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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Dinamik Seminar Nasional Aplikasi Teknologi Informasi (SNATI) ELKHA : Jurnal Teknik Elektro Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Elkom: Jurnal Elektronika dan Komputer TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Prosiding SNATIF Jurnal Transformatika Scientific Journal of Informatics Seminar Nasional Informatika (SEMNASIF) Sisforma: Journal of Information Systems JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Journal of Information System Jurnal Informatika Upgris Jurnal Teknologi dan Sistem Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika SISFOTENIKA JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Wikrama Parahita : Jurnal Pengabdian Masyarakat Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Eksplora Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING J-SAKTI (Jurnal Sains Komputer dan Informatika) Building of Informatics, Technology and Science Jurnal Informatika dan Rekayasa Perangkat Lunak JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) JUDIMAS (Jurnal Inovasi Pengabdian Kepada Masyarakat) Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat Advance Sustainable Science, Engineering and Technology (ASSET) J-SAKTI (Jurnal Sains Komputer dan Informatika) Prosiding Seminar Nasional Hasil-hasil Penelitian dan Pengabdian Pada Masyarakat Jurnal Maklumatika Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Journal of Computing Theories and Applications Seminar Nasional Teknologi dan Multidisiplin Ilmu Scientific Journal of Informatics Journal of Future Artificial Intelligence and Technologies Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) INOVTEK Polbeng - Seri Informatika
Ajib Susanto
Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering 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 Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Physics Public Health Social Sciences Other

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Journal : building of informatics technology and science

 1 
Implementation of Tesseract OCR and Bounding Box for Text Extraction on Food Nutrition Labels Saputra, The Manuel Eric; Susanto, Ajib; Carmelita, Bastiaans Jessica
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.v6i3.6107

Abstract

This study focuses on implementing Optical Character Recognition (OCR) using the Tesseract engine, integrated with bounding box detection, to extract nutritional information from food nutrition labels. The research addresses the challenge of limited consumer access to and understanding of nutritional data, a factor contributing to health issues such as obesity and related metabolic disorders. Studies indicate that although Indonesian consumers generally have a good level of knowledge and positive attitudes toward nutritional labels, the actual behavior of reading and understanding these labels remains limited. Additionally, packaged foods consumed outside the home constitute a significant portion of daily caloric intake, which can lead to health complications if not properly managed. With obesity levels among adults in Indonesia rising to concerning rates, this study highlights the importance of providing accessible nutritional data. In this work, MobileNetV1 is used as the backbone model for bounding box detection, effectively identifying and isolating label regions to enhance OCR accuracy. Tesseract OCR, known for its LSTM-based architecture, is applied to predict sequential data patterns, such as rows of text on nutrition labels. Preprocessing techniques, including grayscale conversion, brightness adjustment, CLAHE (Contrast Limited Adaptive Histogram Equalization), and denoising, are used to improve text clarity and further refine OCR output accuracy. Post-processing steps involve rule-based and contextual error correction to handle common OCR inaccuracies. Evaluated on 10 different label images, the system achieved a maximum Word Error Rate (WER) of 10% and a Character Error Rate (CER) of 1.6%, demonstrating high accuracy in nutritional information extraction.
Implementation of Item-Based Collaborative Filtering Algorithm for Blangkon Product Recommendation on Web-Based E-commerce System Atmojo, Cahyo Tri; Susanto, Ajib
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.v6i3.6120

Abstract

In the development of technology at this time, especially in the trade sector, there is no escape from the development of information technology which has had a significant impact. The most obvious form in the development of information technology in the trade sector is e-commerce, which allows transactions between sellers and buyers to be easier. Not only that, the problem now is that users must be spoiled with features that help to recommend user desires. This requires a recommendation system to help select user desires based on products with high ratings. Therefore, it must continue to develop a system that has features to support the sales system. To achieve the system needs to require a method that supports such as using the collaborative filtering method. This method focuses the analysis on similarities between items, because it is more stable and not always sensitive to changing data with a large number of users. The collaborative filtering method is used in the recommendation system to predict inter-user preferences for blangkon products based on the similarity of other user patterns, so that product recommendations appear that they have never seen or bought before. This technique uses an item-based model in it. The results of the performance test to determine the level of prediction accuracy of the method in this study using the mean absolute error. With MAE for three times trying to get a value of 0.5, 0.3 and 0.2.
Implementasi dan Evaluasi Model Machine Learning untuk Optimalisasi Prediksi Penjualan Produk Kue Kering Hilmi, Muhammad Abror Auliya; Susanto, Ajib
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8657

