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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Algoritma Jurnal Teknologi Informasi dan Ilmu Komputer REKAYASA Jurnal Ecodemica : Jurnal Ekonomi Manajemen dan Bisnis Jurnal Informatika Jurnal Pilar Nusa Mandiri Forum Ekonomi : Jurnal Ekonomi, Manajemen dan Akuntansi JURNAL MANAJEMEN JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Pengabdian Masyarakat AbdiMas Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) INTI Nusa Mandiri Journal of Intelligent Decision Support System (IDSS) Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences IAIC Transactions on Sustainable Digital Innovation (ITSDI) Tridharmadimas: Jurnal Pengabdian Kepada Masyarakat Jayakarta International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Algoritma International Journal of Advanced Science Computing and Engineering Jurnal Pengabdian Ibnu Sina Journal Medical Informatics Technology SmartComp
Dwiza Riana
Institut Teknologi Bandung
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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Pengenalan Alfabet Sistem Isyarat Bahasa Indonesia (SIBI) Menggunakan Convolutional Neural Network Thira, Indra Jiwana; Riana, Dwiza; Ilhami, Azriel Noer; Dwinanda, Brama Rizky Setia; Choerunisya, Hana
Jurnal Algoritma Vol 20 No 2 (2023): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.20-2.1480

Abstract

Deaf is fourth in the list of persons with disabilities in Indonesia at 7.03%. Deaf people communicate using sign language both when communicating with fellow deaf people and with normal people. The problem that arises is that few normal people master sign language, especially the Indonesian Sign System (SIBI) so that it becomes an obstacle when they have to communicate with deaf people. This study aims to classify the alphabet in SIBI except the letters J and Z with a total of 24 classes. Classification is done by comparing three CNN architectures, namely MobileNetV2, MobileNetV3Small and MobileNetV3Large to get the best model. The results showed that the MobileNetV3Small architecture produced the best model at batch size 32 and the number of epochs 30 with an accuracy of 98.81% for testing data.
WORD2VEC OPTIMALIZATION USING TRANSFER LEARNING IN INDONESIAN LANGUAGE FOR HIGHER EDUCATION Hadianti, Sri; Riana, Dwiza; Tohir, Herdian; Jarwadi, Jarwadi; Rosdiana, Tjaturningsih; Sopandi, Evi; Kristiyanti, Dinar Ajeng
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6051

Abstract

Natural language processing (NLP) in Indonesian faces challenges due to limited linguistic resources, particularly in developing optimal word embedding models. This study optimizes the Word2Vec model for Indonesian in higher education contexts by leveraging transfer learning and lexicon expansion. Using a dataset of 4,463 higher education related tweets consisting of positive and negative sentiment categories, the proposed NewWord2Vec model combined with a Support Vector Machine (SVM) classifier achieved a 4% improvement in word detection accuracy compared to the standard Word2Vec. This enhancement demonstrates better performance in capturing linguistic nuances and sentiment orientation in Indonesian text. However, the model’s applicability remains limited to higher education terminology, and potential biases from transfer learning must be addressed. Future research should expand the dataset to diverse domains and refine the transfer learning process to better capture contextual variations in Indonesian. These findings contribute to advancing NLP applications in Indonesian, particularly for automated assessment systems, recommendation tools, and academic decision-making processes
Classification of regional language dialects using convolutional neural network and multilayer perceptron Marasabessy, Fahmi B.; Riana, Dwiza; Ernawati, Muji
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5017-5026

