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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
Irma Purnamasari, Ade
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences

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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 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 Gitacahyani, Adisty Gunia, Euis 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 Putra Pratama, Aeri R, Nining Rakhmat Hidayat, Rakhmat Ramadhan, Ryan Ramanto, Aditiya Restillah, Syifa Retno Sari Rifa'i, Akhmad Rinaldi Dikananda, Arif Rinaldi Dikanda, Arif Rini Astuti Rosyd, Abdul Rudi Kurniawan Sehabudin Sehabudin, Sehabudin Shodik, Fajar Suarna, Nana Suarna, Nana Sudrajat, Adi Suryana, Aldi Syajida, Hanna Tati Suprapti Wahyudin, Edi Wisnu Saputra, Adrian