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All Journal Jurnal Buana Informatika JIKO (Jurnal Informatika dan Komputer) JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING JUTIM (Jurnal Teknik Informatika Musirawas) Jiko (Jurnal Informatika dan komputer) Jurnal Ilmiah Media Sisfo JURIKOM (Jurnal Riset Komputer) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) MANDAR: Management Development and Applied Research Journal Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Pengabdian kepada Masyarakat Nusantara Information System Journal (INFOS) INFOSYS (INFORMATION SYSTEM) JOURNAL Batara Wisnu : Indonesian Journal of Community Services Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Ilmiah Pengabdian dan Inovasi Jurnal Informatika: Jurnal Pengembangan IT The Indonesian Journal of Computer Science
Nuri Cahyono
Universitas Amikom Yogyakarta
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Social Sciences Other

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Pengembangan Aplikasi Chat Multi Bahasa Berbasis NLP Translation API Sugiharto, M Iqbal Novananda; Cahyono, Nuri
The Indonesian Journal of Computer Science Vol. 11 No. 3 (2022): 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.v11i3.3128

Abstract

Interacting with others is an essential part of human life.. Sometimes users experience problems using chat applications, namely language differences when communicating with foreigners. Based on these problems, the author aims to build a chat application with a mobile-based automatic translator. In this application, the user can choose the language that will be used as needed. This application development uses the React Native framework for mobile applications and uses the NLP translation API for translators. This chat application automatically translates messages into the language used by the user. After doing some testing on the application, it can be concluded that it is according to the design and can make it easier for users to communicate with different languages
Analisis Sentimen Masyarakat Terhadap Penggunaan E-Commerce Menggunakan Algoritma K-Nearest Neighbor Kusuma, Ikhsan Habib; Cahyono, Nuri
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.5734

Abstract

Abstract − E-commerce's rapid growth has resulted in an increase in online transactions and shifts in consumer behavior. In Indonesia, the use of e-commerce has grown rapidly, with many online platforms emerging. Understanding public sentiment towards e-commerce in Indonesia is crucial for businesses to improve their services and maintain customer satisfaction. In this review, study propose a methodology for feeling investigation of popular assessment on the utilization of web-based business in Indonesia, utilizing directed learning calculations. The study involved collecting data from the website Google Play Store. The study performed data preprocessing, including removing stop words, tokenization, and stemming, before applying the K-Nearest Neighbor (K-NN) algorithm to classify sentiments into positive or negative. The evaluation was conducted using confusion matrix and classification report. The results showed that the proposed approach was effective in analyzing public sentiment towards e-commerce in Indonesia, with an accuracy rate of 82%. The study concluded that the proposed strategy could help businesses enhance their services and better satisfy customers' requirements and expectations.Keywords – Sentiment Analysis, E-Commerce, Supervised Learning, Machine Learning, NLP, KNN. Abstrak - Perkembangan e-commerce yang pesat telah menyebabkan peningkatan transaksi online dan perubahan perilaku konsumen. Di Indonesia, penggunaan e-commerce tumbuh pesat dengan banyak platform online bermunculan. Memahami sentimen masyarakat terhadap e-commerce di Indonesia sangat penting bagi bisnis untuk meningkatkan layanan dan menjaga kepuasan pelanggan. Oleh karena itu, dalam penelitian ini peneliti mengusulkan sebuah pendekatan untuk melakukan analisis sentimen opini publik mengenai penggunaan salah satu e-commerce di Indonesia dengan menggunakan algoritma K-Nearest Neighbor. Pengumpulan data dilakukan dari website Google Play Store dengan tujuan untuk memperoleh pandangan dan pengalaman masyarakat terkait penggunaan salah satu e-commerce di Indonesia. Setelah data terkumpul, dilakukan proses preprocessing untuk membersihkan data, termasuk menghilangkan stopwords, tokenisasi, dan stemming. Setelah itu, algoritma K-Nearest Neighbor (K-NN) digunakan untuk mengklasifikasikan sentimen menjadi positif atau negatif. Evaluasi dilakukan dengan menggunakan confusion matrix dan classification report untuk menilai keakuratan algoritma. Hasil penelitian menunjukan bahwa pendekatan yang diusulkan efektif dalam menganalisis sentimen masyarakat terhadap e-commerce di Indonesia, dengan tingkat akurasi 82%. Penelitian ini memiliki implikasi penting bagi bisnis e-commerce di Indonesia dalam meningkatkan layanan dan memenuhi kebutuhan serta harapan pelanggan secara lebih baik.Kata Kunci - Sentimen Analisis, E-Commerce, Supervised Learning, Machine Learning, NLP, KNN.
DIGITALISASI MULYA BIRO JASA UNTUK MENINGKATKAN EFEKTIVITAS PEMASARAN DAN KUALITAS PELAYANAN KEPADA MASYARAKAT DALAM PENGURUSAN PERIZINAN DAN LEGALITAS Kurniawan, Hendra; Cahyono, Nuri; Nurhayanto, Nurhayanto; Gustafito, Naufal Nuha; Yuliana , Dwi Eva
Batara Wisnu : Indonesian Journal of Community Services Vol. 5 No. 3 (2025): Batara Wisnu | September - Desember 2025
Publisher : Gapenas Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53363/bw.v5i3.461

