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All Journal CESS (Journal of Computer Engineering, System and Science) Jurnal Ilmiah Flash Jurnal Pengabdian Pada Masyarakat Jurnal SOLMA Prosiding Seminar Nasional Teknoka Jurnal Sains dan Informatika Jurnal Ilmiah Matrik Jurnal Eksplora Informatika J-SAKTI (Jurnal Sains Komputer dan Informatika) KOMPUTEK JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Seling: Journal of PGRA Study Program Jurnal Linguistik Komputasional Jurnal ICT : Information Communication & Technology MULTINETICS Progresif: Jurnal Ilmiah Komputer Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Sistem Komputer dan Informatika (JSON) Infotech: Journal of Technology Information Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Pendidikan dan Teknologi Indonesia Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Abdimas ADPI Sosial Dan Humaniora J-SAKTI (Jurnal Sains Komputer dan Informatika) International Journal Software Engineering and Computer Science (IJSECS) Jurnal Sains Informatika Terapan (JSIT) Acta Pedagogia Asiana JURNAL TEKNIK INFORMATIKA DAN KOMPUTER The Indonesian Journal of Computer Science
Kamayani, Mia
Universitas Muhammadiyah Prof.Dr.HAMKA, Jakarta, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Social Sciences Other

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ANALISIS SENTIMEN TERHADAP MINAT MASYARAKAT JAKARTA YANG MEMILIH KENDARAAN UMUM MENGGUNAKAN ALGORITMA NAÏVE BAYES Dewanto, Yogga Tolly; Wiranata, Ade Davy; Sulaeman, Mia Kamayani
Infotech: Journal of Technology Information Vol 11, No 2 (2025): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i2.407

Abstract

The worsening traffic congestion in Jakarta highlights the need to understand public interest in using public transportation. Social media platforms such as X serve as valuable sources of real-time public opinion data. This study aims to analyze the sentiments of Jakarta residents toward public transportation to identify the factors influencing their interest in using it. Data was collected from X and analyzed using the Naïve Bayes algorithm through the RapidMiner application. The analysis was conducted by splitting the dataset into 60% training data and 40% testing data. The results of the study show 203 positive sentiment data, 135 negative sentiment data, and 138 neutral sentiment data. Positive sentiments were mostly associated with affordability and ease of access, while negative sentiments were related to discomfort and lack of punctuality. This research is expected to serve as a reference for policymakers in improving the quality of public transportation services in Jakarta.
Redesign UI/UX Aplikasi Mobile JKN Dengan Metode Design Thinking Untuk Efisiensi Akses Layanan Kesehatan Yusri, Ahmad Rayhaan Yusri; Sulaeman, Mia Kamayani
Jurnal Sains Informatika Terapan Vol. 5 No. 1 (2026): Jurnal Sains Informatika Terapan (Februari, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

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

Abstract

Perkembangan teknologi digital mendorong meningkatnya kebutuhan masyarakat terhadap layanan kesehatan yang cepat, efisien, dan mudah diakses. Mobile JKN merupakan aplikasi resmi BPJS Kesehatan, namun masih ditemukan berbagai kendala terkait User Interface (UI) dan User Experience (UX) yang memengaruhi efektivitas penggunaan. Penelitian ini bertujuan untuk merancang ulang (redesign) UI/UX Mobile JKN guna meningkatkan kemudahan, efisiensi, dan kualitas pengalaman pengguna. Dataset penelitian diperoleh melalui wawancara mendalam terhadap pengguna aktif Mobile JKN, serta dilakukan pengujian menggunakan metode System Usability Scale (SUS) yang melibatkan 20 responden dan Usability Testing yang melibatkan 10 narasumber. Penelitian mengadopsi pendekatan Design Thinking melalui lima tahap utama: Empathize, Define, Ideate, Prototype, dan Testing. Evaluasi dilakukan menggunakan metode SUS dan Usability Testing melalui platform Maze. Hasil pengujian menunjukkan peningkatan signifikan pada nilai SUS dari 36,75 (kategori Not Acceptable) menjadi 71,95 (kategori Acceptable), dengan selisih peningkatan sebesar 35,20 poin. Selain itu, hasil Usability Testing meningkat dari 64,28% menjadi 82,85%, dengan kenaikan sebesar 18,57%. Temuan tersebut membuktikan bahwa proses redesign berhasil meningkatkan efektivitas, efisiensi, dan kepuasan pengguna dalam menggunakan aplikasi Mobile JKN
Social Media Sentiment Analysis of Twitter Regarding People's Housing Savings (TAPERA) Using Naïve Bayes Avry Liyanah Dewy; Mia Kamayani
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4126

