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All Journal Kinesik Jurnal Informatika dan Teknik Elektro Terapan Jurnal ICT : Information Communication & Technology Infotekmesin JATI (Jurnal Mahasiswa Teknik Informatika) JUSTIN (Jurnal Sistem dan Teknologi Informasi) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Journal of Artificial Intelligence and Engineering Applications (JAIEA) AMMA : Jurnal Pengabdian Masyarakat
Irma Purnamasari, Ade
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Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Nursing Social Sciences

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Peningkatan Kompetensi Siswa SMK Melalui Pelatihan Junior Web Developer Dalam Pengembangan Website Irma Purnamasari, Ade; Arie Wijaya, Yudhistira; Arif Firmansyah, Aditiya; Wangi Nur Qibti, Intan
AMMA : Jurnal Pengabdian Masyarakat Vol. 3 No. 2 : Maret (2024): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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

Abstract

This community service program aims to enhance the competencies of vocational high school (SMK) students in web development through a Junior Web Developer training program. The activities were carried out at several SMKs in Cirebon Regency and Cirebon City as an effort to bridge the gap between school curricula and industry needs. The implementation methods included preparation and planning, training execution, monitoring and evaluation, as well as dissemination of results. The outcomes of this program showed a significant improvement in students' understanding and skills in web development technologies, particularly in the use of HTML, CSS, JavaScript, and modern frameworks. In addition, participating teachers also benefited from workshops designed to enhance their ability to teach industry-based materials. This program successfully produced several outputs such as learning modules, student web projects, and collaborations with industry partners to open up job opportunities for graduates. With this program, it is expected that vocational students will have better competencies to face the workforce and the digital industry. The sustainability of this program can be expanded by increasing industrial partnerships and broadening the scope of training participants.
MODEL KLASIFIKASI SENTIMEN PADA ULASAN PENGGUNA APLIKASI GAME WEPLAY DI GOOGLE PLAY STORE MENGGUNAKAN ALGORITMA NAIVE BAYES Karunia Nurul Asry, Kintan; Irma Purnamasari, Ade; Bahtiar, Agus; Wahyudin, Edi
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 4 (2025): JATI Vol. 9 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i4.13909

Abstract

Penelitian ini bertujuan untuk mengklasifikasikan sentimen ulasan pengguna pada aplikasi Game WePlay di Google Play Store menggunakan algoritma Naive Bayes. Dengan menganalisis sentimen, penelitian ini berupaya memahami persepsi dan pengalaman pengguna terhadap aplikasi game. Salah satu tantangan utama adalah keragaman data ulasan, seperti penggunaan bahasa informal dan distribusi data yang tidak seimbang. Data ulasan diambil dari Google Play Store melalui proses web scraping, kemudian diproses melalui tahapan praproses seperti normalisasi, tokenisasi, dan penghapusan kata-kata yang tidak relevan. Proses ekstraksi fitur dilakukan menggunakan pendekatan Term Frequency-Inverse Document Frequency (TF-IDF). Algoritma Naive Bayes digunakan untuk mengelompokkan sentimen menjadi kategori positif dan negatif. Hasil penelitian menunjukkan bahwa model memiliki tingkat akurasi sebesar 86%, dengan presisi rata-rata 84,9%, recall 82,7%, dan F1-score 83,6%. Dalam evaluasi lebih lanjut, sentimen positif tercatat memiliki F1-score sebesar 89,9%, sementara sentimen negatif mencapai F1-score sebesar 77,3%. Hasil ini mengindikasikan bahwa model lebih efektif dalam mengidentifikasi pola kata pada ulasan positif. Penelitian ini memberikan kontribusi penting untuk pengembangan aplikasi Game WePlay dengan menyediakan pemahaman yang lebih baik tentang ulasan pengguna.
PENINGKATAN MODEL KLASIFIKASI SENTIMEN PUBLIK DI YOUTUBE CNBC INDONESIA TERHADAP MOBIL LISTRIK MENGGUNAKAN ALGORITMA SUPPORT VEKTOR MACHINE Sehabudin, Sehabudin; Irma Purnamasari, Ade; Bahtiar, Agus; Wahyudin, Edi
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 4 (2025): JATI Vol. 9 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i4.14184