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a vital role in Indonesia’s economy, with a total of 64.2 million units contributing 61.07% to the national Gross Domestic Product (GDP), equivalent to IDR 8,573.89 trillion. However, only about 12% of these MSMEs have effectively adopted digital technologies. Digitalization holds a strategic role in enhancing business competitiveness and efficiency in the era of the Industrial Revolution 4.0 toward Society 5.0, particularly in marketing activities that have shifted from conventional systems to digital-based marketing through e-commerce, social media, and online marketplaces. This Community Service Program (PKM) was conducted to assist a partner, Mulya Biro Jasa, an MSME engaged in administrative and legal documentation services located in Kalasan, Sleman, Yogyakarta. Based on observations, the partner faced challenges in marketing and management facilities that remained conventional, such as the absence of an official website, unregistered business location on Google Maps, and underutilization of social media platforms. Through this PKM activity, the implementation team provided several solutions, including the development of a website, social media development, Google Maps optimization, logo design, business cards, and a neon box sign as a business identity. In addition, the implementing team also installed a 1 TB SSD to provide larger digital data storage capacity and added CCTV to enhance the security of important documents. The program successfully improved the partner’s knowledge and skills in utilizing digital technology to support business development. Based on the questionnaire results, the average pre-test score of 3.09 increased to 3.60 in the post-test, indicating a 16,5% improvement in understanding. Furthermore, this activity provided valuable hands-on experience for students in applying information technology knowledge in real-world business contexts.
Optimization of IndoBERT for Sentiment Analysis of FOMO on Social Media Through Fine-Tuning and Hybrid Labeling Adhim, Nadhif Fauzil; Cahyono, Nuri
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.v9i6.11686

Abstract

The rapid growth of social media in Indonesia has given rise to social phenomena such as Fear of Missing Out (FOMO). Expressions of FOMO on platforms like X (previously Twitter) often written informally, filled with abbreviations, slang, and emotional nuances, posing challenges for traditional Natural Language Processing (NLP) methods. This research aims to develop an optimized sentiment classification model for FOMO-related posts by fine-tuning the IndoBERT architecture and applying comprehensive data enhancement strategies. The study introduces three key innovations: (1) systematic text normalization to handle informal expressions, (2) a hybrid labeling framework combining automated model prediction, lexicon-based validation, and manual annotation to construct high-quality ground-truth data, and (3) hyperparameter tuning using both GridSearchCV for traditional machine learning models and Bayesian Optimization (Optuna) for deep learning models to maximize performance. The experimental results demonstrate that the optimized IndoBERT achieved superior performance with an Accuracy of 94.50%, F1-Score of 94.52%, and Macro AUC of 0.987. These results significantly surpass comparative models, including BiLSTM (Accuracy 86.60%), Support Vector Machine (88.06%), and Naive Bayes (80.73%). These results confirm that integrating hybrid labeling and fine-tuned IndoBERT significantly enhances sentiment classification performance. The findings contribute to developing reliable sentiment analysis systems for detecting social anxiety dynamics and computational social science research in Indonesian contexts.
Evaluation of YOLOv8 and Faster R-CNN for Image-Based Food Detection Hananta, Julian Kiyosaki; Cahyono, Nuri
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11684