Abstract

The advancement of technology has transformed how people interact and express opinions on social media platforms. This research examines Twitter conversations regarding Indonesia's government-initiated Housing Savings Program (TAPERA) through sentiment analysis. The study employed Naïve Bayes classification methodology, with data acquisition conducted via Google Colab platform utilizing the tweet-harvest library. The collection process yielded 1,800 tweets matching predetermined search parameters. Data underwent rigorous preprocessing, including text cleaning and manual sentiment annotation to establish reliable training datasets. Examination of 720 test tweets revealed 473 (65.69%) expressed negative sentiment while 247 (34.31%) conveyed positive sentiment toward the program. The implemented Naïve Bayes model achieved 84.17% accuracy, with negative class precision at 88.71% and recall at 88.60%, while positive class precision reached 78.54% with 76.08% recall. Results indicate the Naïve Bayes approach effectively categorizes public sentiment regarding the TAPERA program, offering valuable feedback for stakeholders responsible for program assessment and enhancement.
Penerapan Metode User-Centered Design dalam Redesign UI/UX Aplikasi Mobile Transjakarta Luthfia Masruroh Syah; Mia Kamayani
Progresif: Jurnal Ilmiah Komputer Vol 21, No 2 (2025): Agustus
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v21i2.2900

Abstract

The Transjakarta mobile application plays a vital role in supporting public transportation in Jakarta, but many users report poor usability, complicated navigation, and unintuitive interface design. Based on an initial survey involving 20 active users, the application scored only 26.8 on the System Usability Scale (SUS), placing it in the “Poor” category. This study adopts the User-Centered Design (UCD) approach in the redesign process of the application's UI/UX, involving four key stages: understanding the context of use, identifying user needs, designing appropriate solutions, and evaluating the alignment of those solutions with the identified needs. Evaluation was conducted using the SUS method and A/B Testing. The redesign process led to a significant improvement, raising the SUS score 85.25 and showing that 86% of users preferred the new design. Additional features such as “Top-up Balance” and “Transaction History” also received positive feedback. These findings demonstrate that the UCD approach effectively improves usability and user satisfaction, and can serve as a reference for future developments in public service applications.Keywords: A/B Testing; Redesign; System Usability Scale; Transjakarta; User Centered Design AbstrakAplikasi mobile Transjakarta merupakan layanan pendukung transportasi publik di Jakarta yang penting, namun masih menghadapi berbagai masalah dalam kemudahan penggunaan, integrasi fitur, dan tampilan antarmuka yang kurang intuitif. Berdasarkan survei awal terhadap 20 pengguna, skor System Usability Scale (SUS) hanya mencapai 26,8 yang termasuk kategori Poor. Penelitian ini menggunakan pendekatan User-Centered Design (UCD) dalam proses perancangan ulang UI/UX aplikasi, yang melibatkan empat tahapan inti: memahami konteks penggunaan, mengidentifikasi kebutuhan pengguna, merancang solusi desain, serta mengevaluasi kesesuaian solusi terhadap kebutuhan tersebut. Evaluasi dilakukan menggunanan SUS dan A/B Testing. Hasilnya menunjukkan peningkatan signifikan, dengan skor SUS yang naik menjadi 85,25 (kategori Excellent) dan 86% responden lebih menyukai desain baru. Selain itu, fitur baru seperti “Isi Saldo” dan “Riwayat Transaksi” mendapat tanggapan positif dari mayoritas pengguna. Penelitian ini menunjukkan bahwa UCD efektif dalam meningkatkan kualitas pengalaman pengguna serta dapat menjadi acuan dalam pengembangan aplikasi layanan publik berbasis digital.Kata kunci: A/B Testing; Perancangan Ulang; System Usability Scale; Transjakarta; User Centered Design
Pendekatan Metode UCD dan SUS dalam Redesign UI/UX Aplikasi Mobile iPusnas Oktaviani Ariyaningsih; Mia Kamayani
Progresif: Jurnal Ilmiah Komputer Vol 21, No 2 (2025): Agustus
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v21i2.2826