Abstract

The modern retail sector, such as Transmart, faces difficulties in maintaining stable sales performance due to changes in consumer behavior, variations in product types, and differing store characteristics. To address this issue, this study proposes the use of the Extreme Gradient Boosting (XGBoost) machine learning algorithm to predict retail product sales volumes based on historical data from 2024–2025. The research utilizes the CRISP-DM framework, which consists of the following stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The data cleaning and preprocessing processes involve several steps such as data cleaning, label encoding, feature selection, and data splitting with an 80:20 ratio. The model is further evaluated using the Mean Absolute Error (MAE) and the coefficient of determination (R²) metrics to assess prediction accuracy. The findings indicate that XGBoost is capable of effectively capturing sales patterns and generating accurate predictions to support decision-making strategies in the retail sector, particularly in stock planning and sales optimization. Therefore, the implementation of this data-driven predictive approach is expected to assist companies in enhancing operational management as well as improving competitiveness in the market.
Analisis Sentimen Twitter Terhadap Isu Royalti Lagu di Industri Musik Indonesia Menggunakan Naive Bayes dan Support Vector Machine Berbasis TF-IDF Alif Fadhil Wibowo; Ajib Susanto
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9842

Abstract

The development of digital platforms in Indonesia’s music industry has triggered various debates regarding the song royalty system, particularly those related to copyright and income distribution for songwriters. Public opinions on these issues are widely expressed through Twitter, making it a valuable data source for sentiment analysis. This study aims to analyze public sentiment toward song royalty issues in the Indonesian music industry and compare the performance of Multinomial Naive Bayes and Support Vector Machine (SVM) algorithms using TF-IDF weighting. This study contributes through the implementation of semi-manual labeling, the use of a stratified 5-fold cross-validation approach, and multi-metric evaluation to obtain more representative sentiment classification results on song royalty issues in Indonesian social media. The initial dataset was collected through Twitter scraping using keywords related to song royalties and music copyright. The data were then processed through preprocessing stages, including case folding, cleaning, tokenization, stopword removal, and stemming. Sentiment labeling was conducted using a semi-manual approach, involving lexicon-based pre-labeling followed by manual verification into three sentiment categories: positive, negative, and neutral. Model evaluation was performed using stratified 5-fold cross-validation with accuracy, precision, recall, and F1-score metrics. The results indicate that the SVM algorithm outperformed Multinomial Naive Bayes, achieving an accuracy of 93.21%, while Multinomial Naive Bayes obtained an accuracy of 82.53%. These findings demonstrate that SVM is more effective in handling high-dimensional textual data represented using TF-IDF for Indonesian sentiment analysis. This study is expected to provide insights into public perceptions regarding song royalty issues and serve as a reference for sentiment analysis applications on Indonesian social media data.
