Article Per Year (5 Year)
p-Index From 2021 - 2026
6.174
P-Index
This Author published in this journals
All Journal Voteteknika (Vocational Teknik Elektronika dan Informatika) JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Jurnal Mantik Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Jurnal Informatika dan Rekayasa Elektronik Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Informatika dan Sistem Informasi (JUISI) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Pendidikan dan Teknologi Indonesia Bulletin of Computer Science Research Journal of Information Technology (JIfoTech) Jurnal Abdimas Bina Bangsa COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Algoritme Jurnal Mahasiswa Teknik Informatika Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science DEVOTE: Jurnal Pengabdian Masyarakat Global Jurnal INFOTEL SmartComp SAINTEK Infact: Jurnal Sains dan Komputer Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) UPGRADE: Jurnal Pendidikan Teknologi Informasi
Haeni Budiati
Universitas Kristen Immanuel
Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering 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 Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

Published : 48 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 48 Documents
Search

 1   2   3   4   5 
MODEL PEMBELAJARAN AR INTERAKTIF UNTUK PENGEMBANGAN KOGNITIF ANAK TK DALAM PENGENALAN WARNA DAN BENTUK Tasane, Elshaddai L; Himamunanto, Agustinus Rudatyo; Budiati, Haeni
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 2 (2025): EDISI 24
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i2.5885

Abstract

Sistem pembelajaran untuk Anak TK masih banyak menggunakan media konvensional seperti buku cetak dan gambar dua dimensi, yang dinilai kurang efektif dalam menyampaikan konsep abstrak seperti warna dan bentuk. Namun, pengenalan warna dan bentuk sangat penting dalam mendukung perkembangan kognitif dan persepsi visual anak. Penelitian ini bertujuan untuk mengembangkan dan menguji aplikasi pembelajaran interaktif berbasis Augmented Reality (AR) yang dirancang untuk memperkenalkan warna dan bentuk secara menarik dan mudah dipahami. Pengembangan aplikasi menggunakan metode Multimedia Development Life Cycle (MDLC), dan dilakukan pengujian sebanyak 30 kali terhadap total 35 objek, yang terdiri dari 24 objek warna dan bentuk 11 objek bentuk. Hasil menunjukkan bahwa seluruh objek warna dikenali dengan akurasi 100%, sedangkan objek bentuk dikenali dengan akurasi 90,9% dengan objek bola gagal dikenali karena pencahayaan dan desain marker. Akurasi keseluruhan sistem sebesar 97,14%. Uji coba terhadap 25 anak usia 4-5 tahun di Tk Kanisius Kadirojo menunjukkan bahwa 88% anak mampu mengoperasikan aplikasi secara mandiri. Penelitian ini memberikan kontribusi dalam pemanfaatan teknologi Marker-Based AR sebagai media pembelajaran inovatif yang mendukung pengembangan kognitif anak Tk secara lebih interaktif dan menyenangkan.
Comparative Study of Fuzzy Inference System and Adaptive Neuro-Fuzzy Inference System in Public Sentiment Analysis of Kabinet Merah Putih Budiati, Haeni; Setyawan, Gogor Christmass; P.Hurit, Lud Gerdus; Lase, Kristian J.D.
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1538