Abstract

This study aims to improve the quality of user experience when accessing the iPusnas mobile application, a digital library platform available in Indonesia. Based on observations and interviews, several issues were identified, including an overly crowded user interface that lacks visual appeal, a navigation flow that is perceived as unintuitive, and features that are considered less relevant to users’ needs. To address these problems, a redesign process was carried out by applying the User Centered Design (UCD) approach, which emphasizes user involvement at every stage of development. The evaluation of the redesigned version was conducted using the System Usability Scale (SUS) method. The test results showed an increase in the SUS score from 33.5 to 82.83 after the redesign. This improvement indicates that applying the UCD approach, accompanied by SUS evaluation, can effectively enhance the usability level of the iPusnas application.Keywords: iPusnas application; System usability scale; User centered design; Usability AbstrakPenelitian ini dimaksudkan untuk meningkatkan mutu pengalaman pengguna ketika menggunakan aplikasi iPusnas versi mobile, yang merupakan platform perpustakaan digital di Indonesia. Berdasarkan hasil observasi dan wawancara, ditemukan sejumlah permasalahan yang dialami oleh pengguna, antara lain tampilan antarmuka yang dianggap terlalu padat sehingga kurang menarik secara visual, alur navigasi yang dirasa kurang intuitif, serta beberapa fitur yang dinilai kurang relevan dengan kebutuhan pengguna. Untuk menjawab permasalahan tersebut, dilakukan proses perancangan ulang dengan menerapkan pendekatan User Centered Design (UCD), yaitu metode yang berfokus pada pengguna di setiap langkah proses pengembangan. Evaluasi terhadap desain baru dilakukan melalui pengukuran menggunakan metode System Usability Scale (SUS). Hasil pengujian menunjukkan bahwa nilai SUS meningkat dari 33.5 menjadi 82,83 setelah dilakukan redesign. Peningkatan ini mengindikasikan bahwa penerapan pendekatan UCD yang disertai evaluasi menggunakan SUS mampu secara efektif meningkatkan tingkat kegunaan aplikasi iPusnas.Kata Kunci: Aplikasi iPusnas; System usability scale; User centered design; Usability   
Analisis Sentimen Ulasan Pelanggan Online Ubi Madu Cilembu Abah Nana Menggunakan Algoritma Naïve Bayes Muhammad Rafly Al Fattah Zain; Mia Kamayani
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 1 (2023): September 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i1.6646

