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All Journal Sainteks Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) EDUMATIC: Jurnal Pendidikan Informatika Journal of Information Systems and Informatics Zonasi: Jurnal Sistem Informasi JIKA (Jurnal Informatika) TIN: TERAPAN INFORMATIKA NUSANTARA Idealis : Indonesia Journal Information System Jurnal Pendidikan dan Teknologi Indonesia INTERNATIONAL JOURNAL OF EDUCATION, INFORMATION TECHNOLOGY, AND OTHERS J-Icon : Jurnal Komputer dan Informatika Teknomatika: Jurnal Informatika dan Komputer
Erik Iman Heri Ujianto
Universitas Teknologi Yogyakarta
Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT 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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Analisis Sentimen Publik di Twitter Pasca Debat Kelima Pilpres 2024 dengan Naive Bayes Zharifa, Anjana Haya Atha; Ujianto, Erik Iman Heri
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.28048

Abstract

The presidential election in Indonesia is a frequently discussed topic on social media, especially Twitter. This platform provides a space for people to express their views on presidential candidates and election issues, making it suitable as a data source for this study. This study aims to analyze public sentiment towards presidential election news on Twitter using the Naïve Bayes Classifier method. Data was taken from Twitter for the period 5–13 February 2024 with a total of 2,561 comments. The research process includes data collection, preprocessing, data labeling, and model training and testing. Naïve Bayes was chosen because it is efficient in text classification and has several variants for model experiments. Sentiment is classified into three main categories, namely positive, negative, and neutral. The results showed that negative comments dominated (41%), followed by positive (37.3%) and neutral (21.7%). The Multi Naïve Bayes Classifier model provided the highest accuracy (81%), followed by Bernoulli Naïve Bayes (80%) and Gaussian Naïve Bayes (76%). This difference in accuracy is influenced by the model's sensitivity to data characteristics, such as the number of features and sentiment distribution. This research has the potential to help campaign teams understand the issues that trigger negative responses and support policy makers in designing more effective political communication strategies.
Sentiment Analysis of Indonesian Responses to the Conflict in Palestine Using KNN and SVM Methods Fauzi, Rizky; Ujianto, Erik Iman Heri
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8725

Abstract

The prolonged conflict between Palestine and Israel has attracted worldwide attention, including Indonesia, which has a history of strong support for the Palestinian cause. This study aims to analyze the sentiment of Indonesian people towards the Palestinian-Israeli conflict using the K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) methods. The subject of this research is user data X (Twitter) which contains opinions about the conflict. After preprocessing, weighting, and labeling, 2960 tweets were collected and classified into three sentiment categories: positive, negative, and neutral. The KNN+SVM method is applied to classify the sentiment of the processed tweet data. The results showed that of the 2960 data analyzed, 33.8% were labeled positive, 38.9% were labeled negative, and 27.4% were labeled neutral with 82% accuracy, 83% precision, 82% recall, and 82% F1-Score. These results show that the majority of Indonesians tend to be negative in expressing their views on the Palestinian-Israeli conflict. This analysis provides greater insight into sentiment patterns in Indonesian responses to sensitive issues, and contributes to the study of public opinion and social dynamics on social media.
A DECISION SUPPORT SYSTEM FOR THE DETERMINATION OF ADDITIONAL STOCK ITEMS USING THE TOPSIS METHOD BASED ON ANDROID Santoso, Firdaus Restu Rafi; Ujianto, Erik Iman Heri
IDEALIS : InDonEsiA journaL Information System Vol. 8 No. 1 (2025): Jurnal IDEALIS Januari 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v8i2.3339

