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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 JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Informatika dan Sistem Informasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Pendidikan dan Teknologi Indonesia Journal of Information Technology (JIfoTech) Jurnal Abdimas Bina Bangsa COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat 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
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Implementasi Metode ViSQOL Dalam Mengidentifikasi Noise pada Kualitas Suara Streaming Spotify Setiawan Matangkin, Jimmi; Rudatyo Himamunanto, Agustinus; Budiati, Haeni; Sumihar, Yo'el Pieter
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 7 (2025): JPTI - Juli 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.848

Abstract

Kualitas suara pada layanan streaming Spotify seringkali tidak konsisten akibat gangguan noise dan variasi parameter jaringan, yang berdampak pada kualitas pengalaman pengguna (QoE). Penelitian ini bertujuan mengevaluasi kualitas audio Spotify menggunakan algoritma ViSQOL dengan menganalisis pengaruh jenis noise seperti pink noise, background noise, compression noise, dan impulse noise. Network noise juga diuji berdasarkan parameter jaringan yaitu throughput, delay, packet loss, dan jitter. Sebanyak 800 sampel audio direkam menggunakan Audacity dan dianalisis di MATLAB untuk memperoleh nilai Mean Opinion Score (MOS), Signal-to-Noise Ratio (SNR), dan Spectral Distortion. Hasil menunjukkan bahwa pink noise 50% menurunkan MOS menjadi 61–65%, sementara impulse noise memberikan dampak paling signifikan dengan MOS 15–17%. Background noise masih dapat ditoleransi. Pada parameter jaringan, MOS tertinggi diangka 4.31 terjadi pada delay 132.16 ms dan packet loss 0.49%, sedangkan MOS terendah diangka 4.26 tercatat saat delay 62.15 ms dan packet loss 1.9%. Temuan ini menegaskan pentingnya pengendalian terhadap noise dan stabilitas jaringan untuk menjaga kualitas layanan audio.
Analisis Beban Kendaraan Terhadap Karakteristik Jalan Menggunakan Metode YOLOv5 Dan Perhitungan ESAL Wanda, Melifan; Himamunanto, Agustinus Rudatyo; Budiati, Haeni
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 2: Agustus 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i2.2713

Abstract

Roads are essential infrastructure supporting various types of vehicles; however, excessive loads are a primary cause of surface damage. The increasing volume of vehicles and imbalanced infrastructure development contribute significantly to road deterioration, leading to a reduction in road service life and increased maintenance costs. This study aims to address these issues by developing a system for vehicle detection, classification, and load estimation using the YOLO (You Only Look Once) algorithm a deep learning method capable of detecting and classifying vehicle objects in real time with high speed and accuracy. The data were obtained from CCTV surveillance video recordings. The results indicate that a total of 4,395 vehicles were successfully detected. These detections were then used to estimate the vehicle load using the Equivalent Single Axle Load (ESAL) method. The estimated total daily traffic reached 632,880 vehicles, with a corresponding daily load estimation of 284,214.74 ESAL. The findings highlight the significant impact of vehicle loads on road characteristics and demonstrate the effectiveness of YOLOv5 as a real time tool for monitoring and detecting vehicular load.Keywords: Computer Vision; YOLOv5; Vehicle detection; Vehicle load; Equivalent Single Axle LoadAbstrakJalan merupakan infrastruktur yang penting dalam  menopang berbagai jenis  kendaraan, namun beban berlebih menjadi penyebab utama kerusakan permukaan  pada jalan. Volume kendaraan yang meningkat dan pembangunan infrastruktur yang tidak seimbang  menyebabkan kerusakan pada jalan   yang menyebabkan  pengurangan umur jalan dan meningkatkan biaya perbaikan. Penelitian ini bertujuan untuk mengatasi permasalahan tersebut yaitu dengan membangun Pendeteksi, Klasifikasi dan menghitung  beban kendaraan berbasis Algoritma YOLO (You Only Look Once), sebuah algoritma deep learning yang mampu mendeteksi dan mengklasifikasikan objek kendaraan secara  real-time dengan kecepatan dan akurasi  yang sangat baik. Data yang digunakan diambil dari  rekaman video pengawas CCTV.  Hasil penelitian menunjukan  kendaraan  yang terdeteksi sebanyak 4.395 unit, kendaraan yang  berhasil terdeteksi kemudian dilakukan untuk  perhitungan estimasi beban kendaraan menggunakan perhitungan  Equivalent Single Axle Load (ESAL). Hasil  terhitung dengan total lalu lintas harian mencapai 632.880 unit kendaraan dengan estimasi beban harian sebesar 284.214,74 ESAL. Hasil penelitian menegaskan adanya  pengaruh signifikan beban kendaraan terhadap karakteristik jalan serta menunjukkan efektivitas YOLOv5 sebagai alat dalam memantau  dan mendeteksi beban kendaraan secara  real time 
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.
Sistem Pengenalan Citra Dokumen Teks Terdistorsi menjadi Teks Menggunakan Metode Deep Learning Zalukhu, Talenta Teholi; Himamunanto, Agustinus Rudatyo; Budiati, Haeni
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.
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%.
Identifikasi Pola Obyek Kain Tenun Sumba dengan Menggunakan Metode K-Nearest Neighbor (KNN) Budiati, Haeni; Himamunanto, Agustinus Rudatyo; Bolo, Naomi Tena
Upgrade : Jurnal Pendidikan Teknologi Informasi Vol 1 No 1 (2023): Vol. 1 No. 1 Agustus 2023
Publisher : Pendidikan Teknologi Informasi Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/upgrade.v1i1.3149

Abstract

Woven fabrics originating from Sumba have their own patterns that distinguish them from other woven fabric patterns throughout Indonesia. The pattern is a distinctive feature that describes the culture of the people in Sumba which is very diverse. To distinguish fabric patterns, one of the algorithms for object recognition is the K-Nearest Neighbor (KNN) algorithm. The KNN algorithm classifies objects based on training data that is closest to the object. Processing works by using metric and eccentricity parameters on training data and input images. This processing will produce text data which is the identification of objects in Sumba woven fabric motifs. Based on the testing that has been done, it successfully identifies the type of object contained in the training data. For types of objects that are not contained in the training data, identification is based on their proximity to the types of objects in the group that contain Sumba woven fabric patterns. The accuracy level of Sumba woven fabric pattern object identification in testing 70 different fabric motif images obtained 62 objects in the input image can be identified correctly (88.57%), while 8 objects in the input image cannot be identified (11.43%).
Co-Authors Agustinus Rudatyo Himamunanto Antonius Bima Murti Wijaya Apriani, Apriani Barita Suci Vernando Zebua Bobby Girsang Bolo, Naomi Tena Budet, Vincensa Woytimena Delima Zai Emerita Setyowati Febe Maedjaja Febe Maedjaja Fornieli Gulo 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 Lukas Agung Heriputranto Mendrofa, Milka Justine Nauli, Sumitta 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 Sunneng Sandino Berutu Supriadi, Joseph Surjawirawan Dwiputranto Swasono, Natan Enggal Tasane, Elshaddai L Timothy Ageng Pracaya Veren, Yolanda Wanda, Melifan Yeong-Chin Chen Yo’el Pieter Sumihar Zalukhu, Talenta Teholi Zendrato, Agus Dirgahayu Ziliwu, Johni Revormasi