Abstract

This research aims to analyze the sentiment of online customer reviews for Ubi Madu Cilembu Abah Nana using the Naïve Bayes algorithm. The study has two main objectives: to classify the sentiment analysis of reviews into positive and negative categories regarding the service and products of Ubi Madu Cilembu Abah Nana, as well as to evaluate the accuracy level of the final classification results. The data was collected from online food delivery applications such as Gofood, Grabfood, and Shopeefood. The data used in this study amounts to 259 entries, with 310 positive and 49 negative data points. After conducting experiments, an accuracy result of 86.29% was obtained in Experiment 1 using the Split Data operator, and an accuracy of 86.12% was achieved in Experiment 2 utilizing Cross Validation with the assistance of language experts. The findings of this research indicate that the Naïve Bayes algorithm can be employed to classify customer sentiment towards the service and products of Ubi Madu Cilembu Abah Nana with a significantly high accuracy rate. These results can be valuable for Ubi Madu Cilembu Abah Nana in enhancing their service and product quality based on customer feedback. Additionally, this study also contributes to the field of sentiment analysis and natural language processing by applying classification algorithms to customer review data.
Perbandingan Algoritma SVM Dan Naïve Bayes Pada Analisis Sentimen Penghapusan Kewajiban Skripsi Yunita, Rani; Kamayani, Mia
The Indonesian Journal of Computer Science Vol. 12 No. 5 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i5.3415

Abstract

Pada Agustus 2023 Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi membuat peraturan salah satunya menghapus kewajiban skripsi sebagai syarat kelulusan di semua perguruan tinggi di Indonesia. Pro dan kontra saling bertukar tempat di berbagai media sejak peraturan menteri tersebut diumumkan. Banyak yang mendukung kebijakan tersebut tetapi tidak sedikit yang menentang. Dari Issue tersebut, peneliti melakukan analisis sentimen di twitter tentang kebijakan yang menghapus kewajiban skripsi sebagai syarat kelulusan menggunakan 700 data. Penelitian ini membandingkan hasil evaluasi algoritma Support Vector Machine (SVM) dengan Naïve Bayes. Berdasarkan hasil yang diperoleh dari penelitian ini didapatkan 331 sentimen positif serta 369 sentimen negatif dan ditarik kesimpulan bahwa Support Vector Machine (SVM) menjadi algoritma yang terbaik dengan accuracy 80%, recall 83%, precision 76%, dan F1-Score 79%.
Perbandingan Algoritma Klasifikasi untuk Prediksi Kelulusan Mahasiswa Teknik Informatika dengan Orange Data Mining Attyyatullatifah, Iqlimah; Kamayani, Mia
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3796

Abstract

Penyelesaian studi tepat waktu merupakan indikator penting dalam menilai kompetensi lulusan. Meskipun demikian, muncul tantangan karena tidak semua mahasiswa dapat menyelesaikan studi mereka sesuai jadwal yang telah ditentukan. Penelitian ini mengembangkan model prediksi status kelulusan mahasiswa menggunakan empat algoritma klasifikasi: Decision Tree, Naïve Bayes, K-NN, dan SVM. Data penelitian mencakup 500 data mahasiswa angkatan 2018-2020 di Universitas Muhammadiyah Prof. Dr. Hamka, dengan 60% data latihan dan 40% data uji. Analisis dilakukan menggunakan perangkat lunak Orange Data Mining, dengan evaluasi menggunakan K-Fold Cross Validation (k=5), Confusion Matrix, dan ROC. Hasil analisis menunjukkan bahwa model K-NN memiliki performa tertinggi dengan akurasi 92%, recall 90%, dan presisi 92%. Decision Tree menempati posisi kedua dengan akurasi 90%, presisi 87%, dan recall 90%. SVM mencapai akurasi sebesar 84%, dengan presisi 90%, recall 73%. Sementara itu, model Naïve Bayes menunjukkan akurasi 83%, presisi 80%, dan recall 83%.
Analis Sentimen Aplikasi Maskapai Penerbangan Lion Air Menggunakan Metode SVM dan Naïve Bayes Sulistiawati, Risa; Kamayani, Mia
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3836

