan yang dinilai kurang transparan, termasuk indikasi adanya praktik pola gelap . Penelitian ini bertujuan mengkaji persepsi konsumen terhadap isu tersebut melalui pendekatan analisis sentimen berbasis machine learning dan deteksi pola manipulatif. Data penelitian diperoleh dari ulasan pengguna di platform media sosial X yang kemudian diproses melalui serangkaian tahapan text mining meliputi pembersihan data, tokenisasi, stopword removal , dan stemming . Analisis sentimen menggunakan algoritma Support Vector Machine (SVM) menunjukkan hasil yang signifikan, dimana 55-78% ulasan di platform ketiga e-commerce (Shopee, Tokopedia, Lazada) tergolong negatif. Analisis TF-IDF mengidentifikasi kata kunci seperti "biaya", "layan" (layanan), dan "mahal" sebagai istilah paling dominan dalam ulasan negatif. Model SVM menunjukkan kinerja yang cukup baik dengan akurasi mencapai 87% dalam mengklasifikasikan sentimen negatif. Lebih lanjut, analisis tematik terhadap ulasan negatif berhasil mengidentifikasi indikasi pola gelap , khususnya dalam kategori biaya tersembunyi (biaya tersembunyi) dan menyelinap ke keranjang (penambahan produk tanpa disadari) yang muncul secara konsisten di semua platform. Temuan ini tidak hanya menegaskan adanya pola manipulatif yang berulang dalam industri e-commerce Indonesia, tetapi juga menegaskan urgensi bagi para pelaku industri untuk meningkatkan transparansi dalam kebijakan biaya. Secara praktis, hasil penelitian ini dapat menjadi bahan pertimbangan penting bagi regulator dalam merumuskan kebijakan perlindungan konsumen di era digital yang lebih komprehensif.
Klasifikasi Sentimen Masyarakat Terhadap Aplikasi Tiktok Menggunakan Algoritma Naive Bayes Nahdhudin, Muhammad; Wibowo, Nur Cahyo Hendro; Handayani, Maya Rini; Umam, Khotibul
Jurnal Teknologi informasi dan Ilmu Komputer Vol. 1 No. 3 (2025): Juli 2025
Publisher : Nolsatu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65258/jutekom.v1.i3.13

Abstract

Perkembangan pesat media telah menciptakan ruang digital baru bagi masyarakat untuk mengekspresikan opini, salah satunya melalui aplikasi TikTok. Aplikasi ini menjadi fenomena global dengan jutaan pengguna aktif, namun juga menuai pro dan kontra. Penelitian ini bertujuan untuk mengklasifikasikan sentimen masyarakat terhadap TikTok menggunakan algoritma Naive Bayes. Data yang digunakan  berupa 100.000 ulasan pengguna TikTok dari Google Play Store yang dikumpulkan melalui web scraping. Proses penelitian mencakup tahapan pra-pemrosesan teks, pelabelan sentimen berdasarkan rating, ekstraksi fitur menggunakan TF-IDF, dan klasifikasi menggunakan algoritma Multinominal Naive Bayes. Hasil evaluasi menunjukkan bahwa model memiliki akurasi sebesar 88,2% precision 89,1%, recall 87,4% dan F1-score 88,2%. Mayoritas ulasan pengguna bersentimen positif. Hasil ini menunjukkan bahwa Naive Bayes efektif dalam mengklasifikasi opini berbasis teks dalalam bahasa Indonesia. Penelitian ini dapat dijadikan acuan dalam memahami persepsi publik serta penerapan maching learning dalam analisis opini masyarakat.
Application of SVM and Naive Bayes with PSO for the Classification of Saloka Amusement Park Reviews Putri, Indira Alifia; Umam, Khothibul; Handayani, Maya Rini; Mustofa, Hery
Journal La Multiapp Vol. 6 No. 6 (2025): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v6i6.2505

Abstract

Visitor opinions on tourist destinations can be evaluated through sentiment analysis based on textual reviews. This study aimed to compare the performance of Support Vector Machine (SVM) and Naive Bayes (NB) algorithms in classifying visitor sentiments toward reviews of Saloka Theme Park, while also assessing the impact of parameter optimization using Particle Swarm Optimization (PSO). A total of 740 reviews were collected from the Traveloka platform and underwent text preprocessing. The optimization process targeted key parameters of each algorithm to improve the F1-score. Experimental results showed that the unoptimized SVM achieved an accuracy of 89 percent, while NB reached 86 percent. After applying PSO, SVM's accuracy dropped to 84 percent, whereas NB improved to 85 percent with more balanced classification across sentiment classes. These results recommend the integration of Naive Bayes with Particle Swarm Optimization as a potential approach for sentiment classification of tourism reviews, particularly in the case study of Saloka Theme Park.
Perbandingan Model SpaCy dan BERT untuk Persebaran Penggemar di Platform X (Twitter) Rahmadani, Nurul; Umam, Khothibul; Dwi Yuniarti, Wenty; Rini Handayani, Maya
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2310