Abstract

Difficulties in manually tracking nutrition lead to the need for automatic food detection systems. However, Indonesian food presents tough challenges to recognize because similar-looking foods and different serving styles make it hard. This study looks at two deep learning models that follow different approaches: YOLOv8, which is known for being fast and efficient, and Faster R-CNN, which is known for being very accurate. The goal is to find the best model for use on mobile devices. This research uses a strict and standardized way to test the models to make sure the comparison is fair. A public dataset with 1,325 images from Roboflow was used. To deal with uneven class distribution, the images were split using Stratified Random Sampling. Before training, the images were resized using letterbox method to keep their original shape and normalized for pixel values. Both models were trained for the same number of epochs (100) and used the same optimizer (SGD) to ensure fair comparisons. The results show that YOLOv8 performs better in all areas. It achieved 88.6% mAP@50 accuracy and 62.0% mAP@50-95 precision. Faster R-CNN got 85.5% and 55.6% respectively. YOLOv8 also excels in sensitivity or Recall, reaching 87.7% compared to 61.7% for Faster R-CNN. The F1-Score, which balances accuracy and sensitivity, is 84.0% for YOLOv8 and 72% for Faster R-CNN. In terms of speed and size, YOLOv8 is much better. It runs in 13.5 ms and is 21.5 MB in size. That makes it 7.7 times faster and 7.3 times smaller than Faster R-CNN. Based on these results, YOLOv8 is the best choice for developing mobile-based nutrition tracking systems.
Estimasi Data Insight Social Media Ads Menggunakan Neural Network, Linear Regression dan Deep Learning Zurni Laila; Nuri Cahyono
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 3 (2023): Maret 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5451

Abstract

PT IlmuKomputerCom Braindevs is a professional training company that sells products in the form of training services (courses). In service companies such as PT IlmuKomputerCom Braindevs Sistema, holding courses with high demand is the key to increasing company profits. The marketing division is a very important division in the context of holding a course/training. To manage the target participants needed in organizing training. In addition, PT IlmuKomputerCom Braindevs also needs to estimate advertising costs and ad duration in the training promotions that will be held. To analyze the marketing division using data mining techniques and the Cross-industry standard process for data mining (CRISP-DM) method to obtain the desired estimate. So to get the final result of the participant's estimated value, ad duration and ad cost, an algorithm that has the most accurate accuracy is needed according to the reference from the results of the comparison of algorithms by looking at the value of RMSE (Root Mean Square Error). The closer the resulting value is to 0, the better the estimated accuracy of the RMSE (Root Mean Square Error) estimate will be.
Analisis Sentimen Terhadap Cyberbullying Pada Komentar Di Instagram Menggunakan Algoritma Naïve Bayes Fauzan Baehaqi; Cahyono, Nuri
The Indonesian Journal of Computer Science Vol. 13 No. 1 (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.v13i1.3301

Abstract

Cyberbullying, penggunaan teknologi digital yang disengaja untuk menyakiti, mempermalukan, atau menggertak orang lain secara online, telah menjadi isu penting dalam masyarakat saat ini. Dampak dari cyberbullying bisa sangat parah, menyebabkan masalah kesehatan mental, rendah diri, dan, dalam beberapa kasus tragis, hilangnya nyawa. Memahami fenomena ini secara mendalam dan menemukan solusi efektif untuk mengatasinya sangatlah penting. Analisis sentimen menggunakan algoritma Naïve Bayes sebagai pendekatan yang layak untuk mengatasi cyberbullying di Instagram. Tujuan utama dari penelitian ini adalah untuk mempelajari dan menganalisis sentimen terkait cyberbullying pada komentar Instagram menggunakan algoritma Naïve Bayes. Dengan menganalisis konten yang terkait dengan cyberbullying, penelitian ini bertujuan untuk memberikan wawasan yang lebih baik tentang masalah tersebut dan mengidentifikasi pola dan karakteristik khusus yang dapat membantu upaya pencegahan dan intervensi. Temuan ini dapat berkontribusi untuk menciptakan lingkungan online yang lebih aman dan melindungi kesejahteraan pengguna media sosial.
Analisis Sentimen Ulasan Aplikasi TikTok Shop Seller Center di Google Playstore Menggunakan Algoritma Naive Bayes Anggista Oktavia Praneswara; Cahyono, Nuri
The Indonesian Journal of Computer Science Vol. 12 No. 6 (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.v12i6.3473