Abstract

Purpose: This study aims to compare two fuzzy logic-based approaches, namely the Fuzzy Inference System (FIS) and the Adaptive Neuro-Fuzzy Inference System (ANFIS), in analyzing public sentiment toward the Kabinet Merah Putih.Methods: A dataset of 1,197 tweets was collected from Twitter (X) between October 2024 and April 2025 using specific keywords. After preprocessing and polarity measurement with TextBlob, the sentiment values were mapped into seven categories: strongly negative, negative, weakly negative, neutral, weakly positive, positive, and strongly positive. The classification was performed using both FIS and ANFIS. Evaluation metrics included accuracy, precision, recall, F1-score, and error rate (MSE and RMSE).Result: Experimental results show that FIS achieved an overall accuracy of 79.2%, performing well on majority classes but failing to identify several minority classes. In contrast, ANFIS obtained an accuracy of 92.5% with very low error (MSE = 0.0341, RMSE = 0.1848), demonstrating strong capability in classifying majority and several minority categories. Overall, ANFIS outperformed FIS, proving more effective in capturing sentiment patterns and aligning with the actual distribution of public opinion..Novelty: This study offers novelty by explicitly comparing the performance of FIS and ANFIS in multi-level sentiment analysis of Indonesian social media data, an approach that has not been explored in prior research.
Penerapan ESP32-CAM dan TinyML dalam Klasifikasi Gambar Buah dan Sayuran Ziliwu, Johni Revormasi; Setyawan, Gogor C; Budiati, Haeni
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 13, No 1: April 2024
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v13i1.1869

Abstract

The classification of fruit and vegetable images still requires high costs, posing challenges in developing efficient solutions. Fruits and vegetables are crucial in healthy food, so efficiency in managing their images impacts the agricultural industry. The aim of this study is to apply ESP32-CAM and TinyML in Fruit and Vegetable Image Classification with efficiency and low cost. The method involves Edge Impluse Studio and training the model with Convolutional Neural Network (CNN). From the testing, the F1 score accuracy reached 68.3% for each class of fruit and vegetables. From the demonstration using ESP32-CAM, the obtained accuracies are Apple (89%), Banana (91%), Orange (89%), Carrot (83%), and Cabbage (66%). The results indicate that applying ESP32-CAM and TinyML has the potential to improve efficiency and reduce costs in managing images.Keywords: ESP32-CAM; TinyML; Image Clasification; Fruit and Vegetables; Solution Efficiency; AbstrakPengklasifikasian gambar buah dan sayuran masih memerlukan biaya yang tinggi, sehingga menghadirkan kesulitan dalam mengembangkan solusi yang efisien. Buah dan sayuran penting dalam makanan sehat, sehingga efisiensi dalam pengelolaan gambar mereka berdampak pada industri pertanian. Tujuan penelitian ini adalah menerapkan ESP32-CAM dan TinyML dalam Klasifikasi Gambar Buah dan Sayuran dengan efisiensi dan biaya rendah. Metode melibatkan Edge Impluse Studio dan pelatihan model dengan Convolutional Neural Network (CNN). Dari pengujian, akurasi F1 score mencapai 68.3% untuk setiap kelas buah dan sayuran. Dari demonstrasi menggunakan ESP32-CAM, akurasi yang diperoleh adalah Apel (89%), Pisang (91%), Jeruk (89%), Wortel (83%), dan Kubis (66%). Hasil menunjukkan penerapan ESP32-CAM dan TinyML memiliki potensi untuk meningkatkan efisiensi dan mengurangi biaya pengelolaan gambar. 
Analisis Sentimen Opini Masyarakat Terhadap Presiden Jokowi Sebelum Dan Sesudah Pilpres 2024 Menggunakan Metode Naive Bayes Classification Nehe, Pius Hermanto; Berutu, Sunneng Sandino; Budiati, Haeni
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 13, No 1: April 2024
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v13i1.1841

