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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Profesi Medika: Jurnal Kedokteran dan Kesehatan JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of New Media Technology Compiler JOURNAL OF SCIENCE AND APPLIED ENGINEERING IJID (International Journal on Informatics for Development) Informatika : Jurnal Informatika, Manajemen dan Komputer JISKa (Jurnal Informatika Sunan Kalijaga) Cyber Security dan Forensik Digital (CSFD) Jurnal Berdaya Mandiri Indonesian Journal of Data and Science Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) International Journal of Health Science and Technology Indonesian Journal on Data Science Teknomatika: Jurnal Informatika dan Komputer International Journal of Artificial Intelligence in Medical Issues
Muhammad Habibi
Universitas Jenderal Achmad Yani Yogyakarta
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Ekstraksi Aspek Aksesibilitas untuk Peningkatan Pengalaman Pengguna Menggunakan NER dengan CNN dan LSTM Dwijayanti, Irmma; Rizqi Lahitani, Alfirna; Kusumaningtyas, Kartikadyota; Habibi, Muhammad
JURNAL FASILKOM Vol. 14 No. 3 (2024): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v14i3.8032

Abstract

Transportasi online memberikan dampak positif bagi sebagian besar masyarakat, namun penggunaan aplikasi oleh penyandang disabilitas masih menghadapi sejumlah tantangan. Belum adanya fasilitas yang memadai dan pengalaman pengguna yang baik menjadi kendala utama. Realitas ini menunjukkan bahwa perlunya perhatian khusus terhadap prinsip aksesibilitas untuk meningkatkan pengalaman pengguna dan kenyamanan bagi penyandang disabilitas. Melalui ulasan pengguna dapat diidentifikasi aspek-aspek aksesibilitas untuk mendukung peningkatan pengalaman pengguna. Penelitian ini bertujuan mengekstraksi informasi dari ulasan pengguna terkait aksesibilitas menggunakan metode NER dengan pendekatan CNN dan LSTM. Data yang dikumpulkan melalui web scraping terdiri dari 6.255 ulasan aplikasi Gojek, Grab, Maxim, dan Indriver. Hasil evaluasi menunjukkan bahwa kedua model memiliki akurasi tinggi yaitu CNN 99,84%, dan LSTM 99,48%. Namun memerlukan perbaikan dalam mendeteksi entitas yang jarang muncul atau berkonteks kompleks. Hasil analisis menunjukkan bahwa ulasan lebih banyak membahas fitur aplikasi dan keluhan yang berkaitan dengan aksesibilitas. CNN lebih efektif dalam menangkap pola spesifik, sedangkan LSTM lebih kuat dalam menangkap variasi kata.
Analisis Tren Topik dalam Ulasan Negatif Aplikasi M-Banking Menggunakan Latent Dirichlet Allocation Kusumaningtyas, Kartikadyota; Dwijayanti, Irmma; Rizqi Lahitani, Alfirna; Habibi, Muhammad
JURNAL FASILKOM Vol. 14 No. 3 (2024): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v14i3.8035