Abstract

Inefficient stock management can lead to problems such as overstocking or stockouts, incorrect pricing, and difficulties in identifying best-selling products, which negatively affect the performance and profitability of Toko Twins Pancing Temanggung while reducing customer satisfaction. To overcome these issues, this study develops a mobile-based decision support system (DSS) using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, designed to assist store owners in determining stock additions, managing inventory, and identifying best-selling products. The steps taken include problem identification, data collection, system architecture design, TOPSIS implementation, and system testing. TOPSIS was selected for its ability to provide accurate recommendations by considering various relevant criteria. Decision-making is influenced by factors such as price, stock quantity, and weekly sales. The study results indicate that this system can effectively recommend stock additions by prioritizing products with sufficient stock and good sales performance. For example, the product with the highest preference value is the Red Angle fishing rod (0.8963), which is prioritized for restocking. The system, tested with data from Toko Twins Pancing Temanggung, achieved a 98% accuracy rate compared to manual calculations. Users can conveniently access this system via mobile devices, enabling decision-making anytime and anywhere. This DSS enhances operational efficiency and business performance at Toko Twins Pancing Temanggung, providing a significant solution for stock management and offering a more structured and efficient approach to achieving higher profits.
SISTEM REKOMENDASI PEMILIHAN TEMPAT KOS MAHASISWA DI WILAYAH SLEMAN MENGGUNAKAN SIMPLE ADDITIVE WEIGHTING BERBASIS MOBILE Purwanto, Candra Ihsan; Ujianto, Erik Iman Heri
ZONAsi: Jurnal Sistem Informasi Vol. 7 No. 1 (2025): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode Januari 2025
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v7i1.24740

Abstract

Pengambilan keputusan dalam memilih tempat tinggal sementara, seperti kos, merupakan tantangan bagi mahasiswa yang pindah ke Kota Sleman untuk melanjutkan pendidikan, karena terbatasnya informasi mengenai lokasi dan fasilitas kos. Penilitian ini bertujuan untuk merancang sistem rekomendasi berbasis mobile untuk pemilihan tempat kos bagi mahasiswa di wilayah Sleman dengan kriteria mereka, seperti harga, fasilitas, dan jarak dari kampus. Metode penelitian dilakukan melalui empat tahap: pengumpulan data dari sumber terkait, analisis sistem untuk menyusun alur kerja, perancangan sistem rekomendasi, dan implementasi penuh pada platform mobile. Berdasarkan hasil perhitungan perangkingan, Kos Merapi Green Hills Mezzanie terpilih sebagai rekomendasi terbaik dengan nilai akhir 11, diikuti oleh Kos Khalia Exclusive A dengan nilai 8,33, dan Kos Graha Rahma 2 Ekslusif dengan nilai 7,67. Kesimpulan yang diperoleh dari sistem rekomendasi ini dapat mengurangi kebingungan dan meningkatkan kepuasan mahasiswa dalam memilih kos di Wilayah Sleman.
Efficient Pattern Recognition of Sundanese Script Variants Using CNN Muhammad Husni Wahid; Erik Iman Heri Ujianto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.6122

Abstract

This research aims to apply pattern recognition technology, specifically through the Convolutional Neural Network (CNN) approach, in identifying and translating Sundanese script accurately. This research is focused on recognizing rarangken script patterns based on ngalagena script in Indonesian cultural heritage. This study uses the MobileNetV2 based CNN model, utilizing transfer learning and trained for 50 epochs using the Adam optimizer with a learning rate of 0.0001, to achieve a training accuracy of 98.75% and test accuracy of 96.95% in 1 hour and 23 minutes, respectively. The results of the study show that the simpler CNN architecture without augmentation achieved the highest accuracy of 99.26%, and the augmented CNN model achieved 94.42% accuracy in 2 hours and 22 minutes. These results enable practical applications in both education and cultural preservation, demonstrating how modern technology can effectively contribute to maintaining traditional cultural elements in the digital era.
Analisis Pengaruh Citra Terhadap Kombinasi Kriptografi RSA dan STEGANOGRAFi LSB Mido, Agus Rakhmadi; Ujianto, Erik Iman Heri
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 2: April 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022914852

