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All Journal JURNAL SISTEM INFORMASI BISNIS Techno.Com: Jurnal Teknologi Informasi Jurnal Inovasi Teknologi Pendidikan Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Jurnas Nasional Teknologi dan Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING InComTech: Jurnal Telekomunikasi dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING JSiI (Jurnal Sistem Informasi) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Building of Informatics, Technology and Science Journal of Information Systems and Informatics Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia Jurnal Pengabdian Masyarakat Indonesia Internet of Things and Artificial Intelligence Journal Journal of Information Technology and Cyber Security CSRID Journal of Informatics and Interactive Technology (JIITE)
Imam Tahyudin
Program Studi Sistem Informasi STMIK AMIKOM Purwokerto
Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences

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Journal : Building of Informatics, Technology and Science

 1 
Analisis Sentimen dan Pemodelan Topik pada Ulasan Pengguna Aplikasi myIM3 Menggunakan Support Vector Machine dan Latent Dirichlet Allocation Prastyo, Priyo Agung; Berlilana, Berlilana; Tahyudin, Imam
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6268

Abstract

In the current digital era, mobile applications play a crucial role in enhancing user experience. This study analyzes user sentiment towards the myIM3 application and identifies key topics discussed in user reviews using Support Vector Machine (SVM) and Latent Dirichlet Allocation (LDA). The dataset comprises 1,000 user reviews from the Google Play Store, including review text, star ratings, review dates, and application versions. Data preprocessing involved cleaning, normalization, stop word removal, and lemmatization. Text data was transformed using Term Frequency-Inverse Document Frequency (TF-IDF). The dataset was split into training and testing sets (80:20 ratio). The SVM model, optimized with a linear kernel, achieved an accuracy of 84.65%, with a precision of 85% for negative sentiment, 84% for positive sentiment, and challenges in classifying neutral sentiment. Cross-validation ensured model robustness. LDA identified five primary topics: general user experience, application usability and purchase experience, positive feedback and functionality, general application evaluation, and network issues and pricing concerns. Techniques like oversampling, undersampling, and hybrid methods addressed imbalanced datasets to enhance model performance. The results revealed that 43% of reviews were positive, 42% were negative, and 15% were neutral. The key topics indicated that network issues and pricing were significant user concerns. These findings provide valuable insights for developers and stakeholders to improve user experience and refine application features based on user feedback.
Perbandingan Efficientnet, Visual Geometry Group 16, dan Residual Network 50 Untuk Klasifikasi Kendaraan Bermotor Andrianto, Andrianto; Tahyudin, Imam; Karyono, Giat
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6450

Abstract

This study compares the performance of three Convolutional Neural Network (CNN) models—EfficientNet, VGG16, and ResNet50—in motor vehicle classification tasks using the "Car vs Bike" dataset. Transfer learning was applied using pretrained weights from ImageNet. The results indicate that VGG16 achieved the best performance with 95% accuracy, precision of 0.95, recall of 0.96, and an F1-score of 0.95, demonstrating high balance in recognizing both classes. ResNet50 attained 87% accuracy on the test dataset with a precision of 0.89, recall of 0.84, and an F1-score of 0.87, offering a trade-off between accuracy and computational efficiency. Conversely, EfficientNet exhibited the lowest performance with 50% accuracy, failing to recognize the "Car" class effectively, as evidenced by precision and recall values of 0.00. Factors such as architectural complexity, dataset bias, and computational efficiency influenced these outcomes. This study reinforces previous findings on the strengths and weaknesses of CNN models in motor vehicle classification applications. Furthermore, it highlights the importance of balanced data management and model selection tailored to specific application requirements. However, the dataset's limitation of only two classes and reliance on transfer learning remain areas for future improvement. These findings provide valuable insights for developing intelligent transportation systems requiring high accuracy and efficiency.
Penerapan CNN dan RNN untuk Pembuatan Deskripsi Konten Visual Menggunakan Deep Learning Hermanto, Aldy Agil; Karyono, Giat; Tahyudin, Imam
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6958

