Article Per Year (5 Year)
p-Index From 2021 - 2026
7.928
P-Index
This Author published in this journals
All Journal Jurnal Buana Informatika Jurnal Transformatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science (JOINTECS) JURNAL MEDIA INFORMATIKA BUDIDARMA INOVTEK Polbeng - Seri Informatika Indonesian Journal of Information System JTAM (Jurnal Teori dan Aplikasi Matematika) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Sebatik Indonesian Journal of Computing and Modeling Jurnal Informatika JUSIM (Jurnal Sistem Informasi Musirawas) Journal of Information Systems and Informatics Aiti: Jurnal Teknologi Informasi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Jurnal Indonesia : Manajemen Informatika dan Komunikasi JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Ilmiah Sains Jurnal Ilmiah Ilmu Terapan Universitas Jambi Magistrorum et Scholarium: Jurnal Pengabdian Masyarakat IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Jurnal Pendidikan Teknologi Informasi (JUKANTI) Jurnal Indonesia : Manajemen Informatika dan Komunikasi d'Cartesian: Jurnal Matematika dan Aplikasi Indonesian Journal of Intelligence Data Science (IJIDS) International Journal of Information Technology and Business JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal DIMASTIK
Yessica Nataliani
Universitas Kristen Satya Wacana
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Social Sciences

Published : 59 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 59 Documents
Search

 2   3   4   5   6 
Academic Document Authentication using Elliptic Curve Digital Signature Algorithm and QR Code Wellem, Theophilus; Nataliani, Yessica; Iriani, Ade
JOIV : International Journal on Informatics Visualization Vol 6, No 3 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.6.3.872

Abstract

Paper-based documents or printed documents such as recommendation letters, academic transcripts, and diplomas are prone to forgery. Several methods have been used to protect them, such as watermarking, security holograms, or using paper with specific security features. This paper presents a document authentication system that utilizes QR code and ECDSA as the digital signature algorithm to protect this kind of document from counterfeiting. A digital signature is a well-known technique in modern cryptography used for providing data integrity and authentication. The idea proposed herein is to put a QR code in the printed documents where the QR code includes a digital signature. The signature can later be authenticated using the proposed system by uploading the document for authentication or scanning the document's QR code. The proposed system is particularly developed for digital signature generation and verification of students' final project approval documents as the case study. In traditional settings, the approval form is typically signed directly by the student's advisor dan co-advisor using handwritten signatures. However, using the conventional handwritten signature, the signature on the approval form can be falsified. Therefore, a digital signature generation and verification system is implemented herein to avoid handwritten signature falsification. The advisors can use this system to sign the approval form using a digital signature instead of a handwritten one. The signature is stored in a QR code and is generated using ECDSA with SHA-256 as the hash function. The proposed system is evaluated using documents (i.e., approval forms) with genuine and forged QR codes.  The evaluation results showed that the system could verify the authenticity of the approval forms, which contain genuine QR codes. The approval forms that contained forged QR codes were correctly identified.
Frieze Group in Generating Traditional Cloth Motifs of the East Nusa Tenggara Province Nataliani, Yessica
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 6, No 3 (2022): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v6i3.8568

Abstract

Ethnomathematics studies the relationship between mathematics and culture. Indonesia has many traditional cultures. One of them is traditional cloth. The traditional cloth from East Nusa Tenggara (NTT) province is called tenun ikat. Since the motif of tenun ikat consists of symmetrical and repeated patterns, it can be generated using Frieze groups. The Frieze groups are the plane symmetry groups of patterns whose subgroups of translations are isomorphic to Z. There are seven groups in the Frieze groups, i.e., F_1, F_2, F_3, F_4, F_5, F_6, and F_7. Translation, reflection, rotation, and glide reflection are the transformation used in the Frieze groups. In this paper, Frieze groups are used to generate digital tenun ikat motifs from the basic pattern. Since one piece of original tenun ikat may consist of some basic patterns, the basic patterns are identified first, and then each of them is generated into the desired pattern, according to the suitable Frieze groups. Furthermore, a GUI Matlab program is developed to generate the Frieze groups. Three motifs of tenun ikat are presented to demonstrate the implementation of Frieze groups. With the Frieze group, users can generate other patterns from a basic pattern, so users can generate new motifs of tenun ikat without leaving the cultural characteristics of NTT province. 
PENGENALAN GAME EDUKASI BAGI SISWA TK KRISTEN 1 SATYA WACANA SALATIGA USIA 5-6 TAHUN Anton Hermawan; Nataliani, Yessica; Hindriyanto Dwi Purnomo; Christianto, Erwien; Atik Setyanti, Angela; Yulia, Hanita; Krismiyati; Juliastomo Gundo, Adriyanto; Wellem, Theophilus; Hendry
Jurnal DIMASTIK Vol. 3 No. 2 (2025): Juli
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/dimastik.v3i2.12302