Abstract

Pertumbuhan teknologi kendaraan listrik di Indonesia memicu beragam opini publik, khususnya di platform media sosial seperti YouTube. CNBC Indonesia, sebagai salah satu sumber informasi terpercaya, sering menjadi pusat diskusi terkait isu ini. Penelitian ini bertujuan untuk meningkatkan model klasifikasi sentimen publik terhadap kendaraan listrik menggunakan algoritma Support Vector Machine (SVM), yang dikenal memiliki performa unggul dalam klasifikasi berbasis teks. Data penelitian berupa komentar publik dari kanal YouTube CNBC Indonesia yang dikumpulkan dari video-video bertema kendaraan listrik selama periode tertentu. Tahapan penelitian meliputi preprocessing data seperti tokenisasi, penghapusan stopwords, stemming, pembobotan dengan Term Frequency-Inverse Document Frequency (TF-IDF), dan implementasi algoritma SVM untuk klasifikasi sentimen menjadi positif dan negatif. Evaluasi model dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score, serta membandingkan hasilnya dengan algoritma lain seperti Naïve Bayes. Hasil penelitian menunjukkan bahwa algoritma SVM memiliki performa terbaik dengan akurasi mencapai 93% dan F1-score yang konsisten tinggi. Sebagian besar sentimen publik terhadap kendaraan listrik bersifat negatif, meskipun terdapat kritik terkait infrastruktur dan biaya yang masih menjadi tantangan. Keberhasilan algoritma SVM menunjukkan potensinya untuk analisis teks yang lebih kompleks di masa depan, meskipun penelitian ini menghadapi tantangan seperti bias data dan perlunya memperluas dataset.
Pengembangan Media Pembelajaran Tari Topeng Berbasis Android dengan Metode Analysis Design Development Implementation and Evaluation Irma Purnamasari, Ade; Setiawan, Andi; Kaslani, Kaslani
Infotekmesin Vol 12 No 1 (2021): Infotekmesin: Januari 2021
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v12i1.473

Abstract

Mask Dance is one of Cirebon's traditional arts in Indonesia and has a variety of types and meanings in accordance with the symbols depicting characters such as in popular stories. The young generation in their activities cannot be separated from using internet technology, one of which is social media, so that it affects their less concern for art and traditions that have been inherent in society. From these problems, it is necessary to make Mask Dance learning media by utilizing android technology. This study uses the ADDIE method through the stages of Analysis, Design, Development, Implementation, and Evaluation as a guide in building learning tools. The results of this study are an Android-based application of Mask Dance learning media to introduce Mask Dance starting from history, descriptions, types of dance, how to dance to Mask Dance, and quizzes to evaluate understanding of learning. With this learning media, it can make it easier for students to learn mask dance among students and other young generations. From the test results using a black box, it shows that the functions in the application have been running according to their function, while the test results to the respondents obtained a value of 84.27% with good criteria for the application so that this application can be used as a medium for learning Mask Dance.
PERANCANGAN SISTEM BERBASIS WEB PEMBELAJARAN BAHASA JEPANG DENGAN TEMA SERAH TERIMA AKTIVITAS Khoerudin, Ikna; Irma Purnamasari, Ade
Kinesik Vol. 11 No. 1 (2024): April
Publisher : Fakultas Ilmu Sosial dan Ilmu Politik Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/ejk.v11i1.1212