Abstract

The 2024 presidential election in Indonesia is an important moment in political dynamics. This research analyzes changes in public sentiment towards President Jokowi before and after the 2024 Presidential Election using the naive bayes classification method. Datasets consist of 10,014 tweets that have gone through the process of crawling, preprocessing, translating, labeling, classification, and model evaluation. The analysis results show that before the 2024 presidential election, positive sentiment reached 41.17%, neutral sentiment 34.30%, and negative sentiment 24.53%. After the 2024 presidential election, positive sentiment decreased to 39.08%, neutral sentiment increased to 37.59%, and negative sentiment decreased to 23.33%. Prediction accuracy increased to 64 and neutral sentiment had a precision of 88, with a dataset focusing on President Jokowi after the 2024 Presidential Election, while recall for positive sentiment was 87, and f1-score for neutral sentiment was 69, with a dataset of President Jokowi before the 2024 Presidential Election. Keywords: Presiden Jokowi; Public opinion dynamics; Naive Bayes Classification; Presidential Election 2024; Sentiment AbstrakPemilihan Presiden 2024 di Indonesia merupakan momen penting dalam dinamika politik. Penelitian ini menganalisis perubahan sentimen publik terhadap Presiden Jokowi sebelum dan sesudah Pilpres 2024 dengan menggunakan metode klasifikasi naive bayes. Datasets terdiri dari 10.014 tweets yang telah melalui proses crawling, preprocessing, translating, labeling, classification, dan evaluation model. Hasil analisis menunjukkan bahwa sebelum Pilpres 2024, sentimen positif mencapai 41,17%, sentimen netral 34,30%, dan sentimen negatif 24,53%. Setelah Pilpres 2024, sentimen positif menurun menjadi 39,08%, sentimen netral meningkat menjadi 37,59%, dan sentimen negatif menurun menjadi 23,33%. Akurasi prediksi meningkat menjadi 64 dan Sentimen netral memiliki precision 88, dengan dataset yang berfokus pada Presiden Jokowi setelah pelaksanaan Pilpres 2024, sementara recall untuk sentimen positif adalah 87, dan f1-score untuk sentimen netral adalah 69, dengan dataset Presiden Jokowi sebelum Pilpres 2024.
PELATIHAN DAN PERAKITAN LAMPU PANEL SURYA BAGI MASYARAKAT OPAK-GREMBYANGAN MADUREJO PRAMBANAN SLEMAN Setyawan, Gogor Christmass; Budiati, Haeni; Jatmika, Jatmika; Jacobus, Liefson; Dwiputranto, Surjawirawan
Devote: Jurnal Pengabdian Masyarakat Global Vol. 1 No. 2 (2022): Devote : Jurnal Pengabdian Masyarakat Global, Desember 2022
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (495.824 KB) | DOI: 10.55681/devote.v1i2.361

Abstract

A program of community service has been implemented, with a focus on designing and producing low-cost, easily-assembled solar panels for the Opak-Grembyangan Madurejo Pramabanan village community. Creation of solar panel lighting systems that are practical to use and are capable of producing up to 100 Watts of electricity. The actions taken serve to introduce people to renewable energy sources, educate them about it, and help with solar panel lighting for the master plan area for village tourism development. Village communities may be equipped with the knowledge and skills necessary to create solar panel lights independently and for mass production after receiving continual mentoring and training. Alternative energy management is a more broad goal to be able to lessen reliance on fossil fuels and create a green tourism sector.
Pengembangan Model Klasifikasi Sentimen Dengan Pendekatan Vader dan Algoritma Naive Bayes Terhadap Ulasan Aplikasi Indodax Zendrato, Agus Dirgahayu; Berutu, Sunneng Sandino; Sumihar, Yo’el Pieter; Budiati, Haeni
Journal of Information System Research (JOSH) Vol 5 No 3 (2024): April 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i3.5050

Abstract

Cryptocurrency trading applications such as Indodax have grown rapidly, the understanding of user sentiment towards the platform is still lacking, so it is interesting to analyze user sentiment towards the platform. To measure sentiment, this research proposes a combined approach of Vader and Naïve Bayes methods. The data used is a collection of user comments on the google play store platform related to user experience using Indodax. The Vader method is used to analyze sentiment directly from the comment text, while Naïve Bayes is adopted to improve accuracy in sentiment classification. The sentiment analysis process involves various steps, starting from data preparation, data pre-processing, labeling of training and testing data and performance evaluation of the Naive Bayes model. At the sentiment analysis stage with the Vader Sentiment method, the positive category obtained the highest percentage of 63.5%, followed by the neutral category at 18.9% and negative at 17.6%. Meanwhile, based on the performance evaluation of the Naïve Bayes model, the accuracy value is 78% while the highest precision value is achieved by the negative sentiment category at 80% and recall in the positive sentiment category at 44%.
Deteksi Anomali Traffic Pada Jaringan Komputer Menggunakan Naive Bayes, Decison Tree Dan Isolation Forest Mendrofa, Milka Justine; Lase, Kristian Juri Damai; Budiati, Haeni
COMSERVA : Jurnal Penelitian dan Pengabdian Masyarakat Vol. 4 No. 11 (2025): COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/comserva.v4i11.3012