Abstract

Kemajuan teknologi informasi dan komunikasi menuntut adanya adaptasi dalam dunia pendidikan, termasuk pada jenjang pendidikan anak usia dini. Pengenalan teknologi sejak dini menjadi langkah strategis dalam membentuk kesiapan anak menghadapi era digital. Kegiatan pengabdian ini bertujuan untuk mengenalkan teknologi, khususnya komputer, kepada siswa Taman Kanak-kanak (TK) melalui game edukasi berbasis online. Kegiatan dilaksanakan secara luring di TK Kristen 1 Satya Wacana, Salatiga, dengan melibatkan siswa berusia 5–6 tahun. Metode pelatihan dilakukan dalam beberapa sesi, mencakup pengenalan bagian-bagian komputer, penggunaan mouse, serta pelatihan melalui game edukasi seperti pengenalan huruf, angka, bentuk geometri, dan jenis-jenis kendaraan. Hasil evaluasi menunjukkan bahwa siswa menunjukkan antusiasme tinggi selama pelatihan, serta mulai mengenal komputer sebagai media pembelajaran alternatif selain perangkat yang biasa digunakan di rumah seperti handphone dan tablet. Kegiatan ini membuktikan bahwa pendekatan belajar sambil bermain dengan teknologi dapat meningkatkan minat belajar dan keterampilan dasar siswa dalam menggunakan komputer.
Feature-reduction Fuzzy c-means Clustering for Basketball Players Positioning Nataliani, Yessica
JOIV : International Journal on Informatics Visualization Vol 5, No 4 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.4.651

Abstract

One of the best-known clustering methods is the fuzzy c-means clustering algorithm, besides k-means and hierarchical clustering. Since FCM treats all data features as equally important, it may obtain a poor clustering result. To solve the problem, feature selection with feature weighting is needed. Besides feature selection by assigning feature weights, there is also feature selection by assigning feature weights and eliminating the unrelated feature(s). THE Feature-reduction FCM (FRFCM) clustering algorithm can improve the FCM clustering result by weighting the features and discarding the unrelated feature(s) during the clustering process. Basketball is one of the famous sports, both international and national. There are five players in basketball, each with a different position. A player can generally be in guard, forward, or center position. Those three general positions need different characteristics of players’ physical conditions. In this paper, FRFCM is used to select the related physical feature(s) for basketball players, consisting of height, weight, age, and body mass index. to determine the basketball players’ position. The result shows that FRFCM can be applied to determine the basketball players’ position, where the most related physical feature is the player’s height. FRFCM gets one incorrect player’s position, so the error rate is 0.0435. As a comparison, FCM gets five incorrect player’s positions, with an error rate of 0.2174. This method can help the coach decide the basketball new player’s position.
INFORMATION SYSTEMS ADOPTION AND USE BEHAVIOR OF QRIS AS A DIGITAL PAYMENT INFRASTRUCTURE AMONG GENERATION Z Sugiarto, Adrian Herma; Maria, Evi; Nataliani, Yessica
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 10 No. 2 (2026): Volume 10, Nomor 2, April 2026
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v10i2.54186