Abstract

Regional languages are vital for communication and preserving cultural identity, safeguarding local heritage. However, globalization and modernization endanger their existence as they are increasingly replaced by national or global languages. Despite progress in dialect recognition research, particularly for certain languages, further studies are needed to improve model performance and address less-represented dialects, including those in Indonesia. This study enhances a custom-built dataset for dialect recognition through the application of data augmentation techniques, specifically adding noise, time stretching, and pitch shifting. Using Mel-frequency cepstral coefficients (MFCC) for feature extraction, it evaluates the performance of convolutional neural network (CNN) and multilayer perceptron (MLP) in classifying six Indonesian dialects. Results indicate that CNN outperformed, achieving 97.92% accuracy, 97.90% recall, 97.97% precision, 97.92% F1-score, and a kappa score of 97.49% with combined augmentation techniques, setting a foundation for further research.
PEMANFAATAN AI UNTUK ORGANISASI SOSIAL Riana, Dwiza; Na'am, Jufriadif; Ernawan, Ferda Ernawan; Hadianti, Sri; Mardiana, Tati; Khasanah, Nurul
Jurnal Pengabdian Masyarakat AbdiMas Vol 12, No 1 (2025): Jurnal Pengabdian Masyarakat Abdimas
Publisher : Universitas Esa Unggul

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47007/abd.v12i1.9675

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan kapasitas digital organisasi sosial melalui pelatihan pemanfaatan teknologi kecerdasan buatan (AI) generatif. Madaris Jakarta Islamic Centre dipilih sebagai mitra dalam kegiatan ini karena memiliki peran strategis dalam pembinaan generasi muda Islam, namun belum sepenuhnya mengadopsi teknologi AI dalam operasional organisasinya. Pelatihan dilaksanakan secara tatap muka di Universitas Nusa Mandiri Kampus Margonda pada tanggal 17 Mei 2025, dengan pendekatan teori dan praktik yang mencakup pengenalan AI generatif, penggunaan tools seperti ChatGPT, DALL·E, Canva AI, dan Notion AI, serta penerapannya dalam pembuatan konten dakwah, administrasi, dan promosi kegiatan organisasi. Kegiatan ini diikuti oleh staf dan pengurus Madaris yang menunjukkan antusiasme tinggi selama pelatihan berlangsung. Evaluasi dilakukan melalui pre-test, post-test, observasi, serta kuesioner peserta dan mitra. Hasilnya menunjukkan peningkatan pemahaman dan kemampuan peserta dalam mengintegrasikan teknologi AI ke dalam aktivitas organisasi. Kegiatan ini tidak hanya memberikan manfaat langsung kepada mitra, tetapi juga menjadi sarana implementasi Tri Dharma Perguruan Tinggi, khususnya dalam pengabdian kepada masyarakat berbasis teknologi. Kegiatan ini diharapkan dapat mendorong transformasi digital organisasi sosial serta membuka peluang kolaborasi lanjutan antara perguruan tinggi dan komunitas berbasis keagamaan di era Society 5.0 dan menuju visi Indonesia Emas 2045.
Validation of the pSUAPP Questionnaire and User Experience Evaluation of the Satu Sehat Health Application in Indonesia Eriyandi, Vina; Tanebeth, Riki Daniel; Riana, Dwiza; Hadianti, Sri
Journal Medical Informatics Technology Volume 4 No. 1, March 2026
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v4i1.110

Abstract

The increasing use of digital health applications in Indonesia requires valid and reliable instruments to evaluate usability and user experience. This study aims to adapt and validate the pSUAPP questionnaire in the Indonesian context and to assess the usability of the Satu Sehat application. A cross-sectional validation study was conducted from May to June 2025 involving 102 active users of the Satu Sehat application, with 90 respondents included in the final psychometric analysis. The adapted pSUAPP questionnaire consists of 27 items covering four domains: first contact, registration, features, and overall user experience. Reliability and validity were assessed using Cronbach’s alpha, correlation analysis with SUS, and exploratory factor analysis (EFA). The results showed that the mean pSUAPP score was 67.76 (SD = 18.39), indicating moderate usability. The registration domain obtained the highest score (mean = 87.50; SD = 12.50), while the feature (mean = 70.36) and experience (mean = 69.62) domains showed relatively lower scores. The questionnaire demonstrated high internal consistency, with strong correlations across domains and with SUS. EFA identified four factors explaining 76.7% of the total variance. No significant differences were observed across sociodemographic characteristics. In conclusion, the Indonesian version of the pSUAPP questionnaire is a valid and reliable instrument for evaluating digital health applications. While the Satu Sehat application performs well in registration, improvements are needed in monitoring features and user experience to support long-term engagement.
Butterfly species identification using glcm features and edge detection using KNN (K-Nearest Neighbor) and decision tree algorithm (C.45) Hasan, Muhamad; Riana, Dwiza; Merlina, Nita
Journal of Intelligent Decision Support System (IDSS) Vol 9 No 1 (2026): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v9i1.341