Abstract

Mobile banking atau M-banking menjadi semakin populer seiring dengan meluasnya penggunaan ponsel pintar. Pertumbuhan ini didorong oleh beberapa faktor, seperti kebijakan pemerintah melalui Gerakan Nasional Non-Tunai (GNTT) dan inovasi dari bank. Latar belakang penelitian ini berangkat dari pentingnya merespons keluhan pengguna terhadap aplikasi M-banking. Ulasan negatif mencerminkan masalah yang dialami pengguna dan bisa memengaruhi kepercayaan terhadap layanan. Sayangnya, platform seperti Google Play Store tidak menyediakan fitur untuk mengidentifikasi tren dari ulasan negatif. Oleh karena itu, penelitian ini menggunakan metode Latent Dirichlet Allocation (LDA) untuk memodelkan tren topik dalam ulasan negatif guna memberikan wawasan bagi penyedia layanan untuk meningkatkan kualitas aplikasi mereka. Penelitian ini dilakukan melalui beberapa tahap, dimulai dengan pengumpulan data ulasan negatif dari tiga aplikasi M-banking populer. Selanjutnya data akan melalui tahap preprocessing, meliputi: tokenizing, stopwords removal, dan stemming. Sentimen dari ulasan dianalisis menggunakan algoritma Support Vector Machine (SVM) dengan akurasi mencapai 93%, untuk memisahkan ulasan positif dan negatif. Selanjutnya, LDA digunakan untuk memodelkan topik pada ulasan negatif, dengan mengidentifikasi sejumlah topik optimal melalui Coherence Score, yang menunjukkan struktur topik yang logis dan terorganisir. Hasil penelitian menunjukkan bahwa pada BRImo, topik yang dominan adalah biaya dan kecepatan layanan aplikasi. Pada BCA mobile, pengguna lebih banyak membahas fitur dan kemudahan penggunaan aplikasi, sedangkan pada Livin’ by Mandiri, topik utama yang dibahas berkaitan dengan fitur transfer dan jam transaksi. Kesimpulan dari penelitian ini adalah bahwa metode LDA berhasil digunakan untuk menemukan tren utama dari ulasan negatif pengguna, yang diharapkan dapat membantu bank dalam meningkatkan kualitas layanan dan keamanan aplikasi mobile banking.
Optimalisasi Analisis Data Peserta Olimpiade Sains Nasional Indonesia Menggunakan Algoritma K-Means Clustering Purwatiningsih, Agustina; Habibi, Muhammad
JURNAL FASILKOM Vol. 14 No. 3 (2024): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v14i3.8356

Abstract

Penguasaan ilmu pengetahuan dan teknologi dengan nilai integritas tinggi merupakan salah satu syarat utama kemajuan sebuah bangsa. Salah satu program utama untuk pengembangan bakat dan minat peserta didik yang diselenggarakan oleh Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi (Kemendikbudristek) melalui Pusat Prestasi Nasional (Puspresnas) adalah Olimpiade Sains Nasional (OSN). Tujuan dari OSN mendapatkan calon peserta untuk mewakili Indonesia pada kompetisi sains tingkat internasional dan membangun basis data nasional peserta didik yang bertalenta dalam bidang sains. Prinsip OSN adalah inclusive, growth, participative dan sustain, yaitu pemerataan kesempatan bagi seluruh peserta didik Indonesia tanpa membedakan suku, agama, rupa, dan ras. Serta intensifikasi pembinaan di daerah dalam rangka mengupayakan pemerataan prestasi melalui kegiatan pencarian dan pemanduan bakat (talent scouting). Algoritma K-Means clustering cocok untuk menemukan pola dalam data pendidikan seperti performa siswa, efektivitas pembinaan, atau wilayah dengan tingkat pencapaiantertentu. Tujuan dari penelitian ini adalah untuk menemukan pola hasil OSN berdasarkan perolehan medali tiap wilayah di Indonesia, sehingga menghasilkan informasi strategis untuk pemeratan pembinaan. Hasil dari penelitian ini, terdapat 3 klaster untuk provinsi perolehan medali OSN dan 6 klaster kota dan kabupaten menurut perolehan hasil medali. Klaster 1 data provinsi dan klaster 1 data kabupaten atau kota merupakan daerah prioritas untuk pembinaan OSN dari Pusat Prestasi Nasional (Puspresnas). Terdapat 33 provinsi dan 167 kabupaten atau kota yang termasuk dalam klaster 1, yang memerlukan perhatian khusus. Hasil dari penelitian ini dapat menjadi acuan untuk Puspernas dalam menegakkan prinsip OSN yaitu pemerataan kesempatan bagi seluruh peserta didik Indonesia tanpa membedakan suku, agama, rupa, dan ras.
EDUKASI LITERASI KESEHATAN MENTAL BERBASIS DIGITAL MELALUI PENGENALAN PLATFORM DETEKSI DINI PADA GENERASI Z Irmma Dwijayanti; Alfirna Rizqi Lahitani; Kartikadyota Kusumaningtyas; Muhammad Habibi; Kharisma
Jurnal Berdaya Mandiri Vol. 7 No. 1 (2025): JURNAL BERDAYA MANDIRI (JBM)
Publisher : Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jbm.v7i1.7666