Abstract

Anomali jaringan komputer adalah pola atau aktivitas yang tidak biasa yang menyimpang dari perilaku normal jaringan. Deteksi anomali sangat penting untuk mengidentifikasi ancaman keamanan, gangguan operasional, atau kegagalan sistem yang mungkin terjadi. Tujuan penelitian ini adalah untuk mengembangkan dan mengevaluasi tiga metode utama untuk mendeteksi anomali dalam trafik jaringan komputer: Naive Bayes, Decision Tree, dan Isolation Forest. Berbasis pada Teorema Bayes, pendekatan naive Bayes memprediksi kemungkinan suatu kejadian berdasarkan data historis. Model berbasis pohon keputusan, decision tree membagi data secara iteratif berdasarkan karakteristik tertentu untuk mengklasifikasikan atau memprediksi hasil. Algoritma Isolasi Hutan adalah algoritma berbasis kelompok yang dimaksudkan untuk mendeteksi anomali dengan cepat dengan mengisolasi data anomali. Fokus utama penelitian ini adalah membandingkan kinerja ketiga metode tersebut dalam mendeteksi anomali trafik jaringan, termasuk kemampuan masing-masing metode untuk menemukan pola tidak normal secara akurat dan efisien. Tujuan dari penelitian ini adalah untuk memberikan pemahaman tentang metode yang paling efektif dalam hal deteksi anomali jaringan, sehingga dapat membantu membangun sistem keamanan jaringan yang lebih andal dan responsif terhadap ancaman.  
A Conversion of Signal to Image Method for Two-Dimension Convolutional Neural Networks Implementation in Power Quality Disturbances Identification Berutu, Sunneng Sandino; Chen, Yeong-Chin; Wijayanto, Heri; Budiati, Haeni
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.1529

Abstract

The power quality is identified and monitored to prevent the worst effects arise on the electrical devices. These effects can be device failure, performance degradation, and replacement of some device parts. The deep convolutional neural networks (DCNNs) method can extract the complexity of image features. This method is adopted for the power quality disruption identification of the model. However, the power quality signal data is a time series. Therefore, this paper proposes an approach for the conversion of a power quality disturbance signal to an image. This research is conducted in several stages for constructing the approach proposed. Firstly, the size of a matrix is determined based on the sampling frequency values and cycle number of the signal. Secondly, a zero-cross algorithm is adopted to specify the number of signal sample points inserted into rows of the matrix. The matrix is then converted into a grayscale image. Furthermore, the resulting images are fed to the two-dimension (2D) CNNs model for the PQDs feature learning process. When the classification model is fit, then the model is tested for power quality data prediction. Finally, the model performance is evaluated by employing the confusion matrix method. The model testing result exhibits that the parameter values such as accuracy, recall, precision, and f1-score achieve at 99.81%, 98.95%, 98.84, and 98.87 %, respectively. In addition, the proposed method's performance is superior to the previous methods. 
Sistem Pengenalan Citra Dokumen Teks Terdistorsi menjadi Teks Menggunakan Metode Deep Learning Talenta Teholi Zalukhu; Agustinus Rudatyo Himamunanto; Haeni Budiati
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 1 (2026): JANUARY 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v10i1.4700