Abstract

This study was conducted to compare the performance of the SpaCy Named Entity Recognition (NER) model and the Bidirectional Encoder Representation from Transformers (BERT) model in identifying the distribution of Bernadya fans based on the mention of Geo-Political Entity (GPE) locations. The dataset used was collected from X users' tweets using a scraping method with Python and will be analyzed on both NER models. The SpaCy NER model will be built from scratch with manual annotation, while the BERT model will be built using the transforms approach. From the evaluation results, the SpaCy model achieved a precision of 1.00, a recall of 0.92, and an F1-score of 0.96 on the training data, as well as a recall of 0.98 and an F1-score of 0.99 on the test data. The BERT model recorded a precision of 1.00, a recall of 0.95 (training), and 1.00 (testing), with an F1-score of 0.98 and 1.00. The Spacy model can recognize more than two entities well in one test sentence. However, when tested with the entire dataset, it cannot consistently recognize GPE entities. Conversely, the BERT model is better at recognizing GPE entities, with 4 GPE entities identified, including: Karanganyar, Indonesia, Mongolia, and Bandung as regions capable of identifying GPE entities with the most mentions. Therefore, in this study, the BERT model is better at recognizing GPE entities from the dataset used.
E EKSPLORASI PENDAPAT PENGGUNA APLIKASI DEEPSEEK DI PLAY STORE DENGAN NATURAL LANGUAGE PROCESSING putri lathifah, shofi; Khotibul Umam; Maya Rini Handayani
JIFOSI Vol. 6 No. 2 (2025): Enhancing Information System Security and Governance in the Digital Transformat
Publisher : UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Ulasan yang ditulis oleh pengguna di Google Play Store dapat sangat membantu untuk mengetahui bagaimana masyarakat menerima aplikasi. Dalam penelitian ini, kami menggunakan metode pemrosesan bahasa natural (NLP) untuk melihat bagaimana pengguna melihat aplikasi DeepSeek. Metode scraping dari Play Store digunakan untuk mengumpulkan data, yang kemudian dibersihkan dan diproses melalui proses normalisasi teks, tokenisasi, penghapusan stopword, dan stemming. Selanjutnya, kami mengklasifikasikan ulasan menjadi dua kelompok besar, yaitu ulasan positif dan negatif, menggunakan algoritma Naive Bayes. Hasil analisis menunjukkan bahwa sebagian besar ulasan bersifat positif, dengan beberapa kritik yang terkait dengan bug atau kinerja aplikasi. Studi ini menunjukkan bagaimana teknik pemrosesan bahasa natural dapat digunakan untuk menggali wawasan secara otomatis dan efektif dari data ulasan.
Co-Authors ., Kumarudin ., Kumarudin Amal, Muhammad Niltal Amelia Rahmi Apriliyani, Meli Arroyan, Devina Asep Dadang Abdullah Asep Dadang Abdullah Aufan, Muhammad Haikal Azziizah, Almira Farradinda Chairullah, Dimas Dewi, Fiashintha Dina Wulan Yekti rahayu Dwi Yuni Saraswati Dwi Yuniarti, Wenty Ema Hidayanti Fastabiqul Khusna Febrianto, Bagus Heti Aprilianti Hidayati, Ema Jinan, Muhammad Syifaaul Khoirotulmuadiba Purifyregalia Khoirul Adib Khothibul Umam Khotibul Umam Maizaliyanti, Annisa Malikhatul Ibriza Masuzzahra, Tsaura Rafah Masy Ari Ulinuha Mokhamad Iklil Mustofa Muhadzib Al-Faruq, Muhammad Naufal Muhammad Rafid Pratama Mustofa Hilmi Mustofa, Hery Mustofa, Mokhammad Iklil Musyafak, Najahan Musyafak, Najahan Musyaffaq, Mirza Izzal Nahdhudin, Muhammad Nur Cahyo Hendro Wibowo Nur Cahyo Hendro Wibowo, Nur Cahyo Hendro Nurjanna, Afifah Basmah Nur’aini, Siti Pramesti, Arfiana Diah Prasilda, Dina Rahma putri lathifah, shofi Putri, Indira Alifia Qonita, Nuurun Najmi Rahmadani, Nurul Robby Kurniawan Budhi Safitri, Sindy Eka Sajmira, Danita Divka Salmalina, Divana Taricha Saputra, Adika Kaka Sa’adah, Siti Miftahus Septyorini, Talitha Dwi Siti Hikmah Siti Hikmah, Siti Siti Nur Aini, Siti Nur Siti Nur'aini Sufiya, Ikhwanus Syifa, Achrijal Shohib Arya Tsuroyya, Kamiliya Umam, Khothibulu Vensy Vydia wening wihartati, wening Wenty Dwi Yuniarti Wenty Dwi Yuniarti, Wenty Dwi