Abstract

Mental health education in Z Generation still receives less attention, even though mental health disorders in adolescents can develop into serious problems if not treated early. Many students do not understand or recognize the early symptoms of mental health disorders, and there is a lack of school involvement in providing related education. Technology can be a solution to help with early detection, one of which is through the Beck Depression Inventory (BDI)-based platform from Pijar Psikologi. This activity is expected to provide an understanding of the importance of mental health and how to recognize the early symptoms of mental health disorders. Pengabdian kepada Masyarakat (PkM) activity aims to introduce early detection tools for mental health disorders for students of SMK N 02 Yogyakarta. The methods used include pre-test, socialization and dissemination of research results, the practice of using early detection platforms, and post-test. Based on the results of the comparative analysis of pre-test and post-test scores, there was an increase in participants' knowledge regarding mental health by 90%. This shows that the delivery of the material provided is effective in increasing participants' knowledge. This increase in knowledge can be the basis for students to become cadres who are directly involved in disseminating digital literacy related to the use of information technology in mental health. Keyword: mental health, mental health education, Z generation, early detection tools
Mapping User Dissatisfaction in Mobile Banking Applications Using Ensemble Clustering LDA and LSA Kusumaningtyas, Kartikadyota; Lahitani, Alfirna Rizqi; Dwijayanti, Irmma; Habibi, Muhammad
IJNMT (International Journal of New Media Technology) Vol 12 No 1 (2025): Vol 12 No 1 (2025): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v12i1.3804

Abstract

Mobile banking has become one of the most popular choices compared to other online banking services. Google Play Store is an online application store platform provides a review section for users to give ratings and comments on the applications they use. Positive reviews typically contain good experiences that reflect user satisfaction, while negative reviews usually contain poor experiences that indicate complaints and user dissatisfaction. However, Google Play Store does not yet have a feature to automatically map the main topics in both positive and negative reviews. Specifically for negative reviews, this can make it difficult for developers to understand the root problems and take appropriate corrective actions. In some situations, negative reviews need to be handled more quickly. Slow handling of negative reviews can impact the decline in reputation and customer loyalty. This research aims to identify user dissatisfaction topics based on negative reviews of several popular mobile banking applications in Indonesia, namely BCA Mobile and BRImo.
User Requirement Recommendation Model for Waste Reporting Platforms Based on UX Topics and Sentiment Analysis Dwijayanti, Irmma; Lahitani, Alfirna Rizqi; Habibi, Muhammad
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11371

Abstract

Waste management remains a critical issue in Indonesia, as emphasized in the RPJMN 2025–2029. Ineffective collection and processing services, coupled with limited public participation, continue to hinder progress. Meanwhile, social media has emerged as a primary channel for citizens to express complaints and reports on waste, yet the unstructured nature of comments poses challenges for integration into official reporting systems. This study proposes a user requirements recommendation model based on social media data by integrating sentiment analysis, topic modeling, and rule-based recommendation. Data were collected from YouTube and TikTok comments. Sentiment classification was performed using Support Vector Machine (SVM), while Latent Dirichlet Allocation (LDA) was employed for topic modeling, with results mapped onto the UX Honeycomb dimensions. Recommendation rules were then formulated by combining sentiment polarity with UX dimensions. The SVM model achieved an average accuracy of 87.5% with balanced precision, recall, and F1-score. LDA produced 15 coherent topics, which were distributed across seven UX dimensions. The integration revealed that the main user requirements include transparency in report follow-up through real-time notifications and clear status updates. Additional recommendations involve simplifying the reporting process, providing auto-fill features, improving visual design, and establishing a user appreciation system. The findings demonstrate the potential of leveraging social media comments to systematically capture user requirements, offering practical insights for developers to design waste reporting platforms that are effective, user-friendly, and responsive to community expectations.
Topic Analysis of Indonesian Online News on the Free Nutritious Meal Program Using Non-Negative Matrix Factorization Dwijayanti, Irmma; Lahitani, Alfirna Rizqi; Habibi, Muhammad
Compiler Vol 14, No 2 (2025): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v14i2.3499