Abstract

This research explores the Japanese language with a focus on the theme of activity handover. The Japanese language is unique in its use of various characters such as kanji, hiragana, katakana, and romaji. The influence of Japanese popular culture such as anime and manga has sparked many people who want to learn Japanese. The ability to speak foreign languages, especially Japanese, is considered important for children's future. However, learning Japanese has its challenges, especially in understanding the three typefaces, namely hiragana, katakana, and kanji. To master the Japanese language, learners need to pay attention to various aspects such as vocabulary, grammar, conversation, reading, writing, and listening. In the context of the handover theme this activity involves the use of particles, verbs, and nouns. This research emerged as a solution to the imbalance between the number of students and Japanese language teachers at LPK IHMI Cirebon. By analyzing student growth data, it is necessary to create a web-based learning system for Japanese language learning with the theme of handover activities using the waterfall method with several stages, namely system engineering, needs analysis, design, coding, testing, and maintenance. Thus, this research produced a Japanese language learning system with the theme of handover activities in an interesting and interactive manner. This system is expected to be an effective solution to increase the understanding and motivation of Japanese language learners at LPK IHMI Cirebon.
ANALISIS SENTIMEN ULASAN APLIKASI BANK JAGO MENGGUNAKAN SUPPORT VECTOR MACHINE DAN NEURAL NETWORK Mariyani, Dinda; Irma Purnamasari, Ade; Ali, Irfan; Nurdiawan, Odi; Nurdiawan, Rudi
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8775

Abstract

Abstrak. Pertumbuhan layanan perbankan digital di Indonesia menjadikan ulasan pengguna pada Google Play Store sebagai sumber penting untuk mengevaluasi kualitas aplikasi, termasuk Bank Jago. Namun, ulasan tersebut bersifat tidak terstruktur, informal, dan mengandung noise sehingga menyulitkan analisis sentimen. Penelitian ini bertujuan memberikan gambaran objektif kecenderungan opini pengguna serta membandingkan kinerja algoritma Support Vector Machine (SVM) dan Neural Network (MLPClassifier). Sebanyak 10.000 ulasan dikumpulkan melalui scraping dan direduksi menjadi 7.946 ulasan setelah penghapusan duplikasi. Data diproses melalui tahapan preprocessing meliputi cleaning, case folding, normalisasi slang, tokenisasi, stopword removal, dan stemming. Pelabelan sentimen dilakukan menggunakan lexicon InSet, sedangkan ekstraksi fitur menggunakan CountVectorizer berbasis Bag-of-Words. Hasil penelitian menunjukkan bahwa SVM memperoleh akurasi tertinggi sebesar 91,2%, lebih unggul dibandingkan Neural Network dengan akurasi 89,8%. Temuan ini menegaskan bahwa pemilihan preprocessing dan representasi fitur yang tepat berperan penting dalam meningkatkan performa analisis sentimen pada ulasan aplikasi perbankan digital. Abstract. The growth of digital banking services in Indonesia has made user reviews on the Google Play Store an important source for evaluating application quality, including Bank Jago. However, these reviews are unstructured, informal, and noisy, creating challenges for sentiment analysis. This study aims to provide an objective overview of user sentiment and to compare the performance of Support Vector Machine (SVM) and Neural Network (MLPClassifier). A total of 10,000 reviews were collected through scraping and reduced to 7,946 reviews after duplicate removal. The data were processed through preprocessing stages including cleaning, case folding, slang normalization, tokenization, stopword removal, and stemming. Sentiment labeling was conducted using the InSet lexicon, while feature extraction employed a Bag-of-Words approach with CountVectorizer. The results show that SVM achieved the highest accuracy of 91.2%, outperforming the Neural Network model with 89.8%. These findings highlight the importance of appropriate preprocessing and feature representation for improving sentiment analysis performance in digital banking application reviews.
ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI FLO DI GOOGLE PLAY STORE DENGAN MENGGUNAKAN ALGORITMA NAIVE BAYES Kurniawati, Eti; Irma Purnamasari, Ade; Ali, Irfan; Kurniawan, Rudi; Nurdiawan, Odi
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8776

