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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 Informatika dan Rekayasa Elektronik Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Informatika dan Sistem Informasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA 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
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

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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. 
The Design and Evaluation of a Decentralized E-Voting System Using Ethereum Smart Contracts Hurit, Ludgerdus Pati; Sumihar, Yo'el Pieter; Budiati, Haeni
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.8997

Abstract

The widespread implementation of electronic voting systems poses ongoing challenges related to data integrity, transparency, and centralized control, which can increase the risk of vote manipulation and reduce traceability. To address these issues, this study designs and evaluates a decentralized electronic voting system implemented using Ethereum smart contracts. The objective of this research is to test the ability of blockchain technology to support a secure, transparent, and tamper-resistant voting process in a decentralized environment. The research methodology includes requirements analysis, system design, system implementation, and functional testing. Black-box testing was conducted to verify the system's functionality throughout the voting process. The proposed system permanently records voting transactions on the blockchain, preventing unauthorized modifications while allowing transaction verification by network participants. Voter privacy is maintained by separating voter identity data from voting records and implementing blockchain address abstraction, ensuring that individual votes cannot be directly linked to voter identities. System evaluation focuses on transaction costs and confirmation times. Performance testing was conducted using six test transactions on the Sepolia blockchain network. The total transaction cost recorded was 0.006076 ETH, with an average cost of 0.001013 ETH per transaction. The minimum transaction cost of 0.000091 ETH occurred during voting operations, while the maximum cost of 0.005596 ETH was associated with smart contract deployment and higher network base fees. The average transaction confirmation time was approximately 12 seconds. Although the evaluation was based on a limited number of transactions, the results indicate that the proposed system demonstrates reliable transaction execution, acceptable gas usage, and high transparency. Further large-scale testing is recommended for future work.
Implementasi Augmented Reality dalam Pengembangan Media Pembelajaran Biologi di Tingkat Sekolah Menengah Pertama Novela, Depni; Himamunanto, Agustinus Rudatyo; Budiati, Haeni
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 15, No 1 (2026): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v15i1.9177

Abstract

Kemajuan tekonologi digital mendorong perluasan inovasi dalam proses pembelajaran, terutama untuk konsep-konsep abstrak seperti pembelajaran fotosintesis. Siswa mengalami kesulitan dalam memahami materi ini karenamelibatkan proses kimia yang cukup rumit dan tidak dapat dilihat secara langsung. Upaya alternatif dalam menangani permasalahan tersebut adalahmelalui proses perancangan dan pengembangan media pembelajaran berbasis Augmented Reality(AR) yang memvisualisasikan proses fotosintesis dalam bentuk 3D yang interaktif dan mudah dipahami. Penelitian ini menggunakan pendekatan deskriptif dengan model pengembangan Multimedia Development Live Cycle (MDLC) yang terdiri dari lima tahapan: Consept (Pengonsepan), Design (Perancangan), Material Collecting (Pengumpulan Materi), Assembly (Pembuatan), dan Testing (Pengujian). Aplikasi AR dikembangkan menggunakan Unity dan Vuforia, kemudian diuji coba kepada murid kelas VIII di SMP Negeri 2 Ngemplak. Hasil yang diperoleh menunjukkan bahwa aplikasi AR yang dikembangkan mudah digunakan, tampilan visual menarik, dan membantu meningkatkan pemahaman siswa terhadap materi fotosintesis, khususnya reaksi terang dan reaksi gelap. Selain itu, aplikasi ini juga menerima umpan balik yang baik dari pengguna terkait fitur dan desain antarmuka. Dengan  demikian,  penelitian ini mengindikasikan bahwa AR dapat berfungsi sebagai alat edukasi biologi yang interaktif , inovatif dan menyenangkan.
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.
Co-Authors Agustinus Rudatyo Himamunanto Antonius Bima Murti Wijaya Apriani, Apriani Barita Suci Vernando Zebua Bobby Girsang Bolo, Naomi Tena Budet, Vincensa Woytimena Chen, Yeong-Chin 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. Hurit, Ludgerdus Pati 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 Magdalena, Mensiana Mendrofa, Milka Justine Nauli, Sumitta Nehe, Pius Hermanto Niasiska, Niasiska Novela, Depni 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 Talenta Teholi Zalukhu Tasane, Elshaddai L Timothy Ageng Pracaya Veren, Yolanda Victor Crisman Mendrofa Wanda, Melifan Yo’el Pieter Sumihar Zendrato, Agus Dirgahayu Ziliwu, Johni Revormasi