Abstract

The Free Nutritious Meal Program (MBG) represents a key policy of the Indonesian government to address malnutrition and stunting by providing nutritious meals for students. This study applies Non-Negative Matrix Factorization (NMF) for topic modeling on a long-text corpus of 5,390 digital news articles collected from seven national portals, with the aim of mapping public discourse on MBG. The optimal number of topics was determined using the coherence score, yielding nine distinct themes. Findings indicate that media coverage primarily revolves around program distribution in schools, the role of Micro, Small, and Medium Enterprises (MSMEs) and the food sector, budget allocation, political dynamics of national figures, and health-related concerns such as student poisoning cases. The results suggest that MBG is widely perceived as a strategic policy with broad implications for public policy, economic development, political debate, and social welfare. Methodologically, this research demonstrates the effectiveness of NMF in identifying latent thematic structures within long-text news corpora, offering insights into how digital media frames and interprets government initiatives.
Gender-Aware Prediction of Liver Disease Using Machine Learning and Clinical Laboratory Data Umar Zaky; Muhammad Habibi; Adri Priadana; Thomas Edyson Tarigan
International Journal of Artificial Intelligence in Medical Issues Vol. 4 No. 1 (2026): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/wtsdw234

Abstract

Liver disease is a major health problem that may progress silently and lead to severe clinical complications if not detected early. Machine learning offers a promising approach for supporting early screening by identifying predictive patterns from clinical and biochemical patient data. This study developed an explainable gender-aware machine learning framework for liver disease prediction using demographic information and clinical biomarkers. The dataset consisted of 570 patient records after duplicate removal, including age, gender, total bilirubin, direct bilirubin, alkaline phosphatase, SGPT, SGOT, total protein, albumin, albumin/globulin ratio, and liver disease status. Several machine learning algorithms were evaluated under three experimental scenarios: original data, class-weighted learning, and SMOTENC-based oversampling. Model performance was assessed using accuracy, precision, recall, specificity, F1-score, and ROC-AUC. The experimental results showed that Gradient Boosting combined with SMOTENC achieved the best F1-score, with an accuracy of 0.7632, precision of 0.7935, recall of 0.9012, specificity of 0.4242, F1-score of 0.8439, and ROC-AUC of 0.7759. The model correctly identified 73 of 81 liver disease cases in the testing set, indicating strong sensitivity for early screening. Gender-based evaluation showed comparable F1-scores for male and female patients, with values of 0.8430 and 0.8462, respectively. Feature importance analysis identified SGOT, alkaline phosphatase, age, and direct bilirubin as the most influential predictors. These findings suggest that an explainable and gender-aware machine learning approach can support liver disease risk prediction using routinely available clinical biomarkers, although further validation using larger and more balanced datasets is required
Co-Authors A. Sinay, Stani ABIYOGA BAGUS MUSTRIYANTO Adri Priadana Alfirna Rizqi Lahitani Alfun Roehatul Jannah Alfun Roehatul Jannah Aris Wahyu Murdiyanto dayat subekti Dedy Hariyadi Dwijayanti, Irmma Elvika Fit Ari Shanti, Elvika Fit Ari Endah Puji Astuti, Endah Puji Fajar Syahruddin Fresia Nandela, Emilia I Gede Iwan Sudipa Ika Arfiani Iqbal Hadi Subekti Irmma Dwijayanti Jenny Meilila Azani Cahya Permata Jullev Atmadji, Ery Setiyawan Kharisma Kristi, Ria Kusumaningtyas, Kartikadyota Lahitani, Alfirna Rizqi M. Wahyu Indriyanto Muhammad Erwan Syah Muhammad Rifqi Ma'arif Nafisa Alfi Sa'diya Nasron, Ulil Amri Netania Indi Kusumaningtyas Nur Azizah Firdausa Oktaviana, Dela Pangky Putra Aziztiya Puji Winar Cahyo Purnamaningsih, Nur’Aini Purwatiningsih, Agustina Ramses Caniago Rifanny Br Girsang, Ribka Rifqi Ma’arif, Muhammad Risky Setyadi Putra Rizky Fauzi Akbar Rizky Fauzi Akbar Rizqi Lahitani, Alfirna Rukmi, Dwi Kartika Saputra, Andika Bayu Sujono Riyadi Sumiyarini, Retno Suwarno Tegar Agustian Thomas Edyson Tarigan Tri Sunarsih Ulil Amri Nasron Umar Zaky Wahyuningsiam, Astr