Abstract

Abstrak. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna aplikasi Flo pada Google Play Store menggunakan algoritma Multinomial Naive Bayes. Flo merupakan aplikasi mobile health (mHealth) populer yang digunakan untuk memantau siklus menstruasi dan kesehatan reproduksi. Data dikumpulkan melalui web scraping dan menghasilkan 10.000 ulasan yang setelah pembersihan menjadi 6.908 data valid. Proses pra-pemrosesan meliputi case folding, cleaning, normalisasi, tokenisasi, stopword removal, dan stemming menggunakan Sastrawi. Pelabelan sentimen dilakukan secara semi-otomatis berbasis lexicon InSet dan rating. Ekstraksi fitur menggunakan CountVectorizer menghasilkan representasi Bag-of-Words sebagai input model. Hasil evaluasi menunjukkan bahwa algoritma Naive Bayes mencapai akurasi sebesar 73,6% dengan nilai precision, recall, dan F1-score yang seimbang pada tiga kelas sentimen. Temuan ini menunjukkan bahwa Naive Bayes efektif digunakan dalam mengolah ulasan teks pendek dan informal berbahasa Indonesia. Penelitian ini berkontribusi dalam pemanfaatan machine learning untuk analisis sentimen aplikasi mHealth serta menyediakan wawasan yang dapat digunakan pengembang untuk meningkatkan kualitas layanan aplikasi Flo. Abstract. This study aims to analyze user reviews of the Flo application on Google Play Store using the Multinomial Naive Bayes algorithm. Flo is a popular mobile health (mHealth) application for tracking menstrual cycles and reproductive health. Data were collected using web scraping, obtaining 10,000 initial reviews, with 6,908 valid reviews after cleaning. Preprocessing included case folding, cleaning, normalization, tokenization, stopword removal, and stemming using Sastrawi. Sentiment labeling was performed semi-automatically using the InSet lexicon and rating-based rules. Feature extraction used CountVectorizer with the Bag-of-Words approach. The evaluation shows that Naive Bayes achieved an accuracy of 73.6% with balanced precision, recall, and F1-score across sentiment classes. These results indicate that Naive Bayes is effective for processing short and informal Indonesian text reviews. This research contributes to the application of machine learning in mHealth sentiment analysis and provides insights for developers to improve the quality of the Flo application.
Co-Authors Adi Supriyatna Afiatul Ulumik, Rizki Ahmad Jaelani Aji Santoso Aldiyansyah, Aldiyansyah Amdani Ana Amalia, Ana Andi Setiawan Annurfariz, Aditya Arif Firmansyah, Aditiya Arofah, Mila Azrul, Ahmad Azzam, Ahmad Christian Anderson Wint's II, Hans Danar Dana, Raditya Elfrida Ratnawati ETI KURNIAWATI Fajar Fauzan, Muhammad Fatmawati, Aisyah Gifthera Dwilestari Gina Regiana Gitacahyani, Adisty Gunia, Euis Hartini, Tuti Hendiana, Hendiana Hendriyansyah, Hendriyansyah Hurifiani, Alfia Ikbal, Ali Indriyan Dwi Kesuma, Adri Irfan Ali Irfan Ali, Irfan Julkarnaen, Agus Karunia Nurul Asry, Kintan Kaslani Khoerudin, Ikna Kodratul Munawar, Koko Lestari, Hasanah Lutfiah Lutfiah Mariyani, Dinda Maulana, Ali Muharram, Akbar Nining.R.M Novi Rahmawati Nur Alam, Alfian Nur Aziziah, Aldila Nurdiawan, Rudi Odi Nurdiawan Prihartono, Willy Putra Pratama, Aeri R, Nining Rakhmat Hidayat, Rakhmat Ramadhan, Niko Ramadhan, Ryan Ramanto, Aditiya Restillah, Syifa Retno Sari Rifa'i, Akhmad Rinaldi Dikananda, Arif Rinaldi Dikanda, Arif Rini Astuti Rosyd, Abdul Rudi Kurniawan Sakarias Berek, Richardus Sehabudin Sehabudin, Sehabudin Shodik, Fajar Suarna, Nana Suarna, Nana Sudrajat, Adi Suryana, Aldi Syajida, Hanna Tati Suprapti Tohidi, Edi Wahyudin, Edi Wangi Nur Qibti, Intan Wisnu Saputra, Adrian Yudhistira Arie Wijaya