Abstract

A common issue in document image processing is the inability of OCR systems to accurately read text from blurred images. This study aims to develop a deep learning-based OCR pipeline capable of recognizing text in blurred document images. The process begins with image enhancement using the DnCNN model for deblurring, followed by character segmentation and classification of A–Z characters using a CNN trained on the EMNIST Letters dataset. The recognized characters are then reconstructed into complete text. Experiments were conducted on 300 blurred images with varying levels of blur (low, medium, and high). Evaluation using PSNR and SSIM metrics showed improvements in image quality, with an average PSNR of 29,56 dB and SSIM of 0.89. Furthermore, the character classification accuracy reached 95.64%. Compared to the baseline (direct Tesseract OCR without deblurring), the proposed system showed a significant improvement in text readability. These results demonstrate the effectiveness of CNN-based approaches in enhancing OCR performance on blurred document images.
Analisis Sentimen Berbasis ASOQE dan Taksonomi pada Program MBG di X mendrofa, victor crisman; Berutu, Sunneng Sandino; Budiati, Haeni
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 2 (2026): April 2026 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v6i2.15636

Abstract

The Free Nutritious Meal (MBG) program faces implementation challenges regarding distribution, menu quality, and budget sustainability, sparking diverse public discourse on social media. This study analyzes public sentiment toward the MBG program using an Aspect-Opinion-Qualifier Extraction (ASOQE) approach based on policy taxonomy. The dataset was obtained from X (formerly Twitter) via web scraping and processed through standardized text preprocessing. Automatic annotation used a lexicon-based BIO labeling approach to generate a silver-standard dataset. The classification model was trained using an IndoBERT-BiLSTM architecture to identify contextual aspects and opinions. Inference results were mapped into five sentiment classes and five policy dimensions: nutritional quality, implementation, social impact, policy, and effectiveness. Evaluation showed excellent performance, with F1-scores exceeding 0.98. Findings reveal that social impact and implementation dimensions dominate public discourse, showing significantly positive sentiment. This research demonstrates the potential of Aspect-Based Sentiment Analysis as a data-driven tool for comprehensive public policy evaluation.
Co-Authors Agustinus Rudatyo Himamunanto Antonius Bima Murti Wijaya Apriani, Apriani Barita Suci Vernando Zebua Berlian Sakti Yuliana Ndruru Bobby Girsang Bolo, Naomi Tena Budet, Vincensa Woytimena Chen, Yeong-Chin Delima Zai Depni Novela Emerita Setyowati Febe Maedjaja Febe Maedjaja Fornieli Gulo Gabriel Jimmy Richardo Chastelo B Gallu, Apliana Gogor Christmass Setyawan Gulo, Eniria Heri Wijayanto Hermas Rizzal Himamunanto, A. R. Himamunanto, AR Himamunanto, AR. Ina, Osmanila Tamo Jatmika Jatmika Jatmika Jatmika Jatmika Jatmika Juli Supriyanto Gea Kristian Juri Damai Lase Laia, Yardiana Lase, Anan Sosmita Lase, Kristian J.D. Liefson Jacobus, Liefson Ludgerdus Pati Hurit Lukas Agung Heriputranto Magdalena, Mensiana Mendrofa, Milka Justine mendrofa, victor crisman Nehe, Pius Hermanto Niasiska, Niasiska Olam, Enos Nikodemus P.Hurit, Lud Gerdus Peter1 Putri, Farelia Devi Julia Rudatyo Himamunanto, Agustinus Setiawan Matangkin, Jimmi Setyawan, Gogor C Sumihar, Yo'el Pieter Sumihar, Yo’el Pieter Sumitta Nauli Sunneng Sandino Berutu Supriadi, Joseph Surjawirawan Dwiputranto Swasono, Natan Enggal Talenta Teholi Zalukhu Tasane, Elshaddai L Tessy Ocharina Br Pinem Timothy Ageng Pracaya Victor Crisman Mendrofa Wanda, Melifan Yo'el Pieter Sumihar Yolanda Veren Yo’el Pieter Sumihar Zendrato, Agus Dirgahayu Ziliwu, Johni Revormasi