Abstract

Financial technology transformation has positioned QRIS as a core component of the Indonesia’s digital payment infrastructure. This study aims to examine the determinants of QRIS usage among Generation Z in Indonesia by extending the UTAUT2 framework. The novelty of this study lies in the integration of Personal Innovativeness to capture internal psychological dimensions in the adoption of a mature national payment system among digital-native users. Quantitative data were collected from 261 Generation Z respondents through questionnaires and analyzed using the SEM-PLS method to evaluate both measurement and structural models. The findings show that Social Influence, Habit, and Personal Innovativeness significantly predict Behavioral Intention. In contrast, utilitarian constructs (Performance Expectancy, Effort Expectancy, Hedonic Motivation, Price Value) are not significant. Additionally, Facilitating Conditions exhibit a significantly negative effect, suggesting that reliance on technical infrastructure is not perceived as a motivating factor. These findings indicate that social and habitual factors play a more prominent role than utilitarian considerations in shaping QRIS usage intention. Functional aspects are increasingly perceived as baseline expectations, while adoption is more strongly influenced by established usage patterns and individual innovativeness. This study suggests that policymakers and service providers should shift their strategic focus from functional promotion toward socially embedded and community-based approaches to strengthen user engagement and sustained adoption.
Customer Loyalty Analysis Using RFM Model and K-Means Clustering for Marketing Strategy Optimization Sahertian, Vigo Yano; Yessica Nataliani
International Journal of Information Technology and Business Vol. 8 No. 1 (2025): November : International Journal of Information Techonology and Business
Publisher : Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/ijiteb.212025.01-07

Abstract

This study aims to segment customers to measure their level of loyalty using the RFM (Recency, Frequency, Monetary) model approach combined with the k-Means clustering algorithm. The dataset used comes from the Kaggle site and contains motor vehicle sales data, both cars and motorbikes, with a total of 2,747 transactions. The RFM method is used to calculate three important indicators of customer behavior, namely the last time to make a purchase (recency), purchase frequency (frequency), and total transaction value (monetary). The data is then normalized and grouped using the k-Means algorithm. Based on the results of the Elbow Method and Silhouette Score tests, the optimal number of clusters obtained is four. The segmentation results show four groups of customers with different characteristics, ranging from very loyal customers with high frequency and large transaction values, to customers who have been inactive for a long time. This segmentation is very useful for companies to design more targeted marketing strategies and increase customer retention. This study shows that the combination of RFM and k-Means clustering is able to provide significant insights in understanding consumer behavior and supporting data-based strategic decision making.
Sentiment Analysis of Healthcare Services at RSUD Soe Using Machine Learning and Latent Dirichlet Allocation Saekoko, Agatha Marilin; Purnomo, Hindriyanto Dwi; Nataliani, Yessica
Jurnal Ilmiah Sains Volume 26 Issue 1, April 2026
Publisher : Sam Ratulangi University, Manado, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35799/jis.v26i1.67193

Abstract

Healthcare services constitute a crucial aspect in improving public well-being. Every individual has the right to receive healthcare services that are of high quality, safe, efficient, and affordable. This study aims to identify and analyze public perceptions and sentiments toward healthcare services at RSUD Soe, as well as to evaluate the performance of several machine learning methods in classifying such sentiments. The data were collected from 278 respondents through a Likert-scale questionnaire that represents perceptions and levels of satisfaction regarding various service aspects. Sentiment analysis was conducted using four machine learning algorithms, namely Naïve Bayes, C4.5, Random Forest, and Support Vector Machine. The results indicate that Naïve Bayes achieved the highest accuracy of 82.14 percent, followed by SVM at 80 percent, Random Forest at 79 percent, and C4.5 at 73.21 percent. This study also applied the Latent Dirichlet Allocation (LDA) method to identify the main themes within public feedback. LDA generated twelve topics reflecting key issues such as waiting time, availability of medical personnel, facility cleanliness, and the attitudes of healthcare staff. The majority of comments exhibited positive sentiment, particularly concerning staff friendliness and service quality. These findings were used to formulate improvement recommendations, including enhancing service quality, increasing the number of medical personnel, and optimizing facilities. This research demonstrates that a data-driven quantitative approach is effective in evaluating healthcare service quality and supporting more targeted decision-making. The results are expected to assist RSUD Soe in continuously and effectively improving service quality.
ANALISIS MANAJEMEN RISIKO SISTEM INFORMASI BERBASIS ISO 31000:2018 PADA PERUSAHAAN LOGISTIK PENGIRIMAN BARANG Marchelino Nathanael; Yessica Nataliani
Jurnal Pendidikan Teknologi Informasi (JUKANTI) Vol 8 No 2 (2025): JURNAL PENDIDIKAN TEKNOLOGI INFORMASI (JUKANTI) EDISI NOPEMBER 2025
Publisher : Universitas Citra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37792/jukanti.v8i2.1547