Abstract

Lion Air App is a flight ticket purchase application launched on October 21, 2014. It can be downloaded and used anywhere, anytime. Lion Air App application is available on the Google Play Store and also the Appstore, which aims to facilitate users in the process of purchasing airplane tickets online. online. In several news articles reporting that Lion Air is the world's worst airline. in the world. However, it needs to be realized that the Lion Air application also has many users who give positive, negative and neutral reviews due to several factors. neutral due to the existence of several reviews presented in the Play Store application. This problem was researched for sentiment analysis to get a customer satisfaction rating for the Lion Air application. Lion Air application with the acquisition of 2000 data. In this research, Support Vector Machine (SVM) calculation and Naive Bayes calculation were compared using 80% training ratio and 20% test ratio. In this consideration, 795 positive opinions and 805 negative opinions were used. used, where Support Vector Machine (SVM) with Bigram features became the most superior method with 99.23% precision. method with 99.23% precision, 83.03% recall, 91.75% accuracy, F-1 score of 90.51%.         
Deteksi hate speech pada kolom komentar TikTok dengan menggunakan SVM Ariska, Amelia; Kamayani, Mia
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3982

Abstract

The TikTok application provides numerous features, including the comment section for users to interact with each other. Users can exchange their opinions openly through the comment section. However, as the interaction or exchange of opinions among users increases, the use of hate speech, consciously or unconsciously, remains prevalent. Hate speech refers to actions by an individual or group that can incite criminal acts, thereby harming others. This study aims to identify the use of hate speech in TikTok comment sections using the SVM algorithm and to compare two libraries used in the labeling process to observe the performance of the SVM algorithm model. The labeling process employs a lexicon-based approach. The dictionaries used in this study are the Inset lexicon and VaderSentiment. The SVM algorithm is used as the model to test the evaluation results. The results obtained using the Inset lexicon labeling show an accuracy of 82%, while the second labeling method using VaderSentiment yields an accuracy of 96.21%.
Co-Authors Abdul Aziz Alfaraby Ade Davy Wiranata Ahmad Komarudin Aji, Arielio Bayu Al Assyam, Hafizh Dhery Alfaraby, Abdul Aziz Anhari, Tirta Arif, Zainul Ariska, Amelia Atiqah M. Hilda Attyyatullatifah, Iqlimah Avorizano, Arry Avry Liyanah Dewy Ayyubi, Reza Al Azhar, Nur Chalik Azzahra, Nabila Daffa Anas Darman Dais Isnafirlah Dewanto, Yogga Tolly Dwi Anugrah Putra Eka Novianti, Eka Emilia Roza Estu Sinduningrum Estu Sinduningrum Faisal Akbar Pangestu Febriawan, Dimas Fikri Kurnia Ramadhan Firman Noor Hasan Fitri Yatusholihah Gunawan, PH Hafizh Dhery Al Assyam Hilda, Atiqah M. Hilmy Zhafran Muflih Ikhsan Wahyudi Imam Safi'i Intania Widyaningrum Iqbal Musyaffa Khusniyati Masykuroh Komarudin, Ahmad Kusuma, Miftahul Ari Luthfia Masruroh Syah M. Asep Rizkiawan Mahendra, Ristian Miftahul Ari Kusuma Mohammad Reza Saputra Mohammad Suryadi Syarif Muflih, Hilmy Zhafran Muhammad Irfan Faris Muhammad Izzaturrahman Muhammad Rafly Al Fattah Zain Nur Aini Puspitasari Oktaviani Ariyaningsih Pangestu, Faisal Akbar PH Gunawan Prananda, Dimas Adi Pratiwi, Anissya Agsani Rahman Malik, Luqman Abdur Rahmi Imanda Rani Yunita Rasiyah Shafa Azizah Reza Al Ayyubi Ridwan Maulana Subekti Roslaini Roslaini Sherina Nurul Kautsar Siduningrum, Estu Sinduningrum, Estu Sriyono Sriyono Sriyono Sriyono Suciana Wijirahayu Suharman, Anas Suhendra, Renadi Fadino Sulistiawati, Risa Utami, Arneitta Dwicahya Wijaya, Farid Rizky Yogi Fachriyatul Utama Yunita, Rani Yusri, Ahmad Rayhaan Yusri Zainul Arif