Abstract

Keamanan data dan informasi saat ini merupakan aspek penting dalam proses pertukaran pesan melalui jaringan internet. Tanpa adanya keamanan sering kali pesan tersebut dimanfaatkan oleh oknums yang tidak bertanggung jawab. Oleh karena itu, keamanan data dibutuhkan perlindungan informasi yang akan dikirimkan kepada penerima. Teknik keamanan data dan informasi yang saat ini banyak digunakan yaitu kriptografi dan steganografi. Kriptografi dan steganografi adalah teknik keamanan informasi yang memiliki persamaan dalam hal keamanan. Pada penelitian ini, teknik yang digunakan adalah algoritma Kriptografi Rivest Shamir Adleman (RSA) dan algoritma Steganografi Least Significant Bit (LSB) untuk keamanan pesan. Analisis yang dilakukan terhadap kombinasi algoritma dalam penelitian ini meliputi analisis pengaruh variabel citra pada proses enkripsi dan dekripsi. Pengujian kualitas citra menggunakan teknik Normalized Cross Correlation (NCC), Structured Similarity Index Method (SSIM), Peak Signal to Noise Ratio (PSNR), dan Mean Square Error (MSE). Berdasarkan hasil pengujian menggunakan skema kriptografi RSA dan skema steganografi LSB mampu direkonstruksi dengan baik. Pengujian MSE pada ukuran citra 128x128 menghasilkan error terbesar dan terkecil pada ukuran 1024x1024. Pengujian PSNR citra berukuran 64x64 dan 128x128 menghasilkan nilai kurang dari 40 dB. Sedangkan ukuran 512x512 dan 1024x1024 memiliki nilai lebih dari 40 dB. Pengujian NCC dan SSIM menghasilkan nilai yang mendekati 1 dengan semakin besarnya ukuran citra.AbstractData and information security are currently an important aspect in the process of exchanging messages through the internet network. Without security, the message is often utilized by an irresponsible person. Therefore, data security is required to protect the information that will be sent to the recipient. Data security techniques and information that is currently widely used are cryptography and steganography. Cryptography is a technique for encoding data into encrypted data that is not understood, while steganography is a technique for hiding data into a medium that aims to protect messages from unauthorized. Cryptography and steganography have similarities in terms of security. In this study, the technique is Rivest Shamir Adleman (RSA) Cryptographic algorithm and the Least Significant Bit (LSB) Steganography algorithm for message security. The analysis of an algorithmic combination in this research includes analysis of variable influence image of the encryption and decryption process. Image quality testing uses the Normalized Cross Correlation (NCC), Structured Similarity Index Method (SSIM), Peak Signal to Noise Ratio (PSNR), dan Mean Square Error (MSE) techniques. Based on the testing using the scheme RSA cryptography and the scheme LSB steganography capable of reconstructed well. MSE testing on the size of the image of 128x128 produces the error biggest and the smallest on the size of 1024x1024. PSNR testing on the size of images 64x64 and 128x128 produces values under 40 dB. Meanwhile, the image sizes of 512x512 and 1024x1024 have values above 40 dB. Testing NCC and SSIM produce a value close to 1 with increasing size of the image.
Uncovering Security Vulnerabilities in Electronic Medical Record Systems: A Comprehensive Review of Threats and Recommendations for Enhancement Wijayanti, Dian; Ujianto, Erik Iman Heri; Rianto, Rianto
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.28192

Abstract

Cybersecurity is a critical concern for healthcare organizations in the digital era, as patient data privacy faces significant risks from numerous vulnerabilities. Given the escalating cyberattacks in healthcare, understanding EMR system vulnerabilities has become imperative. This study aimed to find the main weaknesses in Electronic Health Record (EHR) systems and suggest proven methods to improve security and keep patient information private. Utilizing a cross-sectional analysis, we assessed the effectiveness of current security protocols against identified threats. We systematically reviewed 25 recent, high-quality articles (from 2020 to 2023) on EMR vulnerabilities, selected based on their relevance and the efficacy of their proposed solutions. Our analysis revealed that system architecture flaws and credential misuse represented the most significant threats, with hacking incidents most frequently targeting these weaknesses. The analysis identified six key threat categories to EMR security: compromised access, system architecture flaws, data sharing challenges, hacking, credential misuse, and non-compliance with regulations. This framework introduced a multi-layered defense strategy, unique in incorporating both technical and behavioral security measures. The study provided a novel framework combining technological and management safeguards, offering a fresh perspective on modern EMR vulnerabilities. The detailed threat categorization gave healthcare organizations a strategic basis for improved security planning and resource allocation. The actionable insights from this study could greatly enhance EMR security protocols in healthcare settings, potentially reducing data breaches and improving patient trust. Further research was warranted to test the effectiveness of the proposed framework across various healthcare environments.
Digital Image Encryption Using Logistic Map Muhammad Rizki; Erik Iman Heri Ujianto; Rianto Rianto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5389