Co-Authors - Wijanarto - Wijanarto -, Wijanarto -, Wijanarto Abdussalam Abdussalam Abdussalam Abdussalam Abdussalam Abiyyi, Ryandhika Bintang Aceng Sambas Adrian Angga Pramono Afrizal Aziz Maulana Agus Winarno Agus Winarno, Agus Akhmad Rizaldy Ali Muqoddas Ali, Rabei Raad Alif Fadhil Wibowo Alviana Dina Putri Anak Agung Gede Sugianthara Anggia Rizkika Hanin Anggraeny, Tiara Antonio Ciputra Antonius Erick Handoyo Antonius Wibowo Atmojo, Cahyo Tri Bayu Wicaksono Briliantino Abhista Prabandanu Bustami, Sri Heri Cahyani, Anis Putma Carmelita, Bastiaans Jessica Christy Atika Sari Ciputra, Antonio D.R.I.M. Setiadi Daniel Nomolas Wicaksono De Rosal Ignatius Moses Setiadi Desi Purwanti Desi Purwanti Kusumaningrum Dian Kristiawan Nugroho Didik Hermanto Dimas Irawan Ihya’ Ulumuddin Doheir, Mohamed Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Eko Hari Rachmawanto Elkaf Rahmawan Pramudya Ericsson Dhimas Niagara Erwin Erwin Etika Kartikadarma Fakhriyan Nur Rofiq Farrel Athaillah Putra Febrian, Muhamad Rizky Fajar Fikri Budiman Fikri Budiman Galih Setyo Wibowo Gan, Hong-Seng Gilang Raharjito Haqikal, Hafidz Hayu Wikan Kinasih Hidayah Rahmalan Hilmi, Muhammad Abror Auliya Hussain Md Mehedul Islam Ibnu Gemaputra Ramadhan Ibnu Utomo Ibnu Utomo W.M. Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono, Ibnu Utomo Ibnu Utomo WM Ihya Ulumuddin, Dimas Irawan Imam Kurniawan Imam Prayogo Pujiono Imanuel Harkespan Indra Kusuma Islam, Hussain Md Mehedul Istiqomah, Annisa Ayu Karis Widyatmoko Khafiizh Hastuti Krismawan, Andi Danang Kusuma, Tiara Widya Kusumawati, Yupie L. Budi Handoko Laksono, Enggar Adji Lalang Erawan Latifah Diah Kumalasari Lutfi Madiono Marjuni, Aris Md Kamruzzaman Sarker Md Kamruzzaman Sarker Mega Bintang Hatmi Moch Arief Soeleman Mochammad Lukman Mohamad Afendee Mohamed Mohamed A. S. Doheir Mohammad Arif Muttaqin Mohd Yaacob, Noorayisahbe Muhammad Atho’il Maula Muhammad Nur Haztinanto Mulyanto, Ibnu Utomo Mulyono, Ibnu Utomo Wahyu Mulyono, Ibnu Utomo Wahyu Mulyono, Ibnu Utomo Wahyu Musfiqur Rahman Sazal Muslih Muslih Muslih Muslih Muslih Nabiha Riandika, Muhammad Afiq Ningrum, Novita Kurnia Nova Rijati Novita Kurnia Ningrum Novita Kurnia Ningrum Nugroho, Widhi Bagus Ojugo, Arnold Adimabua Ozagastra Caluella Prambudi Ozagastra Caluella Prambudi Panjaitan, Yonathan Gani Panjaitan, Yonathan Gani Putri, Clara Edrea Evelyna Sony Raga Nufusula Rahadian Kristiyanto Rachman Raihan Yusuf Ramadhan, Aditya Wahyu Respatria, Nabila Maharani Rico Rian Alvian Rosyida, Ghaitsa Ardelia Sabilillah, Ferris Tita Saputra, The Manuel Eric Saraswati, Galuh Wilujeng Sarker, Md Kamruzzaman Sembiring, Rinawati Setiarso, Ichwan Sinaga, Daurat Sinaga, Daurat Sinar Setyawan Stefanus Santosa Sudaryanto Sudaryanto Sudaryanto Sudaryanto Sudaryanto Sudaryanto SUDARYANTO SUDARYANTO Suprayogi Suprayogi Syamsiar, Syamsiar Teresa Enades Hari Setia Tiara Anggraeny Tiara Widya Kusuma Tri Wulandari Utomo W.M, Ibnu Utomo W.M, Ibnu Wellia Shinta Sari Widyatmoko Karis Wijanarto Wijanarto Wijanarto Wijanarto Wijanarto Wijanarto Yaacob, Noorayisahbe Mohd. Yupie Kusumawati Zahrotul Umami, Zahrotul Zainal Arifin Hasibuan Zuama, Leygian Reyhan