Abstract

In an era of rapid technological development, it is important to recognize the various threats and risks that may arise along with these developments. These threats and risks can disrupt operational activities, including system usage, and even reduce system performance. PT. Merdeka Panji Mulia, a company engaged in logistics and delivery services, uses an online cashier application to support its business processes. The absence of risk management analysis in this application has led to insufficient data monitoring and the emergence of various other issues. This study aims to apply the ISO 31000:2018 framework to analyze risk management in the online cashier application. The analysis was carried out through four stages: risk identification, risk analysis, risk evaluation, and risk treatment. The results of this study identified 21 potential risks, which were classified into three categories: 3 low risks, 15 medium risks, and 3 high risks. These findings reinforce the applicability of ISO 31000:2018 in managing information system risks within the logistics sector.
Machine Learning Model Optimization and Interpretability Analysis for Classifying Student Stress Levels Samuel Wijayadi Sugiharto; Yessica Nataliani
Sistemasi: Jurnal Sistem Informasi Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6239

Abstract

This study aims to compare and analyze the performance of several algorithms in classifying student stress levels. The dataset used in this research is the Student Lifestyle Dataset obtained from the Kaggle repository, consisting of 2,000 records with eight student lifestyle features. The methods employed include the implementation of three classification algorithms: Logistic Regression, K-Nearest Neighbors, and Support Vector Machine (SVM), across four experimental scenarios. These scenarios include a baseline model, handling imbalanced data using the Synthetic Minority Oversampling Technique (SMOTE), feature selection using Recursive Feature Elimination (RFE), and hyperparameter tuning. The results were evaluated using accuracy, precision, recall, and F1-score metrics. Furthermore, interpretability analysis of the best-performing model was conducted using SHAP. The findings indicate that the integration of data balancing techniques, feature selection, and parameter optimization with the SVM algorithm significantly improved performance, achieving an accuracy of 0.98, precision of 0.96, recall of 0.98, F1-score of 0.97, and a computation time of 0.011 seconds. The interpretability analysis revealed that lifestyle factors such as study duration and sleep duration had the most dominant influence on stress levels. These results demonstrate that the integrated optimization strategy successfully supports fast and accurate detection of student stress levels.
Co-Authors Ade Iriani Adriyanto Juliastomo Gundo Advensius Natalis Agustinus Fritz Wijaya Alberaldo Difra Gunawan Alexander Franklyn Alwin Adi Putra Andree Eka Putra Anissa Enggar Pramitasari Anton Hermawan Antoni Erga Arthur, Christian Atik Setyanti, Angela Bryan Adha Elang Praditya Cahyaningtyas, Christian Chrisanty Mariana Rorimpandey Danny Manongga Deinard Yordan Sihombing Diwa Oktario Dacwanda Edi Suharyadi Eko Sediyono Elizabeth Sri Lestari Erdi Amos Saputra Ering, Anatasya Lingkanwene Erwien Christianto Evi Maria Gerian, Matthew Gigih Prima Subakti Gilang Jonathan Phita Gregorry, Febrianus Hanita Yulia Hapsari, Theresia Shinta Hendry Hindriyanto Dwi Purnomo Hui-Ming Wee Imanuel Susanto Indrastanti Ratna Widiasari Irwan Sembiring Jessica Widyadhana Iskandar Kirono, Aryo Sasi Krismiyati Kristoko Dwi Hartomo Leony Martiyana Putri Lorna Yertas Baisa Marchelino Nathanael Maria Feby Tri Martin Martin Merryana Lestari Michael Jonathan Mramra , Welianus Yohanes Yehuda Mulyono, Andronikus Natalis, Advensius Penidas Fodinggo Tanaem Prandiska, Kelvin Putra, Andree Eka Rama Tri Budi Ardianto Saekoko, Agatha Marilin Sahertian, Vigo Yano Samuel Wijayadi Sugiharto Sanjaya, Rian Sediatmoko, Nur Siradj Sugiarto, Adrian Herma Suharyadi Suryady, Irwan Tambunan, Shanto Moyrano Theophilus Wellem Theopillus J. H. Wellem Tigar Cahyo Wiguno Tintien Koerniawati Wahab, Nur Haliza Abdul Willy Thomas Yuda Novianto