Abstract

The development of technology in the field of image and sound processing has had a significant impact on increasing the accessibility of information for various groups, especially for individuals with visual impairments. One of the innovations that emerged was the image to speech system, which allows the conversion of images into sounds that can be understood by its users. The main problem lies in the low accuracy of object recognition in images with high variability, such as poor lighting or complex backgrounds, as well as the challenge of producing suitable text descriptions to be converted into audio. The method used involves extracting image features using InceptionV3-based CNN and forming a sequence of descriptive texts through RNN with an attention mechanism. The dataset consists of 40,455 captions and 8,091 images, processed using text and image pre-processing techniques before being trained using the teacher forcing technique. The evaluation results show a very low BLEU score (5.154827976372712e-153), indicating the model's inability to replicate the original caption well. However, the audio from the text-to-speech conversion using Google Text-to-Speech is quite clear. Future solutions include increasing the dataset, applying regularization, and adjusting the model architecture to improve the accuracy of caption prediction and audio relevance to the image. With these improvements, it is hoped that the system can provide more inclusive visual information accessibility for individuals with visual impairments.
Co-Authors Agustina, Nur Ngaenun Al-Haq, Ahnaf Vanning Al-Haq Alam, Yusuf Nur Alfirnanda, Weersa Talta Ammar Fauzan, Ammar Ananda, Fahesta Ananda, Rona Sepri Andrianto Andrianto Anggraini, Lintang Wahyu ANNISA HANDAYANI Anton Satria Prabuwono Arifa, Pujana Nisya Aris Munandar Azhari Shouni Barkah Bayu Surarso Berlilana Berlilana Che Pee, Ahmad Naim Daffa, Nauffal Ammar Dani Arifudin Dhanar Intan Surya Saputra Diniyati, Faoziyah Fahiya Eko Priyanto Eko Winarto Evania Adna Faiz Ichsan Jaya Fajariyanti, Alya Nur Fandy Setyo Utomo Fatmawati, Karlina Diah Febryanto, Bagas Aji Fitriani, Intan Indri Giat Karyono Hadie, Agus Nur Hellik Hermawan Hermanto, Aldy Agil Hidayah, Septi Oktaviani Nur Ilham, Rifqi Arifin Irfan Santiko Iskoko, Angga Isnaini, Khairunnisak Nur Khoerida, Nur Isnaeni khusnul khotimah Kuat Indartono Kusuma, Bagus Adhi Lestari, Silvia Windri Ma'arifah, Windiya Maulida, Trisna Melia Dianingrum Miftahus Surur, Miftahus Muhammad Reza Pahlevi Murtiyoso Murtiyoso Musyafa, Muhamad Fahmi Nabila, Putri Isma Najibulloh, Imam Kharits Nanjar, Agi Nazwan, Nazwan Nur Adiya, Az Zahra Dwi Nur Faizah Nur holifah, Anggita Oyabu, Takashi Prasetya, Subani Charis Prastyo, Priyo Agung PUJI LESTARI Purwadi Purwadi Purwadi Purwadi Putra, Bernardus Septian Cahya Putra, Feishal Azriel Arya R Rizal Isnanto Rahayu, Dania Gusmi Rahma, Felinda Aprilia Ramadani, Nevita Cahaya Rizaqi, Hanif Rozak, Rofik Abdul Rozak, Rofiq 'Abdul Rozak, Rofiq Abdul Rozak, Rofiq ‘Abdul Rozaq, Hasri Akbar Awal Saefullah, Ufu Samsul Arifin Santoso, Bagus Budi Sarmini Sarmini Satriani, Laela Jati Setiabudi, Rizki Sholikhatin, Siti Alvi Syafaat, Alif Yahya Syafiq, Bayu Ibnu Taqwa Hariguna Tikaningsih, Ades Tri Retnaningsih Soeprobowati Triana, Latifah Adi Triawan, Puas Wardani, Syafa Wajahtu Widiawati, Neta Tri Widya Cholid Wahyudin Wini Audiana Wulandari, Hendita Ayu Yarsasi, Sri Zainal Arifin Hasibuan Zulfa Ummu Hani Zumaroh, Agnis Nur Afa