Abstract

This study focuses on the application of the logistic map algorithm in the Python programming language for digital image encryption and decryption. It investigates the impact of image type, image size, and logistic map parameter values on computational speed, memory usage, encryption, and decryption results. Three image sizes (300px 300px, 500px x 500px, and 1024px x 1024px) are considered in TIFF, JPG, and PNG formats. The digital image encryption and Decryption process utilizes the logistic map algorithm implemented in Python. Various parameter values are tested for each image type and size to analyze encryption and decryption outcomes. The findings indicate that the type of image does not affect memory usage, which remains consistent regardless of image type. However, image type significantly influences the decryption results and computation time. In particular, the TIFF image type exhibits the fastest computation time, with durations of 0.17188 seconds, 0.28125 seconds, and 1.10938 seconds for 300px x 300px, 500px x 500px, and 1024px x 1024px images, respectively. In addition, the encryption results vary depending on the type of image. The logistic map algorithm is unable to restore encryption results accurately for JPG images. Furthermore, research highlights that higher values of x, Mu and Chaos lead to narrower histogram values, resulting in improved encryption outcomes. This study contributes to the field by exploring the application of the logistic map algorithm in Python and analyzing the effects of image type, image size, and Logistic Map parameter values on computation time, memory usage, and digital image encryption and Decryption results. Prior research has not extensively addressed these aspects in relation to the Logistic Map algorithm in Python.
The IoT-Based E-Voting System Using Fingerprint Biometrics for School Elections Wibowo, Wahyu Andre; Ujianto, Erik Iman Heri
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1328

Abstract

This study proposes an Internet of Things (IoT)-based e-voting system to address the limitations of traditional paper-based student council elections, which are prone to errors, inefficiency, and data manipulation. The system is developed using the ADDIE model for Research and Development (R&D), incorporating a Laravel-based administrative dashboard, a Flutter-based mobile voting interface, and a biometric authentication device built with an ESP32 microcontroller and JM-101B fingerprint sensor. Evaluation involved 20 participants who completed six functional test scenarios, achieving a 100% success rate across 120 instances. Usability testing revealed a notable comfort difference, with 30% comfort on mobile phones and 90% on tablets. Performance testing showed a fingerprint scan time of 669.6 ms and a vote submission latency of 437.1 ms, indicating good system responsiveness. The results suggest the system improves security, transparency, and efficiency in the election process. However, the study is limited by a small sample size and evaluation within a single institution. Future work could explore cloud integration, multi-school deployment, and additional authentication methods to enhance scalability and support broader adoption.
Penerapan Teknologi QR Code untuk Meningkatkan Efisiensi Absensi Karyawan Berbasis Android Kastella, Thufail Bintang; Ujianto, Erik Iman Heri
TIN: Terapan Informatika Nusantara Vol 6 No 7 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i7.8735

Abstract

PT. Harta Samudera Ambon faces several challenges in managing employee attendance due to the continued use of manual methods such as signatures and paper-based records. This conventional system creates a number of issues, including a high potential for proxy attendance, frequent recording errors, delayed data recap, and the inability to monitor employee presence accurately and in real time. These shortcomings negatively impact the effectiveness of administrative operations and the reliability of attendance data required for performance evaluation. To address these problems, this study develops an Android-based attendance system using QR Code technology, which enables automatic, accurate, and instant recording through a scanning mechanism. The system also incorporates additional features such as employee data management, work schedule configuration, attendance history tracking, and digital leave submission. The research methodology consists of requirement analysis, system architecture design, application implementation, and system evaluation using Black Box Testing to validate functional performance. The results indicate that the system significantly improves the speed of the attendance process, reduces recording errors by more than 85%, and eliminates the possibility of proxy attendance. Furthermore, the integrated admin dashboard simplifies centralized monitoring and management of attendance data. Despite these positive outcomes, the system still relies on internet connectivity and lacks advanced security features. Future enhancements may include GPS integration, dynamic QR Code encryption, and automated analytics reporting.
Co-Authors Adhi Prahara ADITYA HERMAWAN Agus Rakhmadi Mido Amelia Citra Dewi Apriansyah, Ferryma Arba Dian Wijayanti Enggar Novianto Fauzi, Rizky Gunawan Wibisono Iman, Asri Khayatul Kastella, Thufail Bintang Mido, Agus Rakhmadi Muhammad Husni Wahid Muhammad Rizki Murinto Murinto Purwanto, Candra Ihsan Rianto Rianto Rianto Santoso, Firdaus Restu Rafi Saputra, Candra Heru Syahab, Alfin Syarifuddin Tri Waluyo Triyas Hevianto Saputro Wibowo, Wahyu Andre Zharifa, Anjana Haya Atha