Abstract

In the rapidly developing digital era, users' views on mobile applications are a key factor in the success of an application. Understanding user sentiment can help application developers and management to improve service quality and user satisfaction. One of the social media that is experiencing a revolution is TikTok, a short video sharing platform that presents e-commerce innovations through the TikTok Shop Seller Center. Therefore, sentiment analysis was carried out to find out whether user reviews of the TikTok Shop Seller Center application tended to be positive or negative based on the Naïve Bayes algorithm. The research methodology involves data scrapping, data cleaning, preprocessing (case folding, stopword removing, tokenization, stemming), labeling, TF-IDF, data testing using confusion matrix and visualization using wordcloud. The results of research regarding sentiment analysis of reviews of the TikTok Shop Seller Center application on Google Playstore totaling 5000 data, it was concluded that user reviews were classified as negative with a percentage of 86.3% accuracy value, 83.7% precision value, 94.6% recall value and 88.7% % F1-Score value.
Analisis Topic Modelling Pariwisata Yogyakarta Menggunakan Latent Dirichlet Allocation (LDA) Uray Nur Khadijah; Nuri Cahyono
The Indonesian Journal of Computer Science Vol. 13 No. 4 (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.v13i4.3816

Abstract

Pariwisata Yogyakarta sebagai destinasi yang kaya akan budaya dan sejarah, sering menjadi fokus diskusi di media sosial. Tujuan dari Penelitian ini adalah menelaah topik pariwisata Yogyakarta dari Twitter. Dataset yang diperoleh dalam penelitian ini dari crawling data menggunakan API key Twitter. Penelitian ini menggunakan tahapan dari pengumpulan data, text preprocessing, dan menerapkan metode Topic Modelling, khususnya Latent Dirichlet Allocation (LDA). Hasil penelitian ini pengujian kinerja pemodelan topik dengan metode LDA dapat dilihat dari nilai coherence score, semakin tinggi nilai coherence suatu topik, semakin mudah diinterprestasikan oleh manusia dan Perplexity merupakan salah satu standar pengukuran yang dapat digunakan untuk menilai kinerja model yang baik dari model tersebut ditunjukkan dengan nilai perplexity yang lebih rendah. Nilai coherence score yang ditunjukkan pada num topic ke-1 sebesar 0.331047, untuk nilai perplexity ditunjukkan dengan nilai yang tinggi terletak pada num topic ke-3 sebesar -8.830172565520245. diharapkan dapat memberikan wawasan mendalam tentang topik-topik yang sering dibahas dan berkonsentrasi pada penerapan sistem pemodelan topik untuk membangun sistem keputusan topik berita yang menggunakan metode Latent Dirichlet Allocation (LDA). Pada Penelitian ini efektif dalam menggunakan metode LDA untuk menentukan topik berita yang mencakup tiga kategori topik yang sering dibicarakan pada masing-masing kelas.
Co-Authors Adhim, Nadhif Fauzil Adi Saputro, Johan Afnan Wijaya, Hafid Alfataah, Sevilla Andika Joni, Gusti Angga Reni Dwi Astuti Angga Reni Dwi Astuti Anggista Oktavia Praneswara Anggita, Sharazita Dyah Anggoro Saputro, Uyock Anna Baita Arif Rahmat Hidayatullah Arifiyanto Hadinegoro, Arifiyanto Armadyah Amborowati Armadyah Amborowati Armadyah Amborowati Bagus Candrahutomo, Rio Baita, Anna Bayu Prayoga, Mahendra Bayu Wicaksono Dewi Setiyawati Dewi Setiyawati Dyah Anggita, Sharazita Eli Pujastuti, Eli Ema Utami Ermawan, Bagas Restya Fahlevie, Reychandra Tristan Faisal Salim, Muhammad Fauzan Baehaqi Gustafito, Naufal Nuha Hananta, Julian Kiyosaki Inggar Adi Prasetyo Kamarudin Kamarudin Kusuma Wardana, Raka Kusuma, Ikhsan Habib M Iqbal Novananda Sugiharto Marsel Pratama, Aldino Mas’ud Mahfudz Darmawan, Muhammad Nugroho, Bagas Julio NUR AINI Nurcahyo, Narwanto Nurhayanto, Nurhayanto Nurwijayanti Oktafria, Fikri Pamungkas, Adji Surya Pramudya Hidayat, Muhammad Rifqi Rakarahayu Putri, Raniya Ramadhan Bima Saputra Restu Agung, Akmal Fauzan Rizal Gibran Aldrin Pratama Rizki Gunawan, Rizki Salamun Sukriandi Santoso, Riki Adi Siswahyudianto Subektiningsih Subektiningsih Sugiharto, M Iqbal Novananda Susanti, Rahmadhani Tofa Nurcholis, Muhammad Uray Nur Khadijah Uyock Anggoro Saputro Wahyu Hidayat Wardhana, Raditya Wulandari, Irma Rofni Yoga Pristyanto Yuliana , Dwi Eva Zurni Laila