Abstract

Butterflies are insects come from the kingdom Animalia, which are the Insecta class, the Lepidoptera order, and the sub-order of Rhopalocera. Butterflies can classified according to the patterns found on the butterfly's wings. Butterfly species have different patterns based on pigment, scale structure, and sunlight fall structure. The weakness of the human eye in specific the patterns in butterflies is the foundation in basis butterfly identification based on pattern recognition. This study used 3 butterfly species: Adonis, Black Hairstreak, and Gray Hairstreak. The butterfly dataset used was 150 which were obtained online. The pre-processing stage used segmentation and edge detection methods. The feature extraction stage used the Gray-level Co-occurrence Matrix (GLCM) method which extracted 8 shape and texture features including area, perimeter, metric, eccentricity, contrast, correlation, energy, and homogeneity. Classification phase used K-Nearest Neighbor (KNN) method with the values of k = 3, 5, 7, 9, 11, 13, 15, 17, and 19 as well as the Decision Tree method (C.45). The results of the identification of butterflies with the highest accuracy were obtained by the KNN Algorithm on the testing with a value of k = 3 of 93.33%, and the accuracy results using the Decision Tree method (C.45) is 84.44% while the results of identification using an application made using the GUI Matlab2017 with the KNN algorithm obtained an accuracy of 93.33% with a value of k= 3.
Co-Authors Abbas, Moch Anwar Ahmad Azhari Anton Anton Arif Budiarto Astuti, Rachmawati Darma Aziz, Faruq Choerunisya, Hana Dinar Ajeng Kristiyanti, Dinar Ajeng Dwi Saputra, Heru Dwinanda, Brama Rizky Setia Edi Sabara Efendi, Ade Irfan Efendi eka rahmawati Ernawan, Ferda Ernawan Ernawati, Muji Evi Sopandi, Evi Ferda Ernawan Gata, Windu Hakim, Valianda Farradillah Hasanah, Riyan Latifahul Herwanto, Teguh Hewiz , Alya Shafira Hewiz, Alya Shafira Ilhami, Azriel Noer Jamal Maulana Hudin Jamil, Muh. JARWADI, JARWADI Jumadi, J Kahfi, Ahmad Hafidzul Kodri, Wan Ahmad Gazali Kurniawantoro Laela Kurniawati, Laela Luh Putu Ratna Sundari Mahendra Mahendra, M Marasabessy, Fahmi B Muhamad Hasan Muhammad Qomaruddin Na'am, Jufriadif Nita Merlina, Nita Nur Sulistyowati, Daning Nurhasanah, Fitri Yani Octaviantara, Adi Pardede, Hilman F Pardede, Hilman Ferdinandus Rahman, Irfan Fadholur Ramanudin, Eko Rosdiana, Tjaturningsih Rudini, Edwin Rumintarsih, Ade Sahidillah Nurdin Saputra, Ridwan Saputra, Surya Fajar Siswahyudianto Sri Hadianti Sri Hardianti Sri Watmah Sulistyowati, Daning Nur Susilawati, Desi Susilawati Tanebeth, Riki Daniel Tati Mardiana, Tati Thira, Indra Jiwana Tohir, Herdian Vina Eriyandi Waeisul Bismi Widagdo, Galih Widanto, Frantina Andri Widianto, Tetra Wildan Wiguna WITRIANA ENDAH PANGESTI Zevana, Alda